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Changes in insulin sensitivity over time and associated factors in HIV-infected adolescents

Geffner, Mitchell, E.a; Patel, Kunjalb; Jacobson, Denise, L.b; Wu, Juliab; Miller, Tracie, L.c; Hazra, Rohand; Gerschenson, Marianae; Sharma, Tanvif; Silio, Margaritag; Jao, Jenniferh; Takemoto, Jody, K.i; Van Dyke, Russell, B.g; DiMeglio, Linda, A.jfor the Pediatric HIV/AIDS Cohort Study (PHACS)

doi: 10.1097/QAD.0000000000001731
CLINICAL SCIENCE
Free

Objective: To compare prevalence of insulin resistance between perinatally HIV-infected (PHIV+) and perinatally HIV-exposed, but uninfected adolescents (PHEU), determine incidence of and contributory factors to new and resolved cases of insulin resistance in PHIV+, and evaluate glucose metabolism.

Design: Cross-sectional design for comparison of prevalence among PHIV+ and PHEU. Longitudinal design for incidence and resolution of insulin resistance among PHIV+ at risk for these outcomes.

Methods: The source population was adolescents from pediatric HIV clinics in the United States and Puerto Rico participating in the Pediatric HIV/AIDS Cohort Study, an ongoing prospective cohort study designed to evaluate impact of HIV infection and its treatment on multiple domains in preadolescents and adolescents. Insulin resistance was assessed by homeostatic model assessment of insulin resistance. Those with incident insulin resistance underwent 2-h oral glucose tolerance test and HbA1c. Baseline demographic, metabolic, and HIV-specific variables were evaluated for association with incident or resolved insulin resistance.

Results: Unadjusted prevalence of insulin resistance in PHIV+ was 27.3 versus 34.1% in PHEU. After adjustment for Tanner stage, age, sex, and race/ethnicity, there was no significant difference between groups. Factors positively associated with developing insulin resistance included female sex, higher BMI z score, and higher waist circumference; those associated with resolving insulin resistance included male sex and lower BMI z score.

Conclusion: Prevalence of insulin resistance in PHIV+ and PHEU was substantially higher than that reported in HIV-uninfected nonoverweight youth, but similar to that in HIV-uninfected obese youth. Factors associated with incident or resolved insulin resistance among PHIV+ were similar to those reported in HIV-negative obese youth. However, a contributory role of HIV infection and/or its treatment to the incident risk of insulin resistance cannot be excluded.

aDepartment of Pediatrics, The Saban Research Institute of Children's Hospital Los Angeles, Los Angeles, California

bDepartment of Epidemiology, Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

cDepartment of Pediatrics, Miller School of Medicine, University of Miami, Miami, Florida

dMaternal and Pediatric Infectious Disease Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland

eDepartment of Cell and Molecular Biology, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii

fDivision of Infectious Diseases, Department of Medicine, Boston Children's Hospital, Boston, Massachusetts

gDepartment of Pediatrics, Tulane University School of Medicine, New Orleans, Louisiana

hDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York

iDepartment of Pharmaceutical Sciences, Ben and Maytee Fisch College of Pharmacy, The University of Texas at Tyler, Tyler, Texas

jSection of Pediatric Endocrinology and Diabetology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Correspondence to Mitchell E. Geffner, MD, Children's Hospital Los Angeles, 4650 Sunset Blvd., MS #61, Los Angeles, CA, 90027 USA. Tel: +1 323 361 7032; fax: +323 361 1360; e-mail: mgeffner@chla.usc.edu

Received 9 April, 2017

Revised 26 August, 2017

Accepted 26 September, 2017

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Introduction

Since the advent of combination antiretroviral therapy (cART), patients with HIV have longer life expectancies; however, chronic conditions, such as metabolic and cardiovascular disease (CVD), are becoming more prevalent in this population [1]. In addition to lifestyle factors, cART and HIV itself may be associated with metabolic syndrome in youth and adults that places them at risk for CVD [2]. Increased rates of insulin resistance have been reported in some studies of HIV-infected youth as has the presence of other risks factors for clinical CVD [3–9].

In our initial study, we measured insulin resistance by the homeostatic model assessment (HOMA) of insulin resistance (HOMA-IR) [10] among 402 perinatally HIV-infected (PHIV+) youth (mean age 12.3 years, of whom 47% were male and ∼25% were prepubertal) enrolled in the Adolescent Master Protocol (AMP) of the Pediatric HIV/AIDS Cohort Study (PHACS). We found that 61 (15.2%) had insulin resistance and that insulin resistance was significantly associated with elevated alanine aminotransferase (ALT), Tanner stage 5 puberty, higher BMI, higher nadir CD4%, and ever having received amprenavir [11]. In that study, 45 youth had an oral glucose tolerance test (OGTT) of whom three (6.7%) had impaired fasting glucose (IFG) only, three (6.7%) had impaired glucose tolerance (IGT) only, and one other (2.2%) had both IFG and IGT. In addition, three of 43 tested (7.0%) had an HbA1c higher than 42 mmol/mol, but none was higher than 48 mmol/mol, one of the criteria for diagnosing diabetes as set forth by the American Diabetes Association (ADA) [12]. In the current follow-up study, we proposed to compare the prevalence of insulin resistance between PHIV+ adolescents and perinatally HIV-exposed, but uninfected (PHEU) individuals, determine incidence rates of and contributory factors to both new and resolved cases of insulin resistance in PHIV+ adolescents, and evaluate abnormal glucose metabolism (by OGTT and HbA1c) in incident insulin resistance cases. We hypothesized that PHIV+ adolescents would have an increased prevalence of insulin resistance compared with PHEUs; incident cases of insulin resistance over time among HIV+ adolescents would be associated with increasing age, worsening body composition (e.g. increased adiposity accessed via BMI and waist circumference), less exercise, and specific ART use; and resolution of insulin resistance over time among HIV+ adolescents would be associated with young age, improving body composition (e.g. decreased truncal adiposity), more exercise, and antiretrovirals with better metabolic profiles.

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Methods

Study population

The source population for this study was 451 PHIV+ and 227 PHEU youth enrolled in the PHACS AMP, an ongoing prospective cohort study designed to evaluate the impact of HIV infection and cART on multiple domains in preadolescents and adolescents with perinatally acquired HIV infection. Between March 2007 and December 2009, youth from 15 study sites in the United States and Puerto Rico were eligible for enrollment into AMP if they were born to HIV-infected mothers, were between 7 and 16 years of age, and had available medical record history since birth on antiretroviral exposure, opportunistic infection prophylaxis, HIV viral load and CD4+ cell count, and major medical events. The AMP protocol was approved by the institutional review board at each participating site and by that of the Harvard T.H. Chan School of Public Health. Written informed consent was obtained from each youth's parent or legal guardian, and assent was obtained from youth participants according to local institutional review board guidelines.

To be eligible for the current study, youth had to have had at least one simultaneously obtained set of centrally assessed fasting measurements of both plasma glucose and serum insulin and Tanner stage information. Exclusions included an ALT greater than three times the upper limit of normal or a diagnosis of hepatitis C, a diagnosis of diabetes or use of medication affecting blood glucose, and diagnoses of chronic renal disease, Cushing disease, or mitochondrial disease.

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Outcome definition

Glucose and insulin levels (obtained after a fast of ≥8 hours) were measured centrally at the Diabetes Research Institute Clinical Chemistry Laboratory at the University of Miami Miller School of Medicine in the laboratory of Armando Mendez, PhD. From these glucose and insulin measurements, HOMA-IR was calculated according to the formula: [glucose (mmol/l) × insulin (μU/ml)]/22.5. Insulin resistance was defined for prepubertal individuals (less than Tanner stage 2) as HOMA-IR more than 2.5 and for pubertal individuals as more than 4.0 [13]. Pubertal assessments by Tanner staging for genitalia and pubic hair were done by physical examination by trained examiners and the maximum Tanner stage based on genitalia or pubic hair was assigned to the participant. Of the total HOMA-IR measurements among the study participants, 14% were missing a Tanner stage assessment on the visit that specimens were obtained for glucose and insulin. However, the majority of these missing Tanner stages (88%) had a previous Tanner stage 5 measurement and the remainder was imputed based on available longitudinal assessments of Tanner stage and expert opinion of the study pediatric endocrinologist (M.E.G.).

Prevalence of insulin resistance was assessed from the first centrally measured HOMA-IR measurement (i.e., baseline). Among PHIV+ participants, incidence of insulin resistance was assessed by following those without defined insulin resistance at baseline and resolution of insulin resistance was assessed by following PHIV+ participants with insulin resistance at baseline (i.e. prevalent cases of insulin resistance). Those children with incident insulin resistance were further characterized based on results of a 2-h OGTT and simultaneous HbA1c measurement. Abnormal glucose tolerance was defined according to the ADA criteria: IFG = fasting glucose more than 5.5 mmol/l, but less than 6.9 mmol/l; IGT = 2-h glucose (postglucose load) between 7.7 and 11.0 mmol/l; and diabetes mellitus = fasting plasma glucose at least 6.9 mmol/l, 2-h glucose (postglucose load) at least 11.0 mmol/l, or HbA1c at least 48 mmol/mol [12].

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Covariates

Baseline variables evaluated for their association with incident insulin resistance or resolution of insulin resistance included demographic (sex, race/ethnicity, and age), metabolic (BMI, waist circumference, fasting lipids, physical activity, and diet), and HIV-specific (CD4+ cell count, HIV viral load, and antiretroviral history) characteristics. The specific ART assessed for their association with insulin resistance included those reported to be associated with hyperglycemia (http://www.globalrph.com/glycemia.htm): abacavir, didanosine, stavudine, amprenavir, fosamprenavir, indinavir, lopinavir/ritonavir, nelfinavir, ritonavir, and saquinavir. Current use of an ART was defined as use at baseline. An indicator of use of other medications known to be associated with hyperglycemia (http://www.globalrph.com/glycemia.htm) was also a covariate of interest. All characteristics were collected either through self-report (e.g. race/ethnicity), clinical evaluation (e.g. BMI and waist circumference), or medical chart abstraction (e.g. immunological, virological, and ART characteristics). BMI was expressed as a z score for age and sex [14]. Dietary intakes and physical activity were assessed by child and adolescent-specific food frequency questionnaires and physical activity screeners developed by Block Dietary Data Systems (Nutriquest, Berkeley, California, USA) (http://nutritionquest.com/assessment/).

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Statistical methods

A log-binomial model was fit to calculate an unadjusted prevalence ratio for insulin resistance comparing PHIV+ and PHEU participants, and prevalence ratios adjusted for Tanner stage, age, sex, and race/ethnicity. Inverse probability weights were used to adjust for Tanner stage and age. Cox proportional hazards models were used to identify factors associated with incident insulin resistance and resolution of insulin resistance. Covariates significantly associated with incident insulin resistance or resolution of insulin resistance at P < 0.10 or with univariable hazard ratios showing at least a 50% increase or decrease in the hazard of insulin resistance or resolution of insulin resistance were included in respective multivariable models. Due to the collinearity between BMI z score and waist circumference, separate multivariable models were fit, including each one individually with the other covariates. All analyses were conducted using SAS version 9.2 (SAS Institute, Cary, North Carolina, USA).

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Results

Three hundred and sixty-two PHIV+ and 185 PHEU participants had at least one set of simultaneous glucose and insulin levels measured. After excluding three PHIV+ participants with type 2 diabetes mellitus, two on metformin, four with hepatitis C, one with chronic renal disease, and seven with high ALT values prior to or at baseline, 345 PHIV+ and 185 PHEU participants were available for analyses. PHIV+ were older than PHEU (mean age 15 versus 11.1 years) at baseline. A larger proportion of PHIV+ participants had also achieved sexual maturation (Tanner stage 5) at baseline compared with PHEU participants (52 versus 12%). The unadjusted prevalence of insulin resistance in the PHIV+ was 27.3% [95% confidence interval (CI) 22.6–32.3%] versus 34.1% (95% CI 27.3–41.4%) in the PHEU group. After adjustment for Tanner stage, age, sex, and race/ethnicity, however, there was no significant difference in the prevalence of insulin resistance between the two groups [prevalence ratio (95% CI) 1.02 (0.70–1.48)].

After excluding the 94 PHIV+ participants with insulin resistance at baseline, the incidence of insulin resistance over follow-up among the remaining 251 PHIV+ participants was 14.6/100 person-years (95% CI 11.2–18.8/100 person-years, N = 62 cases, 423.2 person-years at risk). At the start of follow-up, mean (SD) age was 15.1 (2.9) years, 51% were females, mean (SD) BMI z score was 0.0 (1.1), and mean (SD) waist circumference was 73.5 ± 11.4 cm (Table 1). Factors positively associated with the development of insulin resistance assessed by univariable analysis included female sex, higher BMI z score, and higher waist circumference. Race/ethnicity and fasting lipids were not associated with insulin resistance. Viral load, CD4+ cell count, history of AIDS-defining event, and specific antiretroviral use were not significantly associated with incident cases of insulin resistance (Table 1). Additionally, physical activity (min/day), TV exposure, and carbohydrate/fat intake were not significantly associated with incident cases of insulin resistance (data not shown). In multivariable analyses, only higher BMI z score and waist circumference at baseline were significantly associated with development of insulin resistance, although females had a marginally significantly increased hazard of insulin resistance compared with males (Tables 2 and 3). Of the 62 incident cases of insulin resistance, 47 (76%) had OGTT results available. Two (4%) had IFG only, two (4%) IGT only, and one (2%) had both IFG and IGT. None had an HbA1c higher than 48 mmol/mol.

Table 1

Table 1

Table 1

Table 1

Table 2

Table 2

Table 3

Table 3

Among the 94 participants with insulin resistance at baseline, 43 had resolution of their insulin resistance over a follow-up of 155.4 person-years, yielding a resolution rate of 27.7/100 person-years (95% CI 20.0–37.3). At baseline, mean (SD) age was 14.8 (2.5) years, 59% were females, mean (SD) BMI z score was 1.1 (1.0), and mean (SD) waist circumference was 86.7 (16.2) cm (data not shown). Factors positively associated with resolution of insulin resistance assessed by univariable analysis included male sex and lower BMI z score. None of the other metabolic or HIV-specific factors, such as physical activity, diet, CD4+ cell count, viral load, or specific ART use, were significantly associated with resolution of insulin resistance in univariable analyses. In multivariable analyses, only male sex and lower BMI z score were consistently associated with resolution of insulin resistance, although in the model including waist circumference, lower waist circumference was marginally associated with resolution of insulin resistance and those who used lopinavir/ritonavir at baseline were twice as likely to show resolution of insulin resistance (Tables 4 and 5). Of the 27 participants who used lopinavir/ritonavir at baseline, seven (26%) had switched from lopinavir/ritonavir at the time of resolution or the last visit.

Table 4

Table 4

Table 5

Table 5

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Discussion

Insulin resistance is a contributor to the metabolic syndrome, a cluster of risk factors (in childhood including abdominal obesity, hypertriglyceridemia, low HDL cholesterol, hypertension, and elevated fasting plasma glucose) [15] that predisposes to CVD and type 2 diabetes. This was first described in HIV-infected adults 35 years ago [16]. The exact etiology of insulin resistance in the HIV setting remains unknown, but is likely multifactorial, with contributions from traditional risk factors (e.g. obesity, family history, and racial/ethnic background), comorbid conditions (e.g. hepatitis C virus infection), Antiretroviral-related factors (e.g. direct effects of protease inhibitors, cumulative exposure to nucleoside reverse transcriptase inhibitors, hepatic steatosis, and fat redistribution), and HIV itself (e.g. chronic inflammation) [17]. Mechanistically, insulin resistance may result from an abnormal hormonal secretory profile by adipose tissue; noncompetitive, reversible direct inhibition of the insulin-responsive facilitative glucose transporter isoform 4 (GLUT4) in muscle and fat; and/or mitochondrial abnormalities including reduction in mitochondrial DNA (mtDNA) copies/cell and decreased oxygen consumption [18–22].

We demonstrate in this study that the prevalence of insulin resistance in the PHIV+ children in PHACS/AMP was 27.3 and 34.1% in the PHEU group. After adjustments for Tanner stage, age, sex, and race/ethnicity, however, there was no significant difference in the prevalence of insulin resistance between the two groups. In this report, insulin resistance in the PHIV+ cohort was almost twice as prevalent as in our prior study [11]; however, only five of 62 individuals who developed insulin resistance over time had an abnormality of glucose tolerance (versus 10 of 45 with insulin resistance in our previous study) and none met criteria for diabetes in either study. This contrasts with two cases of diabetes at baseline, with no incident cases, and no cases of IFG at baseline and two incident cases in the National Institute of Child Health and Human Development International Site Development Initiative (NISDI) Pediatric Latin American Countries Epidemiologic Study (PLACES) cohort [23]. This increase in prevalence of insulin resistance in the current study (compared to our prior report) is likely related to the older age and greater degree of pubertal maturation in the current PHIV cohort. Use of a central laboratory for assessment of insulin and glucose compared to local laboratories, as was done in the previous analysis, may also account for some of the difference in prevalence of insulin resistance among PHIV in both studies. The prevalence of insulin resistance in HIV+ youth in other recent smaller studies ranged from 5.3 to 6.8% in Latin America [8,23], 10.0% in South Africa [3], 11.2–40.7% in Spain [4,7,24,25], and 42.9% in Thailand [5]. A possible explanation for these disparate results includes the fact that different definitions of insulin resistance by HOMA are used in different series, reflecting a failure to adjust for the normally lower insulin sensitivity that occurs during puberty compared with prepuberty. Historically, the reported prevalence of insulin resistance is 25–33% in HIV-infected adults [26]. Of note, our current prevalence in HIV+ youth approximates the 34.9% prevalence of insulin resistance by HOMA-IR recently reported in 219 HIV-infected Peruvian adults with HIV on highly active ART [27].

In a previous study of this cohort, PHEU children had higher BMI z scores than did PHIV+, and were more likely to be obese [28]. Our finding of an approximately 30% prevalence of insulin resistance in both groups in the current analysis is substantially higher than the prevalence reported in over 2000 Korean nonoverweight, nonobese healthy adolescents (4.9%) [29], but mirrors the high prevalence of insulin resistance found in otherwise healthy obese youth. In a study of 1356 obese youth (2–19 years), 53.8% of 9–11-year-olds, and 79.3% of those more than 12 years old manifested insulin resistance by HOMA-IR, although the cutoff used of 2.5 did not take into account the naturally decreased insulin sensitivity of puberty [30]. In a study of 100 obese Spanish youth (11.6 ± 2.7 years), the prevalence of insulin resistance was 29% by HOMA-IR [31]. Of note, other methods of assessment of insulin resistance may yield different prevalence of insulin resistance. For example, in the aforementioned Spanish study of non-HIV-infected obese youth, when defining insulin resistance by serum insulin responses to an oral glucose load, the prevalence of insulin resistance increased to 50% [30], which may reflect greater sensitivity of detection of insulin resistance derived from 2-h OGTT glucose and insulin data than by fasting values alone [32]. The gold standard for quantifying insulin resistance in adolescents is the euglycemic hyperinsulinemic clamp. The frequently sampled intravenous glucose tolerance test (FSIVGTT) and steady-state plasma glucose (SSPG) methods are also valid measurements. However, for large cohort studies, these methods are labor-intensive, require intravenous infusions and frequent blood sampling, are burdensome for participants, are expensive, and require a research setting for optimal performance [33].

Longitudinal data regarding the natural history of insulin resistance in HIV+ children are relatively sparse [18]. In a Latin American cohort, 3.8% developed insulin resistance during follow-up (in addition to 5.5% of children who had insulin resistance at baseline) [23]. In multivariable analyses, only higher BMI z score and waist circumference were significantly associated with development of new insulin resistance in our study, although females had a marginally significantly increased risk of insulin resistance compared with males. Conversely, only male sex and lower BMI z score were associated with resolution of insulin resistance, although in the model including waist circumference, use of lopinavir/ritonavir at baseline was significantly associated with an over two-fold rate of resolution compared with no use at baseline. A lower waist circumference was also marginally associated with resolution of insulin resistance. The associations of female gender and increased BMI/waist circumference are not unexpected as these are well known to predict risk of insulin resistance in healthy populations [30]. Thus, it stands to reason then that resolution of insulin resistance would be greater in males and with decreasing BMI/waist circumference even in an HIV+ population.

Other studies have suggested a relationship between the use of protease inhibitors and insulin resistance. The association of improvement in insulin sensitivity that we found with usage of lopinavir/ritonavir is somewhat unexpected and of unclear etiology. Lack of adherence to lopinavir/ritonavir is an unlikely explanation given that viral loads were higher during follow-up among those whose insulin resistance did not resolve. In normal men, short-term use of this combination leads to a deterioration in glucose tolerance at the 2-h time point of an OGTT, without a significant change in insulin-mediated glucose disposal rate as determined by a euglycemic hyperinsulinemic clamp [34]. In a cross-sectional study in consecutive HIV-infected adults treated with regimens containing efavirenz, lopinavir/ritonavir, or atazanavir, HOMA-IR was greatest in those individuals treated with lopinavir/ritonavir [35]. Furthermore, in adult men with HIV, the incidence of insulin resistance and metabolic syndrome was increased by use of lopinavir/ritonavir [36].

Factors known to be associated with increasing insulin resistance in healthy children also include puberty and race/ethnicity [30]. Puberty was not a factor in our comparison between PHIV and PHEU youth as we adjusted for Tanner stage in statistical analysis. As to race/ethnicity, there were no significant differences between groups recognizing that insulin resistance is ordinarily most common in African Americans and Hispanics, which constituted the bulk of our cohort (∼90% in both groups) perhaps making it difficult to detect differences between other races/ethnicities.

There are strengths and limitations to the study. This is a well characterized longitudinal cohort in which glucose and insulin levels were assayed in a central laboratory to standardize measurements. We may have had limited power to detect differences in the incidence or resolution of insulin resistance by factors that are not common and we needed to impute Tanner staging in some participants who had not reached Tanner stage 5. Additionally, allocation to treatment with antiretrovirals was not randomized so that these drugs may have been used differently in groups that also had different insulin resistance risks.

Although only a small percentage of those youth in our study with insulin resistance, even when persistent, had associated disturbances in glucose metabolism, the possibility of future abnormalities, including type 2 diabetes, as well as development of other components of the metabolic syndrome, that is, high blood pressure and unfavorable lipid profiles, remains a concern as HIV-infected youth face an increasing risk of obesity in adulthood and a lifetime exposure to both HIV disease and its treatment.

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Acknowledgements

We thank the children and families for their participation in PHACS, and the individuals and institutions involved in the conduct of PHACS.

The following institutions, clinical site investigators, and staff participated in conducting PHACS AMP and AMP Up in 2015, in alphabetical order: Ann & Robert H. Lurie Children's Hospital of Chicago: Ram Yogev, Margaret Ann Sanders, Kathleen Malee, Scott Hunter; Baylor College of Medicine: William Shearer, Mary Paul, Norma Cooper, Lynnette Harris; Bronx Lebanon Hospital Center: Murli Purswani, Mahboobullah Baig, Anna Cintron; Children's Diagnostic & Treatment Center: Ana Puga, Sandra Navarro, Patricia A. Garvie, James Blood; Children's Hospital, Boston: Sandra K. Burchett, Nancy Karthas, Betsy Kammerer; Jacobi Medical Center: Andrew Wiznia, Marlene Burey, Molly Nozyce; Rutgers – New Jersey Medical School: Arry Dieudonne, Linda Bettica; St. Christopher's Hospital for Children: Janet S. Chen, Maria Garcia Bulkley, Latreaca Ivey, Mitzie Grant; St. Jude Children's Research Hospital: Katherine Knapp, Kim Allison, Megan Wilkins; San Juan Hospital/Department of Pediatrics: Midnela Acevedo-Flores, Heida Rios, Vivian Olivera; Tulane University School of Medicine: Margarita Silio, Medea Gabriel, Patricia Sirois; University of California, San Diego: Stephen A. Spector, Kim Norris, Sharon Nichols; University of Colorado Denver Health Sciences Center: Elizabeth McFarland, Juliana Darrow, Emily Barr, Paul Harding; University of Miami: Gwendolyn Scott, Grace Alvarez, Anai Cuadra.

This work was performed as part of the Pediatric HIV/AIDS Cohort Study (PHACS) which is supported by the NICHD with co-funding from NIAID, NIDA, NIMH, NIDCD, NHLBI, NINDS, and NIAAA, through cooperative agreements with the Harvard University School of Public Health (U01 HD052102-04) and the Tulane University School of Medicine (U01 HD052104-01). J.J. is also supported by NICHD K23HD070760 and M.G. is also supported by NIH/DHHS grant number P20 GM113134.

Authors’ contributions: T.L.M. and M.S. contributed to data collection; K.P. analyzed the data; M.E.G., D.L.J., J.W., T.L.M., R.H., M.G., T.S., M.S., J.J., J.K.T., R.B.V.D., and L.A.D. interpreted the data; and M.E.G. and K.P. drafted the manuscript. K.P. takes responsibility for the integrity of the data analysis. All authors revised the manuscript and approved the final version of manuscript.

The study was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development with co-funding from the National Institute on Drug Abuse, the National Institute of Allergy and Infectious Diseases, the Office of AIDS Research, the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, the National Institute on Deafness and Other Communication Disorders, the National Heart Lung and Blood Institute, the National Institute of Dental and Craniofacial Research, and the National Institute on Alcohol Abuse and Alcoholism, through cooperative agreements with the Harvard T.H. Chan School of Public Health (HD052102) (Principal Investigator: George Seage; Project Director: Julie Alperen) and the Tulane University School of Medicine (HD052104) (Principal Investigator: Russell Van Dyke; Co-Principal Investigators: Kenneth Rich, Ellen Chadwick; Project Director: Patrick Davis). Data management services were provided by Frontier Science and Technology Research Foundation (Principal Investigator: Suzanne Siminski), and regulatory services and logistical support were provided by Westat, Inc. (Principal Investigator: Julie Davidson).

Note: The conclusions and opinions expressed in this article are those of the authors and do not necessarily reflect those of the National Institutes of Health or US Department of Health and Human Services.

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Conflicts of interest

M.E.G. is a consultant to Daiichi-Sankyo and receives royalties from McGraw-Hill and UpToDate. L.A.D. has or had research contracts from Medtronic, Merck, Lexicon, Novo Nordisk, and Sanofi; serves on a data safety monitoring board for Janssen; has served on an advisory board for Merck; and receives royalties from Wolters Kluwer. K.P., D.L.J., J.W., T.LM., R.H., M.G., T.S., M.S., J.J., J.K.T., and R.B.V.D. have nothing to disclose.

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Keywords:

BMI; homeostatic model assessment of insulin resistance; insulin resistance; oral glucose tolerance test; waist circumference

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