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1
Identification of Inflammatory Biomarkers for Pediatric Malarial Anemia Severity 2
using Novel Statistical Methods 3
4
5
John M. Ong’echa1*, Gregory C. Davenport2, John M. Vulule3, James B. Hittner4, 6
and Douglas J. Perkins1,2 7
8
9
10
1 University of New Mexico Laboratories of Parasitic and Viral Diseases, Centre for 11
Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya. 12
2 Center for Global Health, Department of Internal Medicine, University of New Mexico 13
School of Medicine, NM, USA. 14
3 Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya 15
4 Department of Psychology, College of Charleston, Charleston, SC, USA. 16
17
18
Running Title: Biomarkers for malarial anemia severity 19
Key words: Severe malarial anemia, inflammatory mediators, Plasmodium falciparum, 20
cytokines, biomarkers 21
Word count: 3499 22
Abstract: 246 23
24
Copyright © 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.Infect. Immun. doi:10.1128/IAI.05161-11 IAI Accepts, published online ahead of print on 22 August 2011
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FOOTNOTE PAGE 25
Conflict of interest: None reported by any of the authors of the manuscript due to 26
commercial or other affiliations. 27
28
Presentation at previous meetings: Results of this study were presented in part at 29
the 57th American Society of Tropical Medicine and Hygiene annual meeting, New 30
Orleans, Louisiana, U.S.A., Abstract # 936, December 2008 31
32
33
34
*Please address any correspondence to: 35
John Michael Ong’echa, Ph.D. 36
University of New Mexico Laboratories of Parasitic and Viral Diseases 37
Centre for Global Health Research 38
Kenya Medical Research Institute 39
P. O. Box 1578, 40100 40
Kisumu, Kenya 41
Phone: 254-203530427 42
Fax: 254-203530427 43
E-mail: [email protected] 44
45
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ABSTRACT 47
Holoendemic Plasmodium falciparum transmission areas are characterized by high 48
rates of pediatric severe malarial anemia (SMA) and associated mortality. Although the 49
etiology of SMA is complex and multi-factorial, perturbations in inflammatory mediator 50
production play an important role in the pathogenic process. As such, the current study 51
focused on identification of inflammatory biomarkers in children with malarial anemia. 52
Febrile children (aged 3-30 mos.) presenting at Siaya District Hospital in western 53
Kenya, underwent a complete clinical and hematological evaluation. Children with 54
falciparum malaria, and no additional identifiable anemia-promoting co-infections, were 55
stratified into three groups: uncomplicated malaria (Hb≥11.0 g/dL, n=31); non-SMA (Hb 56
6.0-10.9 g/dL, n=37); and SMA (Hb<6.0 g/dL, n=80). A Luminex® hu25-plex array was 57
used to determine potential biomarkers (i.e., IL-1β, IL-1ra, IL-2, IL-2R, IL-4, IL-5, IL-6, 58
IL-7, IL-8, IL-10, IL-12p70, IL-13, IL-15, IL-17, TNF-α, IFN-α, IFN-γ, GM-CSF, MIP-1α, 59
MIP-1β, IP-10, MIG, Eotaxin, RANTES, and MCP-1) in samples obtained prior to any 60
treatment interventions. To determine the strongest biomarkers of anemia, a 61
parsimonious set of predictor variables for Hb was generated by least angle regression 62
(LAR), controlling for the confounding effects of age, gender, G6PD deficiency, and 63
sickle cell trait, followed by multiple linear regression analyses. IL-12p70 and IFN-γ 64
emerged as positive predictors of Hb, while IL-2R, IL-13, and eotaxin were negatively 65
associated with Hb. Results presented here demonstrate that the IL-12p70/IFN-γ 66
pathway represents a set of biomarkers that predict elevated Hb levels in children with 67
falciparum malaria, while activation of the IL-13/eotaxin pathway favors more profound 68
anemia. 69
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INTRODUCTION 71
Malaria due to Plasmodium falciparum infections accounts for a large proportion 72
of the pediatric morbidity and mortality in sub-Saharan Africa (50). In addition, P. 73
falciparum infections are a leading cause of pediatric anemia that can culminate in life-74
threatening severe malarial anemia (SMA) (6, 28). In holoendemic transmission areas, 75
SMA primarily manifests in infants and young children with a peak incidence between 7-76
24 months (6). SMA is characterized by dyserythropoiesis and ineffective 77
erythropoiesis (10). Although the etiology of SMA is multi-factorial, a number of studies 78
show that the condition results from increased erythrocyte destruction and decreased 79
red blood cell (RBC) production (reviewed in (10)). Our recent studies in a holoendemic 80
falciparum transmission area of western Kenya demonstrated that suppression of 81
erythropoiesis is a primary feature of SMA (49). 82
In malaria holoendemic areas, where malaria prevalence is greater than 80% in 83
children 1-4 years of age (6), identification of “high-risk” children who are most likely to 84
develop SMA is of great public health importance. Previous studies suggest that 85
cytokines play a pivotal role in the pathogenesis of malarial anemia (reviewed in (11)) 86
and that their levels could be used in the diagnosis and/or prognosis of the disease (13). 87
Elevated levels of pro-inflammatory cytokines in human malaria including interleukin 88
(IL)-1β, IL-6, IL-8, IL-23, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α are 89
associated with enhanced disease severity (17, 22, 27, 38). Conversely, decreased 90
levels of additional pro-inflammatory cytokines such as IL-12 and IFN-α are associated 91
with enhanced malaria pathogenesis in humans (22, 26, 38, 41). 92
Anti-inflammatory cytokines also play an important role in malarial pathogenesis 93
through their ability to modulate the pro-inflammatory response. For example, IL-10 94
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levels increase progressively with enhancing severity of childhood malarial anemia and 95
parasite density (38), and are associated with an inability to clear malaria parasitemia 96
(15). Increased circulating levels of IL-1 receptor antagonist (IL-1Ra) are also 97
associated with enhanced malaria disease severity in African children (17, 20), while 98
reduced production of other anti-inflammatory cytokines such as TGF-β correlate with 99
severe malaria (41). Based on the counter-regulatory effects of cytokines in the 100
inflammatory milieu, a number of previous studies have shown that the relative 101
expression of pro- and anti-inflammatory cytokines (i.e., ratios) are important predictors 102
of the development and outcomes of malarial anemia (24, 26, 39, 41). 103
In addition to cytokines, studies from our laboratory and others have 104
demonstrated that pediatric malaria is associated with altered production of β-105
chemokines, including macrophage inflammatory protein (MIP)-1α/CCL3, MIP-1β/CCL4, 106
and regulated upon activation, normal T-cell expressed and secreted (RANTES/CCL5) 107
(2, 19, 34, 49). Growth factors such as granulocyte-colony stimulating factor (G-CSF) 108
and granulocyte-macrophage-colony stimulating factor (GM-CSF), as well as additional 109
chemokines including eotaxin/CCL11, monokine induced by IFN-γ (MIG/CXCL9), and 110
interferon inducible protein (IP)-10/CXCL10 also appear to play an important role in 111
malaria pathogenesis (2, 16, 51). 112
Based on the important role of cytokines, chemokines, and growth factors in 113
malaria pathogenesis and previous studies showing that inflammatory mediators may 114
serve as biomarkers for cerebral malaria severity and mortality (2, 16, 20), as well as 115
malaria outcomes during pregnancy (21, 51), we investigated the potential role of 116
inflammatory mediators as biomarkers in children with malarial anemia. The biomarkers 117
investigated were: IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12p70, IL-13, IL-15, 118
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IL-17, IFN-γ, IFN-α, TNF-α, IL-1Ra, IL-2R, GM-CSF, MCP-1, MIP-1α, MIP-1β, IP-10, 119
MIG, eotaxin, RANTES, IL-1Ra:IL-1β ratio, and IL-2R:IL-2 ratio. Selection of this 120
particular biomarker panel was based on available technologies that could 121
concomitantly measure an inclusive group of inflammatory mediators with known or 122
suspected importance in malaria immunology in the context of very low available blood 123
sample volumes from young, anemic children. To identify the most relevant biomarkers 124
from the expanded set of inflammatory mediators, we utilized novel statistical modeling 125
with least angle regression (LAR) analysis to determine a parsimonious set of biomarker 126
predictors that were then used in multiple linear regression models to predict Hb levels 127
in children with malaria. In addition, since the degree of anemia in children with malaria 128
is highly influenced by commonly identified concomitant co-infections including human 129
immunodeficiency virus type 1 (HIV-1) (40), bacteremia (5), and helminthic infections 130
(52), all malaria-infected children with these co-infections were excluded from the 131
analyses. 132
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MATERIALS AND METHODS 137
Study area. We undertook the current study at Siaya District Hospital (SDH) in Siaya 138
County, Nyanza Province, western Kenya, a P. falciparum holoendemic transmission 139
area reporting increased pediatric malarial admissions despite recent interventions (35). 140
SMA is the primary clinical manifestation of severe malaria in children under the age of 141
5 years, peaking in children aged 7-24 months (6). In addition, 85% of children under 3 142
years of age admitted to the SDH’s pediatric ward had malarial anemia (MA) which 143
contributed to 53% of all malaria-related deaths (33). The study area and the 144
hematological manifestations of pediatric MA in the study population have been 145
described in detail elsewhere (36). 146
147
Study population. Children (n=148, aged 3-30mos) presenting with acute P. 148
falciparum malaria were recruited into the study at the SDH, western Kenya. The 149
children were stratified according to Hb levels into the following categories: 150
uncomplicated malaria (UM, n=31; Hb≥11.0 g/dL); non-SMA (Non-SMA, n=37; Hb 6.0-151
10.9 g/dL); and SMA (n=80; Hb<6.0 g/dL). SMA was defined based on a geographically 152
referenced population using >14,000 Hb measures in children less than 48 months of 153
age in western Kenya (29). All children were free of severe malaria symptoms such as 154
hypoglycemia. Since HIV-1 promotes anemia in children with falciparum malaria (40), 155
only HIV-1 negative children were included in the present study. HIV-1 status was 156
determined by two rapid serological antibody tests and HIV-1 proviral DNA PCR tests 157
as previously described (40). Similarly, bacteremic children and those with hook-worm 158
infections were excluded from the study. All study participants were also free from 159
cerebral malaria, which is a very rare occurrence in this high malaria transmission area 160
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(6). Children with malaria were treated according to the Ministry of Health, Kenya 161
(MOH) guidelines using Coartem® (artemether and lumefantrin) for uncomplicated 162
malaria and intravenous quinine for severe malaria. Supportive care and blood 163
transfusions were administered according to MOH guidelines. All blood samples were 164
obtained prior to antimalarial and/or any other treatment interventions. All parents or 165
legal guardians of the children gave written informed consent before enrolment into the 166
study. The study was approved by the National Ethical Review Committee of the Kenya 167
Medical Research Institute and the Institutional Review Board of the University of New 168
Mexico. 169
170
Parasitemia determination. Thick and thin peripheral blood smears were prepared 171
from venous blood samples and stained with Giemsa reagent for malaria parasite 172
identification and quantification by microscopy. Asexual malaria parasites were counted 173
against 300 leukocytes, and parasite densities were determined by multiplying the 174
parasite count by the absolute leukocyte counts from an automated hematology 175
analyzer (Beckman Coulter® AcT diff2™, Beckman-Coulter Corporation, Miami, USA). 176
177
Circulating inflammatory mediator measurements. Venous blood samples (1.0-3.0 178
mL) were immediately centrifuged following collection, and plasma was separated, 179
aliquoted, and stored at -70 ºC until use. Circulating cytokine levels were determined by 180
the human Cytokine 25-plex Antibody Bead Kit (BioSource™ International) according to 181
the manufacturer’s instructions. Plates were read on a Luminex® 100™ system 182
(Luminex Corporation) and analyzed using the Bio-plex manager software (Bio-Rad 183
Laboratories). Detection limits for the inflammatory mediators and receptors were as 184
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follows: 3pg/mL (IL-5, IL-6, IL-8); 4pg/mL (MIG); 5pg/mL (IL-1Ra, IL-2R, IL-4, IL-10, 185
IFN-γ, eotaxin-1, IP-10); 6pg/mL (IL-2); 10pg/mL (IL-7, IL-13, IL-15, IL-17, TNF-α, MIP-186
1α, MIP-1β, MCP-1); and 15pg/mL (IL-1β, IL-12p70, IFN-α, GM-CSF, RANTES). 187
188
Statistical analyses. Comparison of continuous variables across the three clinical 189
groups (UM, non-SMA and SMA) were conducted using Kruskal-Wallis tests, and where 190
significant differences were obtained, Mann-Whitney U tests were used for pairwise 191
comparisons. Differences in the proportional measurements were determined using 192
Pearson’s chi-square test (χ2). In addition, to determine the ability of the inflammatory 193
mediators to predict the primary endpoint (Hb), least angle regression (LAR) analysis 194
was performed http://cran.r-project.org/web/packages/lars/lars.pdf. LAR is a regression 195
algorithm for high-dimensional data that utilizes a variant of forward stepwise regression 196
to select a parsimonious set of predictors from a large number of possible covariates for 197
efficient prediction of a response variable (12). For each LAR analysis, the best 198
predictors were identified and then entered into a multiple linear regression analysis to 199
predict Hb. Moreover, each linear regression was hierarchical in that the potentially 200
confounding effects of age, gender, G6PD deficiency, and sickle cell trait were 201
controlled for by entering these variables first as a covariate block. The best LAR 202
predictors were then entered into the regression equation as a second block. To help 203
ensure the stability of the linear regression coefficients, the maximum number of “best” 204
predictors selected from each LAR analysis was maintained at a subject-to-predictor 205
ratio of at least 10-to-1. Given this restriction, for the linear regression on the total 206
sample (n=148), the 15 best predictors from the LAR analysis were retained and then 207
examined as predictors in the linear regression. Likewise, for determining predictors of 208
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SMA (n=80), the 8 strongest LAR predictors were selected for the multiple linear 209
regression analysis. To gauge the influence of each predictor, we interpreted 210
standardized partial regression coefficients (β-weights) and squared semipartial 211
correlations. β-weights represent the influence of a single predictor on an outcome, 212
controlling for all other predictors. Formally, a β-weight indicates how many standard 213
deviations change are expected in the outcome variable when there is a one standard 214
deviation change in the predictor variable (controlling for all other predictors). The 215
squared semipartial correlation represents the unique amount of criterion, or outcome, 216
variance accounted for by a given predictor variable. For all analyses, P≤0.050 was 217
considered statistically significant. 218
219
220
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RESULTS 221
Demographic and clinical characteristics of the study participants. The 222
demographic and clinical characteristics of the study participants are listed in Table 1. 223
Children in the different clinical categories (UM, Non-SMA, and SMA) were comparable 224
in age, gender, glucose levels, and axillary temperature (P>0.050 for all). As expected 225
based on the a priori classification, Hb concentrations differed across the groups 226
(P<0.001). However, peripheral parasite density and prevalence of high density 227
parasitemia (HDP, ≥10,000 parasites/μL) were comparable across the groups (P=0.286, 228
and P=0.668, respectively). Lymphocyte and monocyte counts differed across the 229
groups (P=0.006, and P<0.001, respectively), while granulocyte counts were 230
comparable across the groups (P=0.378). Post-hoc analysis revealed that relative to 231
the UM group, children with SMA group had elevated lymphocyte and monocyte counts 232
(P<0.010 for both). The proportion of children carrying the sickle cell trait differed 233
across the groups (P=0.050), with the SMA group having the lowest prevalence. 234
235
Inflammatory mediator profiles. The first step for identifying important biomarkers for 236
predicting malarial anemia severity was the measurement of pro- and anti-inflammatory 237
cytokines, chemokines, and growth factors in the 3 groups of children. As shown in 238
Table 2, circulating levels of the pro-inflammatory cytokines IL-6, IL-12p70, and IL-17 239
differed significantly across the clinical groups (P=0.003, P=0.016, and P=0.031, 240
respectively). Among the anti-inflammatory cytokines, only IL-4 and IL-10 levels 241
differed significantly across the groups (P=0.020, and P<0.001, respectively). 242
Circulating levels of IL-2R significantly differed across the groups (P<0.001), as well as 243
the IL-2R/IL-2 ratio (P=0.026). Although not statistically significant, levels of both IL-244
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1Ra and IFN-α progressively decreased with increasing disease severity (P=0.063 and 245
P=0.057, respectively). Examination of chemokines and growth factors revealed that 246
only IP-10 levels differed significantly across the groups (P=0.008). Post-hoc 247
comparisons for all of the significant across-group differences are shown in Table 2. 248
249
Inflammatory mediators as predictors of malarial anemia severity. After 250
determining the profiles of inflammatory mediators in the 3 groups of children, we 251
determined which mediators were the strongest predictors of malarial anemia severity. 252
To accomplish this, we first performed a LAR analysis of the inflammatory mediators 253
(n=25) and biologically relevant ratios (n=2) for the total sample and SMA group 254
separately. The following inflammatory mediators (and ratio) emerged as the 15 best 255
predictors of Hb levels in the total sample (listed in order of predictive strength): IL-17, 256
IL-10, RANTES, IL-1ra:IL-1β ratio, eotaxin, IL-1β, IFN-γ, IL-1Ra, IL-2R, IL-13, IFN-α, IL-257
12p70, IL-5, MIP-1α, and IL-15. Entry of the 15 inflammatory mediators as independent 258
predictors in a multiple linear regression model, with Hb as the dependent variable, 259
demonstrated that IL-12p70 (standardized partial regression coefficient, β-260
weight=0.240, P=0.015) and IFN-γ (β-weight=0.293, P=0.001) positively predicted Hb 261
levels (Table 3). Although not reaching statistical significance, IL-10 (β-weight=0.161, 262
P=0.058) and IFN-α (β-weight=0.175, P=0.067,) also appeared to be important positive 263
predictors of Hb levels. Conversely, IL-2R (β-weight=-0.309, P=0.001), eotaxin (β-264
weight=-0.266, P=0.009), and IL-13 (β-weight=-0.290, P=0.004) inversely predicted Hb 265
levels (Table 3). 266
To determine the predictors of Hb among the subset of children with SMA, similar 267
analyses were performed that included only the SMA group (n=80). The LAR analysis 268
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identified the following 8 inflammatory mediators as the best predictors of Hb in the 269
SMA group (listed in order of predictive strength): MIG, IL-5, IL-4, IL-1β, IL-2R, IL-10, 270
IL-12p70, and GM-CSF. Multiple linear regression analysis with the 8 inflammatory 271
mediators as independent predictors and Hb as the dependent variable demonstrated 272
that only IL-12p70 significantly predicted Hb levels (β-weight=0.288, P=0.017) in 273
children with SMA (Table 3). Although none of the inflammatory mediators inversely 274
predicted Hb levels at P<0.050, IL-2R emerged as a marginally significant predictor (β-275
weight=-0.201, P=0.084, Table 3). 276
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DISCUSSION 278
It is of great significance that biomarkers of malaria disease severity be identified 279
to enable a better understanding of how inflammatory mediators influence disease 280
pathogenesis and clinical outcomes. In the past several years, inflammatory mediators 281
have been investigated as potential biomarkers of cerebral malaria and mortality (2, 16, 282
20), as well as malaria outcomes during pregnancy (21, 51). Recently, using an in vitro 283
model of erythropoiesis, we demonstrated that soluble mediators of inflammation 284
associated with childhood SMA can suppress erythropoiesis in the novel model by 285
decreasing erythroid proliferation and maturation (3). In the current study, we took an 286
expanded approach by measuring a large panel of inflammatory mediators (n=25) to 287
identify biomarkers that are predictive of malarial anemia in a holoendemic area of P. 288
falciparum transmission in which the primary clinical manifestation of falciparum malaria 289
is SMA (6). 290
Results presented here are consistent with previous studies showing that low 291
levels of IL-12p70 (22, 26, 41), IL-10 (23), IFN-α (26), and IFN-γ (26) are associated 292
with more severe disease in children with malaria. Data presented here also support 293
previous investigations in which increased levels of IL-1Ra (17, 20), IL-2R (18), IL-6 (17, 294
27), and the IL-2R:IL-2 ratio (43) were correlated with more severe malaria in pediatric 295
cohorts. However, levels of a number of inflammatory mediators previously associated 296
with disease severity, such as IL-1β (19), IL-8 (27), TNF-α (26, 27), MIP-1α, MIP-1β 297
(34), IP-10 (16), the IL-1Ra:IL-1β ratio (20), and RANTES (19, 34, 49) did not 298
significantly differ across the three clinical groups in the current study. This apparent 299
discrepancy could plausibly be explained by the fact that, in the current study, all 300
children had acute malaria infection, while in previous studies, comparisons were made 301
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that included children: 1) without malaria infections (i.e., aparasitemic or healthy 302
controls) (16, 27, 34, 49); 2) during the convalescent period of disease (26); and/or 3) 303
with more previous exposure and acquired immunity based on being older (19, 27). 304
Differences observed here and previously are also likely related to differing malaria 305
disease manifestations: children in the current study included only those with anemia as 306
a clinical outcome, whereas previous investigations included a mixed phenotype of 307
disease characterized by cerebral malaria, hyperparasitemia, and SMA. Furthermore, 308
unlike previous investigations, the current study excluded children with co-pathogens, 309
since additional pathogens in children with malaria will affect the inflammatory milieu. 310
Although a number of recent studies have acknowledged the importance of 311
inflammatory mediators as potential biomarkers of malaria disease severity (2, 16, 20, 312
21, 51), there continues to be a lack of clear insight into the complexity of the immune 313
response. The complexity is underscored by the fact that production of most 314
inflammatory mediators are typically inter-correlated (20) with the directionality of the 315
immune responses and/or the accumulation of the effector cells within the deeper 316
tissues, rather than the absolute magnitude of cytokine levels, being more informative 317
(14). To address immunological complexities, there has been a move towards the use 318
of mathematical/statistical modeling as a tool to unravel these complex relationships 319
(48). Examples of successful modeling includes biomarkers for the prediction of 320
biochemical recurrence following prostatectomy (46) and characteristic inflammatory 321
responses during hemorrhagic shock (47). 322
In the current study, we modeled biomarkers for the prediction of anemia (Hb 323
levels) using LAR and multiple linear regression analysis. LAR was selected for the 324
modeling since it is a versatile computational application for high-dimensional datasets 325
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that can select a parsimonious set of predictors, which can then be used for prediction 326
of a response variable (in our case Hb) (12). In our total sample analysis, LAR 327
identified 15 inflammatory mediators (Table 3); 60% of which had levels that differed 328
significantly, or were of borderline significance, across the groups (Table 2). However, 329
although IL-4, IL-6, and the IL-2R/IL-2 ratio differed significantly across the groups, 330
these were not identified as top priorities in the LAR analysis. This can be explained by 331
the fact that the LAR analysis took into account the correlations among the predictor 332
variables; such predictor co-linearity cannot be disentangled with conventional between-333
group univariate comparisons. After identifying the parsimonious set of predictors with 334
LAR, a multiple linear regression analysis identified IL-12p70 and IFN-γ as significant 335
positive predictors of Hb. Consistent with the analysis that included all children in the 336
dataset, analyses that included only children with SMA identified IL-12p70 as a 337
significant predictor of Hb. Identification of IL-12p70 and IFN-γ as significant positive 338
predictors of Hb using these novel approaches supports previous observations showing 339
that enhanced production of IL-12p70 and IFN-γ are associated with reduced malarial 340
anemia severity (22, 26, 37, 41). In addition, the emergence of IL-12p70 and IFN-γ in 341
the human modeling presented here supports investigations in murine models 342
demonstrating that IL-12 promotes erythropoiesis by augmenting the formation of 343
erythroid burst forming units (BFU-E) and colony forming units (CFU-E) (31, 32). It is 344
important to note that SMA in the current study was defined as Hb<6.0 g/dL based on a 345
previous study in the same region that defined the distribution of Hb in the population by 346
performing >14,000 longitudinal Hb measures in children less than 48 months of age 347
(29). Given that the sample size was reduced from n=80 to n=39 using a cut-off 348
criterion of Hb<5.0 g/dL to define SMA, an appropriate subject-to-predictor ratio for the 349
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modeling translates into the ability to examine only half of the number of predictors. 350
Although we postulate that the same predictors will emerge when using the cut-off 351
criterion of Hb<5.0 g/dL, additional studies with an appropriate number of P. falciparum-352
infected children in the Hb<5.0 g/dL category are required to confirm this prediction. 353
The observation that IL-13 and eotaxin emerged in the modeling as significant 354
negative predictors of Hb levels is intriguing. IL-13 is a powerful anti-inflammatory 355
cytokine that regulates inflammation and immune responses (30), and is primarily 356
associated with allergic responses and helminthic infections (44, 52). Eotaxin was 357
recently reported to prevent hematopoietic cell differentiation by blocking their signaling 358
through suppressor of cytokine (SOCs) expression (45). Furthermore, in the context of 359
HIV-1/malaria co-infection, we recently observed that HIV-1-exposed and HIV-1-positive 360
children with worsening anemia (40) had elevated levels of eotaxin relative to HIV-1-361
negative children [G.C. Davenport et al., submitted). In addition, eotaxin is a strong 362
chemoattractant for eosinophils (42), which are also associated with allergic reactions 363
(44). However, eosinophil responses have been reported during malaria infections (25) 364
and are important for producing functional IL-13 (44). Since eosinophil responses are 365
associated with hematological recovery following malarial treatment (8), and Hb levels 366
typically decrease following successful parasite clearance with anti-malarial drugs (7), it 367
is tempting to postulate that the IL-13/eotaxin pathway may negatively regulate Hb 368
levels during a malaria infection through eosinophilic responses. Kurtzhals et al. (25) 369
observed that during acute infection, eosinophils were sequestered in deep tissues, 370
while recent studies showed that production of high levels of IL-13 in malaria-infected 371
children was associated with hepatomegaly (52). Eosinophils are known to produce 372
granule proteins [e.g. eosinophil cationic protein, (ECP)] whose levels are correlated 373
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with TNF-α and IL-2R during malarial infections (25). Findings presented here showing 374
that soluble IL-2R levels are significant negative predictors of Hb are consistent with 375
previous studies showing that elevated IL-2R is associated with enhanced malaria 376
disease severity (18). The potential effect of the IL-13/eotaxin pathway on 377
erythropoiesis, and the influence of IL-2R in regulating this process is largely 378
unexplored. As such, further investigation of this pathway in children with malaria, 379
coupled with the novel in vitro model of erythropoiesis recently developed (3), may offer 380
important insight into this largely unexplored pathway. 381
Based on findings presented here placed into the context of previous studies, we 382
propose a model in which IL-12 and IFN-γ are two of the primary cytokines responsible 383
for promoting successful erythropoiesis in children with malaria (31, 32), while high 384
levels of IL-2R may serve to dampen excessive type 1 immunity (18). The model 385
further proposes that eotaxin may recruit eosinophils to the deeper tissues (including 386
bone marrow) where they and other cell types may produce IL-13 to dampen the 387
inflammatory responses (30), but in the process, direct effects of protein granules (or 388
other local toxic mediators) may contribute to the suppression of erythropoiesis. This 389
model is consistent with studies showing elevated levels of eotaxin in bone-marrow (9) 390
and suppressed maturation of bone marrow dendritic cells in individuals with chronic 391
graft-vs-host disease (4). Moreover, the suppressive effect of eotaxin on hematopoietic 392
cell differentiation (45) may also explain the presence of numerous eosinophil 393
precursors trapped in the bone marrow of African children with malaria (1). 394
In summary, using statistical modeling in conjunction with high-throughput 395
assays that concomitantly measured 25 inflammatory mediators in a comprehensively 396
phenotyped cohort of children with malaria, we identified IL-12p70 and IFN-γ as 397
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significant positive predictors of Hb, and IL-2R, eotaxin, and IL-13 as significant inverse 398
predictors of Hb. Although some of the molecules examined here have previously 399
shown associations with malaria disease outcomes, concomitant investigation of a large 400
panel of potential biomarkers (n=25) offered the unique advantage of identifying novel 401
pathways, such as the IL-13/eotaxin pathway that may be an important inflammatory 402
network in malaria that requires further exploration. As the technological capacity 403
continues to expand and the magnitude of potential biomarkers increases rapidly, we 404
will continue to be faced with the practical realities associated with the number of study 405
participants that can be recruited and clinically managed, particularly in resource poor-406
settings. The use of mathematical tools such as LAR may offer some ability to deal with 407
the common and growing problems associated with a high number of predictors in a 408
limited participant base. 409
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ACKNOWLEDGMENTS 613
We are grateful to the parents, guardians, and children from the Siaya District 614
community, western Kenya for their participation in the study. We also thank all the 615
University of New Mexico-KEMRI staff and the Siaya District Hospital staff for their 616
support during this study. We thank the Director, Kenya Medical Research Institute 617
(KEMRI), for approving this manuscript for publication. 618
This work was supported by the National Institute of Health [R01 AI51305-07 and 619
D43 TW05884-07 to D.J.P.] and Fogarty International Center [R01 TW007631-2 to 620
J.M.O.]. The content is the responsibility of the authors and does not necessarily 621
represent the official views of the National Institute of Health. 622
623
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Table 1: Demographic and clinical characteristics of the study participants. 637
Characteristics
Uncomplicated malaria
(UM)
Non-severe malarial anemia
(Non-SMA)
Severe malarial anemia (SMA)
P value
Number of participants 31 37 80 N/A
Age (mos.) 12.0 (8) 9.0 (9) 8.0 (8) 0.301a
Gender (Male), n (%) 14 (45.2) 23 (62.2) 39 (48.8) 0.298b
Glucose levels (mmol/L) 4.7 (1.5) 5.1 (1.0) 4.9 (1.4) 0.307a
Temperature (°C) 37.0 (1.8) 37.4 (1.7) 37.5 (1.7) 0.789a
Hemoglobin (g/dL) 11.0 (1.0) 8.8 (1.1)** 5.0 (1.0)** <0.001a
Parasitemia (/μL) 48,354 (87,430) 22,615 (49,929) 26,166 (60,703) 0.286a
HDP (≥10,000/μL), n (%) 24 (77.4) 28 (75.7) 56 (70.0) 0.668b
Lymphocytes (×109/μL) 3.7 (3.0) 4.5 (1.6) 6.8 (4.3)** 0.006a
Monocytes (×109/μL) 0.70 (0.5) 0.65 (1.0) 1.10 (1.0)** <0.001a
Granulocytes (×109/μL) 7.05 (7.6) 4.45 (1.5) 4.30 (4.0) 0.378a
Sickle cell trait, n (%) 8 (25.8) 4 (10.8) 7 (8.8) 0.050b
638
The values are median (interquartile range [IQR]) unless stated otherwise. aDifferences were 639
determined using Kruskal-Wallis tests and where significant differences were observed, pairwise 640
comparisons were performed using Mann-Whitney U test relative to the UM group. bDifferences were 641
determined using Pearson’s chi-square test. * P<0.050, ** P<0.010. 642
643
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Table 2: Inflammatory mediator levels and ratios in children presenting with acute malaria. 644
Characteristics UM (n=31) Non-SMA (n=37) SMA (n=80) P valuea
Cytokines and ratios IL-1β 160.2 (323.6) 162.5 (309.3) 156.8 (277.7) 0.970
IL-1Ra 2,300.7 (2,396.8) 2,297.1 (2,082.3) 1,450.3 (2,021.0) 0.063
IL-2 36.5 (104.5) 24.1 (66.1) 31.4 (61.4) 0.743
IL-2R 1,039.0 (1,577.5) 2,072.0 (1,085.5)** 2,129.0 (2,737.7)** <0.001
IL-4 7.7 (23.1) 1.7 (8.0)* 4.9 (15.6) 0.020
IL-5 1.7 (4.5) 1.4 (3.0) 1.6 (2.6) 0.708
IL-6 50.6 (175.4) 68.7 (189.2) 97.9 (140.5)* 0.003
IL-7 25.8 (47.0) 8.5 (45.2) 1.3 (33.6) 0.456
IL-8 16.8 (27.3) 11.9 (18.6) 15.3 (22.8) 0.716
IL-10 168.4 (660.8) 569.6 (745.5)* 254.5 (570.5) <0.001
IL-12p70 362.0 (279.9) 439.6 (301.3) 340.1 (221.3) 0.016
IL-13 29.6 (53.9) 29.6 (32.9) 29.5 (48.6) 0.157
IL-15 53.0 (87.4) 22.4 (60.8) 26.9 (38.3) 0.106
IL-17 6.5 (24.8) 10.1 (19.3) 4.7 (11.3) 0.031
TNF-α 29.4 (39.3) 22.0 (51.1) 31.3 (49.2) 0.611
IFN-α 32.8 (122.8) 12.3 (51.5) 8.4 (53.5) 0.057
IFN-γ 16.2 (59.2) 8.0 (25.7) 4.2 (14.1) 0.102
IL-1Ra/IL-1β 22.3 (45.4) 15.0 (52.1) 11.8 (46.3) 0.471
IL-2R/IL-2 12.6 (96.7) 71.1 (879.1)* 70.0 (174.3)** 0.026
Growth Factors and Chemokines GM-CSF 84.4 (362.5) 18.3 (137.5) 39.7 (164.5) 0.525
MIP-1α 114.0 (137.2) 146.2 (87.0) 103.3 (105.0) 0.277
MIP-1β 411.7 (688.6) 343.7 (399.6) 404.3 (362.3) 0.888
IP-10 197.1 (694.3) 422.9 (1,155.4)* 204.2 (509.2) 0.008
MIG 110.0 (155.0) 187.0 (216.5) 119.0 (157.5) 0.572
Eotaxin 40.3 (22.5) 35.0 (32.8) 42.8 (27.8) 0.564
RANTES 18,176 (100,486) 13,952 (37,060) 17,270 (144,999) 0.719
MCP-1 235.1 (235.9) 226.2 (235.8) 192.8 (199.1) 0.431
645
The data are median values in pg/mL (IQR). aDifferences were determined using Kruskal-Wallis tests 646
and where significant differences were observed, pairwise comparisons were performed using Mann-647
Whitney U tests relative to the UM group. * P<0.050, ** P<0.010. 648
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Table 3. Predictors of malarial anemia severity. 649
Variable β-weight Semipartial r2 Block Δ statistics
All Children (n=148)
Block 1 Summaryψ: R2=0.058, P=0.071
Age 0.097 0.010
Gender 0.028 0.001
Sickle-cell trait -0.121 0.015
G6PD deficiency 0.166 0.028
Block 2 Summaryψ: R2=0.281, P<0.001
IL-17 -0.031 0.001
IL-10 0.161 0.028
RANTES 0.088 0.009
IL-Ra/IL-1β -0.037 0.002
Eotaxin -0.266 0.052
IL-1β 0.085 0.005
IFN-γ 0.293 0.083
IL-1Ra -0.117 0.014
IL-2R -0.309 0.087
IL-13 -0.290 0.062
IFN-α 0.175 0.026
IL-12p70 0.240 0.045
IL-5 0.118 0.011
MIP-1α 0.063 0.003
IL-15 -0.061 0.003
SMA Cases Only (n=80)
Block 1 Summaryφ: R2=0.197, P=0.002
Age 0.033 0.001
Gender 0.165 0.032
Sickle-cell trait -0.185 0.039
G6PD deficiency -0.374 0.146
Block 2 Summaryφ: R2=0.140, P=0.099
MIG 0.136 0.021
IL-5 0.106 0.012
IL-4 -0.167 0.036
IL-1β 0.087 0.010
IL-2R -0.201 0.044
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IL-10 0.052 0.003
IL-12p70 0.288 0.082
GM-CSF -0.121 0.014
650
The full model was significant at F(19, 128)=3.454, P<0.001, R=0.582, R2=0.339 when all children 651
were considered (n=148); and F(12, 67)=2.839, P=0.003, R=0.581, R2=0.337 for children with SMA 652
(n=80). Block 1 summary represents the β-weights and semipartial r2 values of the covariates on 653
their own among all childrenψ and among children with SMAφ without the inflammatory mediator 654
levels, while Block 2 summary represents the β-weights and semipartial r2 values of the inflammatory 655
mediators among all childrenψ and among children with SMAφ controlling for the confounding effects 656
of the covariates. β-weights and semipartial r2 values with P≤0.050 are marked in bold. 657
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670 671 672
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