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We investigated the association between body mass index (BMI) standard deviation score (SDS) and prenatal exposure to hexachlorobenzene, dichlorodiphenyldichloroethylene (DDE), dioxin-like compounds, and polychlorinated biphenyls (PCBs). In this prospective birth cohort study, we assessed a random sample of mother-infant pairs (n = 138) living in Flanders, Belgium, with follow-up until the children were 3 years of age. We measured body mass index as standard deviation scores (BMI SDS) of children 1-3 years of age as well as pollutants measured in cord blood. DDE correlated with BMI SDS, with effect modification by maternal smoking and the child's age. At 1 year, children of smoking mothers had higher BMI SDS than did children of nonsmoking mothers. At 3 years, this difference was reduced because of the faster rate of decline in BMI SDS in the former group. This relationship held except for children with high levels of DDE. DDE had a small effect on BMI SDS at 3 years of age in children of nonsmoking mothers (difference in BMI SDS for DDE concentrations between the 90th and 10th percentiles = 0.13). On the other hand, smoking enhanced the relation between DDE and BMI SDS at 3 years (difference in BMI SDS for DDE concentrations between the 90th and 10th percentiles = 0.76). Increasing concentrations of PCBs were associated with higher BMI SDS values at all ages (parameter estimate = 0.003 +/- 0.001; p = 0.03). In this study we demonstrated that intrauterine exposure to DDE and PCBs is associated with BMI during early childhood. Future studies are warranted to confirm our findings and to assess possible mechanisms by which these pollutants could alter energy metabolism.
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... ↑D [142] ↔D [376] ↑↓D [445] ↑P [445] ↑D [445] DDT/DDE ↑ [452,[454][455][456][457][458] ↑D [460] ↑D,T [46,49] ↑D [460] ↑D [253,461,[475][476][477][479][480][481][482][483]491,492,739] ↔D [482] ↑A [474] (continued on next page) J.J. Heindel et al. ↑D [632] ↑A [189] ↑D [188] ↓D [480] ↓C [463] ↓A [463,636] ↔A [635] Arsenic ↓, [638,639] ↑D [640,641] ↓A [642] ↓A [642] ↔A [642] ↔D,C [208] DBTs ↑ [371,364] ↑D [364] ↔D [388] Fructose, HFCS ↑A [703] ↑A [703] ↑C [610] ↑C [610] βHCH ↑D [253] ↔D [485,492] ↑A [464,471,478] HCB ↑D [253,482,490,739] ↔D [208,480,485,491,492] ↓C [208,463,490] ↑A [464,467,468] ↑↓A [463] ↔A [469,478] ↔D [482] ↑A [464,474] A = adult exposure; C = childhood/adolescent exposure; D = developmental/early life exposure; D + A = developmental plus adult exposure; P = exposure during pregnancy; T = transgenerational effects. ↑ positive or increased effect/association, or increased obesogenic effect linked to the exposure. ...
... ↑D [142] ↔D [376] ↑↓D [445] ↑P [445] ↑D [445] DDT/DDE ↑ [452,[454][455][456][457][458] ↑D [460] ↑D,T [46,49] ↑D [460] ↑D [253,461,[475][476][477][479][480][481][482][483]491,492,739] ↔D [482] ↑A [474] (continued on next page) J.J. Heindel et al. ↑D [632] ↑A [189] ↑D [188] ↓D [480] ↓C [463] ↓A [463,636] ↔A [635] Arsenic ↓, [638,639] ↑D [640,641] ↓A [642] ↓A [642] ↔A [642] ↔D,C [208] DBTs ↑ [371,364] ↑D [364] ↔D [388] Fructose, HFCS ↑A [703] ↑A [703] ↑C [610] ↑C [610] βHCH ↑D [253] ↔D [485,492] ↑A [464,471,478] HCB ↑D [253,482,490,739] ↔D [208,480,485,491,492] ↓C [208,463,490] ↑A [464,467,468] ↑↓A [463] ↔A [469,478] ↔D [482] ↑A [464,474] A = adult exposure; C = childhood/adolescent exposure; D = developmental/early life exposure; D + A = developmental plus adult exposure; P = exposure during pregnancy; T = transgenerational effects. ↑ positive or increased effect/association, or increased obesogenic effect linked to the exposure. ...
... In a Faroese mother-child cohort, maternal exposure to persistent pollutants was linked to increased BMI z-scores and/or overweight risk at ages 18 months and/or five years, although the associations were unclear for DDT/DDE [490]. Several factors could explain these findings, including the above-mentioned differential exposure levels since null associations were more likely to be reported in populations with higher exposure levels [485] unaccounted effect modifiers such as smoking [491] or prepregnancy BMI [492]. Exposure estimations based on one spot sample may bias results toward the null because they may not reflect pre-and post-natal exposures [493]. ...
... All our findings were consistent with previous literature reporting positive associations between in-utero exposure of individual OCs and increases in childhood BMI measures. Prior studies, also from our group, consistently identified an increase in childhood BMI measures associated with an increase in individual levels of exposure to HCB, DDE, 10,12,14,27,[44][45][46] or PCBs, 10,46 including a previous multiple-pollutant approach. 9 Prior results showed strong associations between childhood BMI and HCB or DDE, and previous analyses have shown nonmonotonic associations for those chemicals. ...
... All our findings were consistent with previous literature reporting positive associations between in-utero exposure of individual OCs and increases in childhood BMI measures. Prior studies, also from our group, consistently identified an increase in childhood BMI measures associated with an increase in individual levels of exposure to HCB, DDE, 10,12,14,27,[44][45][46] or PCBs, 10,46 including a previous multiple-pollutant approach. 9 Prior results showed strong associations between childhood BMI and HCB or DDE, and previous analyses have shown nonmonotonic associations for those chemicals. ...
... 10,47,48 Here we confirmed the detrimental role of elevated HCB and DDE levels, and we leveraged a kernel machine regression to confirm similar nonlinear chemical-response associations. Prior associations of BMI and prenatal exposure to PCBs were inconclusive, and some of the studies reported null results, 11,46 although others showed nonmonotonic associations. 10 Based on those findings, many authors suggested that the BMI associations with PCBs were positively confounded by other OC compounds and potentially masked by the strong correlation structure and dose-response associations. ...
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Background: Prenatal exposure to organochlorine compounds (OCs) has been associated with increased childhood body mass index (BMI); however, only a few studies have focused on longitudinal BMI trajectories, and none of them used multiple exposure mixture approaches. Aim: To determine the association between in-utero exposure to eight OCs and childhood BMI measures (BMI and BMI z-score) at 4 years and their yearly change across 4-12 years of age in 279 Rhea child-mother dyads. Methods: We applied three approaches: (1) linear mixed-effect regressions (LMR) to associate individual compounds with BMI measures; (2) Bayesian weighted quantile sum regressions (BWQSR) to provide an overall OC mixture association with BMI measures; and (3)Bayesian varying coefficient kernel machine regressions (BVCKMR) to model nonlinear and nonadditive associations. Results: In the LMR, yearly change of BMI measures was consistently associated with a quartile increase in hexachlorobenzene (HCB) (estimate [95% Confidence or Credible interval] BMI: 0.10 [0.06, 0.14]; BMI z-score: 0.02 [0.01, 0.04]). BWQSR results showed that a quartile increase in mixture concentrations was associated with yearly increase of BMI measures (BMI: 0.10 [0.01, 0.18]; BMI z-score: 0.03 [0.003, 0.06]). In the BVCKMR, a quartile increase in dichlorodiphenyldichloroethylene concentrations was associated with higher BMI measures at 4 years (BMI: 0.33 [0.24, 0.43]; BMI z-score: 0.19 [0.15, 0.24]); whereas a quartile increase in HCB and polychlorinated biphenyls (PCB)-118 levels was positively associated with BMI measures yearly change (BMI: HCB:0.10 [0.07, 0.13], PCB-118:0.08 [0.04, 012]; BMI z-score: HCB:0.03 [0.02, 0.05], PCB-118:0.02 [0.002,04]). BVCKMR suggested that PCBs had nonlinear relationships with BMI measures, and HCB interacted with other compounds. Conclusions: All analyses consistently demonstrated detrimental associations between prenatal OC exposures and childhood BMI measures.
... ↑D [142] ↔D [376] ↑↓D [445] ↑P [445] ↑D [445] DDT/DDE ↑ [452,[454][455][456][457][458] ↑D [460] ↑D,T [46,49] ↑D [460] ↑D [253,461,[475][476][477][479][480][481][482][483]491,492,739] ↔D [482] ↑A [474] (continued on next page) J.J. Heindel et al. ↑D [632] ↑A [189] ↑D [188] ↓D [480] ↓C [463] ↓A [463,636] ↔A [635] Arsenic ↓, [638,639] ↑D [640,641] ↓A [642] ↓A [642] ↔A [642] ↔D,C [208] DBTs ↑ [371,364] ↑D [364] ↔D [388] Fructose, HFCS ↑A [703] ↑A [703] ↑C [610] ↑C [610] βHCH ↑D [253] ↔D [485,492] ↑A [464,471,478] HCB ↑D [253,482,490,739] ↔D [208,480,485,491,492] ↓C [208,463,490] ↑A [464,467,468] ↑↓A [463] ↔A [469,478] ↔D [482] ↑A [464,474] A = adult exposure; C = childhood/adolescent exposure; D = developmental/early life exposure; D + A = developmental plus adult exposure; P = exposure during pregnancy; T = transgenerational effects. ↑ positive or increased effect/association, or increased obesogenic effect linked to the exposure. ...
... ↑D [142] ↔D [376] ↑↓D [445] ↑P [445] ↑D [445] DDT/DDE ↑ [452,[454][455][456][457][458] ↑D [460] ↑D,T [46,49] ↑D [460] ↑D [253,461,[475][476][477][479][480][481][482][483]491,492,739] ↔D [482] ↑A [474] (continued on next page) J.J. Heindel et al. ↑D [632] ↑A [189] ↑D [188] ↓D [480] ↓C [463] ↓A [463,636] ↔A [635] Arsenic ↓, [638,639] ↑D [640,641] ↓A [642] ↓A [642] ↔A [642] ↔D,C [208] DBTs ↑ [371,364] ↑D [364] ↔D [388] Fructose, HFCS ↑A [703] ↑A [703] ↑C [610] ↑C [610] βHCH ↑D [253] ↔D [485,492] ↑A [464,471,478] HCB ↑D [253,482,490,739] ↔D [208,480,485,491,492] ↓C [208,463,490] ↑A [464,467,468] ↑↓A [463] ↔A [469,478] ↔D [482] ↑A [464,474] A = adult exposure; C = childhood/adolescent exposure; D = developmental/early life exposure; D + A = developmental plus adult exposure; P = exposure during pregnancy; T = transgenerational effects. ↑ positive or increased effect/association, or increased obesogenic effect linked to the exposure. ...
... In a Faroese mother-child cohort, maternal exposure to persistent pollutants was linked to increased BMI z-scores and/or overweight risk at ages 18 months and/or five years, although the associations were unclear for DDT/DDE [490]. Several factors could explain these findings, including the above-mentioned differential exposure levels since null associations were more likely to be reported in populations with higher exposure levels [485] unaccounted effect modifiers such as smoking [491] or prepregnancy BMI [492]. Exposure estimations based on one spot sample may bias results toward the null because they may not reflect pre-and post-natal exposures [493]. ...
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Full-text available
Obesity is a multifactorial disease with both genetic and environmental components. The prevailing view is that obesity results from an imbalance between energy intake and expenditure caused by overeating and insufficient exercise. We describe another environmental element that can alter the balance between energy intake and energy expenditure: obesogens. Obesogens are a subset of environmental chemicals that act as endocrine disruptors affecting metabolic endpoints. The obesogen hypothesis posits that exposure to endocrine disruptors and other chemicals can alter the development and function of the adipose tissue, liver, pancreas, gastrointestinal tract, and brain, thus changing the set point for control of metabolism. Obesogens can determine how much food is needed to maintain homeostasis and thereby increase the susceptibility to obesity. The most sensitive time for obesogen action is in utero and early childhood, in part via epigenetic programming that can be transmitted to future generations. This review explores the evidence supporting the obesogen hypothesis and highlights knowledge gaps that have prevented widespread acceptance as a contributor to the obesity pandemic. Critically, the obesogen hypothesis changes the narrative from curing obesity to preventing obesity.
... ↑D [142] ↔D [376] ↑↓D [445] ↑P [445] ↑D [445] DDT/DDE ↑ [452,[454][455][456][457][458] ↑D [460] ↑D,T [46,49] ↑D [460] ↑D [253,461,[475][476][477][479][480][481][482][483]491,492,739] ↔D [482] ↑A [474] (continued on next page) J.J. Heindel et al. A = adult exposure; C = childhood/adolescent exposure; D = developmental/early life exposure; D + A = developmental plus adult exposure; P = exposure during pregnancy; T = transgenerational effects. ...
... In a Faroese mother-child cohort, maternal exposure to persistent pollutants was linked to increased BMI z-scores and/or overweight risk at ages 18 months and/or five years, although the associations were unclear for DDT/DDE [490]. Several factors could explain these findings, including the above-mentioned differential exposure levels since null associations were more likely to be reported in populations with higher exposure levels [485] unaccounted effect modifiers such as smoking [491] or prepregnancy BMI [492]. Exposure estimations based on one spot sample may bias results toward the null because they may not reflect pre-and post-natal exposures [493]. ...
... Researchers have found a higher risk for b overweight (26-31%) as estimated from the BMI and waist circumference in the DDT posed group [77]. In addition, in utero exposure to DDT was found to be associated higher body weight in the postnatal period during the first and third years of life [78 Moreover, DDT also plays a role in obesity-associated diseases. In a recent st Henríquez-Hernández et al. have shown that DDT and/or its metabolites can contri to obesity development and related diseases as recorded by altered fasting blood glu and metabolic disorders [79]. ...
... Researchers have found a higher risk for being overweight (26-31%) as estimated from the BMI and waist circumference in the DDT exposed group [77]. In addition, in utero exposure to DDT was found to be associated with higher body weight in the postnatal period during the first and third years of life [78]. ...
Article
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Persistent organic pollutants (POPs) are considered as potential obesogens that may affect adipose tissue development and functioning, thus promoting obesity. However, various POPs may have different mechanisms of action. The objective of the present review is to discuss the key mechanisms linking exposure to POPs to adipose tissue dysfunction and obesity. Laboratory data clearly demonstrate that the mechanisms associated with the interference of exposure to POPs with obesity include: (a) dysregulation of adipogenesis regulators (PPARγ and C/EBPα); (b) affinity and binding to nuclear receptors; (c) epigenetic effects; and/or (d) proinflammatory activity. Although in vivo data are generally corroborative of the in vitro results, studies in living organisms have shown that the impact of POPs on adipogenesis is affected by biological factors such as sex, age, and period of exposure. Epidemiological data demonstrate a significant association between exposure to POPs and obesity and obesity-associated metabolic disturbances (e.g., type 2 diabetes mellitus and metabolic syndrome), although the existing data are considered insufficient. In conclusion, both laboratory and epidemiological data underline the significant role of POPs as environmental obesogens. However, further studies are required to better characterize both the mechanisms and the dose/concentration-response effects of exposure to POPs in the development of obesity and other metabolic diseases.
... DDT is an estrogen agonist and its environmentally-persistent breakdown product dichlorodiphenyldichloroethylene (DDE) is an androgen antagonist. [21][22][23] Epidemiological studies of prenatal exposure to DDT and DDE (DDT/E) have been mixed, reporting positive 18,[24][25][26][27][28][29][30][31] or null 19,[32][33][34][35][36][37][38] associations with child adiposity. Cardiometabolic risk factors other than size and adiposity were assessed only in a Greek birth cohort, which found a positive association between maternal serum DDE and blood pressure in children at 4 years of age. ...
Article
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As part of malaria control programs, many countries spray dichlorodiphenyltrichloroethane (DDT) or pyrethroid insecticides inside dwellings in a practice called indoor residual spraying that results in high levels of exposure to local populations. Gestational exposure to these endocrine- and metabolism-disrupting chemicals may influence child cardiometabolic health. Methods: We measured the serum concentration of DDT and dichlorodiphenyldichloroethylene (DDE) and urinary concentration of pyrethroid metabolites (cis-DBCA, cis-DCCA, trans-DCCA, 3-PBA) in peripartum samples collected between August 2012 and December 2013 from 637 women participating in the Venda Health Examination of Mothers, Babies and their Environment (VHEMBE), a birth cohort study based in Limpopo, South Africa. We applied marginal structural models to estimate the relationship between biomarker concentrations and child-size (height and weight), adiposity (body mass index [BMI], body fat percentage, waist circumference) and blood pressure at 5 years of age. Results: Maternal concentrations of all four pyrethroid metabolites were associated with lower adiposity including reduced BMI z-scores, smaller waist circumferences, and decreased body fat percentages. Reductions in BMI z-score were observed only among children of mothers with sufficient energy intake during pregnancy (βcis-DCCA, trans -DCCA=-0.4, 95% confidence interval (CI) = -0.7,-0.1; pinteraction=0.03 and 0.04, respectively) but there was no evidence of effect modification for the other measures of adiposity. Maternal p,p'-DDT concentrations were associated with a reduction in body fat percentage (β = -0.4%, 95% CI = -0.8,-0.0). Conclusions: Gestational exposure to pyrethroids may reduce adiposity in children at 5 years of age.
... Positive, negative, or null associations have also been reported between in utero or prenatal and postnatal, and between early childhood to the elderly concerning exposure to OCs and overweight and obesity indices ( Table 3 and Matrix Table 6) (103, 115-119, 121, 125, 126, 128-136, 139-145, 147-149). Maternal 1 st to 3 rd trimester blood and/or umbilical cord blood levels of OC metabolites, especially DDE and HCB levels, were positively associated with different anthropometric indices of obesity, whereas associations of PCBs, DDT metabolites, and b-HCH concentrations were null or positive in toddlers and preschoolers (115,116,126,132,142,148). Inconsistent associations (positive and null) were also found between PCBs, DDT metabolites, DDE, HCB, and b-HCH levels in the 1 st to 3 rd trimester maternal blood or umbilical cord blood and obesity indices in school-aged children (103, 118, 130, 133-136, 139, 149). ...
Article
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The exponential global increase in the incidence of obesity may be partly attributable to environmental chemical (EC) exposure. Humans are constantly exposed to ECs, primarily through environmental components. This review compiled human epidemiological study findings of associations between blood and/or urinary exposure levels of ECs and anthropometric overweight and obesity indices. The findings reveal research gaps that should be addressed. We searched MEDLINE (PubMed) for full text English articles published in 2006-2020 using the keywords "environmental exposure" and "obesity". A total of 821 articles were retrieved; 102 reported relationships between environmental exposure and obesity indices. ECs were the predominantly studied environmental exposure compounds. The ECs were grouped into phenols, phthalates, and persistent organic pollutants (POPs) to evaluate obesogenic roles. In total, 106 articles meeting the inclusion criteria were summarized after an additional search by each group of EC combined with obesity in the PubMed and Scopus databases. Dose-dependent positive associations between bisphenol A (BPA) and various obesity indices were revealed. Both individual and summed di(2-ethylhexyl) phthalate (DEHP) and non-DEHP metabolites showed inconsistent associations with overweight and obesity indices, although mono-butyl phthalate (MBP), mono-ethyl phthalate (MEP), and mono-benzyl phthalate (MBzP) seem to have obesogenic roles in adolescents, adults, and the elderly. Maternal exposure levels of individual POP metabolites or congeners showed inconsistent associations, whereas dichlorodiphenyldichloroethylene (DDE) and perfluorooctanoic acid (PFOA) were positively associated with obesity indices. There was insufficient evidence of associations between early childhood EC exposure and the subsequent development of overweight and obesity in late childhood. Overall, human evidence explicitly reveals the consistent obesogenic roles of BPA, DDE, and PFOA, but inconsistent roles of phthalate metabolites and other POPs. Further prospective studies may yield deeper insights into the overall scenario.
Chapter
Environmental factors underpin human variation in patterns of growth and development among the world's populations, both between populations in different societal contexts, and between individuals in the same communities. The study of environmental influences on growth has been a centuries long activity of the human sciences. Work in this field encompasses features of the physical environment at the extremes such as high-altitude hypoxia and temperature, as well as socially mediated influences such as poverty, nutrition, stress, infection and pollution. Many of these influences are detrimental to growth. Sub-optimal nutrition, infection, stress, and poverty are known impediments to good growth, disproportionately affecting populations in the global South. Even in the global North, inequalities in environmental exposures continue to contribute to differential growth of children and consequently on health outcomes. Growth patterns also may be seen as adaptations to local stressors. Growth patterns vary by temperature and climate in ways that appear adaptive by facilitating thermal homeostasis. Variation in growth patterns among high altitude populations are attributed to both adaptation to hypoxia and response to economic and subsistence challenges. Further, different high-altitude populations exhibit different growth patterns due in different degrees of hardship and different strategies of adaptation. Pollution is a modern influence on much of the world's population and is not one entity but many entities with different effects on growth and development depending on the chemical and physical properties of each pollutant. Some chemical pollutants even speed up maturation while others slow it and impede growth. The study of environmental factors and growth patterns requires methodologies relevant to assessment of both the social and physical components of the environment and rigorous research designs.
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We conducted a systematic review and meta-analysis of the associations between prenatal exposure to persistent organic pollutants (POPs) and childhood obesity. We focused on organochlorines (dichlorodiphenyltrichloroethane [DDT], dichlorodiphenyldichloroethylene [DDE], hexachlorobenzene [HCB], and polychlorinated biphenyls [PCBs]), perfluoroalkyl and polyfluoroalkyl substances (PFAS), and polybrominated diphenyl ethers (PBDEs) that are the POPs more widely studied in environmental birth cohorts so far. We search two databases (PubMed and Embase) through July/09/2021 and identified 33 studies reporting associations with prenatal organochlorine exposure, 21 studies reporting associations with prenatal PFAS, and five studies reporting associations with prenatal PBDEs. We conducted a qualitative review. Additionally, we performed random-effects meta-analyses of POP exposures, with data estimates from at least three prospective studies, and BMI-z. Prenatal DDE and HCB levels were associated with higher BMI z-score in childhood (beta: 0.12, 95% CI: 0.03, 0.21; I²: 28.1% per study-specific log increase of DDE and beta: 0.31, 95% CI: 0.09, 0.53; I²: 31.9% per study-specific log increase of HCB). No significant associations between PCB-153, PFOA, PFOS, or pentaPBDEs with childhood BMI were found in meta-analyses. In individual studies, there was inconclusive evidence that POP levels were positively associated with other obesity indicators (e.g., waist circumference).
Chapter
Widespread human exposure to known or suspected endocrine-disrupting chemicals (EDCs) has been documented worldwide, while rates of endocrine-related diseases and disorders among children are increasing. This chapter provides an overview of the current state of the epidemiological evidence for the adverse impacts of common persistent and nonpersistent EDCs on child development. The selected health end points discussed here include fetal growth, early reproductive tract development, pubertal development, obesity, and neurodevelopment. Despite their limitations, the studies mentioned here add to a growing body of evidence that exposure to chemicals commonly found in consumer goods, personal care products, food, drinking water, and other sources may adversely affect child development through altered endocrine function in a variety of pathways. Given the range of these potential serious developmental effects, efforts to reduce EDC exposure as a precaution among pregnant women and children are warranted.
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To follow and predict the evolution of adiposity during growth, individual adiposity curves, assessed by the weight/height² index, were drawn for 151 children from the age of 1 month to 16 yr. Adiposity increases during the 1st yr and then decreases. A renewed rise, termed here the adiposity rebound, occurs at about 6 yr. Individual weight/height² curves may differ regarding their percentile range level and age at adiposity rebound. The present study shows a relationship between the age at adiposity rebound and final adiposity. An early rebound (before 5.5 yr) is followed by a significantly higher adiposity level than a later rebound (after 7 yr). This phenomenon is observed whatever the subject's adiposity at 1 yr. The present observations might be connected with the cellularity of adipose tissue.
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Background: South Asians have a muscle-thin but adipose body phenotype and high rates of obesity-related disease. Adult body composition may be predictable in early life. Objective: Anthropometric indexes of adult body composition were examined in relation to birth size and body mass index (BMI) during childhood. Design: A population-based cohort of 1526 men and women aged 26 -32 y in Delhi, India, who were measured sequentially from birth until 21 y of age were followed up. Adult weight, height, skinfold thicknesses, and waist and hip circumferences were measured. BMI and indexes of adiposity (sum of skinfold thicknesses), central adiposity (waist-hip ratio), and lean mass (residual values after adjustment of BMI for skinfold thicknesses and height) were derived. Results: Mean birth weight was 2851 g. As children, many subjects were underweight-for-age (>2 SDs below the National Center for Health Statistics mean; 53% at 2 y), but as adults, 47% were overweight, 11% were obese, and 51% were centrally obese (according to World Health Organization criteria). Birth weight was positively related to adult lean mass (P < 0.001) and, in women only, to adiposity (P = 0.006) but was unrelated to central adiposity. BMI from birth to age 21 y was increasingly strongly positively correlated with all outcomes. BMI and BMI gain in infancy and early childhood were correlated more strongly with adult lean mass than with adiposity or central adiposity. Higher BMI and greater BMI gain in late childhood and adolescence were associated with increased adult adiposity and central adiposity. Conclusions: Birth weight and BMI gain during infancy and early childhood predict adult lean mass more strongly than adult adiposity. Greater BMI gain in late childhood and adolescence predicts increased adult adiposity.
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The prevalence and magnitude of childhood obesity are increasing dramatically. We examined the effect of varying degrees of obesity on the prevalence of the metabolic syndrome and its relation to insulin resistance and to C-reactive protein and adiponectin levels in a large, multiethnic, multiracial cohort of children and adolescents. We administered a standard glucose-tolerance test to 439 obese, 31 overweight, and 20 nonobese children and adolescents. Baseline measurements included blood pressure and plasma lipid, C-reactive protein, and adiponectin levels. Levels of triglycerides, high-density lipoprotein cholesterol, and blood pressure were adjusted for age and sex. Because the body-mass index varies according to age, we standardized the value for age and sex with the use of conversion to a z score. The prevalence of the metabolic syndrome increased with the severity of obesity and reached 50 percent in severely obese youngsters. Each half-unit increase in the body-mass index, converted to a z score, was associated with an increase in the risk of the metabolic syndrome among overweight and obese subjects (odds ratio, 1.55; 95 percent confidence interval, 1.16 to 2.08), as was each unit of increase in insulin resistance as assessed with the homeostatic model (odds ratio, 1.12; 95 percent confidence interval, 1.07 to 1.18 for each additional unit of insulin resistance). The prevalence of the metabolic syndrome increased significantly with increasing insulin resistance (P for trend, <0.001) after adjustment for race or ethnic group and the degree of obesity. C-reactive protein levels increased and adiponectin levels decreased with increasing obesity. The prevalence of the metabolic syndrome is high among obese children and adolescents, and it increases with worsening obesity. Biomarkers of an increased risk of adverse cardiovascular outcomes are already present in these youngsters.