What anthropometric indicators are associated with insulin resistance? Cross-sectional study on children and adolescents with diagnosed human immunodeficiency virus

Carlos Alencar Souza Alves Junior Priscila Custódio Martins Luiz Rodrigo Augustemak de Lima Diego Augusto Santos Silva About the authors

ABSTRACT

BACKGROUND:

Studies that test associations between anthropometric indicators and insulin resistance (IR) need to provide better evidence in the context of the pediatric population (children and adolescents) with human immunodeficiency virus (HIV), as anthropometric indicators present a better explanation of the distribution of body fat.

OBJECTIVE:

To test the associations between anthropometric indicators and insulin resistance (IR) among children and adolescents diagnosed with HIV.

DESIGN AND SETTING:

Cross-sectional study on 65 children and adolescents (8-15 years) infected with HIV through vertical transmission conducted at the Joana de Gusmão Children's Hospital, Florianópolis, Brazil.

METHODS:

The anthropometric indicators measured were the abdominal (ASF), triceps (TSF), subscapular (SSF) and calf (CSF) skinfolds. The relaxed arm (RAC), waist (WC) and neck (NC) circumferences were also measured. Body mass index (BMI) was calculated from the relationship between body mass and height. IR was calculated through the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR). Simple and multiple linear regression analyses were used.

RESULTS:

After adjustment for covariates (sex, bone age, CD4+ T lymphocytes, CD8+ T lymphocytes, viral load, and physical activity), associations between IR and models with SSF and CSF remained. Each of these explained 20% of IR variability. For females, in the adjusted analyses, direct associations between IR and models with ASF (R² = 0.26) and TSF (R² = 0.31) were observed.

CONCLUSIONS:

SSF and CSF in males and ASF and TSF in females were associated with IR in HIV-infected children and adolescents.

KEY WORDS (MeSH terms):
Anthropometry; Child; Adolescent; HIV; Body composition; Skinfold thickness

AUTHORS’ KEY WORDS:
Children; Body fat; Youths; Human immunodeficiency virus

INTRODUCTION

Antiretroviral treatment (ART) and human immunodeficiency virus (HIV) infection itself can cause side effects in individuals with HIV.11 Nuvoli S, Caruana G, Babudieri S, et al. Body fat changes in HIV patients on highly active antiretroviral therapy (HAART): a longitudinal DEXA study. Eur Rev Med Pharmacol Sci. 2018;22(6):1852-9. PMID: 29630136; https://doi.org/10.26355/eurrev_201803_14606.
https://doi.org/10.26355/eurrev_201803_1...
Among the adverse effects, visible changes in the body such as lipodystrophy syndrome22 Arpadi S, Shiau S, Strehlau R, et al. Metabolic abnormalities and body composition of HIV-infected children on Lopinavir or Nevirapine-based antiretroviral therapy. Arch Dis Child. 2013;98(4):258-64. PMID: 23220209; https://doi.org/10.1136/archdischild-2012-302633.
https://doi.org/10.1136/archdischild-201...
and metabolic changes such as dyslipidemia and insulin resistance (IR)33 Viljoen E, MacDougall C, Mathibe M, Veldman F, Mda S. Dyslipidaemia among HIV-infected children on antiretroviral therapy in Garankuwa, Pretoria. South African Journal of Clinical Nutrition. 2020;33(3):86-93. https://doi.org/10.1080/16070658.2019.1575604.
https://doi.org/10.1080/16070658.2019.15...
are among the most common adverse effects. IR is defined as lower capacity of insulin to instigate use of glucose by adipose tissue and muscles, or which leads to expansion of pancreatic insulin formation.44 Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600.
https://doi.org/10.7448/IAS.16.1.18600...

HIV and continued use of ART are considered to be facilitators for development of IR in the pediatric population.44 Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600.
https://doi.org/10.7448/IAS.16.1.18600...
HIV infection, opportunistic infections and intestinal inflammation can culminate in changes to inflammatory cytokines, such as soluble tumor necrosis factor and hormones such as adiponectin and reduced leptin, which impairs glucose homeostasis.55 Vettor R, Milan G, Rossato M, Federspil G. Review article: adipocytokines and insulin resistance. Aliment Pharmacol Ther. 2005;22 Suppl 2:3-10. PMID: 16225463; https://doi.org/10.1111/j.1365-2036.2005.02587.x.
https://doi.org/10.1111/j.1365-2036.2005...
,66 Willig AL, Overton ET. Metabolic complications and glucose metabolism in HIV infection: a review of the evidence. Curr HIV/AIDS Rep. 2016;13(5):289-96. PMID: 27541600; https://doi.org/10.1007/s11904-016-0330-z.
https://doi.org/10.1007/s11904-016-0330-...
In addition, changes to CD4+ and CD8+ T-cell functions may impair glycolysis, which may adversely influence glucose metabolism.66 Willig AL, Overton ET. Metabolic complications and glucose metabolism in HIV infection: a review of the evidence. Curr HIV/AIDS Rep. 2016;13(5):289-96. PMID: 27541600; https://doi.org/10.1007/s11904-016-0330-z.
https://doi.org/10.1007/s11904-016-0330-...
Specifically, ART protease inhibitors have been associated with hyperglycemia and glucose tolerance in adults diagnosed with HIV77 Flynn PM, Abrams EJ. Growing up with perinatal HIV. Aids. 2019;33(4):597-603. PMID: 30531318; https://doi.org/10.1097/QAD.0000000000002092.
https://doi.org/10.1097/QAD.000000000000...
and may inhibit the action of glucose transporter (GLUT4), thus resulting in decreased insulin-mediated glucose intake by muscle and adipose tissue.11 Nuvoli S, Caruana G, Babudieri S, et al. Body fat changes in HIV patients on highly active antiretroviral therapy (HAART): a longitudinal DEXA study. Eur Rev Med Pharmacol Sci. 2018;22(6):1852-9. PMID: 29630136; https://doi.org/10.26355/eurrev_201803_14606.
https://doi.org/10.26355/eurrev_201803_1...
In addition, changes to the body fat distribution pattern may result in changes to the hormonal secretory system of adipose tissue and generate a chronic inflammatory profile, which facilitates IR development.88 Lopez-Sandoval J, Sanchez-Enriquez S, Rivera-Leon EA, et al. Cardiovascular Risk Factors in Adolescents: Role of Insulin Resistance and Obesity. Acta Endocrinol. (Buchar). 2018;14(3):330-7. PMID: 31149280; https://doi.org/10.4183/aeb.2018.330.
https://doi.org/10.4183/aeb.2018.330...

Overweight is among the factors that contribute to the onset of IR in young people without HIV,88 Lopez-Sandoval J, Sanchez-Enriquez S, Rivera-Leon EA, et al. Cardiovascular Risk Factors in Adolescents: Role of Insulin Resistance and Obesity. Acta Endocrinol. (Buchar). 2018;14(3):330-7. PMID: 31149280; https://doi.org/10.4183/aeb.2018.330.
https://doi.org/10.4183/aeb.2018.330...
especially increased body fat. To assess body fat, anthropometric indicators are commonly used.99 Ejtahed HS, Asghari G, Mirmiran P, et al. Body mass index as a measure of percentage body fat prediction and excess adiposity diagnosis among Iranian adolescents. Arch Iran Med. 2014;17(6):400-5. PMID: 24916524. Different measurements have been directly associated with IR, such as neck circumference,1010 Ben-Noun L, Laor A. Relationship of neck circumference to cardiovascular risk factors. Obes Res. 2003;11(2):226-31. PMID: 12582218; https://doi.org/10.1038/oby.2003.35.
https://doi.org/10.1038/oby.2003.35...
waist circumference (WC) and body mass index (BMI) in different populations.1111 Gracia-Marco L, Moreno LA, Ruiz JR, et al. Body composition indices and single and clustered cardiovascular disease risk factors in adolescents: providing clinical-based cut-points. Progress in cardiovascular diseases. 2016;58(5):555-64. PMID: 26545445; https://doi.org/10.1016/j.pcad.2015.11.002.
https://doi.org/10.1016/j.pcad.2015.11.0...
In pediatric populations diagnosed with HIV, it has been reported that WC and BMI were directly associated with development of IR.1212 Geffner ME, Patel K, Miller TL, et al. Factors associated with insulin resistance among children and adolescents perinatally infected with HIV-1 in the pediatric HIV/AIDS cohort study. Horm Res Paediatr. 2011;76(6):386-91. PMID: 22042056; https://doi.org/10.1159/000332957.
https://doi.org/10.1159/000332957...
,1313 Frigati LJ, Jao J, Mahtab S, et al. Insulin resistance in south african youth living with perinatally acquired HIV receiving antiretroviral therapy. AIDS Res Hum Retroviruses. 2019;35(1):56-62. PMID: 30156434; https://doi.org/10.1089/AID.2018.0092.
https://doi.org/10.1089/AID.2018.0092...
This underscores the relevance of easily and cheaply obtained anthropometric indicators for assessing associations with IR in HIV-infected children and adolescents.

Although a relationship between anthropometric indicators (WC and BMI) and IR in HIV-infected children and adolescents has been identified, the indicators used have not enabled analysis of fat distribution.1212 Geffner ME, Patel K, Miller TL, et al. Factors associated with insulin resistance among children and adolescents perinatally infected with HIV-1 in the pediatric HIV/AIDS cohort study. Horm Res Paediatr. 2011;76(6):386-91. PMID: 22042056; https://doi.org/10.1159/000332957.
https://doi.org/10.1159/000332957...
,1414 Geffner ME, Patel K, Jacobson DL, et al. Changes in insulin sensitivity over time and associated factors in HIV-infected adolescents. AIDS. 2018;32(5):613-22. PMID: 29280758; https://doi.org/10.1097/QAD.0000000000001731.
https://doi.org/10.1097/QAD.000000000000...
,1515 Dirajlal-Fargo S, Shan L, Sattar A, et al. Insulin resistance and intestinal integrity in children with and without HIV infection in Uganda. HIV Med. 2020;21(2):119-27. PMID: 31642582; https://doi.org/10.1111/hiv.12808.
https://doi.org/10.1111/hiv.12808...
Skinfolds, which measure the thickness of the underlying layer of subcutaneous fat, are anthropometric indicators that have a direct association with reference methods for body fat assessment among young people diagnosed with HIV.1616 Alves Junior CA, Mocellin MC, Gonçalves ECA, Silva DA, Trindade EB. Anthropometric indicators as body fat discriminators in children and adolescents: a systematic review and meta-analysis. Adv Nutr. 2017;8(5):718-27. PMID: 28916572; https://doi.org/10.3945/an.117.015446.
https://doi.org/10.3945/an.117.015446...
Furthermore, skinfolds are anthropometric markers that indicate accumulation of body fat in the peripheral region (i.e. triceps skinfold, TSF) and central region (i.e., subscapular skinfold, SSF; and abdominal skinfold, ASF).

Studies that test associations between skinfolds and IR need to provide better evidence in the context of the pediatric population (children and adolescents) with HIV, as anthropometric indicators present a better explanation of the distribution of body fat.

OBJECTIVE

The purpose of this study was to test associations between anthropometric indicators and IR in a pediatric population (eight to 15 years of age) with HIV.

METHODS

Population and sample

This was a cross-sectional study, conducted from 2015 to 2016 (November to June) in a city in southern Brazil. The research protocol was approval by the human research ethics committee of Universidade Federal de Santa Catarina (UFSC) (protocol number: 49691815.0.0000.0121; date of approval: February 15, 2016) and was also approved by the research ethics committee of Hospital Infantil Joana de Gusmão, Florianópolis, Santa Catarina (protocol number: 037/2015; date of approval: October 20, 2015).

Participants

Children and adolescents aged between eight and 15 years, with vertical transmission of HIV, were recruited for the study and were followed up clinically at the Hospital Infantil Joana de Gusmão, Florianópolis, Brazil. Eighty-three eligible patients were found. Three patients were excluded from the sample because they presented severe encephalopathy and because they were unable to walk. Three were excluded because we were unable to contact them, four because they had been transferred to another hospital and four because they refused to participate in the research; and another four were losses during the data collection. The final sample consisted of 65 subjects.

The inclusion criteria were the following: a) presence of information in the medical record to prove that HIV infection had been transmitted from mother to child; b) age between 8 and 15 years; c) ability to stand and communicate; and d) presence of laboratory and clinical information about the infection. The exclusion criteria were the following: a) presentation of contraindication against vigorous intensity exercises and existence of motor disability; b) problems that made speech, hearing and/or cognition impossible; c) presence of diseases that change body composition, except for HIV infection itself; and d) use of immunotherapies and regular use of diuretics. Individuals with any pathological condition other than HIV were excluded from the study.

The sample size was calculated a posteriori, taking into account the type I error (α = 0.05) and type II error (β = 0.80) for testing associations between anthropometric indicators and IR, with an average effect size (0.50).1717 Hulley S, Cummings S, Browner W, Grady D, Newman T. Designing clinical research. Philadelphia: LWW. 2013. For simple and multiple linear regression analyses, the posterior analysis indicated that with α = 0.05 and β = 0.80, a sample of 65 HIV+ children and adolescents would make it possible to find associations between anthropometric indicators and IR, with an effect size of 0.50.1717 Hulley S, Cummings S, Browner W, Grady D, Newman T. Designing clinical research. Philadelphia: LWW. 2013. All calculations were performed using the G* Power software version 3.1.9.2 (Universität Düsseldorf, Germany).

Dependent variable

To check IR, we used the Homeostasis Evaluation Model for Insulin Resistance Index (HOMA-IR), calculated through the mathematical model described by Matthews et al.1818 Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412-9. PMID: 3899825; https://doi.org/10.1007/BF00280883.
https://doi.org/10.1007/BF00280883...
We applied the following equation: HOMA-IR = fasting blood glucose (mg/dl) x insulin (μIU/ml). In the mornings, fasting blood samples (15 ml) were collected to measure glucose and insulin concentrations. Glucose levels were determined using the oxidase method (Wiener CB 400i; Wiener Lab Group, Rosario, Argentina) and insulin levels were measured using the chemiluminescence method (Roche Diagnostics Elecsys, Indianapolis, United States).

The gold standard for IR evaluation is the hyperinsulinemic-euglycemic clamp.44 Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600.
https://doi.org/10.7448/IAS.16.1.18600...
However, this technique is expensive and invasive in the research context, and use of alternative methods for IR identification, such as HOMA-IR and the insulin sensitivity check index (QUICKI), is more frequent.44 Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600.
https://doi.org/10.7448/IAS.16.1.18600...
,1919 Borai A, Livingstone C, Heald AH, Oyindamola Y, Ferns G. Delta insulin-like growth factor binding protein-1 (ΔIGFBP-1): a marker of hepatic insulin resistance? Ann Clin Biochem. 2014;51(Pt 2):269-76. PMID: 24056811; https://doi.org/10.1177/0004563213495818.
https://doi.org/10.1177/0004563213495818...
HOMA-IR has high correlation (r = 0.88) with the hyperinsulinemic-euglycemic clamp in the pediatric population and demonstrates two evaluation parameters: plasma insulin and glucose.1919 Borai A, Livingstone C, Heald AH, Oyindamola Y, Ferns G. Delta insulin-like growth factor binding protein-1 (ΔIGFBP-1): a marker of hepatic insulin resistance? Ann Clin Biochem. 2014;51(Pt 2):269-76. PMID: 24056811; https://doi.org/10.1177/0004563213495818.
https://doi.org/10.1177/0004563213495818...

Independent variables

The anthropometric indicators measured were skinfolds: ASF, TSF, SSF and CSF; relaxed arm circumference (RAC); waist circumference (WC) and neck circumference (NC). Body mass index (BMI) was calculated from the relationship between body mass and square of height.

Standardization of measurements was performed in accordance with the guidelines of the International Society for the Advancement of Kinanthropometry (ISAK) by an ISAK level 1 certified anthropometrist. A sample of 32 children of the same age group was also measured to calculate the technical error of intra-rater measurement (TEM).2020 Pederson D, Gore C. Erros de medição em antropometria. In: Kevin N, Tim O, editores. Antropométrica. Porto Alegre: Artmed; 2005. p. 91-104.

To measure the skinfold thickness, a caliper plicometer (Cescorf, Porto Alegre, Brazil) with a resolution of 0.1 mm was used. Anthropometric tape without elasticity was used to measure body circumferences (Sanny, São Paulo, Brazil) with a unit of measurement of 0.1 cm. Portable digital scales (Tanita, 180 Tokyo, Japan) were used to measure body mass, with a total capacity of up to 150 kg, and with a resolution of 0.1 kg. A stadiometer (AlturaExata, Belo Horizonte, Brazil) was used for height verification, with a measuring capacity from 115 cm to 210 cm and a unit of measurement of 0.1 cm.

Control variables

Information on viral load, CD4+ T lymphocyte count (%) and CD8+ T lymphocyte count (%) was obtained from each participant's medical records. Bone age was assessed by means of wrist-carpal radiography, in the radiology sector of Joana de Gusmão Children's Hospital (JGCH). For this measurement, international standardization was used.2121 Greulich WW, Pyle SI. Radiographic Atlas of Skeletal Development of the Hand and Wrist. Stanford: Stanford University Press; 1959. Bone age was treated as a continuous variable.

The GT3X-Plus Actigraph accelerometer (Manufacturing Technology Inc., Fort Walton Beach, United States) was used to measure moderate to vigorous-intensity physical activity (MVIPA), continuously over a seven to 14-day period that included weekend days. To ensure data reliability, the participants were asked to always use the accelerometer on the right side, located at the waist, throughout the day, and only to remove it for activities such as bathing, water activities and sleep. For data analysis, we considered records that extended across at least four days (three on weekdays and one on weekends) for a period of 10 hours or more, after removing times of non-use consisting of at least 60 one-minute records of successive zeros. The cutoff points proposed by Evenson2222 Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557-65. PMID: 18949660; https://doi.org/10.1080/02640410802334196.
https://doi.org/10.1080/0264041080233419...
were used to quantify the MVIPA minutes, and these were adjusted according to the proportional time for which the youths remained awake (14 hours). Verbal and written instructions were made available to participants and guardians before the device was used.

Statistical treatment

Firstly, descriptive analyses were performed on the data (median and interquartile range). Kurtosis and asymmetry analyses were then used to verify data normality (range from −2 to + 2),2323 George D, Mallery P. Using SPSS for Windows step by step: a simple guide and reference. 4th ed. London: Pearson Education; 2003. in addition to histogram analysis to identify data distribution normality. Student's t-test and the chi-square test were used to identify sex (male/female) differences. Simple and multiple linear regression were used to test associations between outcomes and exposures, respectively. For the multiple linear regressions, control variables (sex, bone age, CD4+ T lymphocytes, CD8+ T lymphocytes, viral load and physical activity) were used. Regression coefficients (β), 95% confidence intervals and determination coefficients (R²) for each model analyzed and diagnoses of multicollinearity (VIF) were estimated. For descriptive analyses and simple and multiple linear regressions, the Statistical Package for the Social Sciences software (IBM SPSS Statistics, Chicago, United States), version 22.0, was used, with P ≤ 0.05. All analyses were performed stratified according to sex (male/female), which can be justified through existence of sexual dimorphism, because as age increases, secretion of sex hormones can interfere with the amount of body fat.2424 Malina RM, Bouchard C, Bar-Or O. Growth, maturation, and physical activity. Champaign: Human Kinetics; 2004.

RESULTS

Sixty-five children and adolescents aged 8-15 years (30 males and 35 females), diagnosed with HIV, participated in the study. There were differences between the sexes, such that the females had higher SSF (P < 0.001) and CSF (P = 0.050) than the males. Regarding physical activity, the male adolescents did more minutes/day than the females (P = 0.022) (Table 1).

Table 1
Characteristics of children and adolescents diagnosed with human immunodeficiency virus (HIV), stratified according to sex (n = 65)

Among the males, direct associations were observed in simple linear regressions between IR and SSF (R² = 0.24), CSF (R² = 0.15), WC (R² = 0.15), RAC (R² = 0.11), NC (R² = 0.12) and BMI (R² = 0.15). After adjustment for covariates (sex, bone age, CD4+ T lymphocytes, CD8+ T lymphocytes, viral load and physical activity), associations between IR and models with SSF and CSF remained, and each of these explained 20% of the IR variability (Table 2).

Table 2
Simple and multiple linear regressions between insulin resistance and anthropometric indicators among male and female children and adolescents diagnosed with HIV (n = 65)

For the females, direct associations were observed in simple analyses, such that ASF and TSF explained 20% and 18% of IR variability, respectively. In addition, direct associations with RAC (R² = 0.10) and NC (R² = 0.25) were observed. In the adjusted analyses, direct associations between IR and models with ASF (R² = 0.26) and TSF (R² = 0.31) were observed.

DISCUSSION

The main results from the present study add to the current literature to show that higher values for peripheral and central skin folds are associated with IR.

SSF and CSF (males) and ASF and TSF (females) were directly associated with IR among these pediatric patients with HIV. Several studies have shown associations between different central and peripheral skinfolds and IR among HIV-infected children and adolescents,2525 Freedman DS, Serdula MK, Srinivasan SR, Berenson GS. Relation of circumferences and skinfold thicknesses to lipid and insulin concentrations in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr. 1999;69(2):308-17. PMID: 9989697; https://doi.org/10.1093/ajcn/69.2.308.
https://doi.org/10.1093/ajcn/69.2.308...
2828 Nightingale CM, Rudnicka AR, Owen CG, et al. Influence of adiposity on insulin resistance and glycemia markers among U.K. Children of South Asian, black African-Caribbean, and white European origin: child heart and health study in England. Diabetes Care. 2013;36(6):1712-9. PMID: 23315600; https://doi.org/10.2337/dc12-1726.
https://doi.org/10.2337/dc12-1726...
based on the assumption that accumulation of subcutaneous adiposity is associated with IR due to increased lipotoxicity.2929 Cabanelas N, António S, Esteves MC. Lipotoxicidade e Síndrome Metabólica. Revista Portuguesa de Diabetes. 2008;3(4):209-15. Available from: http://www.revportdiabetes.com/wp-content/uploads/2017/10/RPD-Vol-3-n%C2%BA-4-Dezembro-2008-Artigo-de-Revis%C3%A3o-p%C3%A1gs-209-215.pdf. Accessed in 2021 (Jun 1).
http://www.revportdiabetes.com/wp-conten...
In this context, insulin favors entry of glucose into adipose tissue, which activates lipoprotein lipase, thus promoting storage of triglycerides and preventing the action of protein kinase, an intracellular enzyme that is capable of blocking insulin signaling pathways.2929 Cabanelas N, António S, Esteves MC. Lipotoxicidade e Síndrome Metabólica. Revista Portuguesa de Diabetes. 2008;3(4):209-15. Available from: http://www.revportdiabetes.com/wp-content/uploads/2017/10/RPD-Vol-3-n%C2%BA-4-Dezembro-2008-Artigo-de-Revis%C3%A3o-p%C3%A1gs-209-215.pdf. Accessed in 2021 (Jun 1).
http://www.revportdiabetes.com/wp-conten...
However, in the context of HIV infection, the complexity of the scenario increases due to the adverse effects both of the virus itself and of ART. This is concomitant with possible increases in body fat and, consequently, the lipotoxic effect of lipodystrophy.44 Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600.
https://doi.org/10.7448/IAS.16.1.18600...
Protease inhibitors (PIs) are believed to play an important role in the emergence of IR dyslipidemia and increased quantities of visceral adipose tissue.11 Nuvoli S, Caruana G, Babudieri S, et al. Body fat changes in HIV patients on highly active antiretroviral therapy (HAART): a longitudinal DEXA study. Eur Rev Med Pharmacol Sci. 2018;22(6):1852-9. PMID: 29630136; https://doi.org/10.26355/eurrev_201803_14606.
https://doi.org/10.26355/eurrev_201803_1...
,44 Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600.
https://doi.org/10.7448/IAS.16.1.18600...

Specifically in relation to central skinfolds associated with IR, our data corroborate previous studies among children and adolescents of different ethnicities and without HIV diagnoses, regarding SSF2525 Freedman DS, Serdula MK, Srinivasan SR, Berenson GS. Relation of circumferences and skinfold thicknesses to lipid and insulin concentrations in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr. 1999;69(2):308-17. PMID: 9989697; https://doi.org/10.1093/ajcn/69.2.308.
https://doi.org/10.1093/ajcn/69.2.308...
,2727 Addo OY, Pereira MA, Himes JH. Is skinfold thickness as good as DXA when measuring adiposity contributions to insulin resistance in adolescents? Am J Hum Biol. 2012;24(6):806-11. PMID: 23012045; https://doi.org/10.1002/ajhb.22321.
https://doi.org/10.1002/ajhb.22321...
and ASF.3030 Mueller NT, Pereira MA, Buitrago-Lopez A, et al. Adiposity indices in the prediction of insulin resistance in prepubertal Colombian children. Public Health Nutr. 2013;16(2):248-55. PMID: 22916737; https://doi.org/10.1017/S136898001200393X.
https://doi.org/10.1017/S136898001200393...
,3131 Weber DR, Levitt Katz LE, Zemel BS, et al. Anthropometric measures of abdominal adiposity for the identification of cardiometabolic risk factors in adolescents. Diabetes Res Clin Pract. 2014;103(3):e14-7. PMID: 24552682; https://doi.org/10.1016/j.diabres.2013.12.050.
https://doi.org/10.1016/j.diabres.2013.1...
The android phenotype of body fat accumulation (more in the trunk) has been more associated with IR, which is explained by pancreatic β cell dysfunction due to formation of reactive oxygen species (ROS), which act on metabolic dysregulation to cause IR.3232 Keane KN, Cruzat VF, Carlessi R, de Bittencourt PI Jr, Newsholme P. Molecular events linking oxidative stress and inflammation to insulin resistance and β-cell dysfunction. Oxid Med Cell Longev. 2015;2015:181643. PMID: 26257839; https://doi.org/10.1155/2015/181643.
https://doi.org/10.1155/2015/181643...
Regarding peripheral skinfolds (CSF [male] and TSF [(female]), which demonstrated associations with IR, our data are consistent with the findings from a systematic review that demonstrated that peripheral subcutaneous fat was associated with IR.3333 Zhang M, Hu T, Zhang S, Zhou L. Associations of different adipose tissue depots with insulin resistance: a systematic review and meta-analysis of observational studies. Sci Rep. 2015;5:18495. PMID: 26686961; https://doi.org/10.1038/srep18495.
https://doi.org/10.1038/srep18495...

HIV-infected individuals undergoing ART treatment with protease inhibitors are predisposed to lipodystrophy syndrome (fat loss or accumulation) in peripheral regions such as arms and legs.22 Arpadi S, Shiau S, Strehlau R, et al. Metabolic abnormalities and body composition of HIV-infected children on Lopinavir or Nevirapine-based antiretroviral therapy. Arch Dis Child. 2013;98(4):258-64. PMID: 23220209; https://doi.org/10.1136/archdischild-2012-302633.
https://doi.org/10.1136/archdischild-201...
Antiretroviral protease inhibitor treatment reduces the action of peroxisome proliferator-activated receptors (PPARy), thereby decreasing adiponectin levels and culminating in IR.66 Willig AL, Overton ET. Metabolic complications and glucose metabolism in HIV infection: a review of the evidence. Curr HIV/AIDS Rep. 2016;13(5):289-96. PMID: 27541600; https://doi.org/10.1007/s11904-016-0330-z.
https://doi.org/10.1007/s11904-016-0330-...
Although there is no consensus on the association between different lipodystrophy phenotypes and IR in pediatric patients diagnosed with HIV, high insulin concentrations were found previously in children with lipohypertrophy, and less consistently in children with lipoatrophy.44 Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600.
https://doi.org/10.7448/IAS.16.1.18600...

The skinfolds associated with IR differed according to sex (male/female). This may be explained by the existence of sexual dimorphism. In girls, as their age increases, estradiol hormone secretion also increases, which leads to fat accumulation in the arms and consequently increases the amount of adipocytes in the tricipital region.2424 Malina RM, Bouchard C, Bar-Or O. Growth, maturation, and physical activity. Champaign: Human Kinetics; 2004. In boys, increasing secretion of testosterone hormone inhibits abdominal fat accumulation.2424 Malina RM, Bouchard C, Bar-Or O. Growth, maturation, and physical activity. Champaign: Human Kinetics; 2004.

Regarding the associations of anthropometric indicators with IR, the results from this study demonstrated the potential of skinfold analyses, such that associations were found with SSF and TSF among males, and with ASF and CSF among females. This is important from a practical point of view, for clinical use in monitoring the body composition and metabolic complications of HIV-infected children and adolescents, given that skinfold measurement is a low-cost alternative.

This study had some limitations, such as the fact that HOMA-IR was used as an indicator of glycemic homeostasis impairment. Nonetheless, this method is often used in clinical investigations. Other limitations related to the absence of any clinical diagnosis for lipodystrophy.

Among the strengths of this study, the analyses were controlled for potential confounders (sex, bone age, CD4+ T lymphocytes, CD8+ T lymphocytes, viral load and physical activity) in the multiple linear regression analyses, a strategy that had not previously been addressed in studies making correlations between anthropometric indicators and body fat among children and adolescents diagnosed with IR.

CONCLUSIONS

In conclusion, SSF and CSF in males and ASF and TSF in females were directly associated with IR. It can be suggested that use of these anthropometric indicators should form part of the routine clinical follow-up for HIV-infected children and adolescents. These low-cost anthropometric measurements can contribute to risk stratification among children and adolescents with IR, and consequently may prevent metabolic complications such as type 2 diabetes and other cardiovascular consequences.

  • Joana de Gusmão Children's Hospital, Florianópolis (SC), Brazil
  • Sources of funding: The study did not receive funding
  • This paper was presented as a poster at the 42nd International Symposium on Sport Sciences, a congress that was held in São Paulo, Brazil, in 2019

Acknowledgments:

Dr. Silva was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES) - finance code 001; and Dr. Silva is supported in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - 302028/2018-8)

REFERENCES

  • 1
    Nuvoli S, Caruana G, Babudieri S, et al. Body fat changes in HIV patients on highly active antiretroviral therapy (HAART): a longitudinal DEXA study. Eur Rev Med Pharmacol Sci. 2018;22(6):1852-9. PMID: 29630136; https://doi.org/10.26355/eurrev_201803_14606
    » https://doi.org/10.26355/eurrev_201803_14606
  • 2
    Arpadi S, Shiau S, Strehlau R, et al. Metabolic abnormalities and body composition of HIV-infected children on Lopinavir or Nevirapine-based antiretroviral therapy. Arch Dis Child. 2013;98(4):258-64. PMID: 23220209; https://doi.org/10.1136/archdischild-2012-302633
    » https://doi.org/10.1136/archdischild-2012-302633
  • 3
    Viljoen E, MacDougall C, Mathibe M, Veldman F, Mda S. Dyslipidaemia among HIV-infected children on antiretroviral therapy in Garankuwa, Pretoria. South African Journal of Clinical Nutrition. 2020;33(3):86-93. https://doi.org/10.1080/16070658.2019.1575604
    » https://doi.org/10.1080/16070658.2019.1575604
  • 4
    Barlow-Mosha L, Ross Eckard A, McComsey GA, Musoke PM. Metabolic complications and treatment of perinatally HIV-infected children and adolescents. J Int AIDS Soc. 2013;16(1):18600. PMID: 23782481; https://doi.org/10.7448/IAS.16.1.18600
    » https://doi.org/10.7448/IAS.16.1.18600
  • 5
    Vettor R, Milan G, Rossato M, Federspil G. Review article: adipocytokines and insulin resistance. Aliment Pharmacol Ther. 2005;22 Suppl 2:3-10. PMID: 16225463; https://doi.org/10.1111/j.1365-2036.2005.02587.x
    » https://doi.org/10.1111/j.1365-2036.2005.02587.x
  • 6
    Willig AL, Overton ET. Metabolic complications and glucose metabolism in HIV infection: a review of the evidence. Curr HIV/AIDS Rep. 2016;13(5):289-96. PMID: 27541600; https://doi.org/10.1007/s11904-016-0330-z
    » https://doi.org/10.1007/s11904-016-0330-z
  • 7
    Flynn PM, Abrams EJ. Growing up with perinatal HIV. Aids. 2019;33(4):597-603. PMID: 30531318; https://doi.org/10.1097/QAD.0000000000002092
    » https://doi.org/10.1097/QAD.0000000000002092
  • 8
    Lopez-Sandoval J, Sanchez-Enriquez S, Rivera-Leon EA, et al. Cardiovascular Risk Factors in Adolescents: Role of Insulin Resistance and Obesity. Acta Endocrinol. (Buchar). 2018;14(3):330-7. PMID: 31149280; https://doi.org/10.4183/aeb.2018.330
    » https://doi.org/10.4183/aeb.2018.330
  • 9
    Ejtahed HS, Asghari G, Mirmiran P, et al. Body mass index as a measure of percentage body fat prediction and excess adiposity diagnosis among Iranian adolescents. Arch Iran Med. 2014;17(6):400-5. PMID: 24916524.
  • 10
    Ben-Noun L, Laor A. Relationship of neck circumference to cardiovascular risk factors. Obes Res. 2003;11(2):226-31. PMID: 12582218; https://doi.org/10.1038/oby.2003.35
    » https://doi.org/10.1038/oby.2003.35
  • 11
    Gracia-Marco L, Moreno LA, Ruiz JR, et al. Body composition indices and single and clustered cardiovascular disease risk factors in adolescents: providing clinical-based cut-points. Progress in cardiovascular diseases. 2016;58(5):555-64. PMID: 26545445; https://doi.org/10.1016/j.pcad.2015.11.002
    » https://doi.org/10.1016/j.pcad.2015.11.002
  • 12
    Geffner ME, Patel K, Miller TL, et al. Factors associated with insulin resistance among children and adolescents perinatally infected with HIV-1 in the pediatric HIV/AIDS cohort study. Horm Res Paediatr. 2011;76(6):386-91. PMID: 22042056; https://doi.org/10.1159/000332957
    » https://doi.org/10.1159/000332957
  • 13
    Frigati LJ, Jao J, Mahtab S, et al. Insulin resistance in south african youth living with perinatally acquired HIV receiving antiretroviral therapy. AIDS Res Hum Retroviruses. 2019;35(1):56-62. PMID: 30156434; https://doi.org/10.1089/AID.2018.0092
    » https://doi.org/10.1089/AID.2018.0092
  • 14
    Geffner ME, Patel K, Jacobson DL, et al. Changes in insulin sensitivity over time and associated factors in HIV-infected adolescents. AIDS. 2018;32(5):613-22. PMID: 29280758; https://doi.org/10.1097/QAD.0000000000001731
    » https://doi.org/10.1097/QAD.0000000000001731
  • 15
    Dirajlal-Fargo S, Shan L, Sattar A, et al. Insulin resistance and intestinal integrity in children with and without HIV infection in Uganda. HIV Med. 2020;21(2):119-27. PMID: 31642582; https://doi.org/10.1111/hiv.12808
    » https://doi.org/10.1111/hiv.12808
  • 16
    Alves Junior CA, Mocellin MC, Gonçalves ECA, Silva DA, Trindade EB. Anthropometric indicators as body fat discriminators in children and adolescents: a systematic review and meta-analysis. Adv Nutr. 2017;8(5):718-27. PMID: 28916572; https://doi.org/10.3945/an.117.015446
    » https://doi.org/10.3945/an.117.015446
  • 17
    Hulley S, Cummings S, Browner W, Grady D, Newman T. Designing clinical research. Philadelphia: LWW. 2013.
  • 18
    Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412-9. PMID: 3899825; https://doi.org/10.1007/BF00280883
    » https://doi.org/10.1007/BF00280883
  • 19
    Borai A, Livingstone C, Heald AH, Oyindamola Y, Ferns G. Delta insulin-like growth factor binding protein-1 (ΔIGFBP-1): a marker of hepatic insulin resistance? Ann Clin Biochem. 2014;51(Pt 2):269-76. PMID: 24056811; https://doi.org/10.1177/0004563213495818
    » https://doi.org/10.1177/0004563213495818
  • 20
    Pederson D, Gore C. Erros de medição em antropometria. In: Kevin N, Tim O, editores. Antropométrica. Porto Alegre: Artmed; 2005. p. 91-104.
  • 21
    Greulich WW, Pyle SI. Radiographic Atlas of Skeletal Development of the Hand and Wrist. Stanford: Stanford University Press; 1959.
  • 22
    Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557-65. PMID: 18949660; https://doi.org/10.1080/02640410802334196
    » https://doi.org/10.1080/02640410802334196
  • 23
    George D, Mallery P. Using SPSS for Windows step by step: a simple guide and reference. 4th ed. London: Pearson Education; 2003.
  • 24
    Malina RM, Bouchard C, Bar-Or O. Growth, maturation, and physical activity. Champaign: Human Kinetics; 2004.
  • 25
    Freedman DS, Serdula MK, Srinivasan SR, Berenson GS. Relation of circumferences and skinfold thicknesses to lipid and insulin concentrations in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr. 1999;69(2):308-17. PMID: 9989697; https://doi.org/10.1093/ajcn/69.2.308
    » https://doi.org/10.1093/ajcn/69.2.308
  • 26
    Kondaki K, Grammatikaki E, Pavón DJ, et al. Comparison of several anthropometric indices with insulin resistance proxy measures among European adolescents: The Helena Study. Eur J Pediatr. 2011;170(6):731-9. PMID: 21052739; https://doi.org/10.1007/s00431-010-1322-4
    » https://doi.org/10.1007/s00431-010-1322-4
  • 27
    Addo OY, Pereira MA, Himes JH. Is skinfold thickness as good as DXA when measuring adiposity contributions to insulin resistance in adolescents? Am J Hum Biol. 2012;24(6):806-11. PMID: 23012045; https://doi.org/10.1002/ajhb.22321
    » https://doi.org/10.1002/ajhb.22321
  • 28
    Nightingale CM, Rudnicka AR, Owen CG, et al. Influence of adiposity on insulin resistance and glycemia markers among U.K. Children of South Asian, black African-Caribbean, and white European origin: child heart and health study in England. Diabetes Care. 2013;36(6):1712-9. PMID: 23315600; https://doi.org/10.2337/dc12-1726
    » https://doi.org/10.2337/dc12-1726
  • 29
    Cabanelas N, António S, Esteves MC. Lipotoxicidade e Síndrome Metabólica. Revista Portuguesa de Diabetes. 2008;3(4):209-15. Available from: http://www.revportdiabetes.com/wp-content/uploads/2017/10/RPD-Vol-3-n%C2%BA-4-Dezembro-2008-Artigo-de-Revis%C3%A3o-p%C3%A1gs-209-215.pdf Accessed in 2021 (Jun 1).
    » http://www.revportdiabetes.com/wp-content/uploads/2017/10/RPD-Vol-3-n%C2%BA-4-Dezembro-2008-Artigo-de-Revis%C3%A3o-p%C3%A1gs-209-215.pdf
  • 30
    Mueller NT, Pereira MA, Buitrago-Lopez A, et al. Adiposity indices in the prediction of insulin resistance in prepubertal Colombian children. Public Health Nutr. 2013;16(2):248-55. PMID: 22916737; https://doi.org/10.1017/S136898001200393X
    » https://doi.org/10.1017/S136898001200393X
  • 31
    Weber DR, Levitt Katz LE, Zemel BS, et al. Anthropometric measures of abdominal adiposity for the identification of cardiometabolic risk factors in adolescents. Diabetes Res Clin Pract. 2014;103(3):e14-7. PMID: 24552682; https://doi.org/10.1016/j.diabres.2013.12.050
    » https://doi.org/10.1016/j.diabres.2013.12.050
  • 32
    Keane KN, Cruzat VF, Carlessi R, de Bittencourt PI Jr, Newsholme P. Molecular events linking oxidative stress and inflammation to insulin resistance and β-cell dysfunction. Oxid Med Cell Longev. 2015;2015:181643. PMID: 26257839; https://doi.org/10.1155/2015/181643
    » https://doi.org/10.1155/2015/181643
  • 33
    Zhang M, Hu T, Zhang S, Zhou L. Associations of different adipose tissue depots with insulin resistance: a systematic review and meta-analysis of observational studies. Sci Rep. 2015;5:18495. PMID: 26686961; https://doi.org/10.1038/srep18495
    » https://doi.org/10.1038/srep18495

Publication Dates

  • Publication in this collection
    05 Jan 2022
  • Date of issue
    Jan-Feb 2022

History

  • Received
    13 Apr 2021
  • Reviewed
    13 Apr 2021
  • Accepted
    27 May 2021
Associação Paulista de Medicina - APM APM / Publicações Científicas, Av. Brigadeiro Luís Antonio, 278 - 7º and., 01318-901 São Paulo SP - Brazil, Tel.: +55 11 3188-4310 / 3188-4311, Fax: +55 11 3188-4255 - São Paulo - SP - Brazil
E-mail: revistas@apm.org.br