The Journal of Infectious Diseases M A J O R A R T I C L E Baseline Inflammatory Biomarkers Identify Subgroups of HIV-Infected African Children With Differing Responses to Antiretroviral Therapy Andrew J. Prendergast,1,2,a Alexander J. Szubert,2,a Chipo Berejena,4 Godfrey Pimundu,5 Pietro Pala,8 Annie Shonhai,4 Victor Musiime,5,6 Mutsa Bwakura-Dangarembizi,4 Hannah Poulsom,3 Patricia Hunter,3 Philippa Musoke,7 Macklyn Kihembo,6 Paula Munderi,8 Diana M. Gibb,2 Moira Spyer,2 A. Sarah Walker,2,a and Nigel Klein3,a; the ARROW Trial Teamb 1Queen Mary University of London, 2MRC Clinical Trials Unit at University College London, and 3Institute of Child Health, London, United Kingdom; 4University of Zimbabwe, Harare; 5Joint Clinical Research Centre, 6Makerere University College of Health Sciences, 7Paediatric Infectious Diseases Clinic/Baylor-Uganda, Kampala, and 8MRC/UVRI Uganda Research Unit on AIDS, Entebbe, Uganda Background. Identifying determinants of morbidity and mortality may help target future interventions for human immunode- ficiency virus (HIV)–infected children. Methods. CD4+ T-cell count, HIV viral load, and levels of biomarkers (C-reactive protein, tumor necrosis factor α [TNF-α], interleukin 6 [IL-6], and soluble CD14) and interleukin 7 were measured at antiretroviral therapy (ART) initiation in the ARROW trial (case-cohort design). Cases were individuals who died, had new or recurrent World Health Organization clinical stage 4 events, or had poor immunological response to ART. Results. There were 115 cases (54 died, 45 had World Health Organization clinical stage 4 events, and 49 had poor immuno- logical response) and 485 controls. Before ART initiation, the median ages of cases and controls were 8.2 years (interquartile range [IQR], 4.4–11.4 years) and 5.8 years (IQR, 2.3–9.3 years), respectively, and the median percentages of lymphocytes expressing CD4 were 4% (IQR, 1%–9%) and 13% (IQR, 8%–18%), respectively. In multivariable logistic regression, cases had lower age-associated CD4+ T-cell count ratio (calculated as the ratio of the subject’s CD4+ T-cell count to the count expected in healthy individuals of the same age; P < .0001) and higher IL-6 level (P = .002) than controls. Clustering biomarkers and age-associated CD4+ and CD8+ T-cell count ratios identified 4 groups of children. Group 1 had the highest frequency of cases (41% cases; 16% died) and profound immunosuppression; group 2 had similar mortality (23% cases; 15% died), but children were younger, with less profound immu- nosuppression and high levels of inflammatory biomarkers and malnutrition; group 3 comprised young children with moderate immunosuppression, high TNF-α levels, and high age-associated CD8+ T-cell count ratios but lower frequencies of events (12% cases; 7% died); and group 4 comprised older children with low inflammatory biomarker levels, lower HIV viral loads, and good clinical outcomes (11% cases; 5% died). Conclusions. While immunosuppression is the major determinant of poor outcomes during ART, baseline inflammation is an additional important factor, identifying a subgroup of young children with similar mortality. Antiinflammatory interventions may help improve outcomes. Keywords. HIV; Africa; children; inflammation; immunosuppression. Approximately 760 000 children are currently receiving antire- troviral therapy (ART), leading to a 40% reduction in human immunodeficiency virus (HIV) infection–related mortality since 2005 [1], despite pediatric ART coverage remaining at only 30% in 2012 [2]. Over 90% of HIV-infected children live in sub-Saharan Africa, where advanced disease and undernutrition contribute to high mortality (3%–19%) in the first year of ART [3–8]. HIV infection is characterized by immune activation in adults, in whom baseline markers of inflammation and coagu- lation predict mortality independently of CD4+ T-cell count and HIV viral load [9–17].Much less is known about the causes and consequences of inflammation in HIV-infected children [18], and no studies to date have explored associations between baseline levels of inflammatory biomarkers and mortality in children starting ART. Identifying factors associated with mor- bidity, mortality, and poor immunological response at ART ini- tiation is important, to target future interventions in children who will need to receive lifelong treatment. We therefore char- acterized immunodeficiency and inflammation in a large cohort of children aged 3 months to 17 years who were starting ART in Uganda and Zimbabwe, to determine the impact of these fac- tors on morbidity, mortality, and immune reconstitution. Received 23 December 2015; accepted 4 April 2016; published online 18 May 2016. aA. J. P., A. J. S., A. S. W., and N. K. contributed equally to this work. bThe members of the ARROW Trial Team are listed in the Appendix. Correspondence: A. J. Prendergast, Blizard Institute, 4 Newark Street, London E1 2AT, UK (a.prendergast@qmul.ac.uk). The Journal of Infectious Diseases® 2016;214:226–36 © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: 10.1093/infdis/jiw148 226 • JID 2016:214 (15 July) • Prendergast et al D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 mailto:a.prendergast@qmul.ac.uk http://creativecommons.org/licenses/by/4.0/ METHODS In the ARROW trial (ISRCTN24791884), previously untreated Ugandan/Zimbabwean children aged 3 months to 17 years who were eligible for ART on the basis of 2006 World Health Organization (WHO) criteria were randomized 1:1 to undergo clinically driven monitoring versus laboratory plus clinical monitoring for toxicity (via hematological and biochemical analyses) and efficacy (via CD4+ T-cell data) [19]. Children were also randomized 1:1:1 in a factorial design to receive lamivudine, abacavir, and nonnucleoside reverse transcrip- tase inhibitor (NNRTI) continuously (arm A) or induction- maintenance therapy with lamivudine, abacavir, NNRTI, and zidovudine for 36 weeks, followed by lamivudine, abaca- vir, and NNRTI (arm B) or lamivudine, abacavir, and zidovu- dine (arm C), as previously described [19]. The NNRTI (nevirapine/efavirenz) was chosen by clinicians. Caregivers gave written consent; older children (age, 8–17 years) aware of their HIV status also gave assent or consent, as per local guidelines. The trial and immunology substudy were ap- proved by Research Ethics Committees in Uganda, Zimba- bwe, and the United Kingdom. Study Subjects and Measurements This study used a case-cohort design. Cases were individuals who died, who had new or recurrent WHO clinical stage 4 events during follow-up (to trial closure; median follow-up duration, 4 years), or poor immunological response to ART (percentage of lymphocytes expressing CD4, ≤ 15% through 3 years of first-line ART, allowing a single measurement of >15%). Controls comprised 316 children in a longitudinal immunology substudy (the last 6 months of recruitment [May–November 2008]) and a random 23% sample of all remaining nonsubstudy children, to reach a sample size of 600 children (Supplementary Figure 1). In cryopreserved plasma samples obtained before ART initi- ation (ie, at enrollment) or trial screening (maximum, 30 days before enrollment), baseline levels of inflammatory biomarkers (C-reactive protein [CRP], tumor necrosis factor α [TNF-α], in- terleukin 6 [IL-6], and soluble CD14 [sCD14]) and interleukin 7 (IL-7) were measured by an enzyme-linked immunosorbent assay (R&D Systems, Oxford, United Kingdom), and viral load was measured by the Abbott m2000sp/rt (Uganda) or Roche COBAS Amplicor Monitor v1.5 (Zimbabwe) system. Total CD4+ and CD8+ T cells were measured in real time. In Uganda, children in the immunology substudy underwent whole-blood immunophenotyping, using anti-CD4-PerCP (Bec- ton Dickinson [BD]), anti-CD45RA-APC (Caltag Medsytems), anti-CD31-PE (eBioscience), and either anti-Ki67-FITC (BD; after nuclear membrane permeabilization) or anti-HLA-DR- FITC (BD), with data acquired on a BD FACSCalibur flow cytometer. Analysis was undertaken using Cellquest (BD). In the immunology substudy, viral loads were assayed at weeks 4, 24, 36, and 48 after ART initiation and every 24 weeks there- after. Viral load response was defined as an HIV viral load of <10 000 copies/mL or a >1 log10 decrease in viral load at week 4, a viral load of <5000 copies/mL at week 24, and, subsequently, viral loads of <80 copies/mL at all measurements through 3 years (the lower limit of 80 copies/mL was selected because many samples had to be diluted owing to small volumes), allowing >80 copies/mL at week 36 only, provided that the viral load was exhibiting a decreasing trend (as observed among slow re- sponders). Children were classified as having blips if the viral load returned to <80 copies/mL. Those in whom the viral did not return to <80 copies/mL but remained <5000 copies/mL were defined as having persistent low-level viral load, whereas individuals with a confirmed viral load of ≥5000 copies/mL after week 24 were defined as rebounders. Analysis Analysis considered all inflammatory biomarkers, IL-7, age- associated CD4+ and CD8+ T-cell counts (hereafter termed “CD4 for age” and “CD8 for age,” respectively, and calculated as the ratio of the subject’s CD4+ or CD8+ T-cell count to the count expected in healthy individuals of the same age; a maxi- mum Spearman rho of 0.57 between any pair of parameters in- dicated a low risk of collinearity), and the following CD4+ T-cell subpopulations as a percentage of the total CD4+ T-cell popu- lation: CD45RA+ (naive), CD45RA+CD31+ (recent thymic em- igrants), HLA-DR+ (activated), and Ki67+ (proliferating). These measurements were log2 transformed for normality; viral load was log10 transformed. Height for age and body mass index (BMI) for age were calculated using WHO reference values [20]; because weight for age does not cover the full age range, this was calculated using United Kingdom reference data [21]. To reduce the potential influence of outliers, measurements were truncated at the 2.5th and 97.5th percentiles (that is, values above the 97.5th percentile were set to the 97.5th percentile, and values below the 2.5th percentile were set to the 2.5th percentile). As analyses included all substudy children and a sample of the remaining children, we prespecified an unmatched case- control design, using univariable rank sum and χ2 tests and multivariable logistic regression, forcing immunology substudy into the models as a stratifier. Sensitivity analyses used the Pren- tice method with time-to-event data for a case-cohort design [22], considering children from the immunology substudy as 1 subcohort and sampling 23% of cases who were not from the immunology substudy as a second subcohort. Variable selection was based on backward elimination with an exit P value of .05, including nonlinearity, based on fractional polynomial modeling, when P < .01 (Stata mfp). Interactions between variables in the final model were investigated and included when Pheterogeneity < .01. Inflammatory Biomarkers in HIV-Infected African Children • JID 2016:214 (15 July) • 227 D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 To identify groups of children based on pre-ART laboratory parameters (CRP, TNF-α, IL-6, sCD14, and IL-7 levels; CD4 for age; CD8 for age; and viral load), we used principal components analysis (correlation matrix) followed by hierarchical clustering using complete linkage (with the number of clusters identified using the Calinski/Harabasz stopping rule). The independent interrelationships between laboratory parameters, age at ART initiation, pre-ART weight for age, pre-ART height for age, pre-ART BMI for age, and sex were identified using backward elimination (exit P value, .01; inclusion of nonlinearity when P < .001) from multivariable linear regression models for each laboratory factor in turn as the outcome and all other factors as explanatory variables. In children with immunophenotyping data, additional relationships between each factor and CD4+ T- cell subpopulations were identified (the proportion of recent thymic emigrant CD4+ T cells was not considered as it was strongly associated with the proportion of naive CD4+ T cells; Spearman correlation, 0.89); (exit P value, .05; inclusion of non- linearity when P < .01, owing to smaller numbers). All analyses were performed using Stata 14.1 (StataCorp). All P values are 2 sided. RESULTS A total of 600 of the 1206 ARROW children were included by design; 115 were cases (54 died, 45 had new/recurrent WHO clinical stage 4 events, and 49 had poor immunological response; some children met multiple definitions), and 485 were controls. Among cases, deaths and WHO clinical stage 4 events occurred at a median of 19 weeks (range, 1–232 weeks) and 63 weeks (range, 1–212 weeks), respectively, after starting ART; in immunological non-response, the median CD4+ T-cell per- centage over the first 3 years of ART was 7% (IQR, 3%–11%). Pre-ART biomarker data were available for 113 cases (98%) and 466 controls (96%); 1 control had a missing pre-ART viral load, leaving 578 children in the analyses. Immunophenotyping data were available for 170 controls (37%) and 9 cases (8%; Uganda only). In 299 children in the immunology substudy who had pre-ART biomarker and longitudinal viral load measurements, virological response was defined in 292 (98%) who were followed up at 24 weeks. Characteristics at ART Initiation Cases were significantly older than controls (median age, 8.2 years [IQR, 4.4–11.4 years] vs 5.8 years [IQR, 2.3–9.3 years]) and had a lower pre-ART CD4+ T-cell percentage (4% [IQR, 1%–9%] vs 13% [IQR, 8%–18%]), a lower CD4 for age (median, 0.08 vs 0.29), and a lower ratio of CD4+ to CD8+ T cells (median, 0.1 vs 0.3; P < .0001 for all comparisons; Table 1). The ratio of CD4+ to CD8+ T cells was highly correlated with the CD4 for age (Spearman rho, 0.89; P < .0001). CRP, IL-6, and sCD14 levels were all significantly higher and the TNF-α level significantly lower in cases, compared with controls (P < .01). In multivariable logistic regression analysis considering all factors from Table 1, cases independently had a lower pre-ART CD4 for age (adjusted odds ratio [aOR], 0.56 per 2-fold increase [95% confidence inter- val {CI}, .49–.64]; P < .0001) and a higher pre-ART IL-6 level (aOR, 1.54 per 2-fold increase [95% CI, 1.18–2.01]; P = .002). There were no independent additional effects of pre-ART base- line CRP, sCD14, or TNF-α concentrations (P > .15), although a model containing CRP level instead of IL-6 level was similarly predictive (Akaike information criterion [AIC], 405 vs 404 in the original model), and a model containing CD4+ T-cell per- centage instead of CD4 for age was only slightly less predictive (AIC, 412). There was a marginal trend toward cases indepen- dently having a higher pre-ART viral load (P = .08), a lower weight for age or BMI for age (P = .0503 or P = .08, respectively), and a lower CD4+ T-cell percentage (P = .06) than controls (other model coefficients were unchanged). Despite strong univari- able effects, there was no independent effect of age (P = .23), ratio of CD4+ to CD8+ T cells (P = .22), WHO clinical stage (P = .48), or any other baseline factor (P > .1). Sensitivity anal- yses using Prentice time-to-event methods supported CD4 for age and IL-6 level as the most prognostic biomarkers (Supple- mentary Table 1). At ART Initiation, Children Fall Into Subgroups With Different ART Responses Whereas multivariable regression identifies the key determi- nants of poor outcomes, it does not inform how these risk fac- tors are distributed across individuals. We therefore used principal components analysis to identify the most informative combinations of biomarkers, viral load, CD4 for age, and CD8 for age (Supplementary Table 2). Hierarchical clustering identi- fied 4 groups of children at ART initiation, strongly associated with case versus control status (P < .001, by χ2 analysis; P < .0001, by logistic regression with adjustment for immunol- ogy substudy; Table 2 and Figure 1A). Ordering by decreasing proportion of cases, group 1 (n = 135; 41% cases) was characterized by profound immunosuppression (median CD4+ T-cell percentage, 3.0%; P < .0001 vs other groups); group 2 (n = 48; 23% cases) was less immunosup- pressed, and group 3 (n = 264; 12% cases) and group 4 (n = 131; 11% cases) were least immunosuppressed (P < .01 vs group 2). De- spite their profound immunosuppression, the viral load in group 1 was similar to that in group 3 (P = .14) and only marginally lower than in group 2 (P = .07). Viral load was lower in group 4 than in all other groups (P < .0001). Median levels of the inflammatory markers CRP and IL-6 were highest in group 2 (35.3 mg/L and 26.4 pg/mL, respectively) and lowest in group 4 (P < .0001 for both markers vs all other groups). sCD14 level was significantly lower in group 3 than in both groups 1 and 2 (P < .0001) and lower in group 4 than in all other groups (P < .0001). TNF-α level and CD8 for age were significantly higher in groups 2 and 3 than in groups 4 and 1 (P < .05). 228 • JID 2016:214 (15 July) • Prendergast et al D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 Considering factors not used to define subgroups, weight for age was highest in group 4 (P < .0001) and lowest in group 2 (P < .05; Figure 2). Height for age similarly was highest in group 4 (P < .0001) and lowest in groups 2 and 3 (P < .01). BMI for age generally increased across all groups. Whereas chil- dren in group 1 (median age, 7.9 years) were older than those in Table 1. Characteristics at Antiretroviral Therapy (ART) Initiation and Impact on ART Response Factor at ART Initiation Overall (n = 578) Cases (n = 113) Controls (n = 465) Univariable P, Cases vs Controlsa Multivariable OR (95% CI); P Value Country/center .89 Uganda/Entebbe (nonurban) 81 (14.0) 18 (15.9) 63 (13.5) . . . Uganda/JCRC (urban) 146 (25.3) 28 (24.8) 118 (25.4) . . . Uganda/PIDC (urban) 150 (26.0) 27 (23.9) 123 (26.5) . . . Zimbabwe/Harare (urban) 201 (34.8) 40 (35.4) 161 (34.6) . . . In immunology substudy 299 (51.7) 16 (14.2) 283 (60.9) <.0001 0.08 (.04–.15); <.0001 Male sex 287 (49.7) 62 (54.9) 225 (48.4) .22 . . . Age, y 6.3 (2.4, 9.7) 8.2 (4.4, 11.4) 5.8 (2.3, 9.3) <.0001 . . . Weight for ageb −2.3 (−3.4 to −1.4) −2.7 (−4.2, −1.7) −2.2 (−3.2, −1.3) .0008 . . . Height for agec −2.5 (−3.4 to −1.6) −2.4 (−3.5, −1.5) −2.5 (−3.5, −1.6) .89 . . . BMI for agec −0.7 (−1.6, 0.2) −1.3 (−2.3, −0.5) −0.6 (−1.5, 0.2) <.0001 . . . Viral load, copies/mL 225 600 (73 800–624 200) 275 100 (143 700, 663 800) 212 700 (62 900, 613 800) .03 . . . CD8+ T-cell percentage 51.0 (41.0, 60.0) 54.0 (46.0, 65.0) 50.0 (40.0, 60.0) .0008 . . . CD8 for age 1.9 (1.3, 2.8) 1.6 (1.0, 2.3) 2.0 (1.3, 2.8) .0007 . . . CRP level, mg/L 4.5 (1.4, 15.6) 7.8 (1.9, 23.3) 4.0 (1.3, 13.8) .008 . . . sCD14 level, mg/L 2.1 (1.7, 2.6) 2.4 (1.8, 2.9) 2.1 (1.7, 2.5) .0007 . . . IL-6 level, pg/mL 6.1 (4.7, 9.5) 8.1 (5.6, 11.6) 5.8 (4.6, 8.7) <.0001 1.54 per 2-fold increase (1.18–2.01) ; .002 TNF-α level, pg/mL 22.6 (19.0, 28.0) 20.2 (17.5, 24.1) 23.3 (19.6, 28.6) <.0001 . . . IL-7 level, pg/mL 9.3 (3.8, 16.8) 8.8 (5.4, 17.6) 9.5 (3.5, 16.7) .28 . . . CD4+ T-cell percentage 12.0 (6.0, 17.0) 4.0 (1.0, 9.0) 13.0 (8.0, 18.0) <.0001 . . . CD4 for age 0.25 (0.11, 0.41) 0.08 (0.02, 0.19) 0.29 (0.17, 0.44) <.0001 0.56 per 2-fold increase (.49–.64); <.0001 Ratio of CD4+ to CD8+ T cells 0.2 (0.1, 0.4) 0.1 (0.0, 0.2) 0.3 (0.2, 0.4) <.0001 . . . Hemoglobin level, g/dL 10.5 (9.5, 11.5) 10.5 (9.5, 11.4) 10.5 (9.5, 11.6) .77 . . . WHO clinical stage .048 1 or 2 172 (29.8) 25 (22.1) 147 (31.6) . . . 3 or 4 406 (70.2) 88 (77.9) 318 (68.4) . . . CD4+ T-cell monitoring .73 Yes 295 (51.0) 56 (49.6) 239 (51.4) . . . No 283 (49.0) 57 (50.4) 226 (48.6) . . . ART strategy .52 Arm A (3TC, ABC, NNRTI throughout) 202 (34.9) 44 (38.9) 158 (34.0) . . . Arm B (ZDV for 36 wk) 190 (32.9) 37 (32.7) 153 (32.9) . . . Arm C (long-term ZDV, 3 NRTIs after wk 36) 186 (32.2) 32 (28.3) 154 (33.1) . . . Initial NNRTI .46 Nevirapine 356 (61.6) 73 (64.6) 283 (60.9) . . . Efavirenz 222 (38.4) 40 (35.4) 182 (39.1) . . . Primary caregiver .0005 Mother 317 (54.9) 45 (40.2) 272 (58.5) . . . Other 260 (45.1) 67 (59.8) 193 (41.5) . . . Missingd 1 1 0 . . . Data are median values (interquartile ranges) or no. (%) of subjects. There was no evidence of interactions in the multivariable model (P > .1). See Supplementary Table 1 for sensitivity analysis using time to event rather than binary outcomes and the Prentice method to adjust for the case-control design. Abbreviations: 3TC, lamivudine; ABC, abacavir; BMI, body mass index; CI, confidence interval; CRP, C-reactive protein; IL-6, interleukin 6; IL-7, interleukin 7; NNRTI, nonnucleoside reverse transcriptase inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; OR, odds ratio; sCD14, soluble CD14; TNF-α, tumor necrosis factor α; WHO, World Health Organization; ZDV, zidovudine. a By rank sum or χ2 tests of observed data. b Because WHO reference values for weight for age only cover children <121 months, this variable was calculated using United Kingdom reference values, which cover the full age range of ARROW children (Spearman correlation between United Kingdom and WHO reference values, 0.99 for 451 children age <121 months). c Calculated using WHO reference values. d The mode was assumed in multivariate analyses. Inflammatory Biomarkers in HIV-Infected African Children • JID 2016:214 (15 July) • 229 D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 Table 2. Subgroups of Children at Antiretroviral Therapy (ART) Initiation Identified From Clustering and Impact on ART Response Variable Group 1 (n = 135) Group 2 (n = 48) Group 3 (n = 264) Group 4 (n = 131) P Valuee Factors contributing to clustering CD4 for age 0.03 (0.01, 0.12)a,b,c 0.22 (0.12, 0.33)b,c,d 0.32 (0.22, 0.48)a,d 0.30 (0.21, 0.45)a,d <.0001 Viral load, copies/mL 275 100 (145 100, 748 700)c 626 600 (144 900, 1254 900)b,c 272 000 (72 600, 673 700)a,c 107 200 (30 600, 285 300)a,b,d <.0001 sCD14 level, mg/L 2.5 (2.0, 2.9)b,c 2.6 (2.2, 3.2)b,c 2.2 (1.6, 2.6)a,c,d 1.8 (1.4, 2.1)a,b,d <.0001 CRP level, mg/L 6.4 (5.2, 9.2)a,c 35.3 (17.4, 76.3)b,c,d 4.5 (1.8, 12.1)a,c 1.2 (0.7, 2.8)a,b,d <.0001 IL-6 level, pg/mL 6.4 (5.2, 9.2)a,c 26.4 (16.7, 40.0)b,c,d 6.7 (5.3, 9.4)a,c 4.2 (3.7, 5.0)a,b,d <.0001 TNF-α level, pg/mL 19.8 (17.2, 23.5)a,b 27.8 (20.6, 33.0)c,d 25.8 (22.1, 31.7)c,d 19.1 (17.2, 21.9)a,b <.0001 CD8 for age 1.2 (0.8, 1.6)a,b,c 2.2 (1.5, 2.7)b,c,d 2.4 (1.8, 3.6)a,c,d 1.7 (1.3, 2.4)a,b,d <.0001 IL-7 level, pg/mL 11.0 (6.6, 19.0)a,c 6.4 (2.0, 16.9)b,c,d 11.3 (6.1, 18.1)a,c 2.9 (1.4, 7.0)a,b,d <.0001 Baseline factors not contributing to clustering Age, y 7.9 (5.2, 11.0) 4.1 (1.7, 8.1) 3.6 (1.9, 8.1) 8.4 (5.1, 11.5) <.0001 CD4+ T-cell percentage 3.0 (1.0, 8.0) 12.0 (5.5, 14.0) 13.0 (9.0, 18.0) 15.0 (11.0, 22.0) <.0001 Hemoglobin level, g/dL 10.6 (9.4, 11.5) 9.6 (9.1, 10.6) 10.3 (9.3, 11.1) 11.6 (10.5, 12.1) <.0001 Neutrophil count, ×109 cells/L 1.8 (1.2, 2.6) 2.4 (1.5, 3.6) 2.3 (1.6, 3.1) 1.8 (1.5, 2.5) <.0001 WHO clinical stage .11 1 or 2 44 (32.6) 12 (25.0) 68 (25.8) 48 (36.6) 3 or 4 91 (67.4) 36 (75.0) 196 (74.2) 83 (63.4) Current WHO clinical stage 3 or 4 illness at baselinef 42 (31.1) 21 (43.8) 111 (42.0) 19 (14.5) <.0001 Tuberculosis at baseline 9 (6.7) 3 (6.3) 24 (9.1) 4 (3.1) .17 Receiving antibiotic treatment at baseline (excluding tuberculosis treatment) 20 (14.8) 15 (31.3) 33 (12.5) 17 (13.0) .008 Outcome Case 55 (40.7) 11 (22.9) 32 (12.1) 15 (11.5) <.0001 Died 21 (15.6) 7 (14.6) 18 (6.8) 7 (5.3) .03 Weeks from randomization to death 19.6 (12.4, 85.9) 5.1 (2.3, 15.3) 16.5 (6.7, 32.9) 136.9 (36.3, 172.0) WHO clinical stage 4 event 25 (18.5) 4 (8.3) 11 (4.2) 3 (2.3) <.0001 Weeks from randomization to first WHO clinical stage 4 event 53.0 (6.4, 109.1) 104.1 (47.2, 117.6) 43.0 (26.0, 144.1) 156.4 (4.0, 161.9) Poor immunological response 31 (23.0) 0 (0.0) 8 (3.0) 10 (7.6) <.0001 WHO clinical stage 3 or 4 or death 44 (32.6) 16 (33.3) 35 (13.3) 15 (11.5) <.0001 Malnutrition as WHO clinical stage 3 or 4 or cause of death 10 (7.4) 4 (8.3) 8 (3.0) 2 (1.5) .03 Tuberculosis 11 (8.1) 7 (14.6) 10 (3.8) 7 (5.3) .02 Hospitalized 70 (51.9) 24 (50.0) 109 (41.3) 29 (22.1) <.0001 Viral load responseg .20 Responded 20 (36.4) 5 (23.8) 36 (24.8) 29 (40.8) Had blip 21 (38.2) 8 (38.1) 73 (50.3) 24 (33.8) Had persistent low-level viral load 4 (7.3) 1 (4.8) 13 (9.0) 6 (8.5) Had rebound or no response 10 (18.2) 7 (33.3) 23 (15.9) 12 (16.9) Data are median values (interquartile ranges) or no. (%) of subjects. Abbreviations: CRP, C-reactive protein; IL-6, interleukin 6; IL-7, interleukin 7; sCD14, soluble CD14; TNF-α, tumor necrosis factor α; WHO, World Health Organization. a P≤ .05, by the rank sum test, compared with group 2. b P≤ .05, by the rank sum test, compared with group 3. c P≤ .05, by the rank sum test, compared with group 4. d P≤ .05, by the rank sum test, compared with group 1. e Based on logistic regression analysis, accounting for immunology substudy as a stratifier for the case-control outcome; χ2 or rank sum tests were used otherwise. f See Supplementary Table 3 for details. g Defined only among 292 of 299 children in the immunology substudy who were alive and followed up at 24 weeks (5 died at ≤24 weeks; 2 died at 25–29 weeks without a viral load measurement after week 24). See “Methods” section for definitions. Only 2 children (one each in groups 3 and 4) were nonresponders, defined as never having a viral load of <5000 copies/mL. 230 • JID 2016:214 (15 Ju ly) • P rendergast et al D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 groups 2 and 3 (P < .0001), counterintuitively so were children in group 4 (median age, 8.4 years; P < .0001 vs groups 2 and 3). As expected, group 1 had the lowest proportion of naive (CD45RA+) and recent thymic emigrant (CD45RA+CD31+) CD4+ T cells (P < .01 vs other groups) and the highest propor- tion of activated (HLA-DR+) and proliferating (Ki67+) CD4+ T cells (P < .05). However, recent thymic emigrant proportions were also lower in group 3, compared with group 4 (P = .04), and the proportion of Ki67+ CD4+ T cells was higher in group 3 than in groups 2 and 4 (P < .05); there were no other significant differences in CD4+ T-cell subpopulations or activa- tion markers among groups 2–4 (P > .05). Interestingly, group 2 also had lower pre-ART hemoglobin level, and group 2 (43.8%) and group 3 (42.0%) had more children with current WHO clinical stage 3 or 4 illnesses at baseline, compared with group 1 (31.1%) and group 4 (14.5%; P < .0001; Table 2 and Supple- mentary Table 3). Percentages of children with tuberculosis were similar in group 1 (6.7%; 9 subjects) and group 2 (6.3%; 2 subjects) at baseline; the greatest excess was in unexplained severe wasting/malnutrition (4 subjects [3.0%] in group 1 versus 10 [20.8%] in group 2). However, weight for age was only mod- estly negatively correlated with IL-6 level (Spearman rho, −0.28; P < .0001) and CRP level (Spearman rho, −0.15; P = .0002). Taken together, group 1 comprised older children with pro- found immunosuppression, low proportions of naive and recent thymic emigrant CD4+ T cells, and high proportions of activat- ed and proliferating CD4+ T cells; group 2 comprised younger children with less profound immunosuppression but high levels of inflammation and malnutrition; group 3 similarly comprised younger children with moderate immunosuppression, high TNF-α levels, and CD8 for age; whereas group 4 comprised older children with moderate immunosuppression, low viral loads, and low levels of inflammation. Groups 1 and 2 had sim- ilar overall mortality (16% and 15%, respectively), with deaths occurring shortly after ART initiation in both groups (median, 20 and 5 weeks), despite their significant differences in pre-ART CD4+ T-cell counts. The excess of cases in group 1 was predom- inantly due to immunological nonresponse (Figure 1B). The proportions with WHO clinical stage 3 or 4 events/death or hospitalization during ART generally decreased across groups 1–4, similar to the overall case proportion, although tuberculo- sis was more common in group 2 (14.6%) than in group 1 (8.1%; Supplementary Figure 2). Virological responses following ART initiation were similar between groups; in particular, viral load suppression was not significantly poorer in group 1 (children with profound immu- nosuppression) than in other groups (P = .32, by the χ2 test). The main difference in virological response was a greater pro- portion of children with virological blips in group 3 (younger children with moderate immunosuppression but high TNF-α levels and CD8 for age, 50% of whom had had blips), compared with other groups (P = .02). There was also a trend toward more rebound/nonresponse in group 2 (high inflammatory markers; 33% with rebound or no response), compared with other groups (P = .054). Interrelationships Between Biomarker Levels, Viral Load, and Age at ART Initiation Finally, we explicitly investigated the independent relationships between laboratory parameters and age at ART initiation in a cross-sectional analysis, considering each parameter in turn as the dependent variable (Supplementary Figure 3). CD4 for age was independently positively associated with CD8 for age and TNF-α level and negatively associated with IL-7 level, sCD14 level, viral load, and age at ART initiation; however, there was no independent association between IL-6 or CRP level and CD4 for age after adjustment for these other factors. IL-6 level was positively associated with viral load, CRP level, TNF-α level, and sCD14 level. Pre-ART CRP level was positively associated with only sCD14 and IL-6 levels, and sCD14 level was positively associated with only IL-6 and CRP levels and negatively Figure 1. Subgroups of children at antiretroviral therapy (ART) initiation, identified from clustering of principal components. A, Compared to first 2 principal components. B, Compared to case vs control status. Abbreviation: WHO-4, World Health Organization clinical stage 4. Inflammatory Biomarkers in HIV-Infected African Children • JID 2016:214 (15 July) • 231 D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 Figure 2. Characteristics in subgroups of children before antiretroviral therapy initiation. P < .0001 for all comparisons except the percentage of CD4+HLA-DR+ T cells (P = .03, by the rank sum test). CD4+ T-cell subpopulations (CD45RA+, CD45RA+CD31+, HLA-DR+, and Ki67+) measured in only 37, 13, 81, and 53 children in groups 1, 2, 3, and 4, respectively. The top 8 plots are factors used to define subgroups (through clustering of principal components). Abbreviations: BMI, body mass index; CRP, C-reactive protein; IL-6, interleukin 6; IL-7, interleukin 7; sCD14, soluble CD14; TNF-α, tumor necrosis factor α. 232 • JID 2016:214 (15 July) • Prendergast et al D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 associated with CD4 for age. Viral load was positively associated with TNF-α and IL-6 levels and negatively associated with CD4 for age and age. IL-7 level was negatively associated with CD4 for age and positively associated with TNF-α level but not with any other biomarkers. The only inflammatory biomarker inde- pendently associated with any cell subpopulation was TNF-α (positively with percentage of Ki67+ CD4+ T cells), although, as expected, the percentage of CD45RA+ CD4+ T cells was pos- itively associated with CD4 for age, and the percentage of Ki67+ CD4+ T cells was negatively independently associated with CD4 for age. DISCUSSION ART has transformed outcomes for HIV-infected children in sub-Saharan Africa [1]. However, pediatric treatment coverage is currently only 30%; therefore, large numbers of HIV-infected children will start ART over the next decade [2]. Historically, many children were older with advanced disease at ART initia- tion [23]; recent WHO guidelines recommend treatment for all HIV-infected children [24], so those starting ART might be expected to be younger, with less severe immunosuppression. However, this large cohort of African children shows that inflam- mation is a major pre-ART determinant of morbidity and mor- tality, independently of immunosuppression. Furthermore, a distinct group of younger children with high inflammation were at particularly high risk of early mortality during ART, de- spite only moderate immunosuppression, highlighting the need to target underlying pathogenic processes to improve outcomes in high-risk children, despite universal availability of ART. Immunosuppression and immune activation are the hall- marks of HIV infection [25]. In the setting of chronic in- flammation, there is increased CD4+ T-cell turnover and an impaired regenerative response to homeostatic signals [26, 27]. CD4+ T-cell depletion and inflammation are therefore highly interlinked processes [28] that drive disease progression in HIV-infected adults [9–11, 13, 15–17]. To our knowledge, ours is the first study to evaluate the associations between im- munodeficiency, inflammatory biomarkers, and mortality in HIV-infected children, and it confirms that there are similarly 2 pathways (immunosuppression and inflammation) underly- ing serious clinical outcomes in pediatric HIV infection. IL-6 is the soluble inflammatory marker most strongly associated with mortality in most prior adult studies [9, 10, 13, 15, 16], and was the only biomarker to be associated with mortality independently of CD4+ T-cell count in our cohort. CRP was similarly predictive but did not have an independent additional effect in our models, consistent with their close biological in- terdependence, as IL-6 induces hepatic synthesis of this acute- phase protein [29]. We found no independent effect of TNF-α or sCD14 on morbidity and mortality. Viral load also had a less strong association with outcome than IL-6 or CD4+ T-cell count, and there was no independent effect of age or baseline disease stage. Whereas immune activation and inflammation are recognized to occur in HIV-infected children [30–42], the drivers of these processes are poorly characterized [18], and our analysis highlights the complex network of interrelation- ships between inflammatory markers (Supplementary Figure 3). The relative contributions of viral replication, coinfections, mi- crobial translocation, malnutrition, and other factors across ages and populations may affect the precise inflammatory mi- lieu and clinical outcome of an HIV-infected individual and may explain both the similarities and the differences between this study and prior adult studies [9–11, 13–17]. Using hierarchical clustering, we identified 4 subgroups of children with different patterns of pre-ART laboratory factors that were strongly associated with outcome during ART. Group 1 comprised children with low CD4+ T-cell counts and high clinical event rates, reflecting the well-recognized in- fection risk among children with profound immunosuppression [7]. This group also had the largest number of immunological nonresponders, likely because of failure of homeostatic CD4+ T-cell regenerative mechanisms. Group 2 had similar mortal- ity to group 1 but comprised younger children with less severe immunosuppression but high levels of inflammation and mal- nutrition. This highlights the fact that the 2 important pathways to mortality identified in our study and others [16, 43, 44]— immunodeficiency and inflammation—do not necessarily over- lap: some children have profound immunosuppression with modest inflammation, whereas others have moderate immuno- suppression with profound inflammation. Most children in our cohort fell into groups 3 and 4: despite differences in age and viral load between these groups, pre-ART CD4+ T-cell counts were similar, and both groups had low event rates, reflecting the excellent clinical outcomes of many children initiating ART in sub-Saharan Africa [19]. These findings provide several insights into the pathogenesis of pediatric HIV infection and indicate potential approaches to identifying high-risk children and targeting interventions to im- prove outcomes. Where available, laboratory monitoring cur- rently relies on CD4+ T-cell count and viral load to identify children at highest risk of early death. The CD4+ T-cell count has long been recognized as the best available prognostic mark- er, and, in this study, children with profound immunosuppres- sion had high mortality, as expected. However, we also found that children with very similar pre-ART CD4+ T-cell counts can have very different clinical outcomes, depending on the pre- vailing inflammatory milieu. A point-of-care test measuring a soluble inflammatory biomarker such as IL-6 or CRP may iden- tify children who are considered at high risk on the basis of their inflammatory status, rather than their immune status. These children may benefit from additional interventions at ART ini- tiation to improve outcomes, although further studies are need- ed to characterize the drivers of inflammation in pediatric HIV infection, to inform what these interventions might be. Inflammatory Biomarkers in HIV-Infected African Children • JID 2016:214 (15 July) • 233 D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 http://jid.oxfordjournals.org/lookup/suppl/doi:10.1093/infdis/jiw148/-/DC1 Children in group 2 had higher rates of intercurrent infections and antibiotic use at baseline as compared to other groups, and overt infections may have partly driven pre-ART inflammation. These children also had high a high prevalence of moderate and severe wasting, and it is recognized that even asymptomatic malnourished children may have subclinical infections [45], which may have contributed to inflammation; we found a mod- est inverse association between IL-6 level and weight for age, providing some support for this premise. Furthermore, these children were more likely than those in group 1 to develop tu- berculosis after ART initiation; although we did not have specif- ic data on immune reconstitution inflammatory syndrome, it is plausible that some had subclinical tuberculosis at baseline that was unmasked after ART initiation. A pragmatic approach to reducing mortality may be to provide a package of interventions at ART initiation that are directed at multiple mechanistic pathways, including clinical and subclinical infections and malnutrition. The REALITY trial (ISRCTN43622374), cur- rently underway in 4 countries in sub-Saharan Africa, is eval- uating the independent and combined effect of additional antimicrobial, anti-HIV, and nutritional interventions at ART initiation on early mortality among adults and children with profound immunosuppression. This study has several strengths and limitations. We studied a large, well-characterized cohort of children initiating ART across a range of ages and CD4+ T-cell counts in 2 high-burden coun- tries, with longitudinal biospecimen collection and long-term outcome data. However, death and WHO clinical stage 4 events occurred in a relatively small proportion of children in the immu- nology substudy (the last 6 months of recruitment), who there- fore formed more of the controls. All analyses adjusted for this factor, and analyses based on unmatched case-control or strati- fied case-cohort designs gave similar results, suggesting this had little influence, if any. Assessing WHO clinical stage 3 and 4 events can be difficult in children, andmost diagnoses were pre- sumptive. However, all reportedWHO clinical stage 3 and 4 con- ditions (and deaths) were reviewed by an independent end-point review committee against diagnostic criteria prespecified in the trial protocol ensuring consistency. Since peripheral blood mononuclear cells were not available for the vast majority of chil- dren in this study, we were limited to measuring circulating plas- ma levels of cytokines and acute-phase proteins, rather than cellular responses following stimulation. Children were recruited in 2007–2008, at the start of pediatric ART rollout, and all met WHO criteria for ART initiation at the time and thus had mod- erate immunosuppression; further studies from different settings that include more children treated under universal ART guide- lines, potentially with less severe immunosuppression, are needed to confirm our findings. In summary, independently of immunosuppression, inflam- mation is a major driver of adverse outcomes during ART in HIV-infected children initiating ART in sub-Saharan Africa, similar to findings for adults [9–11, 13–17, 44]. Children most at risk of this inflammation-associated mortality have moderately preserved CD4+ T-cell counts and so will not be identified by cur- rent routine testing. Our data suggest that targeting HIV-related inflammation is a logical management approach in some high- risk children, as in adults. While most children initiating ART in sub-Saharan Africa have excellent long-term outcomes, a sub- stantial minority die despite ART [3, 6, 8], and there is a critical need for novel approaches in this group, particularly as more chil- dren initiate ART under new universal treatment guidelines. Supplementary Data Supplementary materials are available at http://jid.oxfordjournals.org. Consisting of data provided by the author to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the author, so questions or comments should be addressed to the author. Notes Acknowledgments. 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Nakitto-Kesi, M. Kihembo, P. Pala, P. Kaleebu, S. Nassimbwa, and W. Senyonga; Joint Clinical Research Centre, Kampala, Uganda: P. Mugyenyi, V. Musiime, R. Keishanyu, V. D. Afayo, J. Bwomezi, J. Byaruhanga, P. Erimu, C. Karungi, H. Kizito, W. S. Namala, J. Namusanje, R, Nandugwa, T. K. Najjuko, E. Natukunda, M. Ndigendawani, S. O. Nsiyona, R. Kibenge, B. Bainomuhwezi, D. Sseremba, J. Tezikyabbiri, C. S. Tumusiime, A. Balaba, A. Mugumya, F. Nghania, D. Mwebesa, M. Mutumba, E. Bagurukira, F. Odongo, S. Mubokyi, M. Ssenyonga, M. Kasango, E. Lutalo, P. Oronon, G. Pimundu, L. Nakiire, E. D. Williams, O. Senfuma, L. Mugarura, J. Nkalubo, S. Abunyang, O. Denis, R. Lwalanda, I. Nankya, E. Ndashimye, E. Nabulime, and D. Mulima; Uni- versity of Zimbabwe, Harare: K. J. Nathoo, M. F. Bwakura- Dangarembizi, F. Mapinge, E. Chidziva, T. Mhute, T. Vhembo, R. Mandidewa, M. Chipiti, R. Dzapasi, C. Katanda, D. Nyoni, G. C. Tinago, J. Bhiri, S. Mudzingwa, D. Muchabaiwa, M. Phiri, V. Masore, C. C. Marozva, S. J. Maturure, S. Tsikirayi, L. Munetsi, K. M. Rashirai, J. Steamer, R. Nhema, W. Bikwa, B. Tambawoga, E. Mufuka, M. Munjoma, K. Mataruka, Y. Zviuya, C. Berejena, and A. Shonshai; Zvitambo, Harare: P. Kurira and K. Mutasa; Baylor College of Medicine Children’s Foundation Uganda, Mulago Hospital Uganda: A. Kekitiinwa, P. Musoke, S. Bakeera-Kitaka, R. Namuddu, P. Kasirye, A. Babirye, J. Asello, S. Nakalanzi, N. C. Ssemambo, J. Nakafeero, J. Tikabibamu, G. Musoba, J. Ssanyu, and Inflammatory Biomarkers in HIV-Infected African Children • JID 2016:214 (15 July) • 235 D ow nloaded from https://academ ic.oup.com /jid/article/214/2/226/2572123 by Kam pala International U niversity user on 14 D ecem ber 2021 http://www.who.int/childgrowth/standards/technical_report/en/ http://www.who.int/childgrowth/standards/technical_report/en/ http://www.who.int/childgrowth/standards/technical_report/en/ http://www.who.int/hiv/pub/guidelines/earlyrelease-arv/en/ http://www.who.int/hiv/pub/guidelines/earlyrelease-arv/en/ http://www.who.int/hiv/pub/guidelines/earlyrelease-arv/en/ M. Kisekka; and MRC Clinical Trials Unit at University College London, United Kingdom: D. M. Gibb, M. J. Thomason, A. S. Walker, A. D. Cook, A. J. Szubert, B. Naidoo-James, M. J. Spyer, C. Male, A. J. Glabay, L. K. Kendall, J. Crawley, and A. J. Prendergast. Study Monitors and Committee Members. Independent ARROW trial monitors: I. Machingura and S. Ssenyonjo; Trial Steering Committee: I. Weller (chair), E. Luyirika, H. Lyall, E. Malianga, C. Mwansambo, M. Nyathi, F. Miiro, D. M. Gibb, A. Kekitiinwa, P. Mugyenyi, P. Munderi, K. J. Nathoo, and A. S. Walker, with the observers S. Kinn, M. McNeil, M. Roberts, and W. Snowden; Data Monitoring Committee: A. Breckenridge (chair), A. Pozniak, C. Hill, J. Matenga, and J. Tumwine; End-Point Review Committee (in- dependent members): G. Tudor-Williams (chair), H. Barigye, H. A. Mujuru, and G. Ndeezi, with the observers S. Bakeera- Kitaka, M. F. Bwakura-Dangarembizi, J. Crawley, V. Musiime, P. Nahirya-Ntege, A. Prendergast, and M. Spyer; and Econom- ics Group: P. Revill, T. Mabugu, F. Mirimo, S. Walker, and M. J. 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