SHORT REPORT

Abdominal obesity has the highest impact on metabolic profilein an overweight African population

Line N. Handlos1, Daniel R. Witte2, David L. Mwaniki3, Michael K. Boit4, Beatrice Kilonzo3, Henrik Friis5,Andreas W. Hansen6, Knut Borch-Johnsen7, Inge Tetens8 & Dirk L. Christensen1,2

1Department of International Health, University of Copenhagen, Copenhagen, Denmark, 2Steno Diabetes Center, Gentofte,Denmark, 3Centre for Public Health Research, KEMRI, Nairobi, Kenya, 4Department of Exercise, Recreation and Sport Science,Kenyatta University, Nairobi, Kenya, 5Department of Human Nutrition, University of Copenhagen, Frederiksberg, Denmark,6National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark, 7Institute of Public Health,University of Southern Denmark, Odense, Denmark, and 8Division of Nutrition, National Food Institute, Technical Universityof Denmark, Søborg, Denmark

Aim: The aim of this study was to determine the association

between different anthropometric parameters and metabolic

profile in an overweight, adult, black Kenyan population.

Methods: An opportunity sample of 245 overweight adult

Kenyans (body mass index (BMI) $ 25 kg/m2) was analysed.

A score of metabolic profile (metabolic Z-score) was

constructed on the basis of levels of plasma lipids, blood

pressure, blood glucose and serum insulin. Linear regressions

using metabolic Z-score as outcome and six anthropometric

variables (waist circumference (WC), hip circumference,

visceral adipose tissue (VAT), abdominal subcutaneous adipose

tissue, arm fat area and arm muscle area) separately as

independent variables were carried out.

Results: Mean age of study participants was 42.1 years

(SD ¼ 9.6) and 26.5% of the participants were men. The

median BMI was 28.6 kg/m2 (Q1 ¼ 26.3; Q3 ¼ 31.3). Of the six

anthropometric variables tested, WC and VAT thickness had

the strongest negative association with the metabolic profile

(b ¼ 0.17 (0.09; 0.24) and 0.15 (0.08; 0.23), respectively).

Conclusions: WC and VAT thickness were the strongest

anthropometric predictors for the metabolic profile in

overweight adult Kenyans. WC is useful in clinical practice for

the diagnosis of metabolically unhealthy fat accumulation in

an African setting.

Keywords: Abdominal obesity, overweight, waist

circumference, visceral obesity

Abbreviations: AFA, arm fat area, AMA, arm muscle area, BMI,

body mass index, HC, hip circumference, HDL-C, high-density

lipoprotein cholesterol, LDL-C, low-density lipoprotein

cholesterol, VAT, visceral adipose tissue, WC, waist circumference

INTRODUCTION

The estimated number of overweight individuals on a globalscale is 1.5 billion and the number is increasing (WorldHealth Organization Media Centre 2011). The increase isespecially extensive in developing countries and theprevalence of overweight and obesity has tripled duringthe past 20 years in this part of the world (Hossain et al.2007).

Overweight and obesity are associated with metabolicdisorders such as hypertension, insulin resistance,dyslipidaemia and increased levels of glucose and insulinin the blood. These conditions are associated with anincreased risk of developing type 2 diabetes, cardiovasculardisease and cancer (World Health Organization 2009).However, studies have shown that not all people who areoverweight develop metabolic disorders (Bluher 2010).Understanding what characterizes the ‘unhealthy’ over-weight phenotype is essential in order to design relevantpreventive interventions targeting people at high risk ofdeveloping metabolic disorders. Many studies have beenconducted in order to explore this, but most of thesestudies have been performed in Caucasian populations.Since it has been shown that black and white populationsrespond differently to risk factors such as overweight andobesity (Bacha et al. 2003), it is essential to study this inan African context.

The aim of this study was to determine the associationbetween different anthropometric variables and themetabolic profile in an overweight black population ofdifferent ethnic origin from Kenya.

Correspondence: Dirk Lund Christensen, University of Copenhagen, Øster Farimagsgade 5, PO Box 2099, DK-1014 Copenhagen K, Denmark.

Tel: þ 45 3532 7626. Fax: þ 45 3532 7736. E-mail: dirklc@sund.ku.dk

(Received 22 April 2012; accepted 5 August 2012)

Annals of Human Biology, November–December 2012; 39(6): 530–533Copyright q Informa UK, Ltd.ISSN 0301-4460 print/ISSN 1464-5033 onlineDOI: 10.3109/03014460.2012.720279

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MATERIALS AND METHODS

Study area and populationThe study is based on data from the cross-sectional KenyaDiabetes Study, which was conducted in three ruralpopulations—the Luo, Kamba and Maasai—and an urbanpopulation of mixed ethnic origin (Christensen et al. 2009).All participants gave informed consent. The study wasapproved by the National Ethical Review Committee inKenya.

MeasurementsBlood and plasma measurements to determine levels ofblood glucose, plasma lipids and serum insulin were takenafter an overnight fast. A standard 75-g oral glucosetolerance test was performed on all participants who did nothave known diabetes or fasting venous blood glucose $ 6.1mmol/l. Blood pressure (BP) was measured twice on theright mid-upper arm using a BP oscillometric monitor(Omron M6, Kyoto, Japan). Waist circumference (WC),hip circumference (HC) and mid-upper arm circumferencewere measured using body tape and skin-fold thickness ofthe triceps brachii was measured using a Harpenden calliper.Arm fat and arm muscle areas were calculated according toFrisancho (1990). Abdominal fat distribution was measuredusing ultrasonography with an Aquila Basic Unit (Esaote,Pie Medical Equipment, Maastricht, the Netherlands) with a3.5/5.0 MHz transducer (Probe Article no. 41,0638 CurvedArray HiD probe R40 Pie Medical Equipment). Visceral andsubcutaneous adipose tissue (VAT and SAT, respectively)measurements were made of tissue from the spine to thelinea alba, with minimal pressure at the end of expirationand with the transducer placed above the navel from themuscles to the skin and with no pressure on the skin,respectively.

Selection procedureThe inclusion of participants for the study was based on anopportunity sample. The inclusion criteria were $ 17 yearsof age and Luo, Kamba or Maasai ethnicity for the ruralpopulation and the same or biologically and culturallyrelated ethnicity in the urban population. Exclusion criteriawere pregnancy, serious illness and severe mental disease.A total of 1481 individuals were screened for the study.

In the present study only participants who wereoverweight (body mass index (BMI) $ 25 kg/m2) wereincluded. Participants with incomplete observational dataparameters were excluded, as were the participants whoreceived medical treatment for either diabetes or cardio-vascular disease.

Metabolic Z-scoreIn order to explore the risk factors for metabolic disorders, anew variable (metabolic Z-score) was created by summar-izing four standardized metabolic risk factors. The riskfactors were plasma lipids, blood pressure, blood glucoseand serum insulin. These parameters were chosen based ontheir association with metabolic health (World Health

Organization 2009). The score was created assuming thateach risk factor had an equally large impact on the metabolicprofile; the calculations were as follows:

Plasma lipids ¼ ðfasting plasma LDL-C

þ plasma triglycerides

þ plasma total cholesterol

2 plasma HDL – CÞ=4

Blood pressure ¼ ðsystolic blood pressure

þ diastolic blood pressureÞ=2

Blood glucose ¼ ðfasting blood glucose

þ blood glucose 2 hours after intake

of glucose solutionÞ=2

Serum insulin ¼ fasting serum insulin

Metabolic Z-score ¼ ðplasma lipids þ blood pressure

þ blood glucose þ serum insulinÞ=4

The higher the value of the metabolic Z-score, the lower thedegree of metabolic health.

Statistical analysesStatistical analyses were carried out in SAS version 9.2. Allanthropometric variables were standardized and testedseparately in a linear regression model and control forconfounding of age, sex, ethnicity and residence was donestepwise.

RESULTS

A total of 294 participants were overweight (BMI $ 25kg/m2). Of these 49 were excluded; 35 due to missing valuesand 14 due to medical treatment for either diabetes orcardiovascular illness. The final study sample consisted of245 participants, of whom 26.5% were men. The mean agewas 42.1 years (SD ¼ 9.6) and the median BMI was28.6 kg/m2 (Q1 ¼ 26.3; Q3 ¼ 31.3) (Table I).

The degree of metabolic disorders increased withincreasing age ( p , 0.05) and increasing BMI ( p , 0.01).Being male had a negative impact on the metabolic profile( p , 0.01), whereas being of Maasai ethnicity had a positiveimpact on the metabolic profile ( p , 0.01).

Except for arm muscle area (AMA), all anthropometricvariables were significantly associated with the metabolicprofile when controlling for age, sex, ethnicity and residence.Variables connected to abdominal obesity showed the

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strongest association with the metabolic profile ( p , 0.01).

After adjustment for age, sex, ethnicity and residence, WC

and VAT thickness had the strongest association with the

metabolic profile (b ¼ 0.17 (0.09; 0.24) and 0.15 (0.08;

0.23), respectively). The significance of the two variables’

association with the metabolic profile did not differ

(Table II).When analysing individuals with BMI $ 25 kg/m2

and , 30 kg/m2 and individuals with BMI $ 30 kg/m2

separately, abdominal obesity remained having the highest

impact on metabolic profile in both BMI groups. However,

VAT thickness was found to have a higher impact on the

metabolic profile among those with BMI $ 30 kg/m2 thanthose with BMI $ 25 kg/m2 and , 30 kg/m2 (b ¼ 0.26(0.17; 0.34) and b ¼ 0.19 (0.09; 0.28), respectively) andWC was found to have a higher impact among those withBMI $ 25 kg/m2 and , 30 kg/m2 than among those withBMI $ 30 kg/m2 (b ¼ 0.29 (0.18; 0.40) and b ¼ 0.25 (0.14;0.35), respectively).

DISCUSSION

This is the first study to determine anthropometricpredictors of the metabolic profile in an overweight blackKenyan population. Out of six anthropometric variables, theamount of abdominal fat, more specifically WC and VATthickness, was found to have the strongest association withthe metabolic profile. WC was found to be as strong apredictor for metabolic health as the thickness of the VAT.

The harmful effect of abdominal obesity was found inindividuals with BMI$25 kg/m2 and ,30 kg/m2 as well asin individuals with BMI$30 kg/m2.

The findings are in accordance with reports on otherAfrican populations. Fezeu et al. (2007) showed that centralobesity is the key determinant of the prevalence of themetabolic disorders in normal- and overweight adultCameroonians. Further, Jennings et al. (2009) have shownthat VAT and WC are the largest contributors to diagnosis ofthe metabolic syndrome in normal- and overweight SouthAfrican women.

This study also showed that Maasai ethnicity had apositive impact on the metabolic profile. This correspondswith earlier findings from the full study populationincluding individuals of normal weight as well as overweight(Christensen et al. 2008, 2009). Compared to two otherKenyan ethnic groups (the Luo and the Kamba), the authorsfound that the Maasai had the highest VAT accumulation(Christensen et al. 2008), but also that this ethnic group hadthe lowest prevalence of glucose intolerance (Christensenet al. 2009). One explanation for this discrepancy could bethat the Maasai had accumulated their excess fat recentlyand therefore metabolic disorders have not yet occurred.This hypothesis needs careful follow-up.

Our finding that, along with VAT thickness, WC is thebest predictor of the metabolic profile in an overweight,Kenyan population may have great implications for theprospect of diagnosing individuals with an unhealthymetabolism among black Africans. Compared to ultrasound

Table I. Characteristics of 245 overweight, adult Kenyans (BMI $ 25kg/m2).

Mean(SD)/median(1Q; 3Q)

Age (years) 42.1 (9.6)*Sex (% men) 26.5**BMI (kg/m2) 28.6 (26.3; 31.3)**Ethnicity

Luo (%) 23.7Kamba (%) 54.3Maasai (%) 22.0**

Residency (% urban) 33.9Waist circumference (cm) 95.5 (11.0)**Hip circumference (cm) 107.7 (8.5)Visceral adipose tissue (cm) 7.8 (2.1)**Abdominal subcutaneous adipose tissue (cm) 3.0 (1.2)Arm fat area (cm2) 41.0 (28.6; 49.3)Arm muscle area (cm2) 52.6 (13.3)Venous blood glucose 0 minutes (mmol/l) 4.4 (4.0; 4.8)**Venous blood glucose 2 hours (mmol/l) 6.2 (3.5)**Fasting serum insulin (pmol/l) 39.0 (24.0; 57.0)**Individuals with diabetesa (%) 8.2*Systolic blood pressure (mmHg) 126 (16)**Diastolic blood pressure (mmHg) 79 (10)**Hypertensionb (%) 22.9**Total cholesterol (mmol/l) 4.5 (1.1)**HDL-C (mmol/l) 1.1 (0.3)**LDL-C (mmol/l) 2.8 (0.9)**Triglycerides (mmol/l) 1.0 (0.8; 1.4)**Smokers (%) 4.6Alcohol users (%) 12.1

BMI, body mass index, LDL-C, low-density lipoprotein cholesterol,HDL-C, high-density lipoprotein cholesterol. *p , 0.05; **p , 0.01;a Sum of participants who previously had received a diabetes diagnosis and

participants who either had a blood glucose level of $ 6.1 mmol/l at fast

or $ 10.0 mmol/l 2 hours after intake of the glucose solution; b Systolic

blood pressure $ 140 mmHg or diastolic blood pressure $ 90 mmHg.

Table II. Association between anthropometric variables and metabolic health status in 245 adult Kenyans (BMI $ 25 kg/m2).

Model A Model B Model C

b 95% CI b 95% CI b 95% CI

Waist circumference 0.21 0.15; 0.27 ** 0.17 0.10; 0.24 ** 0.17 0.09; 0.24 **Hip circumference 0.06 20.01; 0.12 0.07 0.01; 0.14 * 0.07 0.00; 0.14 *Visceral adipose tissue 0.21 0.15; 0.27 ** 0.16 0.09; 0.23 ** 0.15 0.08; 0.23 **Abdominal subcutaneous adipose tissue 0.04 20.03; 0.10 0.09 0.02; 0.16 * 0.07 0.00; 0.14 *Arm fat area 0.05 20.02; 0.12 0.09 0.02; 0.15 * 0.08 0.01; 0.15 *Arm muscle area 0.07 0.00; 0.13 0.04 20.02; 0.11 0.04 20.02; 0.11

*p , 0.05; **p , 0.01. Model A: unadjusted. Model B: adjusted for age and sex.Model C: adjusted for age, sex, ethnicity and residence.

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scanning of abdominal obesity, measuring WC requires lesscostly and less sophisticated equipment and is less timeconsuming to undertake. Further, it has the advantage ofrequiring fairly limited training to carry out. Altogether thismakes the measurement of WC ideal as the first step of ascreening strategy in a developing country setting wheresimplicity and low cost is essential.

ACKNOWLEDGEMENTS

We are thankful to all study participants, the local chiefs, thelocal elder councils, district politicians and all localassistants. We acknowledge the permission by the Directorof KEMRI to publish this manuscript. DLC, HF, KBJO,DLM, MKB and IT conceived the study. DLC, BK and AWHimplemented the study. LNH, DRW and DLC analysed thedata and LNH wrote the first draft of the manuscript. Allauthors contributed to interpretation of results andcommented on drafts and approved the final version. DLC(E-mail: dirklc@sund.ku.dk) is guarantor of the paper.

Declaration of interest: The authors report no conflicts ofinterest. This study was supported by grants from DANIDA,University of Copenhagen (Cluster of International Health),Steno Diabetes Center, Beckett Foundation, DagmarMarshall’s Foundation, Dr. Thorvald Madsen’s Grant,Kong Christian den Tiende’s Foundation and Brdr.Hartmann Foundation. The funding bodies had no rolein the study design, data collection, data analysis, datainterpretation or decision to publish the findings.

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NOTICE OF CORRECTION

The version of this article published online ahead of print on18 Sept 2012 contained an error. “Plasma insulin” shouldhave read “serum insulin” throughout the text. This hasbeen corrected for this version.

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