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BMJ Open is committed to open peer review. As part of this commitment we make the peer review history of every article we publish publicly available. When an article is published we post the peer reviewers’ comments and the authors’ responses online. We also post the versions of the paper that were used during peer review. These are the versions that the peer review comments apply to. The versions of the paper that follow are the versions that were submitted during the peer review process. They are not the versions of record or the final published versions. They should not be cited or distributed as the published version of this manuscript. BMJ Open is an open access journal and the full, final, typeset and author-corrected version of record of the manuscript is available on our site with no access controls, subscription charges or pay-per-view fees (http://bmjopen.bmj.com). If you have any questions on BMJ Open’s open peer review process please email
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Clinical and Genetic Associations of Renal Function and
Diabetic Kidney Disease in the United Arab Emirates
Journal: BMJ Open
Manuscript ID bmjopen-2017-020759
Article Type: Research
Date Submitted by the Author: 26-Nov-2017
Complete List of Authors: Osman, Wael; Khalifa University of Science Technology and Research, Biotecnology Center Jelinek, H. F.; Macquarie University Faculty of Medicine and Health Sciences, Clinical Medicine Tay, Guan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khandoker , Ahsan ; Khalifa University of Science Technology and Research, Biomedical Engineering
Khalaf, Kinda; Khalifa University of Science Technology and Research, Biomedical Engineering AlMahmeed , Wael; Sheikh Khalifa Medical City, Institute of Cardiac Science Hassan, Mohamed; Sheikh Khalifa Medical City, Medical Institute ALsafar, Habiba; Khalifa University of Science Technology and Research, Biomedical Engineering
<b>Primary Subject Heading</b>:
Diabetes and endocrinology
Secondary Subject Heading: Genetics and genomics, Global health
Keywords: Diabetes, UAE, ARAB, Renal
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Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the 1
United Arab Emirates 2
Wael M. Osman 1, Herbert F. Jelinek
2, 3, Guan K. Tay
1, 4, 5, Ahsan H. Khandoker
6, Kinda 3
Khalaf 6, Wael Almahmeed
7,8, Mohamed H. Hassan
9 & Habiba S. Alsafar
1, 6,* 4
1 Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates. 5
2 School of Community Health, Charles Sturt University, Albury, Australia. 6
3 Clinical Medicine, Macquarie University, Sydney, Australia. 7
4 School of Health and Medical Sciences, Edith Cowan University, Australia. 8
5 School of Psychiatry and Clinical Neurosciences, University of Western Australia, Australia. 9
6 Department of Biomedical Engineering, Khalifa University, United Arab Emirates. 10
7 Institute of Cardiac Science, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 11
8Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates. 12
9 Medical Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 13
Number of words: 298 in the abstract, 3143 in the main text 14
Number of tables: 5; embedded in the main text. No supplementary data. 15
Number of references: 44 16
Key words: type 2 diabetes, United Arab Emirates, diabetic kidney disease, genetics, 17
SHROOM3 gene. 18
* Corresponding author 19
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Dr. Habiba S. Al Safar 20
Director of Biotechnology Center, Assistant Professor 21
Khalifa University 22
PO BOX 127788 Abu Dhabi, UAE 23
Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442 24
E-mail: [email protected] 25
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Abstract 38
Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic 39
kidney disease (DKD) have not been investigated. The aim of this report was to determine 40
possible clinical and laboratory risk factors and investigate the reported genetic loci linked to 41
DKD. 42
Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 43
diabetes mellitus (T2DM) were recruited with and without DKD and their clinical and laboratory 44
data obtained. Following adjustments for possible confounders, a logistic regression model was 45
used to test the associations of 63 SNPs in 43 genetic loci with DKD (145 patients and 265 46
controls). Linear regression models, adjusted for age and gender, were used to test the genetic 47
associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 48
SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration 49
rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. 50
Results: Patients with DKD compared to those without DKD were mostly males (~52% versus 51
38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and 52
dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 53
years), which in an additive manner was the single factor that significantly contributed to the 54
development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the 55
associations of the genetic loci with different renal function traits found that the most notable 56
associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD 57
(Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 58
with serum creatinine and eGFR levels. 59
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Conclusions: Strong associations were found between several genetic loci and risk markers for 60
diabetic kidney disease, which may influence kidney function traits and DKD in a population of 61
Arab ancestry. 62
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Strengths and limitations of this study 79
• This is the first study to investigate clinical and genetic risk factors of diabetic kidney disease 80
(DKD) in a population of Arab descendants from the United Arab Emirates (UAE), where 81
prevalence for type 2 diabetes is 23%. 82
• The mean age of development of DKD is 67 years, and diabetes duration is the single factor 83
that significantly contributed to the development of DKD (mean duration is 16 years). 84
• Patients who develop DKD have significant rates of hypertension and dyslipidemia. 85
• Some genetic loci reported in other populations also influence different renal function traits 86
and DKD in this population of Arab ethnicity, such as SHROOM3 gene with levels of serum 87
creatinine, eGFR, and DKD ; CASR, GC, and CYP2R1 genes with vitamin D levels; as well 88
as WDR72 gene with serum creatinine and eGFR levels. 89
• A larger population sample across the Middle East is required for sufficient statistical power 90
to discover novel clinical and genetic predisposition for DKD in the region. 91
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Introduction 102
Diabetic kidney disease (DKD), considered as the most common cause of nephropathy in many 103
populations, is a public health challenge worldwide. Globally, the prevalence of chronic kidney 104
disease (CKD) among adults in the general population was reported to be around 10%, with the 105
United States showing a figure around 3.3% for DKD in 2008 1 2
. In addition to increasing the 106
risk of cardiovascular morbidity and mortality 3 4, DKD is reported to be the single strongest 107
predictor of mortality in patients with diabetes 5 with five year survival in the range of 30%
6. 108
DKD often progresses to end-stage renal disease (ESRD). Patients with ESRD have a 20% 109
annual mortality rate, which is higher than the rate for many solid cancers 7
. Overall, the risk of 110
DKD in T2DM is approximately 2% per year 8. The current trend suggests that the prevalence of 111
DKD will continue to increase, constituting a significant socioeconomic burdens on global 112
healthcare systems and leading to increased morbidity and mortality 2. A thorough literature 113
search shows that there remains a knowledge gap related to the understanding of risk factors and 114
pathophysiological mechanisms associated with DKD, especially in the Middle East. Since 115
ESRD can only be treated with highly invasive and costly procedures, such as dialysis or kidney 116
transplantation, a better knowledge of genetic, clinical, and epidemiological factors associated 117
with DKD is required to allow earlier and more effective treatment outcomes. 118
In clinical practice, renal function is assessed using a number of tests that are reported to have 119
high heritability rates 9, indicating that genetic factors contribute significantly to inter-individual 120
variance in kidney function, and hence, to the susceptibility to CKD and related conditions. 121
So far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal 122
function traits in adults 10-21
and children 22
. However, in comparison to other diseases, including 123
T2DM and other cardiometabolic disorders, studies of kidney disease and kidney function traits 124
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are insufficient. A recent report by Pierre-Jean Saulnier et al. suggested that three serum 125
biomarkers (midregional-proadrenomedullin: MR-proADM, soluble tumor necrosis factor 126
receptor 1: sTNFR1, and N-terminal prohormone brain natriuretic peptide: NT-proBNP) can 127
improve the risk prediction of the loss of renal function in patients with T2DM in addition to the 128
established risk factors for DKD such as age, sex, diabetes duration, HbA1c, blood pressure, 129
baseline eGFR, and albumin-to-creatinine ratio 23
. However, the issues of whether the levels of 130
these markers are affected by genetic variations, and whether the encoding genes contribute to 131
DKD development and progress need further investigation. 132
The United Arab Emirates (UAE) is among the countries with the highest prevalence rates of 133
T2DM, obesity and cardiovascular conditions 24 25
. Al-Safar and colleagues recently reported that 134
approximately 80% of T2DM patients within UAE present with at least one complication 135
associated with T2DM, including kidney disease (approximately 6%) 26
. Furthermore, there is 136
increasing evidence suggesting that the genome structure of individuals of Arabic descent is 137
different from individuals from other populations 27
. Despite the high prevalence rate of DKD in 138
the UAE, to our best knowledge, there have been no investigations of the genetic associations of 139
chronic kidney conditions and kidney functions in this population, particularly associated with 140
T2DM. Therefore, the current work aimed to investigate the clinical and laboratory variables 141
linked to DKD in a T2DM Emirati population, and to investigate the associations of the reported 142
genetic loci linked to different renal function tests, in CKD and DKD. 143
144
Materials and Methods 145
Study type and subjects 146
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This report describes a prospective cross-sectional study of Emirati patients from the city of Abu 147
Dhabi. The demographic information and clinical data for the participants are presented in 148
Tables 1 and 2. Four hundred and ninety patients with T2DM were included in the study, with 149
145 diagnosed with DKD. The participants were recruited from Sheikh Khalifa Medical City 150
(SKMC) and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were 151
UAE born and with Arabic descent. 152
Clinical variables and laboratory data 153
Various clinical and laboratory measures were assessed and collected during the hospital visits. 154
Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood 155
pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking 156
antihypertensive medications. Dyslipidemia was reported in the clinical records of participants or 157
diagnosed as previously indicated 28
. The presence of T2DM was confirmed by a qualified 158
physician based on the criteria outlined by the World Health Organization (WHO) consultation 159
group report 29
. Trained nurses measured the height and weight of each participant using a 160
calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the 161
weight in kilograms divided by the square of height of each subject (kg/m2). 162
Definition of the DKD 163
DKD was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal 164
damage over a period of at least three months 30
, or based on an albumin-to-creatinine ratio ≥30 165
mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or 166
abnormalities as assessed by imaging or histology 31
. Accordingly, 145 TD2M patients were 167
identified with DKD, while 265 were disease free (Tables 1 and 2). The remaining 80 patients 168
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could not be classified with or without DKD at the time of the study, and were excluded from 169
subsequent analyses. 170
Selection of SNPs 171
The SNPs tested for each trait are summarized in Table 1. These SNPs were selected from a 172
recent Genome Wide Association Study (GWAS) that was intended to determine the genetic 173
associations of T2DM in the UAE population, establishing the Emirates Family Registry for 174
T2DM 24
. The GWAS was performed for the 490 samples with T2DM, and 450 healthy controls 175
on the Infinium Omni5ExomeHuman chip (Illumina Inc., San Diego, USA). To select SNPs that 176
are associated with kidney function traits included in the study (blood urea, serum creatinine, 177
eGFR values, albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search 178
engines and data bases including PubMed, Google Scholar, the GWAS catalog 179
(https://www.ebi.ac.uk/gwas/home), the Phenolyzer database (http://phenolyzer.wglab.org/), the 180
infinome genome interpretation platform (https://www.infino.me/), and the GWAS Central 181
database (http://www.gwascentral.org/) were consulted. 182
Our search strategy consisted of identifying reported SNPs that cleared the GWAS significance-183
level and were found in our GWAS data, and if necessary selecting possible proxy SNPs (r2 and 184
D’ > 0.8) if the original signal SNPs were missing from the GWAS data. Proxy SNPs were 185
selected using the SNAP database for SNP Annotation and Proxy Search 186
(http://archive.broadinstitute.org/mpg/snap/ldsearch.php). 187
All SNPs located within and flanking genes that have been reported with CKD and DKD were 188
included. In total, 43 genetic loci were identified as linked to CKD, DKD, or a decline in renal 189
functions. Specifically, the gene regions are ACACB, ACE, ACTN4, ADIPOQ, ADM, AFF3, 190
AGTR1, APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-ELMO1, ENPP1, 191
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ERBB4, FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16, MYH9, NCALD, 192
NCK1, NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPARγ2, PVT1, RAGE, RGMA, RPS12, SFI1, 193
SHROOM3, TMEM22, TNFRSF1A, and TRPC6. 194
Statistical analyses 195
Continuous variables were presented as the means ± standard deviations. Vitamin D and eGFR 196
levels were normally distributed. However, urea levels, creatinine levels, and albumin-to-197
creatinine ratio data were converted to normal distributions using natural log transformation. The 198
associations of trait values or their natural logs (if transformed) were tested with SNPs using 199
linear regression models, which included age and gender as covariates using PLINK software 200
version 1.07 (http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele 201
frequencies and testing the quality control (QC) variables, where any SNPs with minor allele 202
frequency (MAF) <0.05, >5% missing genotype rate, or those that failed the Hardy-Weinberg 203
equilibrium (HWE) test at the 0.001, were excluded. Associations with P < 0.05 were reported, 204
pointing to the replication of previously-reported associations. 205
All statistical analyses for the clinical and laboratory variables were performed using Stata 206
software version 14 (StataCorp LLC, Texas, USA). For continuous data, statistical differences 207
were assessed using two-sided t-tests, while the Pearson chi-square test was used for percentage 208
data. A P-value < 0.05 was considered as significant. PLINK software was also used for testing 209
the associations between the SNPs and DKD using a case-control logistic regression model, 210
which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates 211
(Table 1). The results are presented as P-values (Padjusted < 0.05) and odds ratios with the 212
corresponding 95% confidence intervals. The same approach was adopted to test the associations 213
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between the possible risk factors and the development of the DKD (Table 3). However, the 214
logistic model in this case was validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) 215
to allow for the inclusion of several covariates. 216
Ethical considerations 217
Each patient agreed to take part in this study and gave signed consent after a brief session to 218
explain aims and methods. The study was approved by the Institutional Ethics Committee of 219
both hospitals (REC-04062014 and R292, respectively) and conforms to the ethical principles 220
outlined in the Helsinki declaration. 221
222
Results 223
Baseline data of kidney function associated traits and SNPs selection 224
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred 225
and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 226
for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum 227
creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications 228
relating to the diagnosis of kidney disease (145 patients and 265 controls). The possible 229
confounding factors which may affect the genetic associations were included for each trait 230
analysis and summarized in Table 1. 231
232
233
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Table 1: Baseline data of tested traits and tested SNPs 234
Trait Mean ± SD
N of subjects
(Male/Female)
N of SNPs
reported
N of SNPs
tested
Covariates
ACR (mg/mmol) 47.8 ± 173.4 115 (56/59) 331 83 Age, Gender
Vitamin D (ng/ml) 63.7 ± 27.8 328 (146/182) 442 92 Age, Gender
eGFR (ml/min/1.73m2) 81.5 ± 28.5 395 (172/223) 1478 288 Age, Gender
Serum Creatinine (µmol/l) 91.4 ± 83.9 474 (246/228) 1792 363 Age, Gender
Blood Urea (mmol/l) 6.4 ± 5.4 450 (263/214) 446 73 Age, Gender
Diabetic Kidney Disease (DKD)
Cases: 145
Controls: 265
288 63
Age, Gender,
Hypertension, T2DM
duration, eGFR levels
Abbreviations: N: number; SD: standard deviation; SNP: single nucleotide polymorphism, ACR: albumin-to-creatinine ratio; T2DM: type 2 235
diabetes mellitus; eGFR: estimated glomerular filtration rate.236
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Clinical and laboratory characteristics of patients with and without DKD 237
Expectedly, both patient groups (with or without kidney disease) had poor glycemic control with 238
blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those with no 239
DKD indicated that the majority were males (~52% versus 38% for controls), older (67 versus 240
~59 years), had significant rates of comorbidities such as hypertension and dyslipidemia, and had 241
a longer T2DM duration (16 versus 10 years). A clear decline in all renal function indices was 242
also observed in patients with DKD. However, DKD patients tended to have lower LDL-243
cholesterol results compared to those without DKD, which suggests these patients received better 244
medical care or at least received intensive statin medication (Table 2). 245
246
247
248
249
250
251
252
253
254
255
256
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Table 2: Demographic, clinical, and laboratory characteristics of T2DM patients with or without 257
kidney disease 258
Type of variables Variable DKD No DKD P 1
Demographic variables
Gender: Female 2 70 (48.3%) 165 (62.3%) 0.006
Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001
Clinical variables
Clinical hypertension 2 140 (96.6%) 197 (74.3 %) < 0.0001
Dyslipidemia 2 138 (95.2%) 241 (90.9%) < 0.0001
Smoking history 2 47 (32.4%) 64 (24.2%) 0.07
Diabetes duration (years) 16.0 ± 9.2 10.1 ± 7.3 < 0.0001
BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.07
Laboratory variables
Glycemic indices HbA1c (%) 7.7 ± 1.5 7.8 ± 1.7 0.63
Fasting plasma glucose
(mmol/l) 8.3 ± 3.1 8.9 ± 3.8 0.43
Random blood glucose
(mmol/l) 9.1 ± 3.9 9.5 ± 4.3 0.34
Lipids profile Total-cholesterol (mmol/l) 3.7 ± 1.0 4.0 ± 1.1 0.003
Triglyceride (mmol/l) 1.5 ± 0.8 1.6 ± 0.8 0.25
HDL-cholesterol (mmol/l) 1.2 ± 0.6 1.2 ± 0.5 0.45
LDL-cholesterol (mmol/l) 1.9 ± 0.8 2.1 ± 0.9 0.01
Renal function
indices
Albumin:Creatinine Ratio
(mg/mmol) 132.9 ± 289.4 7.8 ± 16.3 0.01
Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48
eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001
Creatinine (µmol/l) 142.5 ± 134.8 65.9 ± 17.5 < 0.0001
Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001
1 P-value for continuous data, calculated using two-sided t-test, and for percentage data using Pearson chi-259
square test. 260
2 Proportional data shown as number of positive outcome and its percentage. All remaining continuous 261
data shown as mean ± standard deviation. 262
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Risk factors for developing DKD in UAE patients 263
Table 3 shows that T2DM duration was the single factor that significantly contributed to the 264
development of DKD. Increased risk for DKD was significantly associated with increasing 265
duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the 266
risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-267
8.02). Although the levels of serum creatinine indicated a trend for disease development 268
(P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03 (Table 3). 269
Table 3: Risk factors for development of kidney disease in patients with T2DM from UAE 270
Covariate OR 95% CI SE Z P-value
Age 0.99 0.96 - 1.04 0.020 -0.25 0.80
Gender 0.90 0.34 - 2.36 0.443 -0.21 0.83
BMI 1.02 0.97 – 1.08 0.028 0.69 0.49
Hypertension 2.00 0.60 - 6.74 1.241 1.12 0.26
HbA1c 0.86 0.69 – 1.07 0.098 -1.33 0.19
Cholesterol 0.59 0.19 – 1.76 0.329 -0.95 0.34
Triglyceride 1.09 0.62 – 1.94 0.319 0.30 0.76
HDL-cholesterol 1.25 0.64 – 2.45 0.429 0.65 0.52
LDL-cholesterol 1.38 0.42 – 4.57 0.842 0.52 0.60
Smoking history 0.80 0.34 – 1.87 0.346 -0.52 0.61
Creatinine 1.03 1.00 – 1.06 0.016 2.14 0.03
Urea 1.13 0.92 – 1.38 0.119 1.13 0.26
eGFR 0.97 0.94 – 1.00 0.016 -1.69 0.09
Diabetes duration*
5-10 years 0.75 0.26 – 2.16 0.405 -0.54 0.59
11-15 years 1.49 0.56 – 3.91 0.733 0.80 0.42
16-20 years 3.35 1.22 – 9.23 1.732 2.34 0.02
> 20 years 3.12 1.21 – 8.02 1.503 2.36 0.02
*Diabetes duration reference is duration ≤ 5 years 271
Abbreviations: BMI: body mass index, eGFR: estimated glomerular filtration rate. 272
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Genetic associations of renal function associated traits in UAE participants 273
The results of genetic associations for each tested renal function trait are shown in Table 4. 274
For blood urea, the best observed association was with rs11868441 in BCAS3 (effect size: -0.038 275
log per allele A, P=0.014), followed by multiple SNPs in the RSPO3 gene. 276
For serum creatinine, two SNPs (rs6999484 and rs1705699) in the intergenic region between 277
STC1 and ADAM28, showed the best associations with similar effect sizes (0.03 log per one copy 278
of the corresponding minor allele), followed by the SNP rs2828785, which is located in the non-279
gene area on chromosome 21. In addition, multiple genetic areas were also indicated, although 280
with less significant associations. 281
eGFR levels were significantly associated with SNPs within the MED1 gene, rs2168785 with 282
effect size -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and P=0.015 283
and 0.017. In addition, the SNPs in two genetic regions, WDR72 and SHROOM3, with 284
associations with serum creatinine levels, were also associated with eGFR levels, indicating a 285
strong link to renal function. 286
Vitamin D levels were associated with three genetic regions including rs1801725 in CARS 287
(effect size: -6.923 per allele A, P=0.0078), rs1155563 in GC (effect size: -6.951, P=0.0081), and 288
two SNPs in CYP2R1 with effect sizes of approximately -5.0 and P-values near 0.01. 289
One SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 290
(effect size: -0.323 log per allele A, P=0.027) was strongly associated with the albumin-to-291
creatinine ratio. 292
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This data showed that renal function traits, are linked to several loci in the UAE population, and 293
that some loci (e.g. WDR27 and SHROOM3) are linked to more than one trait. 294
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Table 4: Results of genetic association analyses of different renal function indices 295
SNP Chr: BP Gene A1/A2 1 MAF_% Beta
2 SE P
Blood Urea
rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014
rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028
rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.030
rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.031
rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039
Serum Creatinine
rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.0030
rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.0048
rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.0082
rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019
rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022
rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034
rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038
rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042
Estimated Glomerular Filtration Rate
rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015
rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017
rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031
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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031
rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035
rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037
rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042
Vitamin D (25-OH Cholecalciferol)
rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.0078
rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.0081
rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015
rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016
Albumin:Creatinine Ratio
rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027
1 A1/A2: minor to major alleles. 296
2 Beta: regression coefficient for interaction in the linear regression model, calculated based on A1 (the minor allele). 297
Abbreviations: BP: base pair position; Chr: chromosome; MAF: minor allele frequency; SE: standard error; SNP: single nucleotide polymorphism.298
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Genetic associations of DKD in patients from UAE 299
Sixty-three SNPs in 43 genetic loci that have previously been linked to chronic or diabetic 300
kidney disease were included in our genetic analysis of DKD (145 case versus 265 controls). A 301
logistic regression model including five possible covariates, which may affect the development 302
of kidney disease, was applied (Table 1). As shown in Table 5, the unadjusted analysis indicates 303
two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682, P=0.024). 304
Following the adjustment for possible covariates, only the SHROOM3 rs4859682 remained 305
significant (P=0.04, OR=1.46). This result confirms that SHROOM3 is a string risk locus for 306
DKD, considering the similar associations with serum creatinine and eGFR levels. 307
308
309
310
311
312
313
314
315
316
317
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Table 5: Association between SNPs linked to CKD and DKD patients from UAE. 318
SNP Chr: BP Gene 1 A1/A2
2 MAF Punadjusted Padjusted OR (95% CI)
Case (N=145) Control (N=265)
rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)
rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)
1 CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3. 319
2 A1/A2: minor to major alleles. 320
Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; MAF: minor allele frequency; N: number; OR: odds ratio; 321
SNP: single nucleotide polymorphism. 322
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Discussion 323
A combination of environmental and clinical factors in genetically predisposed individuals have 324
been suggested to be involved in DKD such as persistent hyperglycemia, arterial hypertension 325
and/or dyslipidemia 32
. In fact, familial aggregation of nephropathy in T2DM has been reported 326
in several populations 33
.Therefore, understanding the interactions between genetic, clinical and 327
traditional kidney disease risk factors can provide insight into new treatment strategies for 328
improving DKD and reducing the likelihood of developing ESRD. In this study, we investigated 329
whether genetic markers correspond to DKD and renal function traits that were reported in 330
different populations are similar or different in the Arab population. 331
The current Emirati population sample indicated that most T2DM patients who developed DKD 332
were males, older by about 10 years than those that did not develop the disease, had more 333
frequent comorbidities specifically hypertension, and showed a marked decline in their renal 334
function profiles. However, even T2DM patients who did not develop DKD had higher rates of 335
comorbities and had poor diabetic control, in agreement with our previous results 26
. We also 336
found that the duration of diabetes was the single factor that significantly contributed to the 337
development of kidney disease, regardless any other factor. However, this risk of the DKD 338
development becomes significant when the duration of T2DM reaches the 15 year mark. This is 339
in concordance with previous reports, which suggest that T2DM patients who do not develop 340
signs of kidney disease by 15 years duration of diabetes onset seem to be protected, most likely 341
due to genetic factors 34
. 342
The most notable finding of this study was the association of SHROOM3 (Shroom Family 343
Member 3) with serum creatinine, eGFR, and DKD. The minor allele for the SNP rs4859682 (A) 344
was observed to increase the serum creatinine (0.021 log increase per one copy), and also to 345
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increase the risk for DKD (OR=1.46). SHROOM3 was first reported to be associated with eGFR 346
levels in DKD patients 10
, then with serum creatinine 35
and serum magnesium levels 36
. This 347
association was further replicated in different ethnicities 16 37
. The SHROOM3 gene product is 348
expressed in the human kidney and is reported to play an important role in epithelial cell shape 349
regulation 38
, as well as the maintenance of the glomerular filtration barrier integrity 39
. 350
Furthermore, genetic variants (such as the intronic variant rs17319721), were found to contribute 351
to kidney allograft injury and fibrosis development through a mechanism involving TGF-β1 352
signaling 40
. Although the variant rs17319721 was not found in our dataset, it is highly linked to 353
the SNP rs4859682 (r2=0.85, D’=1), which was reported in this study to increase the risk for 354
DKD and affect the levels of serum creatinine. Overall, this suggests that SHROOM3 may be 355
considered as a multi-ethnic risk gene for DKD and its related kidney function traits in various 356
populations, including the UAE. 357
Similarly, the two loci; WDR72 (WD Repeat Domain 72, associated with eGFR and serum 358
creatinine levels), and BCAS3 (Breast Carcinoma-Amplified Sequence 3, associated with blood 359
urea levels), are also well-known trans-ethnic renal function traits loci 41
. In addition, RSPO3 (R-360
Spondin 3) was previously reported to be associated with blood urea nitrogen concentration, in 361
line with the results of the current study 37
. The association of rs4528660 near MYOM1 362
(Myomesin 1) with the albumin-to-creatinine ratio was also in alignment with previous work, 363
which linked this locus to albuminuria in patients with diabetes 42
. The analyses performed also 364
replicated the associations of CASR (Calcium-sensing receptor), GC (Group-specific 365
component), and CYP2R1 (Cytochrome P450 Family 2 Subfamily R Member 1) with levels of 366
vitamin D 43
. These genes encode proteins which are involved in vitamin D function including 367
activation by hydroxylation (CYP2R1), transportation (GC), and serum calcium level sensoring 368
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(CASR) 43
. These genes have also been reported to be associated with calcium-vitamin D 369
biology and pathology, such as serum calcium levels, familial hypocalciuric hypercalcemia, 370
tertiary hyperparathyroidism, and vitamin D deficiency presenting as Rickets (see OMIM entries: 371
CARS: 601199; CYP2R1: 608713; and GC: 139200). Furthermore, these genes have recently 372
been shown to influence outcome of Vitamin D3 supplementation, which in the Arab context is 373
an important finding of our study 44
. 374
In summary, this work presents the first study to investigate the clinical and genetic factors 375
influencing kidney function traits and DKD in a population of Arab ancestry. The results 376
demonstrate that the duration of T2DM is the single most important risk factor for DKD 377
development in patients with T2DM from UAE. Our study highlights that several genetic loci, 378
which have been previously linked to renal function associated traits, are shared between diverse 379
ethnic groups. As such we have replicated previous findings of the association of SHROOM3 380
with DKD. It is worthwhile to note that a larger population sample across the Middle East is 381
required to confirm the extent of the shared genetic predisposition reported in the current study. 382
Considering the high prevalence of T2DM in this population and the recent evidence of genomic 383
structure variations among different ethnic groups, more genetic-driven population studies are 384
warranted towards effective genetically guided personalized medicine. 385
386
Acknowledgments: We acknowledge the patients who volunteered to make this study possible. 387
388
Author contribution: HSA obtained the fund for this study. WMO, HSA and HFJ have 389
designed the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and 390
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KK reviewed/edited the manuscript and contributed to the discussion and reviewed/edited the 391
manuscript. WA and MHH provided assistance in patients’ recruitment and clinical data 392
collection. 393
394
Funding: This study was supported by research incentive funds from Khalifa University Internal 395
Research Fund Level 2 granted to Dr. Habiba Al Safar. 396
397
Conflict of interests: None declared 398
399
Patient consent: Obtained. 400
401
Ethics approval: The Institutional Ethics Committee of Mafraq and Shaikh Khalifa Medical 402
Centre hospitals (REC-04062014 and R292, respectively). 403
404
Provenance and peer review: Not commissioned; externally peer reviewed. 405
406
Data sharing statement: No additional data are available. 407
408
References 409
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Joint National Committee (JNC 8). JAMA 2014;311(5):507-20. 482
30. Levey AS, Eckardt K-U, Tsukamoto Y, et al. Definition and classification of chronic kidney 483
disease: a position statement from Kidney Disease: Improving Global Outcomes 484
(KDIGO). Kidney International 2005;67(6):2089-100. 485
31. Andrassy KM. Comments on'KDIGO 2012 clinical practice guideline for the evaluation and 486
management of chronic kidney disease'. Kidney International 2013;84(3):622. 487
32. Murussi M, Coester A, Gross JL, et al. Diabetic nephropathy in type 2 diabetes mellitus: risk 488
factors and prevention. Arquivos Brasileiros de Endocrinologia & Metabologia 489
2003;47(3):207-19. 490
33. Palmer ND, Freedman BI. Insights into the genetic architecture of diabetic nephropathy. 491
Current Diabetes Reports 2012;12(4):423-31. 492
34. Krolewski AS. Genetics of diabetic nephropathy: evidence for major and minor gene effects. 493
Kidney International 1999;55(4):1582-96. 494
35. Pattaro C, De Grandi A, Vitart V, et al. A meta-analysis of genome-wide data from five 495
European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum 496
creatinine level. BMC Medical Genetics 2010;11(1):41. 497
36. Meyer TE, Verwoert GC, Hwang S-J, et al. Genome-wide association studies of serum 498
magnesium, potassium, and sodium concentrations identify six Loci influencing serum 499
magnesium levels. PLoS Genetics 2010;6(8):e1001045. 500
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37. Okada Y, Sim X, Go MJ, et al. Meta-analysis identifies multiple loci associated with kidney 501
function–related traits in east Asian populations. Nature Genetics 2012;44(8):904. 502
38. Nishimura T, Takeichi M. Shroom3-mediated recruitment of Rho kinases to the apical cell 503
junctions regulates epithelial and neuroepithelial planar remodeling. Development 504
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39. Yeo NC, O’Meara CC, Bonomo JA, et al. Shroom3 contributes to the maintenance of the 506
glomerular filtration barrier integrity. Genome Research 2015;25(1):57-65. 507
40. Menon MC, Chuang PY, Li Z, et al. Intronic locus determines SHROOM3 expression and 508
potentiates renal allograft fibrosis. The Journal of Clinical Investigation 509
2015;125(1):208. 510
41. Mahajan A, Rodan AR, Le TH, et al. Trans-ethnic Fine Mapping Highlights Kidney-511
Function Genes Linked to Salt Sensitivity. The American Journal of Human Genetics 512
2016;99(3):636-46. 513
42. Teumer A, Tin A, Sorice R, et al. Genome-wide association studies identify genetic loci 514
associated with albuminuria in diabetes. Diabetes 2015:db151313. 515
43. Wang TJ, Zhang F, Richards JB, et al. Common genetic determinants of vitamin D 516
insufficiency: a genome-wide association study. The Lancet 2010;376(9736):180-88. 517
44. Barry EL, Rees JR, Peacock JL, et al. Genetic variants in CYP2R1, CYP24A1, and VDR 518
modify the efficacy of vitamin D3 supplementation for increasing serum 25-519
hydroxyvitamin D levels in a randomized controlled trial. The Journal of Clinical 520
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For peer review onlyClinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A
Cross-sectional Study
Journal: BMJ Open
Manuscript ID bmjopen-2017-020759.R1
Article Type: Research
Date Submitted by the Author: 07-Jul-2018
Complete List of Authors: Osman, Wael; Khalifa University of Science Technology and Research, Biotecnology Center Jelinek, H. F.; Macquarie University Faculty of Medicine and Health Sciences, Clinical MedicineTay, Guan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khandoker , Ahsan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khalaf, Kinda; Khalifa University of Science Technology and Research, Biomedical Engineering AlMahmeed , Wael; Sheikh Khalifa Medical City, Institute of Cardiac ScienceHassan, Mohamed; Sheikh Khalifa Medical City, Medical InstituteALsafar, Habiba; Khalifa University of Science Technology and Research, Biomedical Engineering
<b>Primary Subject Heading</b>: Diabetes and endocrinology
Secondary Subject Heading: Genetics and genomics, Global health
Keywords: Diabetes, UAE, ARAB, Renal
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Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the 1
United Arab Emirates: A Cross-sectional Study 2
Wael M. Osman 1, Herbert F. Jelinek
2, 3, Guan K. Tay
1, 4, 5, Ahsan H. Khandoker
6, Kinda 3
Khalaf 6, Wael Almahmeed
7,8, Mohamed H. Hassan
9 & Habiba S. Alsafar
1, 6,* 4
1 Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates. 5
2 School of Community Health, Charles Sturt University, Albury, Australia. 6
3 Clinical Medicine, Macquarie University, Sydney, Australia. 7
4 School of Health and Medical Sciences, Edith Cowan University, Australia. 8
5 School of Psychiatry and Clinical Neurosciences, University of Western Australia, Australia. 9
6 Department of Biomedical Engineering, Khalifa University, United Arab Emirates. 10
7 Institute of Cardiac Science, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 11
8Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates. 12
9 Medical Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 13
Number of words: 299 in the abstract, 3971 in the main text 14
Number of tables: 5; embedded in the main text. No supplementary data. 15
Number of references: 53 16
Key words: type 2 diabetes, United Arab Emirates, diabetic kidney disease, genetics, 17
SHROOM3 gene. 18
* Corresponding author 19
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Dr. Habiba S. Al Safar 20
Director of Biotechnology Center, Associate Professor 21
Khalifa University 22
PO BOX 127788 Abu Dhabi, UAE 23
Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442 24
E-mail: [email protected] 25
26
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Abstract 38
Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic 39
kidney disease (DKD) have not been investigated. The aim of this report was to determine 40
possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. 41
Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 42
diabetes mellitus (T2DM) were recruited with and without DKD and their clinical and laboratory 43
data obtained. Following adjustments for possible confounders, a logistic regression model was 44
used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci 45
with DKD (145 patients and 265 controls). Linear regression models, adjusted for age and 46
gender, were used to test the genetic associations of five renal function traits, including 83 SNPs 47
with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs 48
with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs 49
with blood urea. 50
Results: Patients with DKD compared to those without DKD were mostly males (~52% versus 51
38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and 52
dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 53
years), which in an additive manner was the single factor that significantly contributed to the 54
development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the 55
associations of the genetic loci with different renal function traits found that the most notable 56
associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD 57
(Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 58
with serum creatinine and eGFR levels. 59
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Conclusions: Strong associations were found between several genetic loci and risk markers for 60
diabetic kidney disease, which may influence kidney function traits and DKD in a population of 61
Arab ancestry. 62
63
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65
66
67
68
69
70
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Strengths and limitations of this study 79
• This is a cross-sectional study to determine the clinical, laboratory, and genetic associations 80
of the diabetic kidney disease (DKD) and renal function traits in a sample of patients with 81
type 2 diabetes mellitus (T2DM) from the United Arab Emirates. 82
• For the DKD associations, analyses were performed using logistic regression models, and 83
for renal function traits’ associations, linear regressions were performed; all included 84
adjustment for possible covariates. 85
• Genetic association studies were performed using the candidate-gene approach; in which we 86
selected single nucleotide polymorphisms (SNPs) which reported with different tested traits 87
and being exist in database of the Emirates Family Registry for type 2 diabetes mellitus 88
(T2DM). 89
• A limitation of this study is that analyses carried in this study did not include treatment 90
modalities because most of patients had multiple conditions and they used several treatments; 91
which made the models largely unstable and difficult to interpret. 92
• Current analyses of the DKD genetic association had a statistical power ~ 57%; necessitating 93
a larger population sample across the Middle East for sufficient statistical power to discover 94
novel clinical and genetic predisposition for DKD in the region. 95
96
97
98
99
100
101
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Introduction 102
Diabetic kidney disease (DKD), considered as the most common cause of nephropathy in many 103
populations, is a public health challenge worldwide. Globally, the prevalence of chronic kidney 104
disease (CKD) among adults in the general population was reported to be around 10%, with the 105
United States showing a figure around 3.3% for DKD in 2008 1 2
. In Arab world, the prevalence 106
of DKD is widely variable (10.8% to 61.2%) depending on the study design, source of study 107
population, sample selection, race, age, sex structure of the study population, diagnostic criteria 108
among other factors 3. Meta-analysis showed that DKD is the leading cause of the end-stage 109
renal disease (ESRD) in the Gulf Cooperation Council (GCC) with a prevalence of ~ 17% 4. In 110
addition to increasing the risk of cardiovascular morbidity and mortality 5 6, DKD is reported to 111
be the single strongest predictor of mortality in patients with diabetes 7 with five year survival in 112
the range of 30% 8. DKD often progresses to end-stage renal disease (ESRD). Patients with 113
ESRD have a 20% annual mortality rate, which is higher than the rate for many solid cancers 9
. 114
Overall, the risk of DKD in T2DM is approximately 2% per year 10
. The current trend suggests 115
that the prevalence of DKD will continue to increase, constituting a significant socioeconomic 116
burdens on global healthcare systems and leading to increased morbidity and mortality 2. A 117
thorough literature search shows that there remains a knowledge gap related to the understanding 118
of risk factors and pathophysiological mechanisms associated with DKD, especially in the 119
Middle East. Since ESRD can only be treated with highly invasive and costly procedures, such 120
as dialysis or kidney transplantation, a better knowledge of genetic, clinical, and epidemiological 121
factors associated with DKD is required to allow earlier and more effective treatment outcomes. 122
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In clinical practice, renal function is assessed using a number of tests that are reported to have 123
high heritability rates 11
, indicating that genetic factors contribute significantly to inter-individual 124
variance in kidney function, and hence, to the susceptibility to CKD and related conditions. 125
So far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal 126
function traits in adults 12-23
and children 24
. However, in comparison to other diseases, including 127
T2DM and other cardiometabolic disorders, studies of kidney disease and kidney function traits 128
are insufficient. In spite of efforts to describe novel biomarkers for DKD, no tested candidates 129
outperform albumin. A recent report by Pierre-Jean Saulnier et al. suggested that three serum 130
biomarkers (midregional-proadrenomedullin: MR-proADM, soluble tumor necrosis factor 131
receptor 1: sTNFR1, and N-terminal prohormone brain natriuretic peptide: NT-proBNP) can 132
improve the risk prediction of the loss of renal function in patients with T2DM in addition to the 133
established risk factors for DKD such as age, sex, diabetes duration, HbA1c, blood pressure, 134
baseline eGFR, and albumin-to-creatinine ratio 25
. However, the issues of whether the levels of 135
these markers are affected by genetic variations, and whether the encoding genes contribute to 136
DKD development and progress need further investigation. 137
The United Arab Emirates (UAE) is among the countries with the highest prevalence rates of 138
T2DM, obesity and cardiovascular conditions 26 27
. Al-Safar and colleagues recently reported that 139
approximately 80% of T2DM patients within UAE present with at least one complication 140
associated with T2DM, including kidney disease (approximately 6%) 28
. Furthermore, there is 141
increasing evidence suggesting that the genome structure of individuals of Arabic descent is 142
different from individuals from other populations 29
. Despite the high prevalence rate of DKD in 143
the UAE, to our best knowledge, there have been no investigations of the genetic associations of 144
chronic kidney conditions and kidney functions in this population, particularly associated with 145
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T2DM. Therefore, the current work aimed to investigate the clinical and laboratory variables 146
linked to DKD in a T2DM Emirati population, and to investigate the associations of the reported 147
genetic loci linked to different renal function tests, in CKD and DKD. 148
149
Materials and Methods 150
Study type and subjects 151
This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi. The 152
demographic information and clinical data for the participants are presented in Tables 1 and 2. 153
Four hundred and ninety patients with T2DM were included in the study, with 145 diagnosed 154
with DKD. The participants were recruited from Sheikh Khalifa Medical City (SKMC) and 155
Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were UAE born and 156
with Arabic descent. 157
Patient and Public Involvement 158
Patients and public were not involved in setting the research question, results analyses or results 159
writing. However the study was designed because T2DM is a major health problem in the UAE 160
and has a growing public interest. 161
Clinical variables and laboratory data 162
Various clinical and laboratory measures were assessed and collected during the hospital visits. 163
Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood 164
pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking 165
antihypertensive medications. Dyslipidemia was reported in the clinical records of participants or 166
diagnosed as previously indicated 30
. The presence of T2DM was confirmed by a qualified 167
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physician based on the criteria outlined by the World Health Organization (WHO) consultation 168
group report 31
. Trained nurses measured the height and weight of each participant using a 169
calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the 170
weight in kilograms divided by the square of height of each subject (kg/m2). 171
Definition of the DKD 172
DKD definition was according to Ref #2. Briefly, this was defined as either decreased levels of 173
eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 174
32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour 175
period in the setting of T2DM and/or abnormalities as assessed by imaging or histology 33
. 176
eGFR was calculated according to CKD-EPI Creatinine Equation 34
.Accordingly, 145 TD2M 177
patients were identified with DKD, while 265 were disease free (Tables 1 and 2). The remaining 178
80 patients could not be classified with or without DKD at the time of the study, and were 179
excluded from subsequent analyses. 180
Selection of SNPs 181
Genetic studies in this study follow the candidate gene approach. The SNPs tested for each trait 182
are summarized in Table 1. These SNPs were selected from a recent Genome Wide Association 183
Study (GWAS) that was intended to determine the genetic associations of T2DM in the UAE 184
population, establishing the Emirates Family Registry for T2DM 26
. The GWAS was performed 185
for the 490 samples with T2DM, and 450 healthy controls on the Infinium Omni5ExomeHuman 186
chip (Illumina Inc., San Diego, USA). To select SNPs that are associated with kidney function 187
traits included in the study (blood urea, serum creatinine, eGFR values, albumin-to-creatinine 188
ratio, and vitamin D levels) as well as DKD, various search engines and data bases including 189
PubMed, Google Scholar, the GWAS catalog (https://www.ebi.ac.uk/gwas/home), the 190
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Phenolyzer database (http://phenolyzer.wglab.org/), the infinome genome interpretation platform 191
(https://www.infino.me/), and the GWAS Central database (http://www.gwascentral.org/) were 192
consulted. 193
Our search strategy consisted of identifying reported SNPs that cleared the GWAS significance-194
level and were found in our GWAS data. If the original signal SNP was missing from the GWAS 195
data, we searched for a possible proxy SNP utilizing the concept of linkage disequilibrium (LD), 196
which indicates a non-random association of alleles at different genetic loci in a given population 197
and their tendency to be inherited as a block (mathematical values r2 and D’ > 0.8 indicates high 198
LD). Proxy SNPs were selected using the SNAP database for SNP Annotation and Proxy Search 199
(http://archive.broadinstitute.org/mpg/snap/ldsearch.php). 200
All SNPs located within and flanking genes that have been reported with CKD and DKD were 201
included. In total, 43 genetic loci were identified as linked to CKD, DKD, or a decline in renal 202
functions. Specifically, the gene regions are ACACB, ACE, ACTN4, ADIPOQ, ADM, AFF3, 203
AGTR1, APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-ELMO1, ENPP1, 204
ERBB4, FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16, MYH9, NCALD, 205
NCK1, NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPARγ2, PVT1, RAGE, RGMA, RPS12, SFI1, 206
SHROOM3, TMEM22, TNFRSF1A, and TRPC6. 207
Statistical analyses 208
Continuous variables were presented as the means ± standard deviations or lower / median and 209
upper quartiles where the distributions are highly skewed. Vitamin D and eGFR levels were 210
normally distributed. However, urea levels, creatinine levels, and albumin-to-creatinine ratio data 211
were converted to normal distributions using natural log transformation. The associations of trait 212
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values or their natural logs (if transformed) were tested with SNPs using linear regression 213
models, which included age and gender as covariates using PLINK software version 1.07 214
(http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele frequencies 215
and testing the quality control (QC) variables, where any SNPs with minor allele frequency 216
(MAF) <0.05, >5% missing genotype rate, or those that failed the Hardy-Weinberg equilibrium 217
(HWE) test at the 0.001, were excluded. Hardy-Weinberg equilibrium is an important QC test for 218
the genetic association studies assumes that allele and genotype frequencies can be estimated. If 219
the frequencies of the measured genotypes significantly differ from the HWE assumptions, it can 220
indicate genotyping errors among other possible factors, such as ethnic diversity and high level 221
of consanguinity in the population; therefore those SNPs were excluded from further analyses. 222
Association with P < 0.05 were reported, pointing to the replication of previously-reported 223
associations. 224
All statistical analyses for the clinical and laboratory variables were performed using Stata 225
software version 14 (StataCorp LLC, Texas, USA). For continuous data, statistical differences 226
were assessed using two-sided t-tests for normally distributed data or Wilcoxon rank-sum 227
(Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for 228
percentage data or Fishers Exact test when expected frequencies are less than 5. A P-value < 229
0.05 was considered as significant. PLINK software was also used for testing the associations 230
between the SNPs and DKD using a case-control logistic regression model, which included age, 231
gender, hypertension status, T2DM duration, and eGFR levels as covariates (Table 1). The 232
results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% 233
confidence intervals. The same approach (patients with DKD vs. patients without DKD) was 234
adopted to test the associations between the possible risk factors and the development of the 235
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DKD (Table 3). However, the logistic model which includes all the associated factors in Table 4 236
was validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) to allow for the inclusion 237
of several covariates. Analyses between SNPs and renal function traits were conducted in PLINK 238
using linear regression models that included age and gender as covariates. Results were 239
presented as beta (regression coefficient for the linear regression model, calculated based on the 240
minor allele), standard errors, and P-values. 241
As this is a replication study, we reported here all P-values < 0.05, suggesting possible 242
replications. However, using a Bonferroni correction for multiple testing, P-values with 243
statistically significant associations are like the following: < 0.00079 for the DKD associations, < 244
0.0006 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the 245
creatinine, and 0.00068 for the urea associations. 246
Statistical power considerations: 247
Assuming the current sample size (145 cases vs. 265 controls for DKD), prevalence of DKD 248
~20% (depending on references # 3 and #4), genotype risk (odds ratio) = 1.5, significance level 249
of 0.0008 (following correction of multiple testing using Bonferroni correction), disease allele 250
frequency = 0.5, and multiplicative model, the current study has a power ~ 57%. Calculation are 251
made using the Genetic Association Study (GAS) Power Calculator 252
(http://csg.sph.umich.edu//abecasis/cats/gas_power_calculator/index.html ), which was 253
developed by the Department of Biostatistics Center for Statistical Genetics, University of 254
Michigan School of Public Health. 255
Ethical considerations 256
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Each patient agreed to take part in this study and gave signed consent after a brief session to 257
explain aims and methods. The study was approved by the Institutional Ethics Committee of 258
both hospitals (REC-04062014 and R292, respectively) and conforms to the ethical principles 259
outlined in the Helsinki declaration. 260
Funding: This study was supported by research incentive funds from Khalifa University Internal 261
Research Fund Level 2 granted to Dr. Habiba Al Safar. 262
263
Results 264
Baseline data of kidney function associated traits and SNPs selection 265
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred 266
and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 267
for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum 268
creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications 269
relating to the diagnosis of kidney disease (145 patients and 265 controls) according to the 270
diagnostic criteria [Reference #2] or patients’ medical records. The possible confounding factors 271
which may affect the genetic associations were included for each trait analysis and summarized 272
in Table 1. 273
274
275
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Table 1: Baseline data of tested traits and tested SNPs 276
Trait
Mean ± SD or
quartiles
N of subjects
(Male/Female)
N of SNPs
reported **
N of SNPs
tested **
Covariates
ACR (mg/mmol) 1.05 / 3.9/ 12.7 * 115 (56/59) 331 83 Age, Gender
Vitamin D (ng/ml) 63.7 ± 27.8 328 (146/182) 442 92 Age, Gender
eGFR (ml/min/1.73m2) 81.5 ± 28.5 395 (172/223) 1478 288 Age, Gender
Serum Creatinine (µmol/l) 60 / 74/ 95.8 * 474 (246/228) 1792 363 Age, Gender
Blood Urea (mmol/l) 6.4 ± 5.4 450 (263/214) 446 73 Age, Gender
Diabetic Kidney Disease (DKD)
Cases: 145
Controls: 265
288 63
Age, Gender,
Hypertension, T2DM
duration, eGFR levels
* For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25
th/ 50
th/ 75
th quartiles and not as mean ± standard 277
deviation because their distributions are skewed. 278
** Reported: means the total number of SNPs found in the search in the literature, tested: means the actual number of SNPs found from the 279
reported SNPs and used for the analyses in this study for each corresponding trait. 280
Abbreviations: N: number; SD: standard deviation; SNP: single nucleotide polymorphism, ACR: albumin-to-creatinine ratio; T2DM: type 2 281
diabetes mellitus; eGFR: estimated glomerular filtration rate.282
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Clinical and laboratory characteristics of patients with and without DKD 283
Expectedly, both patient groups (with or without kidney disease) had poor glycemic control with 284
blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those without 285
DKD indicated that the majority of those with DKD were males (~52% versus 38% for controls), 286
older (67 versus ~59 years), had significant rates of comorbidities such as hypertension and 287
dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal 288
function indices was also observed in patients with DKD vs. those who don’t have DKD 289
indicated by the significantly higher rates of ACR, urea, and creatinine; and significantly lower 290
eGFR (Table 2). However, DKD patients tended to have lower LDL-cholesterol results 291
compared to those without DKD, which suggests these patients received better medical care or at 292
least received intensive statin medication (Table 2). 293
294
295
296
297
298
299
300
301
302
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Table 2: Demographic, clinical, and laboratory characteristics of T2DM patients with or without 303
kidney disease 304
Type of variables Variable DKD No DKD P *
Demographic variables
Gender: Female **
70 (48.3%) 165 (62.3%) 0.006
Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001
Clinical variables
Clinical hypertension **
96.6% 74.3 % < 0.0001
Dyslipidemia **
95.2% 90.9% 0.17
Smoking history **
32.4% 24.2% 0.07
Diabetes duration (years) 16.0 ± 9.2 10.1 ± 7.3 < 0.0001
BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.078
Laboratory variables
Glycemic indices HbA1c (%) 7.7 ± 1.5 7.8 ± 1.7 0.63
Fasting plasma glucose
(mmol/l) 8.3 ± 3.1 8.9 ± 3.8 0.43
Random blood glucose
(mmol/l) 9.1 ± 3.9 9.5 ± 4.3 0.34
Lipids profile Total-cholesterol (mmol/l) 3.7 ± 1.0 4.0 ± 1.1 0.003
Triglyceride (mmol/l) 1.5 ± 0.8 1.6 ± 0.8 0.25
HDL-cholesterol (mmol/l) 1.2 ± 0.6 1.2 ± 0.5 0.45
LDL-cholesterol (mmol/l) 1.9 ± 0.8 2.1 ± 0.9 0.01
Renal function
indices
Albumin:Creatinine Ratio
(mg/mmol) ***
3.3 / 10.1/ 69.6 0.8/ 2.2/ 9.0 0.0001
Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48
eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001
Creatinine (µmol/l) ***
82/ 111/ 146 55/ 64/ 76 < 0.0001
Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001
* P-value for continuous data, calculated using two-sided t-test except for Albumin: Creatinine Ratio and 305
Creatinine where it is for Wilcoxon rank-sum (Mann-Whitney) test. P-value for percentage data 306
calculated using Pearson chi-square test with except of hypertension and Dyslipidemia where Fisher 307
Exact test was used. 308
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** Proportional data shown as number of positive outcome and its percentage. All remaining continuous 309
data shown as mean ± standard deviation except of Albumin: Creatinine Ratio and Creatinine. 310
*** For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25
th/ 50
th/ 311
75th quartiles and not as mean ± standard deviation because their distributions are highly skewed. 312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
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Associated factors for developing DKD in UAE patients 329
Table 3 shows that T2DM duration was the single factor that significantly contributed to the 330
development of DKD. Increased risk for DKD was significantly associated with increasing 331
duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the 332
risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-333
8.02). Although the levels of serum creatinine indicated a trend for disease development 334
(P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03 (Table 3). 335
Table 3: Risk factors for development of kidney disease in patients with T2DM from UAE 336
Covariate OR 95% CI P-value
Age 0.99 0.96 - 1.04 0.80
Gender 0.90 0.34 - 2.36 0.83
BMI 1.02 0.97 – 1.08 0.49
Hypertension 2.00 0.60 - 6.74 0.26
HbA1c 0.86 0.69 – 1.07 0.19
Cholesterol 0.59 0.19 – 1.76 0.34
Triglyceride 1.09 0.62 – 1.94 0.76
HDL-cholesterol 1.25 0.64 – 2.45 0.52
LDL-cholesterol 1.38 0.42 – 4.57 0.60
Smoking history 0.80 0.34 – 1.87 0.61
Creatinine 1.03 1.00 – 1.06 0.03
Urea 1.13 0.92 – 1.38 0.26
eGFR 0.97 0.94 – 1.00 0.09
Diabetes duration*
5-10 years 0.75 0.26 – 2.16 0.59
11-15 years 1.49 0.56 – 3.91 0.42
16-20 years 3.35 1.22 – 9.23 0.02
> 20 years 3.12 1.21 – 8.02 0.02
*Diabetes duration reference is duration ≤ 5 years 337
Abbreviations: BMI: body mass index, eGFR: estimated glomerular filtration rate. 338
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Genetic associations of renal function associated traits in UAE participants 339
The results of genetic associations for each tested renal function trait are shown in Table 4. In 340
summary, no association passed the Bonferroni correction for multiple testing. However, we 341
report here the most suggestive associations which point to replications of previous reports. 342
For blood urea, the best observed association was with rs11868441 in BCAS3 (effect size: -0.038 343
log per allele A, P=0.014), followed by multiple SNPs in the RSPO3 gene. 344
For serum creatinine, two SNPs (rs6999484 and rs1705699) in the intergenic region between 345
STC1 and ADAM28, showed the best associations with similar effect sizes (0.03 log per one copy 346
of the corresponding minor allele), followed by the SNP rs2828785, which is located in the non-347
gene area on chromosome 21. In addition, multiple genetic areas were also indicated, although 348
with less significant associations. 349
eGFR levels were significantly associated with SNPs within the MED1 gene, rs2168785 with 350
effect size -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and P=0.015 351
and 0.017. In addition, the SNPs in two genetic regions, WDR72 and SHROOM3, with 352
associations with serum creatinine levels, were also associated with eGFR levels, indicating a 353
strong link to renal function. 354
Vitamin D levels were associated with three genetic regions including rs1801725 in CARS 355
(effect size: -6.923 per allele A, P=0.0078), rs1155563 in GC (effect size: -6.951, P=0.0081), and 356
two SNPs in CYP2R1 with effect sizes of approximately -5.0 and P-values near 0.01. 357
One SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 358
(effect size: -0.323 log per allele A, P=0.027) was strongly associated with the albumin-to-359
creatinine ratio. 360
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This data showed that renal function traits, are linked to several loci in the UAE population, and 361
that some loci (e.g. WDR27 and SHROOM3) are linked to more than one trait. 362
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Table 4: Results of genetic association analyses of different renal function indices 363
SNP Chr: BP Gene A1/A2 * MAF_% Beta
** SE P
Blood Urea
rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014
rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028
rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.03
rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.03
rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039
Serum Creatinine
rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.003
rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.005
rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.008
rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019
rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022
rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034
rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038
rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042
Estimated Glomerular Filtration Rate
rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015
rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017
rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031
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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031
rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035
rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037
rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042
Vitamin D (25-OH Cholecalciferol)
rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.008
rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.008
rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015
rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016
Albumin:Creatinine Ratio
rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027
* A1/A2: minor to major alleles. 364
** Beta: regression coefficient for the linear regression model, calculated based on A1 (the minor allele). 365
Abbreviations: BP: base pair position; Chr: chromosome; MAF: minor allele frequency; SE: standard error; SNP: single nucleotide polymorphism.366
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Genetic associations of DKD in patients from UAE 367
Sixty-three SNPs in 43 genetic loci that have previously been linked to chronic or diabetic 368
kidney disease were included in our genetic analysis of DKD (145 case versus 265 controls). A 369
logistic regression model including five possible covariates, which may affect the development 370
of kidney disease, was applied (Table 1). As shown in Table 5, the unadjusted analysis indicates 371
two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682, P=0.024). 372
Following the adjustment for possible covariates, only the SHROOM3 rs4859682 remained 373
significant (P=0.04, OR=1.46). This result confirms that SHROOM3 is a string risk locus for 374
DKD, considering the similar associations with serum creatinine and eGFR levels. 375
376
377
378
379
380
381
382
383
384
385
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Table 5: Association between SNPs linked to CKD and DKD patients from UAE. 386
SNP Chr: BP Gene * A1/A2
** MAF Punadjusted Padjusted OR (95% CI)
Case (N=145) Control (N=265)
rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)
rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)
* CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3. 387
** A1/A2: minor to major alleles. 388
Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; MAF: minor allele frequency; N: number; OR: odds ratio; 389
SNP: single nucleotide polymorphism. 390
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Discussion 391
A combination of environmental and clinical factors in genetically predisposed individuals have 392
been suggested to be involved in DKD such as persistent hyperglycemia, arterial hypertension 393
and/or dyslipidemia 35
. In fact, familial aggregation of nephropathy in T2DM has been reported 394
in several populations 36
.Therefore, understanding the interactions between genetic, clinical and 395
traditional kidney disease risk factors can provide insight into new treatment strategies for 396
improving DKD and reducing the likelihood of developing ESRD. In this study, we investigated 397
whether genetic markers correspond to DKD and renal function traits that were reported in 398
different populations are similar or different in the Arab population. 399
The current Emirati population sample indicated that most T2DM patients who developed DKD 400
were males, older by about 10 years than those that did not develop the disease, had more 401
frequent comorbidities specifically hypertension, and showed a marked decline in their renal 402
function profiles. However, even T2DM patients who did not develop DKD had higher rates of 403
comorbities and had poor diabetic control, in agreement with our previous results 28
. We also 404
found that the duration of diabetes was the single factor that significantly contributed to the 405
development of kidney disease, regardless any other factor. However, this risk of the DKD 406
development becomes significant when the duration of T2DM reaches the 15 year mark. This is 407
in concordance with previous reports, which suggest that T2DM patients who do not develop 408
signs of kidney disease by 15 years duration of diabetes onset seem to be protected, most likely 409
due to genetic factors 37
. 410
The most notable finding of this study was the association of SHROOM3 (Shroom Family 411
Member 3) with serum creatinine, eGFR, and DKD. The minor allele for the SNP rs4859682 (A) 412
was observed to increase the serum creatinine (0.021 log increase per one copy), and also to 413
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increase the risk for DKD (OR=1.46). SHROOM3 was first reported to be associated with eGFR 414
levels in DKD patients 12
, then with serum creatinine 38
and serum magnesium levels 39
. This 415
association was further replicated in different ethnicities 18 40
. The SHROOM3 gene product is 416
expressed in the human kidney and is reported to play an important role in epithelial cell shape 417
regulation 41
, as well as the maintenance of the glomerular filtration barrier integrity 42
. Defective 418
Shroom3 protein leads to decreased actin organisation and affects mechanical characteristics and 419
integrity of the glomerular podocyte resulting in thinning of the glomerular filtration membrane 420
42. Additionally, Shroom3 heterozygous (Shroom3
Gt/+) mice showed developmental irregularities 421
that manifested as adult-onset glomerulosclerosis and proteinuria 43
. Furthermore, genetic 422
variants (such as the intronic variant rs17319721), were found to contribute to kidney allograft 423
injury and fibrosis development through a mechanism involving TGF-β1 signaling 44
. Although 424
the variant rs17319721 was not found in our dataset, it is highly linked to the SNP rs4859682 425
(r2=0.85, D’=1), which was reported in this study to increase the risk for DKD and affect the 426
levels of serum creatinine. Overall, this suggests that SHROOM3 may be considered as a multi-427
ethnic risk gene for DKD and its related kidney function traits in various populations, including 428
the UAE. 429
Similarly, the two loci; WDR72 (WD Repeat Domain 72, associated with eGFR and serum 430
creatinine levels), and BCAS3 (Breast Carcinoma-Amplified Sequence 3, associated with blood 431
urea levels), are also well-known trans-ethnic renal function traits loci 45
. WDR72 in particular 432
was well studied in association with kidney function traits and pathologies. For instance, WDR72 433
was reported to be associated with creatinine production or secretion 46
, and suggested to be 434
associated with signal transduction, cell cycle regulation, and vesicular trafficking that affects 435
already mentioned podocyte activity, reduced eGFR and a progression of CKD 47
.In addition, 436
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RSPO3 (R-Spondin 3) was previously reported to be associated with blood urea nitrogen 437
concentration, in line with the results of the current study 40
. The association of rs4528660 near 438
MYOM1 (Myomesin 1) with the albumin-to-creatinine ratio was also in alignment with previous 439
work, which linked this locus to albuminuria in patients with diabetes 48
. The analyses performed 440
also replicated the associations of CASR (Calcium-sensing receptor), GC (vitamin D-binding 441
protein (VDBP) is also known as group-specific component (GC)- globulin), and CYP2R1 442
(Cytochrome P450 Family 2 Subfamily R Member 1) with levels of vitamin D 49
. These genes 443
encode proteins which are involved in vitamin D function including activation by hydroxylation 444
(CYP2R1), transportation (GC), and serum calcium level sensoring (CASR) 49
. The genes have 445
also been reported to be associated with calcium-vitamin D biology and pathology, such as 446
serum calcium levels, familial hypocalciuric hypercalcemia, tertiary hyperparathyroidism, and 447
vitamin D deficiency presenting as Rickets (see OMIM entries: CARS: 601199; CYP2R1: 448
608713; and GC: 139200). For instance, CASR protein is expressed in the kidney amongst other 449
tissue and regulates ion metabolism including calcium and magnesium. Mutations in CASR lead 450
to abnormalities in the regulation of parathyroid gland and renal function causing hypercalcemia 451
and increased blood pressure that in turn may affect kidney function 50
. Similarly, GC proteins 452
binds actin and acts as an actin scavenger (as such it may play a role in podocyte integrity) as 453
well as a binding site for Vit D. GC is the precursor to Gc-macrophage activating factor 454
(GcMAF), a macrophage activator and suggests together with Vit D that GC has an important 455
role in immune function and the pathogenesis of CKD 51
. For CYP2R1, it is reported that vitamin 456
D is hydroxylated at the C25 position by specific hydroxylase coded by the CYP2R1 gene to 25-457
hydroxyvitamin D (25(OH) D), which is the main circulating form of vitamin D. Low Vit D 458
observed in CKD due to reduced CYP2R1 production by the liver or due to a mutation in the 459
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gene, disturbs calcium balance and leads to hyperparathyroidism. Conversely the loss of renal 460
protection brought about by Vitamin D and an increase in the renin-angiotensin pathway leads to 461
hypertension that further advances kidney disease 52
. Furthermore, these genes have recently 462
been shown to influence outcome of Vitamin D3 supplementation, which in the Arab context is 463
an important finding of our study 53
. 464
In summary, this work presents the first study to investigate the clinical and genetic factors 465
influencing kidney function traits and DKD in a population of Arab ancestry. The results 466
demonstrate that the duration of T2DM is the single most important risk factor for DKD 467
development in patients with T2DM from UAE. Our study highlights that several genetic loci, 468
which have been previously linked to renal function associated traits, are shared between diverse 469
ethnic groups. As such we have replicated previous findings of the association of SHROOM3 470
with DKD. The logistic analyses carried in this study did not included treatment modalities 471
because most of patients have multiple conditions and their usage of several treatments, which 472
make the logistic largely unstable and difficult to interpret. It is worthwhile to note that a larger 473
population sample across the Middle East is required to confirm the extent of the shared genetic 474
predisposition reported in the current study. Considering the high prevalence of T2DM in this 475
population and the recent evidence of genomic structure variations among different ethnic 476
groups, more genetic-driven population studies are warranted towards effective genetically 477
guided personalized medicine. 478
479
Acknowledgments: We acknowledge the patients who volunteered to make this study possible. 480
We also acknowledge patients’ care advisers for their roles in this study. 481
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482
Author contribution: HSA obtained the fund for this study. WMO, HSA and HFJ have 483
designed the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and 484
KK reviewed/edited the manuscript and contributed to the discussion and reviewed/edited the 485
manuscript. WA and MHH provided assistance in patients’ recruitment and clinical data 486
collection. 487
488
Conflict of interests: None declared 489
490
Patient consent: Obtained. 491
492
Ethics approval: The Institutional Ethics Committee of Mafraq and Shaikh Khalifa Medical 493
Centre hospitals (REC-04062014 and R292, respectively). 494
495
Provenance and peer review: Not commissioned; externally peer reviewed. 496
497
Data sharing statement: No additional data are available. 498
499
References 500
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STREGA reporting recommendations, extended from STROBE Statement
Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A Cross-sectional Study
By Wael Osman et al.
Item Item
number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Title and Abstract
1
(a) Indicate the study’s design with a commonly used term in the title or the abstract.
Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A Cross-sectional Study
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number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic kidney disease (DKD) have not been investigated. The aim of this report was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD and their clinical and laboratory data obtained. Following adjustments for possible confounders, a logistic regression model was used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients and 265 controls). Linear regression models, adjusted for age and gender, were used to test the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. Results: Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels. Conclusions: Strong associations were found between several genetic loci and risk markers for diabetic kidney disease, which may influence kidney function traits and DKD in a population of Arab ancestry.
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Item Item
number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Introduction
Background rationale
2 Explain the scientific background and rationale for the investigation being reported.
Explain the scientific background and rationale for the investigation being reported.
Objectives 3 State specific objectives, including any pre-specified hypotheses.
The aim of this report was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of diabetic kidney disease in UAE population.
State if the study is the first report of a genetic association, a replication effort, or both.
This is a replication study for the genetic association part
Methods
Study design 4 Present key elements of study design early in the paper.
This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi.
Setting 5 Describe the setting, locations and relevant dates, including periods of recruitment, exposure, follow-up, and data collection.
The participants were recruited from Sheikh Khalifa Medical City (SKMC)
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Item Item
number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE.
Participants 6 (a) Cohort study – Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up.
Case-control study – Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls.
Cross-sectional study – Give the eligibility criteria, and the sources and methods of selection of participants.
All subjects were UAE born and with Arabic descent.
Give information on the criteria and methods for selection of subsets of participants from a larger study, when relevant.
145 diagnosed with DKD and 265 did not.
(b) Cohort study – For matched studies, give matching criteria and number of exposed and unexposed.
Case-control study – For matched studies, give matching criteria and the number of controls per case.
145 diagnosed with DKD and 265 did not.
Variables 7 (a) Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable.
Various clinical and laboratory measures were assessed and collected
(b) Clearly define genetic exposures (genetic variants) using a widely-used nomenclature system. Identify
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number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
during the hospital visits. Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation.
To select SNPs that are associated with kidney function traits included in the study (blood urea, serum creatinine, eGFR values, albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and data bases including PubMed, Google Scholar, the GWAS catalogue (https://www.ebi.ac.uk/gwas/home ), the Phenolyzer database (http://phenolyzer.wglab.org/ ), the infinome genome interpretation platform (https://www.infino.me/ ), and the GWAS Central database (http://www.gwascentral.org/ ) were consulted.
variables likely to be associated with population stratification (confounding by ethnic origin).
Genetic studies in this study follow the candidate gene approach. The SNPs tested for each trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide Association Study (GWAS) that was intended to determine the genetic associations of T2DM in the UAE population, establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).
Data sources measurement
8*
(a) For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group.
(b) Describe laboratory methods, including source and storage of DNA, genotyping methods and platforms
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Item Item
number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation. Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2).
(including the allele calling algorithm used, and its version), error rates and call rates. State the laboratory/centre where genotyping was done. Describe comparability of laboratory methods if there is more than one group. Specify whether genotypes were assigned using all of the data from the study simultaneously or in smaller batches.
The original Genome Wide Association Study (GWAS) from which we selected the tested SNPs was reported to establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).
Bias 9 (a) Describe any efforts to address potential sources of bias.
We applied filters for genotyping like H-W testing, allele frequencies
(b) For quantitative outcome variables, specify if any investigation of
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and genotype call rates filters. We also excluded data which is missed > 20% of participants from the analyses, and used covariates adjustment for each analysis.
potential bias resulting from pharmacotherapy was undertaken. If relevant, describe the nature and magnitude of the potential bias, and explain what approach was used to deal with this.
Not applicable
Study size 10 Explain how the study size was arrived at.
No prior calculations conducted
Quantitative variables
11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen, and why.
Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2). Others quantitative variables like age or laboratory variables were collected without handling.
If applicable, describe how effects of treatment were dealt with.
For genetic associations with quantitative variables in this study, data was normalized using log transformation.
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Statistical methods
12
(a) Describe all statistical methods, including those used to control for confounding.
For continuous data, statistical differences were assessed using two-sided t-tests for normally distributed data or Wilcoxon rank-sum (Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for percentage data or Fishers Exact test when expected frequencies are less than 5. A P-value < 0.05 was considered as significant. PLINK software was also used for testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. The results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% confidence intervals. The same approach (patients with DKD vs. patients without DKD) was adopted to test the associations between the possible risk factors and the development of the DKD. However, the logistic model which includes all the associated factors in Table 4 was validated by the Hosmer–Lemeshow goodness of fit test to allow for the inclusion of several covariates. Analyses between SNPs and renal function traits were conducted in PLINK using linear regression models that included age and gender as covariates. Results were presented as beta (regression coefficient for the linear regression model, calculated based on the minor allele), standard errors, and P-values.
State software version used and options (or settings) chosen.
PLINK software version 1.07
Stata software version 14 (StataCorp LLC, Texas, USA)
(b) Describe any methods used to examine subgroups and interactions.
No methods used to examine subgroups and interactions.
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(c) Explain how missing data were addressed.
Any missing data in > 20% of participants were excluded to avoid bias.
(d) Cohort study – If applicable, explain how loss to follow-up was addressed.
Case-control study – If applicable, explain how matching of cases and controls was addressed.
Cross-sectional study – If applicable, describe analytical methods taking account of sampling strategy.
No specific sampling strategy.
(e) Describe any sensitivity analyses.
No sensitivity analyses.
(f) State whether Hardy-Weinberg equilibrium was considered and, if so, how.
SNPs failed the Hardy-Weinberg equilibrium (HWE) test at the 0.001 were excluded.
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(g) Describe any methods used for inferring genotypes or haplotypes.
No methods used for inferring genotypes or haplotypes.
(h) Describe any methods used to assess or address population stratification.
The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the population stratification. This current study is not a GWAS, so we did not address this issue again.
(i) Describe any methods used to address multiple comparisons or to control risk of false positive findings.
We mentioned that the current
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study is a replication study, therefore we reported here all P-values < 0.05, suggesting possible replications. However, using a Bonferroni correction for multiple testing, P-values with statistically significant associations are like the following: < 0.00079 for the DKD associations, < 0.0006 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine, and 0.00068 for the urea associations.
(j) Describe any methods used to address and correct for relatedness among subjects.
The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the relatedness among subjects. This current study is not a GWAS, so we did not address this issue again.
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Results
Participants
13* (a) Report the numbers of individuals at each stage of the study – e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed.
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls)
Report numbers of individuals in whom genotyping was attempted and numbers of individuals in whom genotyping was successful.
In all of participants genotyping was successful.
(b) Give reasons for non-participation at each stage.
Non-participation were excluded. This is a one stage study
(c) Consider use of a flow diagram.
No flow diagram
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Descriptive data
14*
(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders.
Both patient groups (with or without kidney disease) had poor glycemic control with blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those without DKD indicated that the majority of those with DKD were males (~52% versus 38% for controls), older (67 versus ~59 years), had significant rates of comorbidities such as hypertension and dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal function indices was also observed in patients with DKD vs. those who don’t have DKD indicated by the significantly higher rates of ACR, urea, and creatinine; and significantly lower eGFR. However, DKD patients tended to have lower LDL-cholesterol results compared to those without DKD, which suggests these patients received better medical care or at least received intensive statin medication. T2DM duration was the single factor that significantly contributed to the development of DKD. Increased risk for DKD was significantly associated with increasing duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-8.02). Although the levels of serum creatinine indicated a trend for disease development (P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03. We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.
Consider giving information by genotype.
- Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.
- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with creatinine.
- SNPs within the MED1 gene (rs2168785 and rs12452509), and SNPs in two genetic regions, WDR72 and SHROOM3 with eGFR.
- Rs1801725 in CARS, rs1155563 in GC, and two SNPs in CYP2R1 with vitamin D levels.
- SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 with albumin-to-creatinine ratio.
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(b) Indicate the number of participants with missing data for each variable of interest.
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls).
(c) Cohort study – Summarize follow-up time, e.g. average and total amount.
The study was not a cohort study.
Outcome data 15 * Cohort study-Report numbers of outcome events or summary measures over time.
Report outcomes (phenotypes) for each genotype category over time
Case-control study – Report numbers in each exposure category, or summary measures of exposure.
Report numbers in each genotype category
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Cross-sectional study – Report numbers of outcome events or summary measures.
There is no exposure-outcome measurements.
Report outcomes (phenotypes) for each genotype category
Not applicable
Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence intervals). Make clear which confounders were adjusted for and why they were included.
For testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. Analyses between SNPs and renal function traits were conducted using linear regression models that included age and gender as covariates.
(b) Report category boundaries when continuous variables were categorized.
Continuous variables were not categorized.
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period.
We did not translating estimates of relative risk into absolute risk for a meaningful time period.
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(d) Report results of any adjustments for multiple comparisons.
We discussed the Bonferroni correction for multiple testing in the study.
Other analyses 17 (a) Report other analyses done – e.g., analyses of subgroups and interactions, and sensitivity analyses.
No analyses for subgroups, interactions, or sensitivity analyses.
(b) If numerous genetic exposures (genetic variants) were examined, summarize results from all analyses undertaken.
Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.
- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with
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creatinine.
- SNPs within the MED1 gene (rs2168785 and rs12452509), and SNPs in two genetic regions, WDR72 and SHROOM3 with eGFR.
- Rs1801725 in CARS, rs1155563 in GC, and two SNPs in CYP2R1 with vitamin D levels.
- SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 with albumin-to-creatinine ratio.
- SHROOM3 rs4859682 remained significant (P=0.04, OR=1.46) with DKD.
(c) If detailed results are available elsewhere, state how they can be accessed.
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Not applicable
Discussion
Key results 18 Summarize key results with reference to study objectives. Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.
Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias. A limitation of this study is that analyses carried in this study did not included treatment modalities because most of patients had multiple conditions and they used several treatments; which make the models largely unstable and difficult to interpret. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for
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sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.
Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.
Generalizability 21 Discuss the generalizability (external validity) of the study results. Some of the loci which reported previously in other populations did not replicated because of the statistical power issue. In addition, due to lack of results in this topic in the other populations, we cannot confirm the results of this study will be replicated
Other Information
Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based. This study was supported by research incentive funds from Khalifa University Internal Research Fund Level 2 granted to Dr. Habiba Al Safar. No role of funders taken in this study.
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For peer review onlyClinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A
Cross-sectional Study
Journal: BMJ Open
Manuscript ID bmjopen-2017-020759.R2
Article Type: Research
Date Submitted by the Author: 16-Sep-2018
Complete List of Authors: Osman, Wael; Khalifa University of Science Technology and Research, Biotecnology Center Jelinek, H. F.; Macquarie University Faculty of Medicine and Health Sciences, Clinical MedicineTay, Guan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khandoker , Ahsan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khalaf, Kinda; Khalifa University of Science Technology and Research, Biomedical Engineering AlMahmeed , Wael; Sheikh Khalifa Medical City, Institute of Cardiac ScienceHassan, Mohamed; Sheikh Khalifa Medical City, Medical InstituteALsafar, Habiba; Khalifa University of Science Technology and Research, Biomedical Engineering
<b>Primary Subject Heading</b>: Diabetes and endocrinology
Secondary Subject Heading: Genetics and genomics, Global health
Keywords: Diabetes, UAE, ARAB, Renal
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Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the 1
United Arab Emirates: A Cross-sectional Study 2
Wael M. Osman 1, Herbert F. Jelinek
2, 3, Guan K. Tay
1, 4, 5, 6, Ahsan H. Khandoker
6, Kinda 3
Khalaf 6, Wael Almahmeed
7,8, Mohamed H. Hassan
9 & Habiba S. Alsafar
1, 6,* 4
1 Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates. 5
2 School of Community Health, Charles Sturt University, Albury, Australia. 6
3 Clinical Medicine, Macquarie University, Sydney, Australia. 7
4 School of Health and Medical Sciences, Edith Cowan University, Australia. 8
5 School of Psychiatry and Clinical Neurosciences, University of Western Australia, Australia. 9
6 Department of Biomedical Engineering, Khalifa University, United Arab Emirates. 10
7 Institute of Cardiac Science, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 11
8Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates. 12
9 Medical Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 13
Number of words: 299 in the abstract, 3999 in the main text 14
Number of tables: 5; embedded in the main text. No supplementary data. 15
Number of references: 55 16
Key words: type 2 diabetes, United Arab Emirates, diabetic kidney disease, genetics, 17
SHROOM3 gene. 18
* Corresponding author 19
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Dr. Habiba S. Al Safar 20
Director of Biotechnology Center, Associate Professor 21
Khalifa University 22
PO BOX 127788 Abu Dhabi, UAE 23
Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442 24
E-mail: [email protected] 25
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Abstract 38
Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic 39
kidney disease (DKD) have not been investigated. The aim of this research was to determine 40
possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. 41
Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 42
diabetes mellitus (T2DM) were recruited with and without DKD and clinical and laboratory data 43
obtained. Following adjustments for possible confounders, a logistic regression model was used 44
to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with 45
DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for 46
age and gender, were used to test the genetic associations of five renal function traits, including 47
83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 48
288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 49
73 SNPs with blood urea. 50
Results: Patients with DKD compared to those without DKD were mostly males (52% versus 51
38% for controls), older (67 versus 59 years), and had significant rates of hypertension and 52
dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus 10 53
years), which in an additive manner was the single factor that significantly contributed to the 54
development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the 55
associations of the genetic loci with different renal function traits and found that the most notable 56
associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD 57
(Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 58
with serum creatinine and eGFR levels. 59
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Conclusions: Associations were found between several genetic loci and risk markers for DKD, 60
which may influence kidney function traits and DKD in a population of Arab ancestry. 61
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Strengths and limitations of this study 79
• This cross-sectional study to determine the clinical, laboratory, and genetic associations of 80
diabetic kidney disease (DKD) and renal function traits in a sample of patients with type 2 81
diabetes mellitus (T2DM), was the first in a population of Arab ancestry from the United 82
Arab Emirates (UAE). 83
• The study designed led to the identification of DKD associated SNPs and risk markers in the 84
UAE population. 85
• A limitation of this study was that the analyses carried out did not include treatment 86
modalities due to patients with multiple conditions and treatments, which made the models 87
largely unstable and difficult to interpret. 88
• Current analyses of the DKD genetic association had a statistical power of ~ 57%; suggesting 89
that a larger population sample across the Middle East is required to discover with better 90
statistical power novel clinical and genetic predisposition for DKD in the region. 91
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Introduction 102
Diabetic kidney disease (DKD) is considered the most common cause of nephropathy in many 103
populations and is a public health challenge worldwide. Globally, the prevalence of chronic 104
kidney disease (CKD) among adults in the general population was reported to be around 10% 1. 105
Thomas et al. reported that about 20% of adults with type 2 diabetes mellitus (T2DM) will have 106
DKD depending on the eGFR measurements (<60 ml/min/1.73 m2), and a diagnosis between 107
30% and 50% depending on the urinary albumin excretion levels. The considerable variation is 108
due to settings, geographical area, and ethnicity 2. Overall, the risk of DKD in T2DM is 109
approximately 2% per year 3. In the Arab world, the prevalence of DKD is highly variable 110
(10.8% to 61.2%) depending on the study design, source of study population, sample selection, 111
race, age, sex structure of the study population, and diagnostic criteria among other factors 4. 112
Meta-analysis has shown that DKD is the leading cause of end-stage renal disease (ESRD) in the 113
Gulf Cooperation Council (GCC) with a prevalence of ~ 17% 5. Patients with ESRD have a 20% 114
annual mortality rate, which is higher than the rate for many solid cancers 6
. In addition to 115
increasing the risk of cardiovascular morbidity and mortality 7 8, DKD is reported to be the single 116
strongest predictor of mortality in patients with diabetes 9 with a five year survival in the range 117
of 30% 10
. The current trend suggests that the prevalence of DKD will continue to increase, 118
constituting a significant socioeconomic burdens on global healthcare systems and leading to 119
increased morbidity and mortality 11
. A thorough literature search has shown that there remains a 120
knowledge gap related to the understanding of risk factors and pathophysiological mechanisms 121
associated with DKD, especially in the Middle East. Since ESRD can only be treated with highly 122
invasive and costly procedures, such as dialysis or kidney transplantation, a better knowledge of 123
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genetic, clinical, and epidemiological factors associated with DKD is required to allow earlier 124
and more effective treatment outcomes. 125
In clinical practice, renal function is assessed using a number of tests that are reported to have 126
high heritability rates 12
, indicating that genetic factors contribute significantly to inter-individual 127
variance in kidney function, and hence, to the susceptibility to CKD and related conditions. So 128
far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal 129
function traits in adults 13-24
and children 25
. However, in comparison to other diseases, including 130
T2DM and other cardiometabolic disorders, studies of kidney disease and kidney function traits 131
are insufficient and inconclusive. In spite of efforts to describe novel biomarkers for DKD, no 132
tested candidates outperform albumin. A recent report by Saulnier et al. suggested that three 133
serum biomarkers (midregional-proadrenomedullin: MR-proADM, soluble tumor necrosis factor 134
receptor 1: sTNFR1, and N-terminal prohormone brain natriuretic peptide: NT-proBNP) can 135
improve risk prediction of the loss of renal function in patients with T2DM in addition to the 136
established risk factors for DKD such as age, sex, diabetes duration, HbA1c, blood pressure, 137
baseline eGFR, and albumin-to-creatinine ratio 26
. However, the issues of whether the levels of 138
these markers are affected by genetic variations, and whether the encoding genes contribute to 139
DKD development and progress need further investigation. 140
The United Arab Emirates (UAE) is among the countries with the highest prevalence rates of 141
T2DM, obesity and cardiovascular conditions 27 28
. Al-Safar and colleagues recently reported that 142
approximately 80% of T2DM patients within UAE present with at least one complication 143
associated with T2DM, including kidney disease (approximately 6%) 29
. Furthermore, there is 144
increasing evidence suggesting that the genome structure of individuals of Arabic descent is 145
different from individuals from other populations 30
. Despite the high prevalence rate of DKD in 146
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the UAE, there have been no investigations of the genetic associations of chronic kidney 147
conditions and kidney functions in this population, particularly associated with T2DM. 148
Therefore, the current work aimed to investigate the clinical and laboratory variables linked to 149
DKD in a T2DM Emirati population, and to investigate the associations of the reported genetic 150
loci linked to different renal function tests, in CKD and DKD. 151
152
Materials and Methods 153
Study type and subjects 154
This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi. The 155
demographic information and clinical data for the participants are presented in Tables 1 and 2. 156
Four hundred and ninety (n=490) patients with T2DM were included in the study, with 145 157
diagnosed with DKD. The participants were recruited from Sheikh Khalifa Medical City 158
(SKMC) and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were 159
UAE born and of Arabian descent. 160
Patient and Public Involvement 161
The study was designed because T2DM is a major health problem in the UAE and has a growing 162
public interest. However, patients and public were not involved in setting of the research 163
question, analyses, interpretation or writing of the results. 164
Clinical variables and laboratory data 165
Various clinical and laboratory measures were assessed and collected during the hospital visits. 166
Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood 167
pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or if patients were taking 168
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antihypertensive medications. Dyslipidemia was reported in the clinical records of participants or 169
diagnosed as previously indicated 31
. The presence of T2DM was confirmed by a qualified 170
physician based on the criteria outlined by the World Health Organization (WHO) consultation 171
group report 32
. Trained nurses measured the height and weight of each participant using a 172
calibrated wall-mounted stadiometer and a weight scale, respectively. Body mass index (BMI) 173
was calculated as the weight in kilograms divided by the square of height (kg/m2). 174
Diabetic Kidney Disease 175
DKD was defined as either decreased levels of estimated glomerular filtration rate (eGFR) <60 176
ml/min/1.73 m2 with or without renal damage over a period of at least three months
33, or based 177
on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria >500 mg over a 24 hour period in the 178
setting of T2DM and/or abnormalities as assessed by imaging or histology 34
. eGFR was 179
calculated according to CKD-EPI Creatinine Equation 35
.Accordingly, 145 TD2M patients were 180
identified with DKD, while 265 were disease free (Tables 1 and 2). The remaining 80 patients 181
could not be classified with or without DKD at the time of the study, and were excluded from 182
subsequent analyses. 183
Selection of SNPs 184
Genetic studies in this study followed the candidate gene approach 36
. The SNPs tested for each 185
trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide 186
Association Study (GWAS) that was intended to determine the genetic associations of T2DM in 187
the UAE population and establishing the Emirates Family Registry for T2DM 27
. The GWAS 188
was performed for the 490 samples with T2DM, and 450 healthy controls on the Infinium 189
Omni5ExomeHuman chip (Illumina Inc., San Diego, USA). To select SNPs that are associated 190
with kidney function traits included in the study (blood urea, serum creatinine, eGFR values, 191
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albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and 192
data bases including PubMed, Google Scholar, the GWAS catalog 193
(https://www.ebi.ac.uk/gwas/home), the Phenolyzer database (http://phenolyzer.wglab.org/), the 194
infinome genome interpretation platform (https://www.infino.me/), and the GWAS Central 195
database (http://www.gwascentral.org/) were consulted. 196
Our search strategy consisted of identifying reported SNPs that cleared the GWAS significance-197
level and were found in our GWAS data. If the original signal SNP was missing from the GWAS 198
data, we searched for a possible proxy SNP utilizing the concept of linkage disequilibrium (LD), 199
which indicates a non-random association of alleles at different genetic loci in a given population 200
and their tendency to be inherited as a block (mathematical values r2 and D’ > 0.8 indicates high 201
LD). Proxy SNPs were selected using the SNAP database for SNP Annotation and Proxy Search 202
(http://archive.broadinstitute.org/mpg/snap/ldsearch.php). 203
All SNPs located within and flanking genes that have been reported with CKD and DKD were 204
included. In total, 43 genetic loci were identified as linked to CKD, DKD, or a decline in renal 205
functions. Specifically, the gene loci are ACACB, ACE, ACTN4, ADIPOQ, ADM, AFF3, AGTR1, 206
APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-ELMO1, ENPP1, ERBB4, 207
FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16, MYH9, NCALD, NCK1, 208
NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPARγ2, PVT1, RAGE, RGMA, RPS12, SFI1, 209
SHROOM3, TMEM22, TNFRSF1A, and TRPC6. 210
Statistical analyses 211
Continuous variables were presented as mean ± standard deviation or lower / median and upper 212
quartiles where the distributions are highly skewed. Vitamin D and eGFR levels were normally 213
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distributed. However, urea levels, creatinine levels, and albumin-to-creatinine ratio data were 214
converted to normal distributions using natural log transformation. The associations of trait 215
values or their natural logs (if transformed) were tested with SNPs using linear regression 216
models, which included age and gender as covariates using PLINK software version 1.07 217
(http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele frequencies 218
and testing the quality control (QC) variables, where any SNPs with minor allele frequency 219
(MAF) <0.05, and >5% missing genotype rate, or those that failed the Hardy-Weinberg 220
equilibrium (HWE) test at the 0.001, were excluded. Hardy-Weinberg equilibrium is an 221
important QC test for genetic association studies and assumes that allele and genotype 222
frequencies can be estimated. If the frequencies of the measured genotypes significantly differ 223
from the HWE assumptions, it can indicate genotyping errors among other possible factors, such 224
as ethnic diversity and high level of consanguinity in the population; therefore those SNPs were 225
excluded from further analyses. Association with P < 0.05 were reported, pointing to the 226
replication of previously-reported associations. 227
All statistical analyses for the clinical and laboratory variables were performed using Stata 228
software version 14 (StataCorp LLC, Texas, USA). For continuous data, statistical differences 229
were assessed using two-sided t-tests for normally distributed data or the Wilcoxon rank-sum 230
(Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for 231
percentage data or Fishers Exact test when expected frequencies were less than 5. A P-value < 232
0.05 was considered as significant. PLINK software was also used for testing the associations 233
between the SNPs and DKD using a case-control logistic regression model, which included age, 234
gender, hypertension status, T2DM duration, and eGFR levels as covariates (Table 1). The 235
results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% 236
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confidence intervals. The same approach (patients with DKD vs. patients without DKD) was 237
adopted to test the associations between the possible risk factors and the development of the 238
DKD (Table 3). However, the logistic model, which included all the associated factors listed in 239
Table 4 was validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) to allow for the 240
inclusion of several covariates. Analyses between SNPs and renal function traits were conducted 241
in PLINK using linear regression models that included age and gender as covariates. Results 242
were presented as beta (regression coefficient for the linear regression model, calculated based 243
on the minor allele), standard errors, and P-values. 244
As this was a replication study, we reported all P-values < 0.05, suggesting possible replications. 245
However, using a Bonferroni correction for multiple testing, some model P-values with 246
statistically significant associations included P < 0.00079 for the DKD associations, < 0.0006 for 247
the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine, and 248
P< 0.00068 for associations with urea. 249
Statistical power considerations: 250
Taking the current sample size (145 with DKD vs. 265 without DKD), prevalence of DKD ~20% 251
(depending on reference # 2), genotype risk (odds ratio) = 1.5, significance level of 0.0008 252
(following correction of multiple testing using Bonferroni correction), disease allele frequency = 253
0.5, and multiplicative model, the current study had a power ~ 57%. Calculations were made 254
using the Genetic Association Study (GAS) Power Calculator 255
(http://csg.sph.umich.edu//abecasis/cats/gas_power_calculator/index.html ). 256
Ethical considerations 257
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Each patient agreed to take part in this study and gave signed consent after a brief session to 258
explain aims and methods. The study was approved by the Institutional Ethics Committee of 259
both hospitals (REC-04062014 and R292, respectively) and conformed to the ethical principles 260
outlined in the Helsinki declaration. 261
Funding: This study was supported by research incentive funds from Khalifa University Internal 262
Research Fund Level 2 granted to Dr. Habiba Al Safar. 263
264
Results 265
Baseline data of kidney function associated traits and SNPs selection 266
Of the 490 patients with T2DM that were recruited for this study, 115 patients were tested for 267
genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 268
25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood 269
urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney 270
disease (145 patients with DKD and 265 without DKD) according to the diagnostic criteria 271
[References #33 and #34] or patient medical records. The possible covariates which may affect 272
the genetic associations were included for each trait analysis and summarized in Table 1. 273
274
275
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Table 1: Baseline data of tested traits and tested SNPs 276
Trait
Mean ± SD or
quartiles
N of subjects
(Male/Female)
N of SNPs
reported **
N of SNPs
tested **
Covariates
ACR (mg/mmol) 1.05 / 3.9/ 12.7 * 115 (56/59) 331 83 Age, Gender
Vitamin D (ng/ml) 63.7 ± 27.8 328 (146/182) 442 92 Age, Gender
eGFR (ml/min/1.73m2) 81.5 ± 28.5 395 (172/223) 1478 288 Age, Gender
Serum Creatinine (µmol/l) 60 / 74/ 95.8 * 474 (246/228) 1792 363 Age, Gender
Blood Urea (mmol/l) 6.4 ± 5.4 450 (263/214) 446 73 Age, Gender
Diabetic Kidney Disease (DKD)
with: 145
without: 265
288 63
Age, Gender,
Hypertension, T2DM
duration, eGFR levels
* For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25
th/ 50
th/ 75
th quartiles and not as mean ± standard 277
deviation because their distributions are skewed. 278
** Reported: means the total number of SNPs found in the search in the literature, tested: means the actual number of SNPs found from the 279
reported SNPs and used for the analyses in this study for each corresponding trait. 280
Abbreviations: N: number; SD: standard deviation; SNP: single nucleotide polymorphism, ACR: albumin-to-creatinine ratio; T2DM: type 2 281
diabetes mellitus; eGFR: estimated glomerular filtration rate.282
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Clinical and laboratory characteristics of patients with and without DKD 283
Both patient groups (with or without kidney disease) had poor glycemic control with blood 284
glucose levels above 8 mmol/L. A comparison of patients with DKD to those without DKD 285
indicated that the majority of those with DKD were males (52% versus 38% for controls), older 286
(67 versus 59 years), had significant rates of comorbidities such as hypertension and 287
dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal 288
function indices was also observed in patients with DKD compared to those who did not have 289
DKD, which was indicated by the significantly higher rates of ACR, urea, and creatinine, and 290
significantly lower eGFR (Table 2). However, DKD patients tended to have lower LDL-291
cholesterol results compared to those without DKD, which suggested that these patients received 292
were more likely to have received intensive statin therapy (Table 2). 293
294
295
296
297
298
299
300
301
302
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Table 2: Demographic, clinical, and laboratory characteristics of T2DM patients with or without 303
kidney disease 304
Type of variables Variable DKD No DKD P *
Demographic variables
Gender: Female **
70 (48.3%) 165 (62.3%) 0.006
Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001
Clinical variables
Clinical hypertension **
96.6% 74.3 % < 0.0001
Dyslipidemia **
95.2% 90.9% 0.17
Smoking history **
32.4% 24.2% 0.07
Diabetes duration (years) 16.0 ± 9.2 10.1 ± 7.3 < 0.0001
BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.078
Laboratory variables
Glycemic indices HbA1c (%) 7.7 ± 1.5 7.8 ± 1.7 0.63
Fasting plasma glucose
(mmol/l) 8.3 ± 3.1 8.9 ± 3.8 0.43
Random blood glucose
(mmol/l) 9.1 ± 3.9 9.5 ± 4.3 0.34
Lipids profile Total-cholesterol (mmol/l) 3.7 ± 1.0 4.0 ± 1.1 0.003
Triglyceride (mmol/l) 1.5 ± 0.8 1.6 ± 0.8 0.25
HDL-cholesterol (mmol/l) 1.2 ± 0.6 1.2 ± 0.5 0.45
LDL-cholesterol (mmol/l) 1.9 ± 0.8 2.1 ± 0.9 0.01
Renal function
indices
Albumin:Creatinine Ratio
(mg/mmol) ***
3.3 / 10.1/ 69.6 0.8/ 2.2/ 9.0 0.0001
Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48
eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001
Creatinine (µmol/l) ***
82/ 111/ 146 55/ 64/ 76 < 0.0001
Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001
* P-value for continuous data, calculated using two-sided t-test except for Albumin: Creatinine Ratio and 305
Creatinine where it is for Wilcoxon rank-sum (Mann-Whitney) test. P-value for percentage data 306
calculated using Pearson chi-square test with except of hypertension and Dyslipidemia where Fisher 307
Exact test was used. 308
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** Proportional data shown as number of positive outcome and its percentage. All remaining continuous 309
data shown as mean ± standard deviation except of Albumin: Creatinine Ratio and Creatinine. 310
*** For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25
th/ 50
th/ 311
75th quartiles and not as mean ± standard deviation because their distributions are highly skewed. 312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
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Factors associated with developing DKD in UAE patients with T2DM 331
Table 3 shows that T2DM duration was the single factor that significantly contributed to the 332
development of DKD. Increased risk for DKD was significantly associated with increasing 333
duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the 334
risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-335
8.02). Although the levels of serum creatinine indicated a significant difference between no 336
DKD and DKD patients, (P=0.03), the odds ratio of 1.03 showed no increased risk (Table 3). 337
Table 3: Risk factors for development of kidney disease in patients with T2DM from the UAE 338
Covariate OR 95% CI P-value
Age 0.99 0.96 - 1.04 0.80
Gender 0.90 0.34 - 2.36 0.83
BMI 1.02 0.97 – 1.08 0.49
Hypertension 2.00 0.60 - 6.74 0.26
HbA1c 0.86 0.69 – 1.07 0.19
Cholesterol 0.59 0.19 – 1.76 0.34
Triglyceride 1.09 0.62 – 1.94 0.76
HDL-cholesterol 1.25 0.64 – 2.45 0.52
LDL-cholesterol 1.38 0.42 – 4.57 0.60
Smoking history 0.80 0.34 – 1.87 0.61
Creatinine 1.03 1.00 – 1.06 0.03
Urea 1.13 0.92 – 1.38 0.26
eGFR 0.97 0.94 – 1.00 0.09
Diabetes duration*
5-10 years 0.75 0.26 – 2.16 0.59
11-15 years 1.49 0.56 – 3.91 0.42
16-20 years 3.35 1.22 – 9.23 0.02
> 20 years 3.12 1.21 – 8.02 0.02
*Diabetes duration reference is duration ≤ 5 years 339
Abbreviations: BMI: body mass index, eGFR: estimated glomerular filtration rate. 340
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Genetic associations of renal function associated traits in UAE participants 341
The results of genetic associations for each tested renal function trait are shown in Table 4. In 342
summary, no association passed the Bonferroni correction for multiple testing. However, we 343
report here the most suggestive associations which point to replications of previous reports. 344
For blood urea, the best observed association was with rs11868441 in BCAS3 (effect size: -0.038 345
log per allele A, P=0.014), followed by multiple SNPs in the RSPO3 gene. 346
For serum creatinine, two SNPs (rs6999484 and rs1705699) in the intergenic region between 347
STC1 and ADAM28, showed the best associations with similar effect sizes (0.03 log per one copy 348
of the corresponding minor allele), followed by SNP rs2828785 (P=0.008, effect size = -0.030 349
log per one copy of allele A), which is located in the non-coding gene area on chromosome 21. 350
In addition, multiple genetic areas were also indicated, although with less significant 351
associations. 352
eGFR levels were significantly associated with SNPs within the MED1 gene, rs2168785 with 353
effect size of -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and 354
P=0.015 and 0.017 respectively. In addition, the SNPs in two genetic regions, WDR72 and 355
SHROOM3, that are associated with serum creatinine levels, were also associated with eGFR 356
levels, indicating a strong link to renal function. 357
Vitamin D levels were associated with three genetic regions including rs1801725 in CARS 358
(effect size: -6.923 per allele A, P=0.0078), rs1155563 in GC (effect size: -6.951, P=0.0081), and 359
two SNPs, rs12794714 and rs10500804, in CYP2R1 with effect sizes of approximately -5.0 and 360
P-values of 0.015 and 0.016, respectively. 361
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One SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 362
(effect size: -0.323 log per allele A, P=0.027) was strongly associated with the albumin-to-363
creatinine ratio. 364
This data showed that renal function traits, are linked to several loci in the UAE population, and 365
that some loci (e.g. WDR27 and SHROOM3) are linked to more than one trait. 366
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Table 4: Results of genetic association analyses of different renal function indices 367
SNP Chr: BP Gene A1/A2 * MAF_% Beta
** SE P
Blood Urea
rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014
rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028
rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.03
rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.03
rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039
Serum Creatinine
rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.003
rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.005
rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.008
rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019
rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022
rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034
rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038
rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042
Estimated Glomerular Filtration Rate
rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015
rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017
rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031
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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031
rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035
rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037
rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042
Vitamin D (25-OH Cholecalciferol)
rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.008
rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.008
rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015
rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016
Albumin:Creatinine Ratio
rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027
* A1/A2: minor to major alleles. 368
** Beta: regression coefficient for the linear regression model, calculated based on A1 (the minor allele). 369
Abbreviations: BP: base pair position; Chr: chromosome; MAF: minor allele frequency; SE: standard error; SNP: single nucleotide polymorphism.370
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Genetic associations of DKD in patients from UAE 371
Sixty-three SNPs in 43 genetic loci that have previously been linked to chronic or diabetic 372
kidney disease were included in our genetic analysis of DKD (145 patients with DKD versus 265 373
without DKD). A logistic regression model including five possible covariates, which may affect 374
the development of kidney disease, was applied (Table 1). As shown in Table 5, the unadjusted 375
analysis indicates two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682, 376
P=0.024). Following the adjustment for possible covariates, only the SHROOM3 rs4859682 377
remained significant (P=0.04, OR=1.46). This result confirms that SHROOM3 is a string risk 378
locus for DKD, considering the similar associations with serum creatinine and eGFR levels. 379
380
381
382
383
384
385
386
387
388
389
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Table 5: Association between SNPs linked to CKD and DKD patients from UAE. 390
SNP Chr: BP Gene * A1/A2
** MAF Punadjusted Padjusted OR (95% CI)
DKD (N=145) No DKD (N=265)
rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)
rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)
* CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3. 391
** A1/A2: minor to major alleles. 392
Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; DKD: diabetic kidney disease, MAF: minor allele frequency; 393
N: number; OR: odds ratio; SNP: single nucleotide polymorphism. 394
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Discussion 395
A combination of environmental and clinical factors in genetically predisposed individuals have 396
been suggested to be involved in DKD including persistent hyperglycemia, arterial hypertension 397
and/or dyslipidemia 37
. In addition, familial aggregation of nephropathy in T2DM has been 398
reported in several populations 38
. Therefore, understanding the interactions between genetic, 399
clinical and traditional kidney disease risk factors can provide insight into new treatment 400
strategies for improving DKD and reducing the likelihood of developing ESRD. In this study, we 401
investigated whether genetic markers that correspond to DKD and renal function traits and 402
reported in different populations are similar or different in the Arab population. 403
The current Emirati population sample indicated that most T2DM patients who developed DKD 404
were males, older by about 10 years than those that did not develop the disease, had more 405
frequent comorbidities specifically hypertension, and showed a marked decline in their renal 406
function profiles. However, even T2DM patients who did not develop DKD had higher rates of 407
comorbidities and had poor diabetic control, in agreement with our previous results 29
. We also 408
found that the duration of diabetes was the single factor that significantly contributed to the 409
development of kidney disease. The risk of developing DKD became significant when the 410
duration of T2DM reached the 15 year mark. This is in concordance with previous reports, which 411
suggested that T2DM patients who do not develop signs of kidney disease by 15 years duration 412
of diabetes seem to be protected, most likely due to genetic factors 39
. 413
The most notable finding of this study was the association of SHROOM3 (Shroom Family 414
Member 3) with serum creatinine, eGFR, and DKD. The minor allele for the SNP rs4859682 (A) 415
was observed to increase the serum creatinine (0.021 log increase per one copy), and also to 416
increase the risk for DKD (OR=1.46). SHROOM3 was first reported to be associated with eGFR 417
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levels in DKD patients 13
, then with serum creatinine 40
and serum magnesium levels 41
. This 418
association was further replicated in different ethnicities 19 42
. The SHROOM3 gene product is 419
expressed in the human kidney and is reported to play an important role in epithelial cell shape 420
regulation 43
, as well as the maintenance of the glomerular filtration barrier integrity 44
. Defective 421
Shroom3 protein leads to decreased actin organisation and affects mechanical characteristics and 422
integrity of the glomerular podocyte resulting in thinning of the glomerular filtration membrane 423
44. Additionally, Shroom3 heterozygous (Shroom3
Gt/+) mice showed developmental irregularities 424
that manifested as adult-onset glomerulosclerosis and proteinuria 45
. Furthermore, genetic 425
variants (such as the intronic variant rs17319721), were found to contribute to kidney allograft 426
injury and fibrosis development through a mechanism involving TGF-β1 signaling 46
. Although 427
the variant rs17319721 was not found in our dataset, it is highly linked to the SNP rs4859682 428
(r2=0.85, D’=1), which was reported in this study to increase the risk for DKD and affect the 429
levels of serum creatinine. Overall, this suggests that SHROOM3 may be considered as a multi-430
ethnic risk gene for DKD and its related kidney function traits in various populations, including 431
the Arabs who inhabit the UAE. 432
Similarly, the two loci; WDR72 (WD Repeat Domain 72, associated with eGFR and serum 433
creatinine levels), and BCAS3 (Breast Carcinoma-Amplified Sequence 3, associated with blood 434
urea levels), are also well-known trans-ethnic renal function traits loci 47
. WDR72 in particular 435
has been well studied in association with kidney function traits and pathologies. For instance, 436
WDR72 was reported to be associated with creatinine production or secretion 48
, and suggested to 437
be associated with signal transduction, cell cycle regulation, and vesicular trafficking that affects 438
podocyte activity, reduced eGFR and progression of CKD 49
. In addition, RSPO3 (R-Spondin 3) 439
was previously reported to be associated with blood urea nitrogen concentration, in line with the 440
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results of the current study 42
. The association of rs4528660 near MYOM1 (Myomesin 1) with the 441
albumin-to-creatinine ratio is also in agreement with previous work, which linked this locus to 442
albuminuria in patients with diabetes 50
. The current analyses performed also replicated the 443
associations of CASR (Calcium-sensing receptor), GC (vitamin D-binding protein (VDBP) is 444
also known as group-specific component (GC)- globulin), and CYP2R1 (Cytochrome P450 445
Family 2 Subfamily R Member 1) with levels of vitamin D 51
. These genes encode proteins 446
which are involved in vitamin D function including activation by hydroxylation (CYP2R1), 447
transportation (GC), and serum calcium level sensoring (CASR) 51
. These genes have also been 448
reported to be associated with calcium-vitamin D physiology and pathology, such as serum 449
calcium levels, familial hypocalciuric hypercalcemia, tertiary hyperparathyroidism, and vitamin 450
D deficiency presenting as Rickets (see OMIM entries: CARS: 601199; CYP2R1: 608713; and 451
GC: 139200). For instance, CASR protein is expressed in the kidney amongst other tissue and 452
regulates ion metabolism including calcium and magnesium. Mutations in CASR lead to 453
abnormalities in the regulation of parathyroid gland and renal function causing hypercalcemia 454
and increased blood pressure that in turn may affect kidney function 52
. Similarly, GC proteins 455
bind actin and act as actin scavengers (as such GC may play a role in podocyte integrity) as well 456
as a binding site for Vitamin D. The GC protein is the precursor to the Gc-macrophage 457
activating factor (GcMAF), a macrophage activator and suggests together with Vitamin D that 458
GC has an important role in immune function and the pathogenesis of CKD 53
. Vitamin D is 459
hydroxylated at the C25 position by specific hydroxylase coded by the CYP2R1 gene to 25-460
hydroxyvitamin D (25(OH) D), which is the main circulating form of vitamin D. Low Vitamin D 461
observed in CKD due to reduced CYP2R1 production by the liver or due to a mutation in the 462
gene, disturbs calcium balance and leads to hyperparathyroidism. Conversely the loss of renal 463
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protection brought about by Vitamin D and an increase in the renin-angiotensin pathway leads to 464
hypertension that further advances kidney disease 54
. Furthermore, these genes have recently 465
been shown to influence outcome of Vitamin D3 supplementation, which in the Arab context is 466
an important finding of our study 55
. 467
In summary, this work presents the first study to investigate the clinical and genetic factors 468
influencing kidney function traits and DKD in a population of Arab ancestry. The results 469
demonstrate that the duration of T2DM is the single most important risk factor for DKD 470
development in patients with T2DM in the UAE. Our study highlights that several genetic loci, 471
which have been previously linked to renal function associated traits, are shared between diverse 472
ethnic groups. As such we have replicated previous findings of the association of SHROOM3 473
with DKD. The logistic analyses carried in this study did not include treatment modalities 474
because most of the patients had multiple conditions and underwent multiple treatments, which 475
makes logistic regression largely unstable and difficult to interpret. A larger population sample 476
across the Middle East is now being considered to confirm the extent of the shared genetic 477
predisposition reported in the current study. Considering the high prevalence of T2DM in this 478
population and the recent evidence of genomic structure variations among different ethnic 479
groups, more genetic-driven population studies are warranted towards effective genetically 480
guided personalized medicine. 481
482
Acknowledgments: We acknowledge the patients who volunteered to make this study possible. 483
We also acknowledge patient care advisers for their roles in this study. 484
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Author contribution: HSA obtained the funding for this study. WMO, HSA and HFJ designed 485
the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and KK 486
reviewed/edited the manuscript and contributed to the discussion and reviewed/edited the 487
manuscript. WA and MHH provided assistance in patient recruitment and clinical data 488
collection. 489
Conflict of interests: None declared 490
Patient consent: Obtained. 491
Ethics approval: The Institutional Ethics Committee of Mafraq and Shaikh Khalifa Medical 492
Centre hospitals (REC-04062014 and R292, respectively). 493
Provenance and peer review: Not commissioned; externally peer reviewed. 494
Data sharing statement: No additional data are available. 495
496
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disease: a position statement from Kidney Disease: Improving Global Outcomes 579
(KDIGO). Kidney International 2005;67(6):2089-100. 580
34. Andrassy KM. Comments on 'KDIGO 2012 clinical practice guideline for the evaluation and 581
management of chronic kidney disease'. Kidney International 2013;84(3):622. 582
35. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration 583
rate. Annals of Internal Medicine 2009;150(9):604-12. 584
36. Kwon JM, Goate AM. The candidate gene approach. Alcohol Research and Health 585
2000;24(3):164-68. 586
37. Murussi M, Coester A, Gross JL, et al. Diabetic nephropathy in type 2 diabetes mellitus: risk 587
factors and prevention. Arquivos Brasileiros de Endocrinologia & Metabologia 588
2003;47(3):207-19. 589
38. Palmer ND, Freedman BI. Insights into the genetic architecture of diabetic nephropathy. 590
Current Diabetes Reports 2012;12(4):423-31. 591
39. Krolewski AS. Genetics of diabetic nephropathy: evidence for major and minor gene effects. 592
Kidney International 1999;55(4):1582-96. 593
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40. Pattaro C, De Grandi A, Vitart V, et al. A meta-analysis of genome-wide data from five 594
European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum 595
creatinine level. BMC Medical Genetics 2010;11(1):41. 596
41. Meyer TE, Verwoert GC, Hwang S-J, et al. Genome-wide association studies of serum 597
magnesium, potassium, and sodium concentrations identify six Loci influencing serum 598
magnesium levels. PLoS Genetics 2010;6(8):e1001045. 599
42. Okada Y, Sim X, Go MJ, et al. Meta-analysis identifies multiple loci associated with kidney 600
function–related traits in east Asian populations. Nature Genetics 2012;44(8):904. 601
43. Nishimura T, Takeichi M. Shroom3-mediated recruitment of Rho kinases to the apical cell 602
junctions regulates epithelial and neuroepithelial planar remodeling. Development 603
2008;135(8):1493-502. 604
44. Yeo NC, O’Meara CC, Bonomo JA, et al. Shroom3 contributes to the maintenance of the 605
glomerular filtration barrier integrity. Genome Research 2015;25(1):57-65. 606
45. Khalili H, Sull A, Sarin S, et al. Developmental origins for kidney disease due to Shroom3 607
deficiency. Journal of the American Society of Nephrology 2016:ASN. 2015060621. 608
46. Menon MC, Chuang PY, Li Z, et al. Intronic locus determines SHROOM3 expression and 609
potentiates renal allograft fibrosis. The Journal of Clinical Investigation 610
2015;125(1):208. 611
47. Mahajan A, Rodan AR, Le TH, et al. Trans-ethnic Fine Mapping Highlights Kidney-612
Function Genes Linked to Salt Sensitivity. The American Journal of Human Genetics 613
2016;99(3):636-46. 614
48. O'seaghdha CM, Fox CS. Genome-wide association studies of chronic kidney disease: what 615
have we learned? Nature Reviews Nephrology 2012;8(2):89. 616
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49. Good MC, Zalatan JG, Lim WA. Scaffold proteins: hubs for controlling the flow of cellular 617
information. Science 2011;332(6030):680-86. 618
50. Teumer A, Tin A, Sorice R, et al. Genome-wide association studies identify genetic loci 619
associated with albuminuria in diabetes. Diabetes 2015:db151313. 620
51. Wang TJ, Zhang F, Richards JB, et al. Common genetic determinants of vitamin D 621
insufficiency: a genome-wide association study. The Lancet 2010;376(9736):180-88. 622
52. Riccardi D, Brown EM. Physiology and pathophysiology of the calcium-sensing receptor in 623
the kidney. American Journal of Physiology-Renal Physiology 2009;298(3):F485-F99. 624
53. Thyer L, Ward E, Smith R, et al. A novel role for a major component of the vitamin D axis: 625
vitamin D binding protein-derived macrophage activating factor induces human breast 626
cancer cell apoptosis through stimulation of macrophages. Nutrients 2013;5(7):2577-89. 627
54. Jean G, Souberbielle JC, Chazot C. Vitamin D in chronic kidney disease and dialysis 628
patients. Nutrients 2017;9(4):328. 629
55. Barry EL, Rees JR, Peacock JL, et al. Genetic variants in CYP2R1, CYP24A1, and VDR 630
modify the efficacy of vitamin D3 supplementation for increasing serum 25-631
hydroxyvitamin D levels in a randomized controlled trial. The Journal of Clinical 632
Endocrinology & Metabolism 2014;99(10):E2133-E37. 633
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STREGA reporting recommendations, extended from STROBE Statement
Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A Cross-sectional Study
By Wael Osman et al.
Item Item
number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Title and Abstract
1
(a) Indicate the study’s design with a commonly used term in the title or the abstract.
Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A Cross-sectional Study
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(STREGA)
Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic kidney disease (DKD) have not been investigated. The aim of this research was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD and clinical and laboratory data obtained. Following adjustments for possible confounders, a logistic regression model was used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for age and gender, were used to test the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. Results: Patients with DKD compared to those without DKD were mostly males (52% versus 38% for controls), older (67 versus 59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus 10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits and found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels. Conclusions: Associations were found between several genetic loci and risk markers for DKD, which may influence kidney function traits and DKD in a population of Arab ancestry.
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Item Item
number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Introduction
Background rationale
2 Explain the scientific background and rationale for the investigation being reported.
Explain the scientific background and rationale for the investigation being reported.
Objectives 3 State specific objectives, including any pre-specified hypotheses.
The aim of this report was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of diabetic kidney disease in UAE population.
State if the study is the first report of a genetic association, a replication effort, or both.
This is a replication study for the genetic association part
Methods
Study design 4 Present key elements of study design early in the paper.
This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi.
Setting 5 Describe the setting, locations and relevant dates, including periods of recruitment, exposure, follow-up, and data collection.
The participants were recruited from Sheikh Khalifa Medical City (SKMC)
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE.
Participants 6 (a) Cohort study – Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up.
Case-control study – Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls.
Cross-sectional study – Give the eligibility criteria, and the sources and methods of selection of participants.
All subjects were UAE born and with Arabic descent.
Give information on the criteria and methods for selection of subsets of participants from a larger study, when relevant.
145 diagnosed with DKD and 265 did not.
(b) Cohort study – For matched studies, give matching criteria and number of exposed and unexposed.
Case-control study – For matched studies, give matching criteria and the number of controls per case.
145 diagnosed with DKD and 265 did not.
Variables 7 (a) Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable.
Various clinical and laboratory measures were assessed and collected
(b) Clearly define genetic exposures (genetic variants) using a widely-used nomenclature system. Identify
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
during the hospital visits. Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation.
To select SNPs that are associated with kidney function traits included in the study (blood urea, serum creatinine, eGFR values, albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and data bases including PubMed, Google Scholar, the GWAS catalogue (https://www.ebi.ac.uk/gwas/home ), the Phenolyzer database (http://phenolyzer.wglab.org/ ), the infinome genome interpretation platform (https://www.infino.me/ ), and the GWAS Central database (http://www.gwascentral.org/ ) were consulted.
variables likely to be associated with population stratification (confounding by ethnic origin).
Genetic studies in this study follow the candidate gene approach. The SNPs tested for each trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide Association Study (GWAS) that was intended to determine the genetic associations of T2DM in the UAE population, establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).
Data sources measurement
8*
(a) For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group.
(b) Describe laboratory methods, including source and storage of DNA, genotyping methods and platforms
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation. Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2).
(including the allele calling algorithm used, and its version), error rates and call rates. State the laboratory/centre where genotyping was done. Describe comparability of laboratory methods if there is more than one group. Specify whether genotypes were assigned using all of the data from the study simultaneously or in smaller batches.
The original Genome Wide Association Study (GWAS) from which we selected the tested SNPs was reported to establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).
Bias 9 (a) Describe any efforts to address potential sources of bias.
We applied filters for genotyping like H-W testing, allele frequencies
(b) For quantitative outcome variables, specify if any investigation of
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
and genotype call rates filters. We also excluded data which is missed > 20% of participants from the analyses, and used covariates adjustment for each analysis.
potential bias resulting from pharmacotherapy was undertaken. If relevant, describe the nature and magnitude of the potential bias, and explain what approach was used to deal with this.
Not applicable
Study size 10 Explain how the study size was arrived at.
No prior calculations conducted
Quantitative variables
11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen, and why.
Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2). Others quantitative variables like age or laboratory variables were collected without handling.
If applicable, describe how effects of treatment were dealt with.
For genetic associations with quantitative variables in this study, data was normalized using log transformation.
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Statistical methods
12
(a) Describe all statistical methods, including those used to control for confounding.
For continuous data, statistical differences were assessed using two-sided t-tests for normally distributed data or Wilcoxon rank-sum (Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for percentage data or Fishers Exact test when expected frequencies are less than 5. A P-value < 0.05 was considered as significant. PLINK software was also used for testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. The results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% confidence intervals. The same approach (patients with DKD vs. patients without DKD) was adopted to test the associations between the possible risk factors and the development of the DKD. However, the logistic model which includes all the associated factors in Table 4 was validated by the Hosmer–Lemeshow goodness of fit test to allow for the inclusion of several covariates. Analyses between SNPs and renal function traits were conducted in PLINK using linear regression models that included age and gender as covariates. Results were presented as beta (regression coefficient for the linear regression model, calculated based on the minor allele), standard errors, and P-values.
State software version used and options (or settings) chosen.
PLINK software version 1.07
Stata software version 14 (StataCorp LLC, Texas, USA)
(b) Describe any methods used to examine subgroups and interactions.
No methods used to examine subgroups and interactions.
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
(c) Explain how missing data were addressed.
Any missing data in > 20% of participants were excluded to avoid bias.
(d) Cohort study – If applicable, explain how loss to follow-up was addressed.
Case-control study – If applicable, explain how matching of cases and controls was addressed.
Cross-sectional study – If applicable, describe analytical methods taking account of sampling strategy.
No specific sampling strategy.
(e) Describe any sensitivity analyses.
No sensitivity analyses.
(f) State whether Hardy-Weinberg equilibrium was considered and, if so, how.
SNPs failed the Hardy-Weinberg equilibrium (HWE) test at the 0.001 were excluded.
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Item Item
number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
(g) Describe any methods used for inferring genotypes or haplotypes.
No methods used for inferring genotypes or haplotypes.
(h) Describe any methods used to assess or address population stratification.
The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the population stratification. This current study is not a GWAS, so we did not address this issue again.
(i) Describe any methods used to address multiple comparisons or to control risk of false positive findings.
We mentioned that the current
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
study is a replication study, therefore we reported here all P-values < 0.05, suggesting possible replications. However, using a Bonferroni correction for multiple testing, P-values with statistically significant associations are like the following: < 0.00079 for the DKD associations, < 0.0006 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine, and 0.00068 for the urea associations.
(j) Describe any methods used to address and correct for relatedness among subjects.
The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the relatedness among subjects. This current study is not a GWAS, so we did not address this issue again.
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Results
Participants
13* (a) Report the numbers of individuals at each stage of the study – e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed.
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls)
Report numbers of individuals in whom genotyping was attempted and numbers of individuals in whom genotyping was successful.
In all of participants genotyping was successful.
(b) Give reasons for non-participation at each stage.
Non-participation were excluded. This is a one stage study
(c) Consider use of a flow diagram.
No flow diagram
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number
STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
Descriptive data
14*
(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders.
Both patient groups (with or without kidney disease) had poor glycemic control with blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those without DKD indicated that the majority of those with DKD were males (~52% versus 38% for controls), older (67 versus ~59 years), had significant rates of comorbidities such as hypertension and dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal function indices was also observed in patients with DKD vs. those who don’t have DKD indicated by the significantly higher rates of ACR, urea, and creatinine; and significantly lower eGFR. However, DKD patients tended to have lower LDL-cholesterol results compared to those without DKD, which suggests these patients received better medical care or at least received intensive statin medication. T2DM duration was the single factor that significantly contributed to the development of DKD. Increased risk for DKD was significantly associated with increasing duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-8.02). Although the levels of serum creatinine indicated a trend for disease development (P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03. We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.
Consider giving information by genotype.
- Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.
- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with creatinine.
- SNPs within the MED1 gene (rs2168785 and rs12452509), and SNPs in two genetic regions, WDR72 and SHROOM3 with eGFR.
- Rs1801725 in CARS, rs1155563 in GC, and two SNPs in CYP2R1 with vitamin D levels.
- SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 with albumin-to-creatinine ratio.
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Item Item
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STROBE Guideline
Extension for Genetic Association Studies
(STREGA)
(b) Indicate the number of participants with missing data for each variable of interest.
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls).
(c) Cohort study – Summarize follow-up time, e.g. average and total amount.
The study was not a cohort study.
Outcome data 15 * Cohort study-Report numbers of outcome events or summary measures over time.
Report outcomes (phenotypes) for each genotype category over time
Case-control study – Report numbers in each exposure category, or summary measures of exposure.
Report numbers in each genotype category
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Extension for Genetic Association Studies
(STREGA)
Cross-sectional study – Report numbers of outcome events or summary measures.
There is no exposure-outcome measurements.
Report outcomes (phenotypes) for each genotype category
Not applicable
Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence intervals). Make clear which confounders were adjusted for and why they were included.
For testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. Analyses between SNPs and renal function traits were conducted using linear regression models that included age and gender as covariates.
(b) Report category boundaries when continuous variables were categorized.
Continuous variables were not categorized.
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period.
We did not translating estimates of relative risk into absolute risk for a meaningful time period.
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(d) Report results of any adjustments for multiple comparisons.
We discussed the Bonferroni correction for multiple testing in the study.
Other analyses 17 (a) Report other analyses done – e.g., analyses of subgroups and interactions, and sensitivity analyses.
No analyses for subgroups, interactions, or sensitivity analyses.
(b) If numerous genetic exposures (genetic variants) were examined, summarize results from all analyses undertaken.
Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.
- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with
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creatinine.
- SNPs within the MED1 gene (rs2168785 and rs12452509), and SNPs in two genetic regions, WDR72 and SHROOM3 with eGFR.
- Rs1801725 in CARS, rs1155563 in GC, and two SNPs in CYP2R1 with vitamin D levels.
- SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 with albumin-to-creatinine ratio.
- SHROOM3 rs4859682 remained significant (P=0.04, OR=1.46) with DKD.
(c) If detailed results are available elsewhere, state how they can be accessed.
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Not applicable
Discussion
Key results 18 Summarize key results with reference to study objectives. Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.
Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias. A limitation of this study is that analyses carried in this study did not included treatment modalities because most of patients had multiple conditions and they used several treatments; which make the models largely unstable and difficult to interpret. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for
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sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.
Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.
Generalizability 21 Discuss the generalizability (external validity) of the study results. Some of the loci which reported previously in other populations did not replicated because of the statistical power issue. In addition, due to lack of results in this topic in the other populations, we cannot confirm the results of this study will be replicated
Other Information
Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based. This study was supported by research incentive funds from Khalifa University Internal Research Fund Level 2 granted to Dr. Habiba Al Safar. No role of funders taken in this study.
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For peer review onlyClinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A
Cross-sectional Study
Journal: BMJ Open
Manuscript ID bmjopen-2017-020759.R3
Article Type: Research
Date Submitted by the Author: 18-Oct-2018
Complete List of Authors: Osman, Wael; Khalifa University of Science Technology and Research, Biotecnology Center Jelinek, H. F.; Macquarie University Faculty of Medicine and Health Sciences, Clinical MedicineTay, Guan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khandoker , Ahsan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khalaf, Kinda; Khalifa University of Science Technology and Research, Biomedical Engineering AlMahmeed , Wael; Sheikh Khalifa Medical City, Institute of Cardiac ScienceHassan, Mohamed; Sheikh Khalifa Medical City, Medical InstituteALsafar, Habiba; Khalifa University of Science Technology and Research, Biomedical Engineering
<b>Primary Subject Heading</b>: Diabetes and endocrinology
Secondary Subject Heading: Genetics and genomics, Global health
Keywords: Diabetes, UAE, ARAB, Renal
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1 Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the
2 United Arab Emirates: A Cross-sectional Study
3 Wael M. Osman 1, Herbert F. Jelinek 2, 3, Guan K. Tay 1, 4, 5, 6, Ahsan H. Khandoker 6, Kinda
4 Khalaf 6, Wael Almahmeed 7,8, Mohamed H. Hassan 9 & Habiba S. Alsafar 1, 6,*
5 1 Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates.
6 2 School of Community Health, Charles Sturt University, Albury, Australia.
7 3 Clinical Medicine, Macquarie University, Sydney, Australia.
8 4 School of Health and Medical Sciences, Edith Cowan University, Australia.
9 5 School of Psychiatry and Clinical Neurosciences, University of Western Australia, Australia.
10 6 Department of Biomedical Engineering, Khalifa University, United Arab Emirates.
11 7 Institute of Cardiac Science, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.
12 8Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates.
13 9 Medical Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.
14 Number of words: 300 in the abstract, 4028 in the main text
15 Number of tables: 5; embedded in the main text. No supplementary data.
16 Number of references: 55
17 Key words: type 2 diabetes, United Arab Emirates, diabetic kidney disease, genetics, SHROOM3
18 gene.
19 * Corresponding author
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20 Dr. Habiba S. Al Safar
21 Director of Biotechnology Center, Associate Professor
22 Khalifa University
23 PO BOX 127788 Abu Dhabi, UAE
24 Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442
25 E-mail: [email protected]
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38 Abstract
39 Objectives: Within the Emirati population, risk factors and genetic predisposition to diabetic
40 kidney disease (DKD) have not yet been investigated. The aim of this research was to determine
41 potential clinical, laboratory, and reported genetic loci as risk factors for DKD.
42 Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2
43 diabetes mellitus (T2DM) were recruited with and without DKD, and clinical and laboratory data
44 were obtained. Following adjustments for possible confounders, a logistic regression model was
45 developed to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci
46 with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for
47 age and gender, were then used to study the genetic associations of five renal function traits,
48 including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH
49 Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum
50 creatinine, and 73 SNPs with blood urea.
51 Results: Patients with DKD, as compared to those without the disease, were mostly males (52%
52 versus 38% for controls), older (67 versus 59 years), and had significant rates of hypertension and
53 dyslipidemia. Furthermore, patients with DKD had T2DM for a longer duration of time (16 versus
54 10 years), which in an additive manner was the single factor that significantly contributed to the
55 development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). Among the replicated associations
56 of the genetic loci with different renal function traits, the most notable included SHROOM3 with
57 levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1
58 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.
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59 Conclusions: Associations were found between several genetic loci and risk markers for DKD,
60 which may influence kidney function traits and DKD in a population of Arab ancestry.
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78 Strengths and limitations of this study
79 This cross-sectional study to determine the clinical, laboratory, and genetic associations of
80 diabetic kidney disease (DKD) and renal function traits in a sample of patients with type 2
81 diabetes mellitus (T2DM) was the first of its kind in a population of Arab ancestry from the
82 United Arab Emirates (UAE).
83 A limitation inherent to this study was that the analyses carried out did not include treatment
84 modalities due to patients having multiple conditions and treatments, which made the models
85 largely unstable and difficult to interpret.
86 Current analyses of the DKD genetic association had a statistical power of ~ 57%; suggesting
87 that a larger population sample across the Middle East is required to discover novel clinical
88 and genetic predisposition to DKD in the region with better statistical power.
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101 Introduction
102 This overall dramatic worldwide increase in the number of people with diabetes has had a major
103 impact on the increasing incidence and prevalence of diabetic kidney disease (DKD) as one of the
104 most frequent complications of both types of diabetes. Globally, the prevalence of chronic kidney
105 disease (CKD) among adults in the general population is reported to be around 10% 1. On the other
106 hand, 20% of adults with type 2 diabetes mellitus (T2DM) are expected to develop DKD based on
107 eGFR measurements (<60 ml/min/1.73 m2), while the number reaches 30% to 50% based on the
108 urinary albumin excretion levels1. This considerable variation is due to variances in settings,
109 geographical area, and ethnicity 2. Overall, the risk of DKD in T2DM is approximately 2% per
110 year 3. In the Arab world, the prevalence of DKD is also highly variable (10.8% to 61.2%)
111 depending on the study design, population, sample selection, race, age, sex, as well as diagnostic
112 criteria among other factors 4. Meta-analysis has shown that DKD is the leading cause of end-stage
113 renal disease (ESRD) in the Gulf Cooperation Council (GCC) with a prevalence of ~ 17% 5.
114 Patients with ESRD have a 20% annual mortality rate, which is higher than the rate for many solid
115 cancers 6. In addition to increasing the risk of cardiovascular morbidity and mortality 7 8, DKD is
116 reported to be the single strongest predictor of mortality in patients with diabetes 9 , with a five
117 year survival in the range of 30% 10.
118 The current trend suggests that the prevalence of DKD will continue to increase worldwide,
119 leading to increased morbidity and mortality and imposing significant socioeconomic burdens on
120 global healthcare systems11. A thorough literature search reveals that there remains a wide
121 knowledge gap related to the understanding of risk factors and pathophysiological mechanisms
122 associated with DKD, especially in the GCC and Middle East. Since ESRD can only be treated
123 with highly invasive and costly procedures, such as dialysis or kidney transplantation, better
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124 knowledge of genetic, clinical, and epidemiological factors associated with DKD is required to
125 allow for timely and more effective treatment options.
126 In clinical practice, renal function is assessed using a number of tests that are reported to have high
127 heritability rates 12. This indicates that genetic factors contribute significantly to inter-individual
128 variance in kidney function, and hence, to the susceptibility to CKD and related conditions. Thus
129 far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal
130 function traits in adults 13-24 as well as children 25. However, in comparison to other diseases,
131 including T2DM and other cardiometabolic disorders, studies of kidney disease and kidney
132 function traits are largely insufficient and inconclusive. In spite of efforts to describe novel
133 biomarkers for DKD, currently no tested candidates outperform albumin. A recent report by
134 Saulnier et al. suggests that three serum biomarkers (midregional-proadrenomedullin: MR-
135 proADM, soluble tumor necrosis factor receptor 1: sTNFR1, and N-terminal prohormone brain
136 natriuretic peptide: NT-proBNP) can improve risk prediction of the loss of renal function in
137 patients with T2DM, in addition to the established risk factors for DKD such as age, sex, diabetes
138 duration, HbA1c, blood pressure, baseline eGFR, and albumin-to-creatinine ratio 26. However,
139 issues such as whether the levels of these markers are affected by genetic variations, and whether
140 the encoding genes contribute to DKD development and progress need further investigation.
141 The United Arab Emirates (UAE) is among the countries with the highest prevalence rates of
142 T2DM, obesity and cardiovascular disease 27 28. Al-Safar and colleagues have recently reported
143 that approximately 80% of T2DM patients within UAE present with at least one complication
144 associated with T2DM, including kidney disease (approximately 6%) 29. Furthermore, there is
145 increasing evidence suggesting that the genome structure of individuals of Arabic descent is
146 different from individuals from other populations 30. Despite the high prevalence rate of DKD in
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147 the UAE, there have been no investigations up to date of the genetic associations of chronic kidney
148 conditions and kidney functions, particularly as associated with T2DM. Therefore, the current
149 work aimed to investigate the clinical and laboratory variables linked to DKD in a T2DM Emirati
150 population, and to study the associations of the reported genetic loci linked to different renal
151 function tests in CKD and DKD.
152
153 Materials and Methods
154 Study type and subjects
155 This work describes a cross-sectional study of Emirati patients from the city of Abu Dhabi. The
156 demographic information and clinical data for the participants are presented in Tables 1 and 2.
157 Four hundred and ninety (n=490) patients with T2DM were included in the study, with 145
158 diagnosed with DKD. The participants were recruited from Sheikh Khalifa Medical City (SKMC)
159 and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were UAE born
160 and of Arabian descent.
161 Patient and Public Involvement
162 The study was designed because T2DM, along with its multi organ complications, is a major health
163 challenge in the UAE with increasingly growing public interest. However, patients and the public
164 at large were not involved in defining the research questions, analyses, interpretation or
165 dissemination of the results.
166
167
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168 Clinical variables and laboratory data
169 Various clinical and laboratory measures were collected/assessed during the hospital visits. Blood
170 pressure was taken on two different occasions. Hypertension was defined as systolic blood
171 pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or if the patients were taking any
172 antihypertensive medications. Dyslipidemia was either reported based on clinical records of the
173 participants or diagnosed as previously indicated 31. The presence of T2DM was confirmed by a
174 qualified physician based on criteria outlined by the World Health Organization (WHO) 32. Trained
175 nurses measured the height and weight of each participant using a calibrated wall-mounted
176 stadiometer and a weight scale, respectively. Body mass index (BMI) was calculated as the weight
177 in kilograms divided by the square of the height in meters (kg/m2).
178 Diabetic Kidney Disease
179 DKD was defined as either decreased levels of estimated glomerular filtration rate ((eGFR) <60
180 ml/min/1.73 m2) with or without renal damage over a period of at least three months 33, or based
181 on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria >500 mg over a 24 hour period in the
182 setting of T2DM and/or abnormalities, as assessed by imaging or histology 34. eGFR was
183 calculated according to CKD-EPI Creatinine Equation 35. Accordingly, 145 TD2M patients were
184 identified with DKD, while 265 were identified as disease free (Tables 1 and 2). The remaining
185 80 patients could not be classified with or without DKD at the time of the study, and were excluded
186 from subsequent analyses.
187 Selection of SNPs
188 Genetic investigation in this study followed the candidate gene approach 36. The SNPs tested for
189 each trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide
190 Association Study (GWAS) that was intended to determine the genetic associations of T2DM in
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191 the UAE population towards establishing the Emirates Family Registry for T2DM 27. The GWAS
192 was performed for 490 samples with T2DM, and 450 healthy controls using the Infinium
193 Omni5ExomeHuman chip (Illumina Inc., San Diego, USA). To select the SNPs associated with
194 the kidney function traits included in the study (blood urea, serum creatinine, eGFR values,
195 albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and data
196 bases including PubMed, Google Scholar, the GWAS catalog (https://www.ebi.ac.uk/gwas/home),
197 the Phenolyzer database (http://phenolyzer.wglab.org/), the infinome genome interpretation
198 platform (https://www.infino.me/), and the GWAS Central database
199 (http://www.gwascentral.org/) were consulted.
200 Our search strategy consisted of identifying reported SNPs that cleared the GWAS significance-
201 level and were found in our GWAS data. If the original signal SNP was missing from the GWAS
202 data, we searched for a possible proxy SNP utilizing the concept of linkage disequilibrium (LD).
203 This typically indicates a non-random association of alleles at different genetic loci in a given
204 population and their tendency to be inherited as a block (mathematical values r2 and D’ > 0.8
205 indicates high LD). Proxy SNPs were selected using the SNAP database for SNP Annotation and
206 Proxy Search (http://archive.broadinstitute.org/mpg/snap/ldsearch.php).
207 All SNPs located within and flanking genes that have been previously reported in association with
208 CKD and DKD were included. In total, 43 genetic loci were identified as linked to CKD, DKD, or
209 a decline in renal function. Specifically, the gene loci comprised: ACACB, ACE, ACTN4, ADIPOQ,
210 ADM, AFF3, AGTR1, APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-
211 ELMO1, ENPP1, ERBB4, FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16,
212 MYH9, NCALD, NCK1, NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPAR2, PVT1, RAGE, RGMA,
213 RPS12, SFI1, SHROOM3, TMEM22, TNFRSF1A, and TRPC6.
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214 Statistical modeling and analyses
215 Continuous variables were presented as mean ± standard deviation or lower / median and upper
216 quartiles where the distributions were highly skewed. Vitamin D and eGFR levels were normally
217 distributed. However, urea levels, creatinine levels, and albumin-to-creatinine ratio data were
218 converted to normal distributions using natural log transformation. The associations of trait values
219 or their natural logs (if transformed) were tested with SNPs using linear regression models, which
220 included age and gender as covariates using PLINK software version 1.07
221 (http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele frequencies
222 and testing the quality control (QC) variables. Any SNPs with minor allele frequency (MAF)
223 0.05, and 5% missing genotype rate, or those that failed the Hardy-Weinberg equilibrium
224 (HWE) test at the 0.001, were excluded. Hardy-Weinberg equilibrium is considered as an
225 important QC test for genetic association studies and assumes that allele and genotype frequencies
226 can be estimated. If the frequencies of the measured genotypes significantly differed from the
227 HWE assumptions, genotyping errors among other possible factors, such as ethnic diversity and
228 high levels of consanguinity in the population, are indicated leading to excluding the SNPs from
229 further analyses. Association with P < 0.05 were reported, indicating the replication of previously-
230 reported associations.
231 Statistical analyses for all clinical and laboratory variables were performed using Stata software
232 version 14 (StataCorp LLC, Texas, USA). For continuous data, statistical differences were
233 assessed using two-sided t-tests for normally distributed data or the Wilcoxon rank-sum (Mann-
234 Whitney) test for highly skewed data. The Pearson chi-square test was used for percentage data as
235 well as the Fishers Exact test when the expected frequencies were less than 5. A P-value < 0.05
236 was considered as significant. PLINK software was also used for testing the associations between
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237 the SNPs and DKD using a case-control logistic regression model, which included age, gender,
238 hypertension status, T2DM duration, and eGFR levels as covariates (Table 1). The results are
239 presented as P-values (Padjusted < 0.05) and odds ratios with corresponding 95% confidence
240 intervals. The same approach (patients with DKD vs. patients without DKD) was adopted to test
241 the associations between the possible risk factors and the development of the DKD (Table 3).
242 However, the logistic model, which included all the associated factors listed in Table 4 was
243 validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) to allow for the inclusion of
244 several covariates. Analyses between SNPs and renal function traits were conducted in PLINK
245 using linear regression models that included age and gender as covariates. The results were
246 presented as beta coefficients (regression coefficients for the linear regression model, calculated
247 based on the minor allele), standard errors, and P-values.
248 As this was a replication study, we reported all P-values < 0.05, suggesting possible replications.
249 However, using a Bonferroni correction for multiple testing, the P-values of some of the models
250 with statistically significant associations included P < 0.00079 for the DKD associations, < 0.0006
251 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine,
252 and P< 0.00068 for associations with urea.
253 Statistical power considerations:
254 The current study had a power ~ 57%. This is based on the current sample size (145 with DKD vs.
255 265 without DKD), prevalence of DKD ~20% (depending on reference # 2), genotype risk (odds
256 ratio) = 1.5, significance level of 0.0008 (following the correction of multiple testing using the
257 Bonferroni correction), disease allele frequency = 0.5, and multiplicative model. Calculations were
258 verified using the Genetic Association Study (GAS) Power Calculator
259 (http://csg.sph.umich.edu//abecasis/cats/gas_power_calculator/index.html ).
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260 Ethical considerations
261 Each patient agreed to take part in this study and provided an informed signed consent after a brief
262 session to explain the aims and methods. The study was approved by the Institutional Ethics
263 Committees of both local hospitals (REC-04062014 and R292, respectively) and conformed to the
264 ethical principles outlined in the Helsinki declaration.
265
266 Results
267 Baseline data of kidney function associated traits and SNPs selection
268 Of the 490 patients with T2DM that were recruited for this study, 115 patients were tested for
269 genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as
270 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood
271 urea levels. Among the 490 patients, 410 had clear classifications for the diagnosis of kidney
272 disease (145 patients with DKD and 265 without DKD) according to the adopted diagnostic criteria
273 [References #33 and #34] and/or patient medical records. The possible covariates that may affect
274 the genetic associations for trait analyses are summarized in Table 1.
275
276
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277 Table 1: Baseline data of tested traits and tested SNPs
TraitMean ± SD or
quartiles
N of subjects
(Male/Female)
N of SNPs
reported **
N of SNPs
tested **Covariates
ACR (mg/mmol) 1.05 / 3.9/ 12.7 * 115 (56/59) 331 83 Age, Gender
Vitamin D (ng/ml) 63.7 ± 27.8 328 (146/182) 442 92 Age, Gender
eGFR (ml/min/1.73m2) 81.5 ± 28.5 395 (172/223) 1478 288 Age, Gender
Serum Creatinine (µmol/l) 60 / 74/ 95.8 * 474 (246/228) 1792 363 Age, Gender
Blood Urea (mmol/l) 6.4 ± 5.4 450 (263/214) 446 73 Age, Gender
Diabetic Kidney Disease (DKD)with: 145
without: 265288 63
Age, Gender,
Hypertension, T2DM
duration, eGFR levels
278 * For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25th/ 50th/ 75th quartiles and not as mean ± standard 279 deviation because their distributions are skewed.
280 ** Reported: means the total number of SNPs found in the search in the literature, tested: means the actual number of SNPs found from the 281 reported SNPs and used for the analyses in this study for each corresponding trait.
282 Abbreviations: N: number; SD: standard deviation; SNP: single nucleotide polymorphism, ACR: albumin-to-creatinine ratio; T2DM: type 2 283 diabetes mellitus; eGFR: estimated glomerular filtration rate.
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284 Clinical and laboratory characteristics of patients with and without DKD
285 Both patient groups (with or without kidney disease) had poor glycemic control with blood glucose
286 levels above 8 mmol/L. A comparison of patients with DKD to those without DKD indicated that
287 the majority of DKD patients were males (52% versus 38% for controls), older in age (67 versus
288 59 years), had significant rates of comorbidities, such as hypertension and dyslipidemia, and had
289 a longer T2DM duration (16 versus 10 years). A clear decline in renal function indices was also
290 observed in patients with DKD compared to those without DKD, as indicated by the significantly
291 higher rates of ACR, urea, and creatinine, and significantly lower eGFR (Table 2). However, DKD
292 patients tended to have lower LDL-cholesterol results as compared to those without DKD, which
293 suggests that these patients were more likely to have received intensive statin therapy (Table 2).
294
295
296
297
298
299
300
301
302
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303 Table 2: Demographic, clinical, and laboratory characteristics of T2DM patients with or without
304 kidney disease
Type of variables Variable DKD No DKD P *
Demographic variables
Gender: Female ** 70 (48.3%) 165 (62.3%) 0.006
Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001
Clinical variables
Clinical hypertension ** 96.6% 74.3 % < 0.0001
Dyslipidemia ** 95.2% 90.9% 0.17
Smoking history ** 32.4% 24.2% 0.07
Diabetes duration (years) 16.0 ± 9.2 10.1 ± 7.3 < 0.0001
BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.078
Laboratory variables
Glycemic indices HbA1c (%) 7.7 ± 1.5 7.8 ± 1.7 0.63
Fasting plasma glucose
(mmol/l)8.3 ± 3.1 8.9 ± 3.8 0.43
Random blood glucose
(mmol/l)9.1 ± 3.9 9.5 ± 4.3 0.34
Lipids profile Total-cholesterol (mmol/l) 3.7 ± 1.0 4.0 ± 1.1 0.003
Triglyceride (mmol/l) 1.5 ± 0.8 1.6 ± 0.8 0.25
HDL-cholesterol (mmol/l) 1.2 ± 0.6 1.2 ± 0.5 0.45
LDL-cholesterol (mmol/l) 1.9 ± 0.8 2.1 ± 0.9 0.01
Renal function
indices
Albumin:Creatinine Ratio
(mg/mmol) ***3.3 / 10.1/ 69.6 0.8/ 2.2/ 9.0 0.0001
Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48
eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001
Creatinine (µmol/l) *** 82/ 111/ 146 55/ 64/ 76 < 0.0001
Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001
305 * P-value for continuous data, calculated using two-sided t-test except for Albumin: Creatinine Ratio and 306 Creatinine where it is for Wilcoxon rank-sum (Mann-Whitney) test. P-value for percentage data calculated 307 using Pearson chi-square test with except of hypertension and Dyslipidemia where Fisher Exact test was 308 used.
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309 ** Proportional data shown as number of positive outcome and its percentage. All remaining continuous 310 data shown as mean ± standard deviation except of Albumin: Creatinine Ratio and Creatinine.
311 *** For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25th/ 50th/ 312 75th quartiles and not as mean ± standard deviation because their distributions are highly skewed.
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
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331 Factors associated with developing DKD in Emirati patients with T2DM
332 Table 3 shows that T2DM duration was the single factor that significantly contributed to the
333 development of DKD. Increased risk for DKD was significantly associated with the increasing
334 duration of T2DM, cumulatively, up to 20 years of duration. At a T2DM duration ≥ 20 years, the
335 DKD risk stabilized at approximately 3.12 times higher as compared to patients with duration ≤ 5
336 years (P=0.02, 95% CI=1.21-8.02). Although the levels of serum creatinine indicated a significant
337 difference between no DKD and DKD patients, (P=0.03), the odds ratio of 1.03 showed no
338 increased risk (Table 3).
339 Table 3: Risk factors for the development of kidney disease in patients with T2DM from the UAE
Covariate OR 95% CI P-value
Age 0.99 0.96 - 1.04 0.80
Gender 0.90 0.34 - 2.36 0.83
BMI 1.02 0.97 – 1.08 0.49
Hypertension 2.00 0.60 - 6.74 0.26
HbA1c 0.86 0.69 – 1.07 0.19
Cholesterol 0.59 0.19 – 1.76 0.34
Triglyceride 1.09 0.62 – 1.94 0.76
HDL-cholesterol 1.25 0.64 – 2.45 0.52
LDL-cholesterol 1.38 0.42 – 4.57 0.60
Smoking history 0.80 0.34 – 1.87 0.61
Creatinine 1.03 1.00 – 1.06 0.03
Urea 1.13 0.92 – 1.38 0.26
eGFR 0.97 0.94 – 1.00 0.09
Diabetes duration*
5-10 years 0.75 0.26 – 2.16 0.59
11-15 years 1.49 0.56 – 3.91 0.42
16-20 years 3.35 1.22 – 9.23 0.02
> 20 years 3.12 1.21 – 8.02 0.02
340 *Diabetes duration reference is duration ≤ 5 years
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341 Abbreviations: BMI: body mass index, eGFR: estimated glomerular filtration rate.
342 Genetic associations of renal function associated traits in Emirati patients
343 The results of genetic associations for each tested renal function trait are shown in Table 4. In
344 summary, no association passed the Bonferroni correction for multiple testing. However, we report
345 here the most suggestive associations which point to replications of previous reports.
346 For blood urea, the best observed association was with rs11868441 in BCAS3 (effect size: -0.038
347 log per allele A, P=0.014), followed by multiple SNPs in the RSPO3 gene.
348 For serum creatinine, two SNPs (rs6999484 and rs1705699) in the intergenic region between
349 STC1 and ADAM28, showed the best associations with similar effect sizes (0.03 log per one copy
350 of the corresponding minor allele), followed by SNP rs2828785 (P=0.008, effect size = -0.030 log
351 per one copy of allele A), which is located in the non-coding gene area on chromosome 21. In
352 addition, multiple genetic areas were also indicated, although with less significant associations.
353 eGFR levels were significantly associated with SNPs within the MED1 gene, rs2168785 with
354 effect size of -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and P=0.015
355 and 0.017, respectively. In addition, the SNPs in two genetic regions, WDR72 and SHROOM3,
356 that are associated with serum creatinine levels, were also associated with eGFR levels, indicating
357 a strong link to renal function.
358 Vitamin D levels were associated with three genetic regions including rs1801725 in CARS (effect
359 size: -6.923 per allele A, P=0.0078), rs1155563 in GC (effect size: -6.951, P=0.0081), and two
360 SNPs, rs12794714 and rs10500804, in CYP2R1 with effect sizes of approximately -5.0 and P-
361 values of 0.015 and 0.016, respectively.
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362 One SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 (effect
363 size: -0.323 log per allele A, P=0.027) was strongly associated with the albumin-to-creatinine
364 ratio.
365 This data demonstrated that renal function traits, are linked to several loci in the UAE population,
366 and that some loci (e.g. WDR27 and SHROOM3) are linked to more than one trait.
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367 Table 4: Results of genetic association analyses of different renal function indices
SNP Chr: BP Gene A1/A2 * MAF_% Beta ** SE P
Blood Urea
rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014
rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028
rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.03
rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.03
rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039
Serum Creatinine
rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.003
rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.005
rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.008
rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019
rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022
rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034
rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038
rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042
Estimated Glomerular Filtration Rate
rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015
rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017
rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031
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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031
rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035
rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037
rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042
Vitamin D (25-OH Cholecalciferol)
rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.008
rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.008
rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015
rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016
Albumin:Creatinine Ratio
rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027
368 * A1/A2: minor to major alleles.
369 ** Beta: regression coefficient for the linear regression model, calculated based on A1 (the minor allele).
370 Abbreviations: BP: base pair position; Chr: chromosome; MAF: minor allele frequency; SE: standard error; SNP: single nucleotide polymorphism.
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371 Genetic associations of DKD in Emirati patients
372 Sixty-three SNPs in 43 genetic loci, which have previously been linked to chronic or diabetic
373 kidney disease, were included in our genetic analyses of DKD (145 patients with DKD versus 265
374 without DKD). A logistic regression model including five possible covariates that may affect the
375 development of kidney disease was applied (Table 1). As depicted in Table 5, the unadjusted
376 analyses indicates two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682,
377 P=0.024), respectively. Following the adjustment for possible covariates, only the SHROOM3
378 rs4859682 remained significant (P=0.04, OR=1.46). This confirms that SHROOM3 is a string risk
379 locus for DKD, considering similar associations with serum creatinine and eGFR levels.
380
381
382
383
384
385
386
387
388
389
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390 Table 5: Association between SNPs linked to CKD and DKD patients from the UAE.
SNP Chr: BP Gene * A1/A2 ** MAF Punadjusted Padjusted OR (95% CI)
DKD (N=145) No DKD (N=265)
rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)
rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)
391 * CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3.
392 ** A1/A2: minor to major alleles.
393 Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; DKD: diabetic kidney disease, MAF: minor allele frequency; 394 N: number; OR: odds ratio; SNP: single nucleotide polymorphism.
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395 Discussion
396 A combination of environmental and clinical factors in genetically predisposed individuals have
397 been suggested to be involved in DKD, including persistent hyperglycemia, arterial hypertension
398 and/or dyslipidemia 37. In addition, familial aggregation of nephropathy in T2DM has been
399 reported in several populations 38. Therefore, understanding the complex multifactorial
400 interactions between genetic, clinical and traditional kidney disease risk factors can provide insight
401 into novel drugs and treatment strategies for DKD towards reducing the likelihood of developing
402 ESRD. In this study, we investigated whether the genetic markers that correspond to DKD and
403 renal function traits reported for different populations are similar to the Arab population.
404 The current Emirati population sample indicated that most T2DM patients who developed DKD
405 were males, about 10 years older, had more frequent comorbidities, specifically hypertension, and
406 showed a marked decline in their renal function profiles, as compared to those who did not develop
407 the disease. However, T2DM patients who did not develop DKD still had higher rates of
408 comorbidities and poor diabetic control, in agreement with our previous results 29. We also found
409 that the duration of diabetes was the single factor that significantly contributed to the development
410 of kidney disease. Specifically, the risk of developing DKD became significant when the duration
411 of T2DM reached the 15 year mark. This is in alignment with previous reports, which suggest that
412 T2DM patients who do not develop signs of kidney disease by 15 years duration of diabetes seem
413 to be protected, most likely due to genetic factors 39.
414 The most notable finding of this study was the association of SHROOM3 (Shroom Family Member
415 3) with serum creatinine, eGFR, and DKD. The minor allele for the SNP rs4859682 (A) was
416 observed to increase the serum creatinine (0.021 log increase per one copy), and also to increase
417 the risk for DKD (OR=1.46). SHROOM3 was first reported to be associated with eGFR levels in
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418 DKD patients 13, then with serum creatinine 40 and serum magnesium levels 41. This association
419 was further replicated in different ethnicities 19 42. The SHROOM3 gene product is expressed in
420 the human kidney and is reported to play an important role in epithelial cell shape regulation 43, as
421 well as the maintenance of the glomerular filtration barrier integrity 44. Defective Shroom3 protein
422 leads to decreased actin organisation and affects the mechanical characteristics and integrity of the
423 glomerular podocyte resulting in thinning of the glomerular filtration membrane 44.
424 Additionally, Shroom3 heterozygous (Shroom3Gt/+) mice showed developmental irregularities that
425 manifested as adult-onset glomerulosclerosis and proteinuria 45. Furthermore, genetic variants
426 (such as the intronic variant rs17319721), were found to contribute to kidney allograft injury and
427 the development of fibrosis through a mechanism involving TGF-β1 signaling 46. Although the
428 variant rs17319721 was not found in our dataset, it is highly linked to the SNP rs4859682 (r2=0.85,
429 D’=1), which was reported in this study to increase the risk for DKD and affect the levels of serum
430 creatinine. Overall, this suggests that SHROOM3 may be considered as a multi-ethnic risk gene
431 for DKD and associated kidney function traits in various populations, including the Arabs who
432 inhabit the UAE.
433 Similarly, the two loci; WDR72 (WD Repeat Domain 72, associated with eGFR and serum
434 creatinine levels), and BCAS3 (Breast Carcinoma-Amplified Sequence 3, associated with blood
435 urea levels), are also well-known trans-ethnic renal function traits loci 47. WDR72, in particular,
436 has been well studied in association with kidney function traits and pathologies. For instance,
437 WDR72 has been reported to be associated with creatinine production or secretion 48, as well as
438 signal transduction, cell cycle regulation, and vesicular trafficking that affects podocyte activity,
439 reduced eGFR and progression of CKD 49. In addition, RSPO3 (R-Spondin 3) has been previously
440 reported to be associated with blood urea nitrogen concentration, in line with the results of the
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441 current study 42. The association of rs4528660 near MYOM1 (Myomesin 1) with the albumin-to-
442 creatinine ratio is also in agreement with previous work, which link this locus to albuminuria in
443 patients with diabetes 50. The current analyses also replicated the associations of CASR (Calcium-
444 sensing receptor), GC (vitamin D-binding protein (VDBP), also known as group-specific
445 component (GC)- globulin), and CYP2R1 (Cytochrome P450 Family 2 Subfamily R Member 1)
446 with levels of vitamin D 51. These genes encode proteins which are involved in vitamin D function,
447 including activation by hydroxylation (CYP2R1), transportation (GC), and serum calcium level
448 sensoring (CASR) 51. They have also been associated with calcium-vitamin D physiology and
449 pathology, such as serum calcium levels, familial hypocalciuric hypercalcemia, tertiary
450 hyperparathyroidism, and vitamin D deficiency presenting as Rickets (see OMIM entries: CARS:
451 601199; CYP2R1: 608713; and GC: 139200). For instance, CASR protein is expressed in the
452 kidney amongst other tissue and regulates ion metabolism including calcium and magnesium.
453 Mutations in CASR lead to abnormalities in the regulation of the parathyroid gland and renal
454 function causing hypercalcemia and increased blood pressure, which in turn may affect kidney
455 function 52. Similarly, GC proteins bind actin and work as actin scavengers (as such GC may play
456 a role in podocyte integrity), as well as a binding site for Vitamin D. The GC protein is the
457 precursor to the Gc-macrophage activating factor (GcMAF), a macrophage activator and suggests,
458 together with Vitamin D, that GC has an important role in the immune function and pathogenesis
459 of CKD 53. Vitamin D is hydroxylated at the C25 position by specific hydroxylase coded by the
460 CYP2R1 gene to 25-hydroxyvitamin D (25(OH) D), which is the main circulating form of vitamin
461 D. The low levels of Vitamin D observed in CKD, due to reduced CYP2R1 production by the liver
462 or due to a mutation in the gene, disturb calcium balance and lead to hyperparathyroidism.
463 Conversely, the loss of renal protection caused by Vitamin D and the increase in the renin-
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464 angiotensin pathway leads to hypertension that further advances kidney disease 54. Furthermore,
465 these genes have recently been shown to influence the outcome of Vitamin D3 supplementation,
466 which in the Arab context is an important finding of our study 55.
467 In summary, this work presents the first study to investigate the clinical and genetic factors
468 influencing kidney function traits and DKD in a population of Arab ancestry. The results
469 demonstrate that the duration of T2DM is the single most important risk factor for DKD
470 development in patients with T2DM in the UAE. Our study highlights that several genetic loci,
471 which have been previously linked to renal function associated traits, are shared between diverse
472 ethnic groups. As such, we have replicated previous findings of the association of SHROOM3 with
473 DKD. The logistic analyses performed here did not include treatment modalities since most of the
474 patients had multiple conditions and underwent multiple treatments, which makes logistic
475 regression largely unstable and difficult to interpret. A larger population sample across the Middle
476 East is now being considered to confirm the extent of the shared genetic predisposition reported in
477 the current study. Considering the high prevalence of T2DM in this population and the recent
478 evidence of genomic structure variations among different ethnic groups, more genetic-driven
479 population studies are warranted towards effective genetically guided personalized medicine.
480
481 Acknowledgments: We acknowledge the patients who volunteered to make this study possible.
482 We also acknowledge patient care advisers for their roles in this study.
483 Author contribution: HSA obtained the funding for this study. WMO, HSA and HFJ designed
484 the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and KK
485 provided critical revision the manuscript, contributed to writing the discussion, and writing the
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486 revision of the manuscript. WA and MHH did the patient recruitment process and provided
487 acquisition clinical data collection. All authors gave final approval of the version to be published.
488 Conflict of interests: None declared
489 Patient consent: Obtained.
490 Ethics approval: The Institutional Ethics Committee of Mafraq and Shaikh Khalifa Medical
491 Centre hospitals (REC-04062014 and R292, respectively).
492 Funding: This study was supported by research incentive funds from Khalifa University Internal
493 Research Fund Level 2 granted to Dr. Habiba Al Safar.
494 Provenance and peer review: Not commissioned; externally peer reviewed.
495 Data sharing statement: No additional data are available.
496
497 References
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STREGA reporting recommendations, extended from STROBE Statement
Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A Cross-sectional Study
By Wael Osman et al.
Item Item number
STROBE Guideline Extension for Genetic Association Studies
(STREGA)
Title and Abstract
1 (a) Indicate the study’s design with a commonly used term in the title or the abstract.
Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A Cross-sectional Study
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Item Item number
STROBE Guideline Extension for Genetic Association Studies
(STREGA)
Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic kidney disease (DKD) have not been investigated. The aim of this research was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD and clinical and laboratory data obtained. Following adjustments for possible confounders, a logistic regression model was used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for age and gender, were used to test the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. Results: Patients with DKD compared to those without DKD were mostly males (52% versus 38% for controls), older (67 versus 59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus 10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits and found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels. Conclusions: Associations were found between several genetic loci and risk markers for DKD, which may influence kidney function traits and DKD in a population of Arab ancestry.
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Item Item number
STROBE Guideline Extension for Genetic Association Studies
(STREGA)
Introduction
Background rationale
2 Explain the scientific background and rationale for the investigation being reported.
Explain the scientific background and rationale for the investigation being reported.
Objectives 3 State specific objectives, including any pre-specified hypotheses.
The aim of this report was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of diabetic kidney disease in UAE population.
State if the study is the first report of a genetic association, a replication effort, or both.
This is a replication study for the genetic association part
Methods
Study design 4 Present key elements of study design early in the paper.
This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi.
Setting 5 Describe the setting, locations and relevant dates, including periods of recruitment, exposure, follow-up, and data collection.
The participants were recruited from Sheikh Khalifa Medical City (SKMC)
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Item Item number
STROBE Guideline Extension for Genetic Association Studies
(STREGA)
and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE.
(a) Cohort study – Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up.
Case-control study – Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls.
Cross-sectional study – Give the eligibility criteria, and the sources and methods of selection of participants.
All subjects were UAE born and with Arabic descent.
Give information on the criteria and methods for selection of subsets of participants from a larger study, when relevant.
145 diagnosed with DKD and 265 did not.
Participants 6
(b) Cohort study – For matched studies, give matching criteria and number of exposed and unexposed.
Case-control study – For matched studies, give matching criteria and the number of controls per case.
145 diagnosed with DKD and 265 did not.
Variables 7 (a) Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable.
Various clinical and laboratory measures were assessed and collected
(b) Clearly define genetic exposures (genetic variants) using a widely-used nomenclature system. Identify
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Item Item number
STROBE Guideline Extension for Genetic Association Studies
(STREGA)
during the hospital visits. Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation.
To select SNPs that are associated with kidney function traits included in the study (blood urea, serum creatinine, eGFR values, albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and data bases including PubMed, Google Scholar, the GWAS catalogue (https://www.ebi.ac.uk/gwas/home ), the Phenolyzer database (http://phenolyzer.wglab.org/ ), the infinome genome interpretation platform (https://www.infino.me/ ), and the GWAS Central database (http://www.gwascentral.org/ ) were consulted.
variables likely to be associated with population stratification (confounding by ethnic origin).
Genetic studies in this study follow the candidate gene approach. The SNPs tested for each trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide Association Study (GWAS) that was intended to determine the genetic associations of T2DM in the UAE population, establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).
Data sources measurement
8* (a) For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group.
(b) Describe laboratory methods, including source and storage of DNA, genotyping methods and platforms
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Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation. Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2).
(including the allele calling algorithm used, and its version), error rates and call rates. State the laboratory/centre where genotyping was done. Describe comparability of laboratory methods if there is more than one group. Specify whether genotypes were assigned using all of the data from the study simultaneously or in smaller batches.
The original Genome Wide Association Study (GWAS) from which we selected the tested SNPs was reported to establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).
Bias 9 (a) Describe any efforts to address potential sources of bias.
We applied filters for genotyping like H-W testing, allele frequencies
(b) For quantitative outcome variables, specify if any investigation of
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and genotype call rates filters. We also excluded data which is missed > 20% of participants from the analyses, and used covariates adjustment for each analysis.
potential bias resulting from pharmacotherapy was undertaken. If relevant, describe the nature and magnitude of the potential bias, and explain what approach was used to deal with this.
Not applicable
Study size 10 Explain how the study size was arrived at.
No prior calculations conducted
Quantitative variables
11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen, and why.
Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2). Others quantitative variables like age or laboratory variables were collected without handling.
If applicable, describe how effects of treatment were dealt with.
For genetic associations with quantitative variables in this study, data was normalized using log transformation.
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(a) Describe all statistical methods, including those used to control for confounding.
For continuous data, statistical differences were assessed using two-sided t-tests for normally distributed data or Wilcoxon rank-sum (Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for percentage data or Fishers Exact test when expected frequencies are less than 5. A P-value < 0.05 was considered as significant. PLINK software was also used for testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. The results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% confidence intervals. The same approach (patients with DKD vs. patients without DKD) was adopted to test the associations between the possible risk factors and the development of the DKD. However, the logistic model which includes all the associated factors in Table 4 was validated by the Hosmer–Lemeshow goodness of fit test to allow for the inclusion of several covariates. Analyses between SNPs and renal function traits were conducted in PLINK using linear regression models that included age and gender as covariates. Results were presented as beta (regression coefficient for the linear regression model, calculated based on the minor allele), standard errors, and P-values.
State software version used and options (or settings) chosen.
PLINK software version 1.07
Stata software version 14 (StataCorp LLC, Texas, USA)
Statistical methods
12
(b) Describe any methods used to examine subgroups and interactions.
No methods used to examine subgroups and interactions.
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(c) Explain how missing data were addressed.
Any missing data in > 20% of participants were excluded to avoid bias.
(d) Cohort study – If applicable, explain how loss to follow-up was addressed.
Case-control study – If applicable, explain how matching of cases and controls was addressed.
Cross-sectional study – If applicable, describe analytical methods taking account of sampling strategy.
No specific sampling strategy.
(e) Describe any sensitivity analyses.
No sensitivity analyses.
(f) State whether Hardy-Weinberg equilibrium was considered and, if so, how.
SNPs failed the Hardy-Weinberg equilibrium (HWE) test at the 0.001 were excluded.
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(g) Describe any methods used for inferring genotypes or haplotypes.
No methods used for inferring genotypes or haplotypes.
(h) Describe any methods used to assess or address population stratification.
The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the population stratification. This current study is not a GWAS, so we did not address this issue again.
(i) Describe any methods used to address multiple comparisons or to control risk of false positive findings.
We mentioned that the current
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study is a replication study, therefore we reported here all P-values < 0.05, suggesting possible replications. However, using a Bonferroni correction for multiple testing, P-values with statistically significant associations are like the following: < 0.00079 for the DKD associations, < 0.0006 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine, and 0.00068 for the urea associations.
(j) Describe any methods used to address and correct for relatedness among subjects.
The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the relatedness among subjects. This current study is not a GWAS, so we did not address this issue again.
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Results
(a) Report the numbers of individuals at each stage of the study – e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed.
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls)
Report numbers of individuals in whom genotyping was attempted and numbers of individuals in whom genotyping was successful.
In all of participants genotyping was successful.
(b) Give reasons for non-participation at each stage.
Non-participation were excluded. This is a one stage study
Participants
13*
(c) Consider use of a flow diagram.
No flow diagram
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Descriptive data
14* (a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders.
Both patient groups (with or without kidney disease) had poor glycemic control with blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those without DKD indicated that the majority of those with DKD were males (~52% versus 38% for controls), older (67 versus ~59 years), had significant rates of comorbidities such as hypertension and dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal function indices was also observed in patients with DKD vs. those who don’t have DKD indicated by the significantly higher rates of ACR, urea, and creatinine; and significantly lower eGFR. However, DKD patients tended to have lower LDL-cholesterol results compared to those without DKD, which suggests these patients received better medical care or at least received intensive statin medication. T2DM duration was the single factor that significantly contributed to the development of DKD. Increased risk for DKD was significantly associated with increasing duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-8.02). Although the levels of serum creatinine indicated a trend for disease development (P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03. We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.
Consider giving information by genotype.
- Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.
- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with creatinine.
- SNPs within the MED1 gene (rs2168785 and rs12452509), and SNPs in two genetic regions, WDR72 and SHROOM3 with eGFR.
- Rs1801725 in CARS, rs1155563 in GC, and two SNPs in CYP2R1 with vitamin D levels.
- SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 with albumin-to-creatinine ratio.
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(b) Indicate the number of participants with missing data for each variable of interest.
Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls).
(c) Cohort study – Summarize follow-up time, e.g. average and total amount.
The study was not a cohort study.
Cohort study-Report numbers of outcome events or summary measures over time.
Report outcomes (phenotypes) for each genotype category over time
Outcome data 15 *
Case-control study – Report numbers in each exposure category, or summary measures of exposure.
Report numbers in each genotype category
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Cross-sectional study – Report numbers of outcome events or summary measures.
There is no exposure-outcome measurements.
Report outcomes (phenotypes) for each genotype category
Not applicable
(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence intervals). Make clear which confounders were adjusted for and why they were included.
For testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. Analyses between SNPs and renal function traits were conducted using linear regression models that included age and gender as covariates.
(b) Report category boundaries when continuous variables were categorized.
Continuous variables were not categorized.
Main results 16
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period.
We did not translating estimates of relative risk into absolute risk for a meaningful time period.
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(d) Report results of any adjustments for multiple comparisons.
We discussed the Bonferroni correction for multiple testing in the study.
Other analyses 17 (a) Report other analyses done – e.g., analyses of subgroups and interactions, and sensitivity analyses.
No analyses for subgroups, interactions, or sensitivity analyses.
(b) If numerous genetic exposures (genetic variants) were examined, summarize results from all analyses undertaken.
Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.
- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with
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creatinine.
- SNPs within the MED1 gene (rs2168785 and rs12452509), and SNPs in two genetic regions, WDR72 and SHROOM3 with eGFR.
- Rs1801725 in CARS, rs1155563 in GC, and two SNPs in CYP2R1 with vitamin D levels.
- SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 with albumin-to-creatinine ratio.
- SHROOM3 rs4859682 remained significant (P=0.04, OR=1.46) with DKD.
(c) If detailed results are available elsewhere, state how they can be accessed.
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Not applicable
Discussion
Key results 18 Summarize key results with reference to study objectives.Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.
Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias.A limitation of this study is that analyses carried in this study did not included treatment modalities because most of patients had multiple conditions and they used several treatments; which make the models largely unstable and difficult to interpret.Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for
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sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.
Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence.Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.
Generalizability 21 Discuss the generalizability (external validity) of the study results.Some of the loci which reported previously in other populations did not replicated because of the statistical power issue. In addition, due to lack of results in this topic in the other populations, we cannot confirm the results of this study will be replicated
Other Information
Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based.This study was supported by research incentive funds from Khalifa University Internal Research Fund Level 2 granted to Dr. Habiba Al Safar. No role of funders taken in this study.
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