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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]. ...
... However, many of these studies implemented single-pollutant models, hampering the interpretability, and the findings haven been discrepant. Some studies reported positive associations with OCs (Iszatt et al., 2015;Mendez et al., 2011;Valvi et al., 2014;Verhulst et al., 2009) and negative associations with PFAS (Andersen et al., 2010;Shoaff et al., 2018), but others reported null associations (Alkhalawi et al., 2016;Chen et al., 2017;Garced et al., 2012). Furthermore, knowledge about whether the possible perturbations are persistent across childhood is scarce. ...
... Our observation that exposure to PCB-153 may contribute to increased infant growth is consistent with one study also using data from FLEHS I cohort but with smaller sample size, which has reported increased BMI-score through 3 years of age in Flemish children was associated with higher concentrations of PCBs (congeners 118, 138, 153, 170, 180) in cord blood (Verhulst et al., 2009). The observed larger mean beta value for PCB-153 in our ENET model compared to the single-pollutant model was fully consistent with the positive correlation between PCB-153 and p,p'-DDE exposures in this population combined with the estimated negative effect of p,p'-DDE on infant growth. ...
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Background Children are born with a burden of persistent organic pollutants (POPs) which may have endocrine disrupting properties and have been postulated to contribute to the rise in childhood obesity. The current evidence is equivocal, which may partly because many studies investigate the effects at one time point during childhood. We assessed associations between prenatal exposure to POPs and growth during infancy and childhood. Methods We used data from two Belgian cohorts with cord blood measurements of five organochlorines [(dichlorodiphenyldichloroethylene (p,p’-DDE), hexachlorobenzene (HCB), polychlorinated biphenyls (PCB-138, -150, −180)] (N = 1418) and two perfluoroalkyl substances [perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS)] (N = 346). We assessed infant growth, defined as body mass index (BMI) z-score change between birth and 2 years, and childhood growth, characterized as BMI trajectory from birth to 8 years. To evaluate associations between POP exposures and infant growth, we applied a multi-pollutant approach, using penalized elastic net regression with stability selection, controlling for covariates. To evaluate associations with childhood growth, we used single-pollutant linear mixed models with random effects for child individual, parametrized using a natural cubic spline formulation. Results PCB-153 was associated with increased and p,p’-DDE with decreased infant growth, although these results were imprecise. No clear association between any of the exposures and longer-term childhood growth trajectories was observed. We did not find evidence of effect modification by child sex. Conclusion Our results suggest that prenatal exposure to PCB-153 and p,p’-DDE may affect infant growth in the first two years, with no evidence of more persistent effects.
... ↑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]. ...
... Moreover, a meta-analysis encompassing seven studies, two of which involved cord blood levels, indicated a negative correlation between PCB exposure and birth weight [11]. However, prenatal exposure to the ΣPCBs had also been linked to higher BMI-z in infancy [46,67], a negative association with the change in weight from birth to 3 months [68] and no association between PCB-153 and weight-for-age from birth to 24 months in a multi-center European study [18]. These varying outcomes may arise from differences in the specific PCB congener profiles studied, variations in exposure levels, and population characteristics. ...
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Background Controversy surrounds the impact of persistent organic pollutants (POPs) on fetal development. This study aimed to investigate levels of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in umbilical cord blood from Şanlıurfa mothers in Turkey, exploring associations with gestational age and birth weight. Methods Participants included voluntary mothers pregnant with a single fetus, providing details on maternal factors. Cord blood samples were collected immediately after delivery. Samples were extracted with a modified QuEChERS method, and OCPs (17 pesticides) and PCBs (11 congeners) compound levels were analyzed with a gas chromatograph/mass spectrometry. Detection frequencies and levels of POPs by single pollutant type and pollutant groups were calculated and compared according to gestational duration and birth weight. We used partial least squares discriminant analysis to identify the key chemicals and distinguish their respective statuses. Results Among 120 infants, 35 were preterm but appropriate for gestational age, 35 were term but small for gestational age (SGA), and 50 were term and appropriate for gestational age (AGA). Beta HCH, Oxy-Chlordan, and PCB 28, were not detected in cord blood samples. Half of the samples contained at least 4 types of OCPs, with a median OCP level of 38.44 ng/g. Among the DDT, 2,4’-DDE was found at the highest concentration in cord plasma samples. The PCB congeners with a frequency exceeding 50% were ranked in the following order: 151, 149, 138, 146. The median level of ∑PCBs was 5.93 ng/g. Male infants born at term with SGA status exhibited lower levels of ∑DDTs, ∑OCPs compared to male infants born preterm or at term with AGA status. Di-ortho-substituted PCBs and hexachlorinated PCBs were higher in male infants born at term with SGA status than male infants born preterm with AGA status. Conclusion Overall, exposure to DDT and PCBs demonstrates varying effects depending on gestational duration and birth weight, with exposure levels also differing by gender. This underscores the necessity for studies across diverse populations that investigate the combined effects of multiple pollutant exposures on gestational age, birth weight, and gender simultaneously.
... This condition is typically assessed using Body Mass Index (BMI) criteria given by the WHO: a BMI over 25 is considered overweight, and over 30 obese. In children aged 5-19 years, being overweight means having a BMI-for-age greater than 1 standard deviation above the WHO Growth Reference median; and obesity is greater than 2 standard deviations above the WHO Growth Reference median (1,2). Factors like overeating, sedentary behavior, and genetic predispositions are commonly associated with obesity. ...
Article
The link between environmental pollution and obesity is of high importance, because understanding the relationship between the two can provide valuable insights into the complex factors contributing to the obesity epidemic. These chemicals, termed "obesogens," are believed to disrupt lipid metabolism processes, therefore promoting the development of obesity. Human activities such as industrialization, urbanization, agriculture, and transportation have significantly contributed to environmental pollution. Therefore, the main identified obesogens are BPA found in plastics, food packaging, and thermal paper receipts, phthalates, commonly used in plastics, personal care products, and food packaging, toxic metal(oid)s, determined in non-stick cookware, water-resistant fabrics, and food packaging, pesticides, used in agriculture, as well as other persistent organic pollutants (POPs), and pharmaceuticals (waste). Addressing environmental pollution not only has the potential to improve environmental quality, but also to promote public health and prevent obesity-related diseases. Addressing the causality between pollutants and obesity could be a new and challenging road map for health professionals.
... The association of early DDT exposure with obesity was observed in children and adults in epidemiological and experimental studies [45][46][47][48]. Verhulst et al. [49] described a positive association between prenatal DDE exposure and Fig. 1 How Pesticides Cause Obesity and Diabetes. There are many possible mechanisms by which pesticides can act as EDCs. ...
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Purpose Exposure to pesticides has been associated with obesity and diabetes in humans and experimental models mainly due to endocrine disruptor effects. First contact with environmental pesticides occurs during critical phases of life, such as gestation and lactation, which can lead to damage in central and peripheral tissues and subsequently programming disorders early and later in life. Methods We reviewed epidemiological and experimental studies that associated pesticide exposure during gestation and lactation with programming obesity and diabetes in progeny. Results Maternal exposure to organochlorine, organophosphate and neonicotinoids, which represent important pesticide groups, is related to reproductive and behavioral dysfunctions in offspring; however, few studies have focused on glucose metabolism and obesity as outcomes. Conclusion We provide an update regarding the use and metabolic impact of early pesticide exposure. Considering their bioaccumulation in soil, water, and food and through the food chain, pesticides should be considered a great risk factor for several diseases. Thus, it is urgent to reformulate regulatory actions to reduce the impact of pesticides on the health of future generations.
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The study objective was to determine a possible association between maternal exposure to organochlorine pesticides (OCPs) and anthropometric measures at birth in group of postpartum women in urban and rural areas of Armenia. The anthropometric measures of infants were obtained from birth records and gamma-hexachlorocyclohexane (γ-HCH), dichlorodiphenyltrichloroethane (DDT), dichlorodiphenyldichloroethylene, and dichlorodiphenyldichloroethane were measured in breast milk. Gas-liquid chromatography with electron capture detection was used to identify OCPs. Total OCPs and DDTs were calculated, and the anthropometrics were analyzed for sex and areas, and group differences were compared (Student's t-test). Both individual OCPs and total OCPs and DDTs were significantly higher in rural samples than in urban ones (P < 0.01-0.000), with lower and upper quartiles differing by 2.6-fold and 3.1-fold, respectively (P < 0.000). There was no association between the anthropometrics and OCPs levels in rural or urban areas. However, this does not rule out the possibility of OCPs impact on health later in life. To our knowledge, this was the first study addressing these issues in Armenia. The results obtained will provide data on the current situation regarding birth outcomes in terms of prenatal exposure to OCPs in Armenia and will contribute to the available results from previous studies.
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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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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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