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All issues > Volume 68(3); 2025
Hidden link between endocrine-disrupting chemicals and pediatric obesity
-
Min Won Shin, MD
, Shin-Hye Kim, MD, PhD
- Corresponding author: Shin-Hye Kim, MD, PhD. Department of Pediatrics, Inje University Sanggye Paik Hospital, 1342, Dongil-ro, Nowon-gu, Seoul, Korea Email: s2635@paik.ac.kr
- Received March 28, 2024 Revised October 22, 2024 Accepted October 22, 2024
- Abstract
-
The increasing prevalence of pediatric obesity has emerged as a significant public health concern. Among various contributing factors, exposure to endocrine-disrupting chemicals (EDCs) has gained recognition for its potential role. EDCs, including bisphenols, phthalates, per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, and organochlorines, disrupt hormonal regulation and metabolic processes, contributing to alterations in fat storage, appetite regulation, and insulin sensitivity. This study offers a comprehensive review of the current research linking EDC exposure to pediatric obesity by integrating the findings from experimental and epidemiological studies. It also addresses the complexities of interpreting this evidence in the context of public health, highlighting the urgent need for further research.
- Introduction
- Introduction
Endocrine-disrupting chemicals (EDCs) are a diverse group of substances that interfere with the endocrine system and the body's network of hormone-producing glands. These chemicals can mimic, block, or disrupt normal hormonal function, leading to a multitude of health issues [1]. EDCs, which encompass a variety of substances, including phthalates, bisphenols, per- and polyfluoroalkyl substances (PFAS), organochlorines, and polycyclic aromatic hydrocarbons (PAHs) [2,3], are found in numerous consumer products, industrial emissions, and natural sources, rendering their exposure ubiquitous and virtually inevitable in daily life [4].Pediatric obesity is a critical and growing public health concern characterized by the excessive accumulation of fat that poses a risk to child and adolescent health [5]. The prevalence of pediatric obesity has increased dramatically in recent decades [6], leading to a surge in obesity-related health complications such as type 2 diabetes, hypertension, precocious puberty, and psychological issues [7-9]. These conditions affect the child's immediate well-being and have long-term implications for their adult health, underscoring the urgency of addressing this epidemic.Emerging research suggests a complex interplay between environmental factors and a genetic predisposition in the development of pediatric obesity. Among these environmental factors, EDC exposure has gained attention owing to its potential role in modulating the risk of obesity. Preliminary findings of epidemiological and experimental studies suggest that EDCs may disrupt hormonal balance and impact metabolism and fat storage mechanisms, potentially contributing to obesity [10].This paper reviews the potential association between EDC exposure and the risk of childhood obesity by addressing the experimental and epidemiological evidence, highlighting the need for comprehensive research and targeted interventions to mitigate this pressing public health issue.
- Bisphenol A
- Bisphenol A
- 1. General characteristics
- 1. General characteristics
Bisphenols, including bisphenol A (BPA), bisphenol S (BPS), bisphenol F (BPF), bisphenol B (BPB), and bisphenol P (BPP), are a group of synthetic organic compounds widely used in the manufacture of polycarbonate plastics and epoxy resins [11]. These materials are prevalent in a variety of consumer products such as water and baby bottles, food storage containers, and food and beverage can linings. The structure of bisphenols, characterized by 2 hydroxyphenyl groups, enables them to mimic estrogen, thereby categorizing them as xenoestrogens. BPA, the most widely recognized bisphenol, was first synthesized in 1891 and has been used in plastic manufacturing since the 1950s [12]. Despite its widespread applications, concerns over the potential health risks of BPA due to its endocrine-disrupting properties have prompted increased scrutiny and the development of alternative bisphenols, including BPS, BPF, BPB, and BPP.The primary route of human exposure to bisphenols is through dietary consumption, predominantly from food and beverages that are contaminated due to contact with bisphenol-containing materials. Other exposure routes include dermal contact, dust inhalation, and direct oral contact with products made from or coated with bisphenols. Once absorbed, BPA enters the bloodstream and is primarily metabolized in the liver through glucuronidation mediated by uridine 5'-diphospho-glucuronosyltransferase enzymes, resulting in BPA glucuronide formation. This metabolite is subsequently excreted, mostly through the urine, with over 90% of the conjugated metabolites eliminated within 24 hours. The measurement of total urinary bisphenol levels, typically assessed after deconjugation with β-glucuronidase, is a reliable biomarker for bisphenol exposure. Notably, the terminal half-life of BPA is longer after dermal exposure versus oral intake because the skin continues to absorb BPA into the bloodstream even after the initial exposure has ended.- 2. Impact on obesity in experimental studies
- 2. Impact on obesity in experimental studies
BPA has been extensively studied for its effects on adipogenesis across various experimental studies (Fig. 1). EDCs such as BPA can also act through estrogen receptor pathways to influence adipogenesis. BPA can bind to estrogen receptors (ERα and ERβ) with lower affinity than estradiol. ER activation by BPA leads to increased expression of genes associated with adipocyte differentiation in human mesenchymal stem cells [13]. Furthermore, the interaction of BPA with ER can impede adiponectin release, thereby exacerbating inflammation in fat tissues and disrupting lipid regulation [14].BPA also activates the expression of peroxisome proliferator-activated receptor γ (PPARγ), which is essential for fat cell development and energy balance [15]. Studies involving human adipose-derived mesenchymal stem cells revealed that BPA exposure increased the mRNA levels of key adipogenic markers such as PPARγ, C/EBPα, and C/EBPβ. This finding indicates the potential of BPA to promote fat cell formation through ER pathways and potentially via an alternative ER pathway [13]. Additionally, BPA exposure leads to increased preadipocyte growth, greater lipid accumulation, higher proinflammatory cytokine levels, and alterations in genes involved in lipid processing, particularly FABP4 and CD36. This effect has been observed in 3T3-L1 adipocyte models and primary adipocytes derived from children [16,17].Experimental studies have shown that BPA exposure leads to dysfunction of the brown adipose tissue, which is crucial for thermogenesis and energy expenditure. BPA reduces the expression of uncoupling protein-1 and β-adrenergic receptors in brown adipocytes, leading to decreased thermogenic capacity and energy expenditure while increasing fat accumulation [18]. In an in vitro model, early-life BPA exposure can induce modifications in DNA methylation and histone marks, leading to epigenetic changes in the genes involved in hepatic oxidation. These epigenetic alterations are associated with increased fat and lean mass as well as hepatic steatosis [19].In utero and adult BPA exposure also affect the hypothalamic neurons involved in appetite regulation, such as the agouti-related peptide and neuropeptide Y, leading to increased appetite and potential weight gain [20]. Moreover, BPA disrupts the gut microbiota, leading to altered microbial composition, reduced short-chain fatty acids, and increased systemic lipopolysaccharides and contributing to obesity, metabolic disorders, and inflammation [21].- 3. Association with childhood obesity in epidemiological studies
- 3. Association with childhood obesity in epidemiological studies
The relationship between prenatal BPA exposure and childhood obesity has garnered extensive attention, particularly in North America, Europe, and Asia (Table 1). A substantial body of evidence has indicated a positive correlation between prenatal BPA exposure and increased childhood adiposity. These studies frequently reported that early BPA exposure was linked to accelerated weight gain during early childhood and higher adiposity indicators such as body mass index (BMI) z scores, skinfold thickness, body fat percentage, and waist circumference (WC) [22-27]. However, a shift in this trend appears in the more recent literature. Studies published after 2020 reported no significant association between prenatal BPA exposure and adiposity measures in children aged 5–11 years [28,29]. This finding suggests a potential reevaluation of earlier findings or indicates that the impact of BPA may be more nuanced than previously understood. Interestingly, a study from China presented a unique finding, suggesting that prenatal BPA exposure correlates with a specific pattern of BMI trajectory characterized by a low BMI that increases rapidly within the first 2 years of life [30]. This suggests that BPA influences growth patterns that are not uniformly observable across different populations or age groups.Research on postnatal BPA exposure and its effects on children, including studies from the United States (US) (through National Health and Nutrition Examination Survey [NHANES] data), Europe, Mexico, China, and Korea, primarily employed cross-sectional methodologies; only a few were prospective studies (Table 2). These studies generally indicated a potential link between postnatal BPA exposure, increased adiposity, and a higher risk of obesity in children. However, 2 prospective studies from the US revealed no significant relationship between BPA exposure in childhood and adiposity in later childhood [31,32]. Additionally, a serial cross-sectional study by Okubo et al. [33] utilizing the NHANES 2003–2014 data identified an association between higher BPA levels and obesity risk in 2003–2008 but variability in 2009– 2014. This inconsistency implies that the relationship between BPA exposure and obesity may be affected by several factors including exposure window, changes in BPA usage patterns, public awareness, and regulatory measures. A recent Korean study using Korean National Environmental Health Survey (KoNEHS) data indicated no significant association between urinary BPA concentrations and obesity in children aged 3–17 years [34]. Therefore, further studies are needed to explore how ethnicity-specific factors influence the relationship between BPA exposure and obesity risk.
- Phthalates
- Phthalates
- 1. General characteristics
- 1. General characteristics
Phthalates, a group of phthalic acid diesters recognized for their ability to make plastics more flexible and durable, have been extensively used since their introduction in the 1920s [35]. These chemicals are added to polyvinyl chloride (PVC) plastics, which are found in diverse consumer products from packaging and flooring to toys and medical devices. Moreover, phthalates are applied in nonplastic products such as personal care products, detergents, and fragrances because of their ability to enhance scents and extend cosmetic durability. Phthalates can leach from these products into the environment and food sources, leading to human exposure.Based on their molecular weight, phthalates are categorized into low molecular weight (LMW) and high molecular weight (HMW). LMW phthalates, which include phthalates with ester side-chain lengths of one to 4 carbons, are primarily used in personal care products and cosmetics [4]. Common LMW phthalates include dimethyl phthalate, diethyl phthalate, dibutyl phthalate (DBP), and diisobutyl phthalate. These phthalates are found in products such as nail polish to minimize chipping as well as in fragrances as scent stabilizers. Due to their lower molecular weight, LMW phthalates are not as tightly bound to the products, allowing them to off-gas or leach out more readily, especially when exposed to high temperatures. HMW phthalates, which have ester side-chain lengths of 5 or more carbons, are used in a wide range of products, particularly in those that require durability and flexibility. The key HMW phthalates include di (2- ethylhexyl) phthalate (DEHP), diisononyl phthalate, and diisodecyl phthalate. These phthalates are commonly found in PVC products such as plastic tubing, food packaging and processing materials, containers, vinyl toys, vinyl floor coverings, and building products. HMW phthalates are preferred in these applications because of their increased permanency and durability attributed to the longer carbon chains in their chemical structures.The primary route of phthalate exposure is through ingestion, particularly from consuming food and beverages that have contacted materials containing them.36) Phthalates can also be absorbed through the skin via personal care products or other items containing these chemicals. Another exposure pathway involves inhaling dust or fumes from products that emit phthalates into the air.Phthalates are metabolized in the liver through phases I and II enzymatic reactions, including hydrolysis, oxidation, and conjugation [36]. The metabolites are subsequently excreted primarily through the urine, although some are excreted via feces. The half-life of phthalates in humans varies depending on the specific compound; however, they are generally metabolized and excreted relatively quickly, often within 24–48 hours. Biomonitoring of phthalate exposure typically involves the measurement of phthalate metabolites in the urine (Supplementary Table 1). These metabolites reflect phthalate processing in the body and are considered reliable indicators of exposure. Advanced analytical methods have enabled the detection of multiple phthalate metabolites and provided a comprehensive assessment of exposure levels.- 2. Impact on obesity in experimental studies
- 2. Impact on obesity in experimental studies
Extensive experimental studies explored the mechanisms by which phthalates contribute to obesity (Fig. 1). Several studies demonstrated that mono (2-ethylhexyl) phthalate activates PPARγ, which induces adipocyte differentiation and lipid accumulation [37,38]. Similarly, research on DEHP substitutes has shown that these compounds and their metabolites can accelerate adipogenesis through PPARγ activation in human mature adipocytes [39]. Phthalates such as DBP and DEHP exhibit antiandrogenic activity in vitro. For example, a study of reporter gene assays demonstrated that DBP, mono-n-butyl phthalate, and DEHP display potent antiandrogenic activity [40]. This antiandrogenic action suggests that phthalates can disrupt the normal function of androgens, which are critical for maintaining metabolic health and preventing obesity.Phthalates, including DEHP, undermine thyroid function by blocking thyroid-stimulating hormone (TSH) receptors, attaching to thyroid hormone transport proteins and modifying iodine absorption in the thyroid follicle cells [41]. Thyroid hormones are crucial for regulating metabolism, and the disruption of thyroid function can lead to weight gain and obesity. Evidence suggests that phthalates can disrupt endocrine function and contribute to obesity via various epigenetic mechanisms. In particular, phthalate exposure is associated with altered DNA methylation and histone modifications in genes related to adipogenesis and insulin resistance [19]. These epigenetic changes can affect metabolic processes and have long-term health implications, including transgenerational effects.Furthermore, a mouse model of prenatal DEHP exposure demonstrated long-term disruptions in glucose metabolism, energy expenditure, adipogenesis, and gut dysbiosis in the offspring. These disturbances demonstrate the interconnectedness between the host and the gut microbiota in terms of modifying energy metabolism [39].- 3. Association with childhood obesity from epidemiological studies
- 3. Association with childhood obesity from epidemiological studies
Research into prenatal phthalate exposure and its effects on childhood obesity has been conducted in the US, China, Mexico, and Spain (Table 1). Their findings are mixed; while nearly half of these studies suggest that prenatal exposure may lead to increased BMI z scores, higher overweight risk, and accelerated adiposity in early childhood, other studies reported no significant effects or negative associations. Notably, 2 cohort studies, one each from China and the US, reported that certain phthalate metabolites had a negative impact on fetal growth but were linked to increased adiposity in early childhood [42,43]. Conversely, studies such as a recent Korean prospective study associated phthalate exposure with a reduced skeletal muscle index by 6 years of age [44]. This finding suggests a complex relationship between prenatal phthalate exposure and various aspects of childhood physical development, highlighting the need for further research to fully understand these dynamics.Research on postnatal phthalate exposure and its impact on childhood adiposity has primarily been conducted in Asian countries, notably China, with several studies emerging from the US (Table 2). These studies consistently showed that LMW and HMW phthalates are associated with increases in BMI z scores, WC, and the likelihood of being overweight and obese in children. Specifically, findings from the KoNEHS highlight that mono-(2-ethyl-5-carboxypentyl) phthalate, a metabolite of DEHP, was linked to a higher odds of obesity among Korean children [45]. Research highlighted the sex-specific effects of phthalate exposure on obesity and revealed that female children might face lower risks of obesity, whereas male children are more likely to exhibit increased obesity odds [46]. This indicates a complex interaction between phthalate exposure and biological sex in determining the obesity risk among children.
- Per- and polyfluoroalkyl substances
- Per- and polyfluoroalkyl substances
- 1. General characteristics
- 1. General characteristics
PFAS are synthetic compounds characterized by multiple fluorine atoms attached to an alkyl chain [47]. Their carbon-fluorine bonds, which are among the strongest in organic chemistry, contribute to their resistance to degradation in the environment and human body. Since their introduction in the 1940s, PFAS have been celebrated for their remarkable resistance to water, oil, and heat, leading to their integration into a wide array of products including non-stick cookware, water-repellent apparel, stain-resistant textiles, cosmetics, firefighting foams, and various grease-resistant applications [47]. These compounds are generally categorized by their functional groups into 2 main types: sulfonic acids, such as perfluorooctane sulfonic acid (PFOS), and carboxylic acids, such as perfluorooctanoic acid (PFOA). Additionally, PFAS are classified into short- and long-chain types based on their carbon chain lengths. PFOS and PFOA, 2 extensively studied and widely used PFAS compounds, have been phased out in several countries because of their environmental persistence and potential health risks [47]. Short-chain PFAS, characterized by 6 or fewer carbon atoms, have emerged as less bioaccumulative alternatives to their long-chain counterparts. However, they also exhibit high environmental mobility [48]. Despite ongoing efforts to identify safer substitutes, identifying alternatives that do not compromise product quality or safety remains a significant challenge (Supplementary Table 2).PFAS, widely used in consumer products and industrial applications, pose significant exposure risks through contaminated drinking water originating from industrial discharges, firefighting foams at military and airport facilities, and waste site leachate [49]. Food packaging materials containing PFAS, including microwave popcorn bags and fast-food wrappers, can leach these chemicals into food. Moreover, PFAS infiltrate the food chain, thereby contaminating fish and seafood. Common items such as non-stick cookware, stain-resistant textiles, water-repellent clothing, and some cosmetics contain PFAS, leading to exposure through direct contact or inhalation of contaminated dust. Although less prevalent, occupational exposure can indirectly affect the families of workers who handle PFAS. Pregnant women and nursing infants are also at risk, as PFAS can transfer through the placenta and breast milk, prompting concerns regarding developmental impacts and long-term health risks [50].Notably, the half-life of PFAS—the duration over which half of the substance leaves the body—can span several years. For instance, the estimated half-life of PFOA in humans is approximately 2.7 years, whereas that of PFOS may be approximately 3.4 years [51]. Thus, PFAS can persist in the body well beyond the cessation of exposure, with longer-chain PFAS and those featuring specific functional groups being eliminated more slowly. PFAS are notable for their resilience against metabolic breakdown, resulting in their prolonged presence and potential accumulation in the human body [51].These substances withstand typical metabolic transformations and remain unchanged, leading to their gradual excretion, primarily through the urine. The elimination rate varies by PFAS variant, individual physiology, and exposure intensity. Key factors influencing excretion rates include PFAS chain length, chemical structure, and personal physiological differences such as age, sex, genetics, and overall health.Blood serum levels serve as the principal biomarkers for PFAS exposure assessments, enabling the detection of diverse PFAS compounds and the evaluation of individual exposures [44]. Long-term monitoring of blood PFAS levels is critical because of their extended half-life and health implications. Additional biomarkers include PFAS detected in the urine, breast milk, and liver tissue, providing a comprehensive overview of an individual's exposure and accumulation.- 2. Impact on obesity in experimental studies
- 2. Impact on obesity in experimental studies
Several in vitro and in vivo studies have provided significant insight into the adipogenic potential of PFAS (Fig. 1). A comprehensive study evaluated the adipogenic potential of 10 different PFAS in murine 3T3-L1 fibroblasts. One study reported that perfluorohexane sulfonate (PFHxS) significantly increased adipogenesis in the absence of rosiglitazone, a known PPARγ agonist, suggesting that PFAS can act through mechanisms independent of PPARγ activation [52]. Further investigations have demonstrated the complex interaction of PFAS with thyroid hormone processes, including competitive binding to thyroid transport proteins and the modulation of clearance enzymes, thereby affecting thyroid hormone signaling in both stimulatory and inhibitory manners [53]. PFAS exposure has also been linked to immune system activation, notably through the AIM2 inflammasome, resulting in increased proinflammatory cytokine production and subsequent tissue inflammation [54]. This suggests that PFAS exposure may foster chronic low-grade inflammation, disrupt metabolic function, and promote fat accumulation. A pivotal study highlighted the role of PFAS in body weight regulation, revealing that individuals with higher initial plasma PFAS levels experienced more significant weight regain postdieting, particularly among women, which is likely attributable to a decelerated decrease in resting metabolic rate, indicating a direct impact of PFAS on energy expenditure regulation [55].- 3. Association with childhood obesity in epidemiological studies
- 3. Association with childhood obesity in epidemiological studies
Longitudinal studies on prenatal exposure to PFAS and their association with childhood obesity are presented in Table 1. Research across Europe, the US, and Asia on prenatal exposure to PFAS and childhood obesity has revealed significant findings. Among 28 reviewed studies, 20 suggested that prenatal PFAS exposure could lead to increased adiposity in children, with varying effects reported across studies. Interestingly, some studies suggested that exposure to legacy PFAS compounds such as PFOS and PFOA might be associated with lower adiposity at birth and during infancy but trigger rapid adiposity accretion in early childhood [56-58]. This pattern suggests a possible link between PFAS exposure and the risk of infants born small for gestational age catching up rapidly, which could predispose them to obesity. Additionally, a sex-specific impact is apparent, with girls exhibiting a higher susceptibility to obesity following PFAS exposure, whereas boys show null or negative associations [59,60].Epidemiological studies, predominantly from Europe and the US, explored the association between postnatal exposure to PFAS and childhood obesity and revealed mixed outcomes (Table 2). Contrary to findings related to prenatal exposure, a significant proportion of these studies (14 of 18) identified negative associations between postnatal PFAS exposure and measures of childhood adiposity such as BMI z scores, WC, and body fat percentage. Importantly, a notable cohort study from the US highlighted that specific PFAS types might influence body composition in children in distinct ways [61]. For instance, higher PFOS and PFHxS levels were linked to decreased subcutaneous fat accumulation, whereas other types, such as perfluorodecanoic acid and perfluorononanoic acid, were associated with increased visceral fat accrual. Visceral fat is of particular concern because of its association with an elevated risk of cardiometabolic disease [62]. These findings imply that postnatal PFAS exposure may not uniformly contribute to obesity but could affect body fat distribution in a way that predisposes children to different health risks, underlining the need for nuanced approaches in assessing the health impacts of PFAS.- Organochlorines
- Organochlorines
- 1. General characteristics
- 1. General characteristics
Organochlorines, synthetic organic compounds characterized by covalently bonded chlorine atoms, have been extensively used in a range of applications including pesticides (e.g., dichlorodiphenyltrichloroethane [DDT] and lindane), solvents (e.g., carbon tetrachloride and trichloroethylene), and plastic production (e.g., PVC) since the early 20th century [63]. Chlorophenols, another important group within this class, have been utilized in wood preservation, disinfection, and as antiseptics owing to their antimicrobial properties [64]. In addition, some chlorophenols are produced during water chlorination or the bleaching of wood pulp with chlorine [64]. The organochlorine pesticide DDT gained prominence in the 1940s because of its ability to control malaria and other insect-borne diseases [63]. However, the environmental persistence and potential health risks associated with organochlorines, including chlorophenols, led to the restriction or outright ban of these compounds across several countries by the late 20th century [63].Organochlorines, known for their lipophilicity, accumulate in fatty tissues because of their resistance to metabolic breakdown [65]. Although they undergo limited metabolism in the liver into more water-soluble metabolites, the process is slow, resulting in their gradual excretion, primarily through the feces and, to a lesser extent, urine [66]. The elimination of these compounds from the body can span years and is influenced by specific organochlorines and individual metabolic differences. For instance, the half-life of DDT and its metabolites in humans can extend to several years, with some studies suggesting a potential duration of 10 years or longer [65]. Conversely, chlorophenols exhibit comparatively shorter persistence, with their half-life in the human body ranging from hours to days depending on factors such as the specific chlorophenol compound, environmental exposure levels, and individual metabolic rates [65].The primary routes of exposure to organochlorines are dietary intake, where they accumulate in the food chain; dermal absorption, particularly in agricultural settings; and inhalation, especially in industrial environments where they are produced or utilized [67].Biomonitoring typically measures the concentrations and metabolites of organochlorine in the blood and adipose tissue (Supplementary Table 3). This approach effectively indicates the extent of chemical accumulation in individuals and underscores the critical need for ongoing surveillance, particularly among at-risk groups, to manage and mitigate associated health risks [67].- 2. Impact on obesity in experimental studies
- 2. Impact on obesity in experimental studies
Evidence from in vitro and in vivo studies suggests a link between organochlorine exposure and obesity, highlighting its potential obesogenic effects (Fig. 1). Organochlorines interfere with thyroid hormone signaling pathways, which are essential for metabolism and energy regulation. In vitro studies have shown that DDT inhibits TSH release and can hinder TSH receptor activity [68]. Additionally, organochlorines facilitate adipogenesis by activating PPARγ in various cell lines, including hepatocytes and 3T3-L1 [69].Exposure to organochlorines results in epigenetic changes that can lead to obesity and metabolic disorders. Studies have shown that organochlorine exposure can result in hypomethylation of the PPARγ promoter and/or histone H3 (H3K27me3), which promotes adipogenesis and contributes to lipid accumulation and obesity [19].Organochlorines reduce the thermogenic capacity of brown adipose tissue by affecting the expression of uncoupling protein-1 and β-adrenergic receptors, key components of the thermogenic machinery in brown adipocytes [18]. Moreover, organochlorines have been implicated in increased oxidative stress and inflammation, thereby contributing to the development of insulin resistance and disruption of glucose metabolism [70].Additionally, organochlorines, including DDT and hexachlorocyclohexane, have been shown to alter the gut microbiota, leading to changes in bile acid metabolism and increased obesity risk in murine models [45].- 3. Association with childhood obesity in epidemiological studies
- 3. Association with childhood obesity in epidemiological studies
Research investigating the impact of prenatal exposure to organochlorines on childhood obesity has been predominantly conducted in Europe and North America (Table 1). Among these studies, a significant number (17 of 22) indicated that prenatal exposure to organochlorines, such as DDT, PCB, and HCB, is linked to various indicators of adiposity in children. These indicators included the BMI z score, fat mass index, trunk fat percentage, and skinfold thickness. Notably, only a small subset of these studies reported no significant association, highlighting the complex nature of the relationship between prenatal organochlorine exposure and childhood obesity.Research is relatively sparse on the effects of postnatal organochlorine exposure on childhood obesity (Table 2). Despite the heterogeneity in the chemicals evaluated across these studies, approximately half suggested that such exposure could contribute to increased childhood adiposity as indicated by metrics such as BMI z score, WC, and body fat percentage. However, the other half found no significant or negative relationships. Nonetheless, a study from the US identified a specific increase in adiposity linked to triclosan exposure, with this effect exclusively observed in overweight girls, suggesting that the effects of organochlorine exposure may be contingent on the child's pre-existing body composition [27]. Furthermore, a study from Korea observed a positive association between urinary chlorophenol levels and central adiposity in 7–8-year-old girls [71], which corroborates the findings of similar cross-sectional studies in Iran [72] and the US [73]. These observations highlight the need for comprehensive research to fully elucidate the relationship between postnatal organochlorine exposure and childhood obesity.
- Polycyclic aromatic hydrocarbons
- Polycyclic aromatic hydrocarbons
- 1. General characteristics
- 1. General characteristics
PAHs are a significant group of organic compounds composed of 2 or more fused aromatic (benzene) rings that are created through incomplete combustion or pyrolysis of organic materials such as coal, oil, gas, wood, garbage, and tobacco [42]. PAHs are ubiquitous in the environment including the air, water, soil, and food. PAHs have been the focal point of environmental health studies for many decades because of their potential carcinogenic and mutagenic properties. In 1983, the United States Environmental Protection Agency identified 16 PAHs as priority pollutants, underscoring concerns regarding their exposure, resilience, and toxicity [43]. These compounds are characterized by low water solubility, low vapor pressure, and high melting and boiling points, which contribute to their stability and persistence in the environment that create long-term ecological and health risks.PAHs are categorized based on their molecular weights into LMW and HMW types [44]. LMW PAHs, which comprise 2 or 3 aromatic rings, are generally more volatile and water-soluble, which enhances their biodegradability. In contrast, HMW PAHs, which comprise 4 or more aromatic rings, tend to persist in the environment and bioaccumulate because of their stability and lipophilicity. Furthermore, PAHs are classified as alternants, which consist of only 6 carbon benzene rings, or nonalternants, which incorporate rings with fewer than 6 carbon atoms. The symmetrical electron distribution of alternant PAHs contributes to their environmental persistence, whereas nonalternant PAHs, with their irregular structures, may display varied chemical reactivities and environmental behaviors (Supplementary Table 4).PAH exposure in humans predominantly occurs through inhalation, ingestion, and dermal contact, all of which are linked to various anthropogenic and natural sources [42]. Anthropogenic activities, including waste incineration; production processes in the iron, steel, aluminum, and cement industries; coal-tar pitch and dye manufacturing; and asphalt and rubber tire manufacturing, significantly contribute to environmental PAH pollution. Although less prominent, natural events such as volcanic eruptions and forest fires also contribute to ambient PAH levels. Inhalation exposure arises from air pollution, encompassing tobacco, wood smoke, and asphalt fumes, and presents a common risk, particularly in urban settings and among smokers [42]. Ingestion is another significant route, with PAHs entering the human diet through the consumption of grilled, smoked, or charred food items, including meat and various plant-based foods prepared over open flames that absorb these compounds during cooking [42]. Additionally, dermal contact with contaminated soil, dust, or direct handling of PAH-containing materials in occupational settings poses risks, facilitating the absorption of PAHs through the skin [46].Upon entering the human body, PAHs undergo metabolism, predominantly in the liver and kidneys, facilitated by cytochrome P450 enzymes [42]. This metabolic process leads to the formation of hydroxy derivatives (OH-PAHs), which are subsequently eliminated via the bile and urine. Moreover, PAHs may accumulate in the adipose tissue and can be secreted in breast milk [74]. The biological half-life of these compounds varies, with 1-hydroxypyrene having a half-life of approximately 3.9 hours, whereas other OH-PAHs have a half-life of 2.5–6.1 hours [75]. In terms of monitoring PAH exposure, 1-hydroxypyrene serves as a commonly used urinary biomarker indicative of PAH exposure a few days before sampling. Other detectable urinary OH-PAHs offer insight into PAH exposure extent and recency (Supplementary Table 4).- 2. Impact on obesity in experimental studies
- 2. Impact on obesity in experimental studies
Several laboratory studies elucidated the biological and molecular pathways through which PAHs contribute to obesity development (Fig. 1). A recent study revealed that PAH exposure leads to the increased proliferation of 3T3-L1 preadipocytes and lipogenesis along with elevated expression levels of genes including Fasn, Acaca, Cebpa, Pparg, Fabp4, Plin1, Rarres2, Adipoq, and Retn, which are crucial for adipocyte differentiation and fat cell development [76]. These changes have been associated with altered methylation of target genes, including PPARγ [19].Benzo [a]pyrene, an extensively studied PAH compound, disrupts the normal process of fat breakdown within adipose tissues by impairing β-adrenergic stimulation, resulting in a notable increase in body weight and fat accumulation in mice [71]. Furthermore, naphthalene metabolites obstruct thyroid hormone receptor-mediated transcription in laboratory settings, potentially decreasing the basal metabolic rate and hindering fat breakdown within adipose tissues [72].Chronic PAH exposure has been linked to increased expression of inflammatory cytokines (e.g., Tnfα, Il-1β, and Il-6) in fat cells and macrophages, indicating an inflammatory response that may further contribute to obesity [73].- 3. Association with childhood obesity from epidemiological studies
- 3. Association with childhood obesity from epidemiological studies
Research into the link between prenatal exposure to PAHs and childhood obesity has predominantly relied on personal air monitoring, although such studies are relatively few (Table 1). Cohort studies in the US demonstrated that prenatal exposure to PAHs is associated with increased measures of adiposity in children, such as higher BMI z scores and body fat percentages, particularly at age 5–7 years [77]. Nevertheless, further analyses of BMI growth patterns suggested that the initial differences in adiposity observed in early childhood tend to decrease by adolescence [78].Research on the association between postnatal exposure to PAHs and childhood obesity primarily used cross-sectional data, predominantly from North America (Table 2). These studies consistently highlighted the potential impact of PAHs on increasing the risk of obesity in children. Notably, NHANES data have shown associations between total urinary levels of PAH metabolites, including naphthalene, and higher instances of overweight and central obesity in the US pediatric population aged 6–19 years [79-81]. Additionally, a national study conducted in Canada observed positive associations between PAH metabolites and central obesity in children aged 3–5 years and 6–18 years, underscoring the pervasive nature of this relationship across different cohorts at younger ages [82]. Remarkably, KoNEHS data identified a significant association between 2-naphthol, a primary naphthalene metabolite, and obesity in Korean children aged 6–11 years, suggesting that regular physical activity might mitigate this effect [42]. These findings underscore the importance of monitoring PAH exposure and advocating preventive measures, including increased physical activity, to protect children against the obesity-related risks associated with environmental pollutants.
- Conclusions
- Conclusions
Research suggests potential associations between early-life exposure to EDCs, including bisphenols, phthalates, and PAHs, and the risk of obesity in children. This body of research, which encompasses both experimental and epidemiological investigations, underscores the adverse impact of EDCs on key biological processes such as hormonal regulation, metabolic function, and adipogenesis, which may collectively predispose children to obesity. Variability in the observed effects has been noted, likely attributable to differences in the concentration of exposures, timing relative to developmental stages, outcome assessment methods, and potential interactive effects of concurrent exposure to multiple EDCs. While the exact mechanisms remain under investigation, these findings highlight the need for further research to understand how EDC mixtures contribute to pediatric obesity and guide public health strategies for reducing EDC exposure.
Supplementary material
Supplementary material
Supplementary Tables 1-4 are available at https://doi.org/10.3345/cep.2024.00556.Supplementary Table 1.
cep-2024-00556-Supplementary-Table-1.pdfBiomonitoring of phthalate exposure: common phthalates and their metabolitesSupplementary Table 2.
cep-2024-00556-Supplementary-Table-2.pdfOverview of widely studied PFAS compoundsSupplementary Table 3.
cep-2024-00556-Supplementary-Table-3.pdfOrganochlorines and their metabolites for biomonitoringSupplementary Table 4.
cep-2024-00556-Supplementary-Table-4.pdfPolycyclic aromatic hydrocarbons representatives and their metabolites
- Footnotes
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Conflicts of interest No potential conflict of interest relevant to this article was reported.
Funding This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Fig. 1.
The potential mechanisms of endocrine-disrupting chemicals (EDCs) in the development of obesity. BPA, bisphenol A; PFAS, per- and polyfluoroalkyl substances; PAHs, polycyclic aromatic hydrocarbons.
Table 1.
Prenatal exposure to EDCs and childhood obesity in epidemiologic studies
| Study | Country | Study design |
Study population |
Outcome | Biomonitoring parameter | Direction of relationship | Findings | ||
|---|---|---|---|---|---|---|---|---|---|
| Sample size (n) | Time of sampling | Age at outcome (yr) | |||||||
| Bisphenols | |||||||||
| Harley (2013) [83] | US | Cohort | 311 | 1st and 2nd trimesters | 9 | BMIZ, WC, OV/OB, BF% | Urine BPA | ▽ | Negative associations with BMIZ, BF%, and odds of OV/OB in girls, not in boys. |
| Valvi (2013) [25] | Spain | Cohort | 348 | 1st and 3rd trimesters | 4 | BMIZ, WC, OV | Urine BPA | ▲ | Positive associations with WC at age 4. |
| Braun (2014) [22] | US | Cohort | 297 | 2nd and 3rd trimesters | 2-5 | BMIZ | Urine BPA | ▲ | Positive associations with BMI increase per year from 2-5 yr. |
| Hoepner (2016) [23] | US | Cohort | 375 | 3rd trimester | 7 | BMIZ, FMI, WC, BF% | Urine BPA | ▲ | Positive associations with FMI, BF%, and WC at age 7. |
| Vafeiadi (2016) [24] | Greece | Cohort | 235 | 1st trimester | 4 | BMIZ, WC, OB, Skinfolds | Urine BPA | ▽ | BPA was negatively associated with BMI and adiposity measures in girls and positively in boys. |
| ▲ | |||||||||
| Yang (2017) [26] | US | Cohort | 223 | 3rd trimester | 8-14 | BMIZ, WC, Skinfolds | Urine BPA | ▲ | Positive associations with sum of skinfolds in girls. |
| Yeung (2019) [27] | US | Cohort | 2920 | At birth | 0-3 | BMIZ | Infants’ dried blood spot BPA | ▲ | Positive associations with rapid infant weight gain and early childhood OB. |
| Braun (2019) [84] | Canada | Cohort | 719 | 1st trimester | 3.5 | BMIZ, WC, WHR, Skinfolds | Urine BPA | ▲ | Positive associations with waist-to-hip ratio at age 3.5. |
| Guo (2020) [85] | China | Cohort | 190 | At delivery | 7 | BMIZ, WC, OB | Urine BPA | ▲ | Positive associations with WC and central OB at age 7. |
| Choi (2020) [86] | Korea | Cohort | 59 | 2nd trimesters | 2-8 | BMI | Urine BPA | ▲ | Methylation at cg19196862 (IGF2R) mediates the association between prenatal exposure and increased BMI at ages 2 and 4. |
| Berger (2021) [28] | US | Cohort | 309 | 1st and 3rd trimesters | 5 | BMIZ, WC, OV | Urine BPA | ↔ | No associations with adiposity at age 5. |
| Güil-Oumrait (2022) [87] | Spain | Cohort | 954 | 1st and 3rd trimesters | 11 | BMIZ | Urine BPA | ↔ | No associations with BMIZ at age 11. |
| Sol (2022) [88] | Netherlands | Cohort | 1128 | 1st, 2nd, and 3rd trimesters | 10 | BMI, FMI | Urine BPA | ↔ | No associations with BMI and FMI at age 10. |
| Xiong (2023) [30] | China | Cohort | 826 | 1st, 2nd, and 3rd trimesters | 2 | BMI trajectories | Urine BPA, BPS, BPF | ▲ | Positive associations with low-increasing BMI patterns in offspring's first 2 years. |
| Phthalates | |||||||||
| Valvi (2015) [89] | Spain | Cohort | 391 | 1st and 3rd trimesters | Birth-7 | BMIZ, WHR, OV | Urine MEP, MiBP, MBzP, MnBP, MEHP, MEHHP, MEOHP, MECPP | ▽ | ∑HMWP was negatively associated with BMIZ at ages 4-7 in boys. |
| ↔ | No associations between LMWP and adiposity. | ||||||||
| Buckley (2016) [90] | US | Cohort | 180 | 3rd trimester | 2-9 | BMIZ, BF% | Urine MEP, MnBP, MiBP, MCPP, MBzP, MEHP, MEHHP, MEOHP, MECPP | ▽ | ∑DEHP was negatively associated with BF% at ages 2-9. |
| ↔ | No associations between LMWP and adiposity. | ||||||||
| Buckley (2016) [91] | US | Cohort | 707 | 2nd and 3rd trimesters | 4-7 | BMIZ, OV/OB | Urine MEP, MnBP, MiBP, MCPP, MEHP, MEHHP, MEOHP, MECPP | ▲ | MCPP was positively associated with OV/OB at ages 4-7. |
| ▽ | MEP was negatively associated with BMIZ in girls. | ||||||||
| Harley (2017) [92] | US | Cohort | 345 | 1st and 2nd trimesters | 5-12 | BMIZ, WC, BF%, OV | Urine MEP, MBP, MiBP, MBzP, MCOP, MCNP, MCPP, MEHP, MEOHP, MEHHP, MECPP | ▲ | MEP, MBzP and ∑DEHP were positively associated with odds for OV at ages 5-12. |
| MEP was positively associated with BMIZ and BF%. | |||||||||
| Shoaff (2017) [93] | US | Cohort | 219 | 2nd trimesters | 8 | BMI, WC, BF% | Urine MEP, MBzP, MCPP, MnBP, MEHHP, MEOHP, MECPP | ↔ | No associations between phthalate metabolites and adiposity. |
| Yang (2017) [26] | US | Cohort | 223 | 3rd trimester | 8-14 | BMIZ, WC, Skinfolds | Urine MEP, MBzP, MEHP, MEHHP, MEOHP, MECPP | ▽ | MBzP was negatively associated with BMIZ. |
| MBHP was negatively associated with WC and skinfolds. | |||||||||
| Vafeiadi (2018) [94] | Greece | Cohort | 500 | 1st trimester | 2-6 | BMIZ, WC, WHR | Urine MEP, MnBP, MiBP, MBzP, MEHP, MEHHP, MEOHP | ↔ | No associations between phthalate metabolites and adiposity. |
| Yang (2018) [95] | Mexico | Cohort | 249 | 3rd trimester | Birth-14 | BMI trajectories | Urine MEP, MiBP, MCPP, MBzP, MEHP, MEHHP, MEOHP, MECPP | ▲ | Highest BMI trajectories for the highest MECPP groups in females. |
| ▽ | Highest BMI trajectories for the lowest MiBP, MBzP, MEHP, MEHHP groups in males. | ||||||||
| Heggeseth (2019) [96] | US | Cohort | 335 | 1st and 2nd trimesters | 2-14 | BMI trajectories | Urine MEP, MnBP, MiBP, MBzP, MCOP, MCPP, MEHP, MEOHP, MEHHP, MECPP | ▲ | MEP was positively associated with BMI level across ages. |
| Bowman (2019) [97] | Mexico | Cohort | 2239 | 1st, 2nd, and 3rd trimesters | 8-17 | BMIZ, WC, skinfolds | Urine MEP, MiBP, MCPP, MBzP, MEHP, MEHHP, MEOHP, MECPP | ▲ | MBP and MiBP were positively associated with BMIZ in girls. |
| ▽ | MBzP was positively associated with BMIZ and WC in boys. | ||||||||
| MBzP was negatively associated with skinfold thickness in girls. | |||||||||
| Lee (2020) [98] | KR | Cohort | 481 | 2nd trimesters | 6 | BMIZ, BF%, SMI | Urine MnBP, MEOHP, MEHHP | ▽ | MEHHP was negatively associated with BMIZ at age 6. |
| Phthalate metabolites were negatively associated with SMI at age 6. | |||||||||
| Berger (2021) [28] | US | Cohort | 309 | 1st and 3rd trimesters | 5 | BMIZ, WC, OV | Urine MEP, MBP, MiBP, MBzP, MCPP, MCOP, MCNP, MEHP, MEHHP, MEOHP, MECPP | ▲ | MEP and MCNP were positively associated with odds of OV at age 5. |
| Li (2021) [99] | China | Cohort | 814 | 1st, 2nd, and 3rd trimesters | Birth-2 | BMIZ | Urine MEHP, MEOHP, MEHHP, MECPP | ▽ | DEHP metabolites were negatively associated with fetal growth, but positively associated with BMI in infancy. |
| ▲ | |||||||||
| Kupsco (2022) [100] | Mexico | Cohort | 514 | 2nd and 3rd trimesters | 4-12 | Adiposity trajectory | ΣDEHP, ΣDiBP, ΣDiNP, ΣDBP, MBzP, MCPP, MECPTP, MCNP, MEP | ▲ | ΣDEHP was associated with increased odds of being in the “high-high” class. |
| ΣDiNP was associated with greater odds of being in the “low-high” class. | |||||||||
| Ferguson (2022) [101] | US | Cohort | 780 | 1st and 3rd trimesters | Birth-6 | BMIZ | Urine MEP, MBP, MiBP, MBzP, MCPP, MCOP, MCNP, MEHP, MEHHP, MEOHP, MECPP | ▽ | MEP, MBP, MBzP, and MiBP were negatively associated with BMIZ at birth and positively associated with BMIZ at ages 3-4. |
| ▲ | |||||||||
| Güil-Oumrait (2022) [87] | Spain | Cohort | 954 | 1st and 3rd trimesters | 11 | BMIZ | Urine MEP, MnBP, MiBP, MBzP, MEHP, MEHHP, MEOHP, MECPP | ↔ | No associations with BMIZ at age 11. |
| Gao (2022) [102] | China | Cohort | 990 | 1st, 2nd, and 3rd trimesters | Birth-6 | BMI trajectories | Urine MMP, MEP, MBP, MBzP, MEHP, MEOHP, MEHHP | ▲ | MEP and DEHP metabolites were positively associated with the highest BMI trajectories in girls. |
| Gao (2023) [103] | China | Cohort | 2950 | 1st, 2nd, and 3rd trimesters | Birth-6 | Adiposity trajectory classes | Urine MMP, MEP, MBP, MEHP, MEOHP, MEHHP | ▲ | MEP and phthalate mixture were positively associated with the rapidly increasing ABSI trajectory class. |
| PFAS | |||||||||
| Andersen (2013) [104] | Denmark | Cohort | 811 | 1st and 2nd trimesters, at delivery | 7 | BMI, WC, OV | Maternal blood PFOS, PFOA | ↔ | No associations with BMI, WC, odds of OV at age 7. |
| Høyer (2015) [105] | Ukraine | Cohort | 1022 | 2nd trimesters | 5-9 | BMI, WHtR | Maternal blood PFOS, PFOA | ▲ | PFOS was positively associated with central OB. |
| Hartman (2017) [106] | UK | Cohort | 359 girls | 1st and 2nd trimesters | 9 | BMI, WC, Fat mass, BF% | Maternal blood PFOS, PFOA, PFHxS, PFNA | ▲ | PFOS and PFOA was positively associated with BF% in girls at age 9. |
| Karlsen (2017) [107] | Denmark | Cohort | 444 | Postpartum 2 weeks | 1.5-5 | BMI | Maternal blood PFOS, PFOA, PFHxS, PFNA, PFDA | ▲ | PFOS and PFOA were associated with increased BMI z scores and/or odds of OV. |
| Chen (2017) [57] | Taiwan | Cohort | 429 | At delivery | 0-9 | BMI | Maternal blood PFOS, PFOA | ▽ | PFOS was associated with smaller body size at birth. |
| ▲ | PFOS was associated with decreased BMI from 6 months to age 5, and then to increased BMI from ages 5-9. | ||||||||
| Manzano-Salgado (2017) [108] | Spain | Cohort | 1154 | 1st trimester | Birth-7 | BMIZ, OV | Maternal blood PFOS, PFOA, PFNA, PFHxS | ↔ | No associated with BMIZ and odds of OV at ages 4-7. |
| Mora (2017) [59] | US | Cohort | 1645 | 1st trimesters | 3-7 | BMI, WC, fat mass, BF% | Maternal blood PFOA, PFOS, PFHxS, PFNA | ▲ | PFASs was associated with increases in adiposity in mid-childhood (mean 7.7 yr) only in girls. |
| ↔ | Null association in boys. | ||||||||
| Lauritzen (2018) [109] | Norway and Sweden | Cohort | 412 | 2nd trimesters | 5 | BMI, Fat mass, BF% | Maternal blood PFOS, PFOA | ▲ | PFOS was positively associated with BMIZ at age 5. |
| Shoaff (2018) [110] | US | Cohort | 334 | various | 0-2 | BMI, WC, BF% | Maternal blood PFOS, PFOA, PFHxS, PFNA | ▽ | PFOA was linked to reduced BMI z score changes from 4 weeks to age 2. |
| Gyllenhammar (2018) [111] | Sweden | Cohort | 182–193 | 3rd trimester, postpartum 3 weeks | Birth-5 | BMI | Maternal blood PFOA, PFNA, PFDA, PFUnDA, PFHxS, PFOS | ▽ | PFNA, PFDA and PFUnDA were associated with reduced birth weight. |
| ▲ | PFOA, PFNA, PFHxS, and PFOS were associated with increased childhood BMI. | ||||||||
| Yeung (2019) [27] | US | Cohort | 2920 | At birth | 0-3 | BMIZ | Infants’ dried blood spot PFOA, PFOS | ▽ | PFOS and PFOA was associated with lower BMI in early childhood. |
| Chen (2019) [112] | China | Cohort | 404 | At birth | 5 | BMI, WC, fat mass, BF%, WHtR | Cord blood | ▲ | PFBS was positively associated with adiposity in girls. |
| PFOS, PFOA, PFNA, PFDA, PFUA, PFDoA, PFHxS, PFBS | ↔ | No associations in boys. | |||||||
| Martinsson (2020) [113] | Sweden | Cohort | 1048 | 2nd trimesters | 4 | BMI, OV | Maternal blood PFOS, PFOA, PFHxS, PFNA | ↔ | No associations with BMIZ and OV at age 4. |
| Vrijheid (2020) [114] | Europe | Cohort | 1301 | various | 6-11 | BMIZ, WCZ, Skinfolds, Fat mass | Maternal blood PFOS, PFOA, PFHxS, PFNA, PFUnDA | ↔ | No associations with adiposity and odds of OV at ages 6-11. |
| Li (2021) [115] | US | Cohort | 221 | various | 12 | WC, Fat mass | Maternal blood PFOS, PFHxS, PFOA, PFNA | ▲ | PFOA and PFHxS were positively associated with WC at age 12. |
| Horikoshi (2021) [58] | Japan | Cohort | 597 | At birth | 1-5 | BMI trajectories | Cord blood | ▽ | PFOS and PFOA was associated with lower BMI during infancy, then associated with an increase in BMI in childhood. |
| PFOS, PFOA | ▲ | ||||||||
| Papadopoulou (2021) [116] | UK, France, Spain, Lithuania, Norway, Greece | Cohort | 1101 | 2nd and 3rd trimesters | 6-12 | WC | Maternal blood PFOA, PFNA, PFHxS, PFOS | ▲ | -PFNA was positively associated with WC. |
| Braun (2021) [56] | US | Cohort | 345 | various | 0-12 | BMI trajectories | Maternal blood PFOA, PFOS, PFNA, PFHxS | ▽ | PFOA was associated with specific BMI trajectory pattern: lower infancy BMI, earlier BMI nadir, accelerated mid-childhood BMI gain, and higher BMI by age 12. |
| ▲ | |||||||||
| Zhang (2022) [60] | China | Cohort | 206 | At delivery | 7 | BMI, Fat mass, BF%, WC, WHtR, OV | Maternal blood PFOA, PFOS, PFNA, PFDA, PFUA, PFHxS, PFDoA, PFBS, PFOSA, PFHpA | ▽ | PFHpA and PFOSA was negatively associated with adiposity. |
| ▽ | PFAS mixture was negatively associated with adiposity in boys. | ||||||||
| ▲ | PFAS mixture was positively associated with adiposity in girls. | ||||||||
| Bloom (2022) [117] | US | Cohort | 803 | 1st trimester | 4-8 | BMI, WC, Fat mass, BF% | Maternal blood PFHxS, PFOS, PFOSA, PFDS, PFOSA, PFDS, PFHpA, PFOA, PFNA, PFDA, PFUnDA, PFDoDA | ▲ | PFUnDA was positively associated with adiposity (fat mass and BF%) at age 4-8. |
| ▲ | Higher PFASs were associated with higher adiposity in NHB and Asian/ Pacific islanders | ||||||||
| ▽ | Higher PFASs were associated with lower adiposity in NHW and Hispanics. | ||||||||
| Sevelsted (2022) [118] | Denmark | Cohort | 675 | 2nd trimesters, postpartum week 1 | 6-10 | BMI, BF% | Maternal blood PFOS, PFOA | ▽ | PFOS was linked to lower BMI and BF% in girls but higher BMI and BF% in boys at age 6. |
| ▲ | |||||||||
| Cai (2023) [119] | Belgium | Cohort | 346 | At birth | Birth-8 | BMIZ | Cord blood | ↔ | No associations with BMI trajectories during infancy and childhood. |
| BMI trajectories | PFOS, PFOA | ||||||||
| Zhang (2023) [120] | US | Cohort | 545 | 1st and 2nd trimesters | 16-20 | BMIZ, OB, BMI trajectories | Maternal blood PFOS, PFOA, PFHxS, PFNA, EtFOSAA, MeFOSAA | ▲ | PFOS and PFNA were associated with higher OB risk at aged 16-20. |
| PFOS, EtFOSAA, and MeFOSAA were associated with higher rates of BMI gain after ages 9-11. | |||||||||
| Dai (2023) [121] | China | Cohort | 887 | At birth | Birth-10 | BMI trajectories | Cord blood | ▲ | Five PFAS congeners (PFBA, PFHpA, PFHxS, PFHpS, and PFDoDA) were associated with the high BMI trajectory group. |
| PFOS, PFOA, PFNA, PFDA, PFUnDA, PFDoA, PFDS, PFOSA, PFBS, PFHxS, PFHpA, PFHpS, | |||||||||
| Cano-Sancho (2023) [122] | Spain | Cohort | 1241 | 1st trimester | 7 | BMIZ, OV | Maternal blood | ▲ | PFNA was associated with increased risk of OV at age 7. |
| PFOS, PFOA, PFHxS, PFNA | |||||||||
| Starling (2024) [123] | US | Cohort | 373 | 2nd and 3rd trimesters | 5 | BF%, Fat mass, FMI, BMI | Maternal blood | ▲ | PFOA and PFNA was positively associated with early childhood adiposity. |
| PFOS, PFOA, PFHxS, PFNA, PFDA | No associations between PFAS mixtures and adiposity. | ||||||||
| Sun (2024) [124] | China | Cohort | 573 | 1st and 2nd trimesters | 4-6 | BMIZ, WC, OV | Maternal blood | ▲ | PFNA was positively associated with adiposity at ages 4-6. |
| PFOS, PFOA, PFNA, PFHxS, PFDA, PFUdA, PFTrDA, PFDoA | PFOS, PFNA, PFUdA, PFTrDA were associated with risk of OV in age 6. | ||||||||
| Chen (2024) [125] | Singapore | Cohort | 783 | At birth | Birth-6 | BMIZ, Fat mass, MRI-derived abdominal adiposity, OV | Cord blood | ↔ | Short-chain PFAS was associated with higher abdominal adiposity at birth but not at age 6 years. |
| PFOS, PFOA, PFNA, PFDA, PFUnDA, PFDoA, PFDS, PFOSA, PFBS, PFHxS, PFHpA, PFHpS, | |||||||||
| Organochlorine | |||||||||
| Gladen (2000) [126] | US | Cohort | 594 females | various | 14 | Weight adjusted for height | Maternal blood, cord blood, placenta DDE, PCB | ▲ | PCB was associated with higher weight for height in girls. |
| DDE was associated with higher weight for height in boys. | |||||||||
| Gladen (2004) [127] | US | Cohort | 304 males | 3rd trimester | 10-20 | BMI, skinfolds, central OB | Maternal blood o,p′-DDT, p,p′-DDT, p,p′-DDE | ↔ | No associations with adiposity in male adolescents. |
| Smink (2008) [128] | Spain | Cohort | 482 | At birth | 6.5 | BMI, OV, OB | Cord blood | ▲ | HCB was associated with an increase in BMI and risk of OV/OB. |
| HCB | |||||||||
| Cupul-Uicab (2013) [129] | US | Cohort | 1915 | 3rd trimester | 7 | BMIZ, OV | Maternal blood p,p′-DDT, p,p′-DDE, β-HCH, HCB, ∑PCB | ↔ | No associations with adiposity at age 7. |
| Valvi (2012) [130] | Spain | Cohort | 344 | 1st trimester | 6.5 | OV | ∑PCB, PCB-118, PCB-138, PCB-153, PCB180, DDE, DDT | ▲ | PCB and DDE was associated with increased risk of OV. |
| DDT was associated to OV in girls only. | |||||||||
| Delvaux (2014) [131] | Belgium | Cohort | 114 | At birth | 7-9 | BMIZ, WC, WHtR | Cord blood | ▲ | p,p'-DDE was associated with higher WC and WHtR in girls. |
| p,p′-DDE, HCB, ∑PCB | |||||||||
| Dallaire (2014) [132] | Canada | Cohort | 290 | At birth | 8-14 | BMIZ | Cord blood | ▲ | PCB-153 was associated with increased child BMI. |
| PCB-153 | |||||||||
| Tang-Péronard (2014) [133] | Denmark | Cohort | 539 | 3rd trimester | 5-7 | BMI, WC | Maternal blood DDE, ∑PCB | ▲ | PCB was associated with WC in 7-year-old girls. |
| DDE was associated with WC in 7-year-old girls only with overweight mothers. | |||||||||
| Vafeiadi (2015) [181] | Greece | Cohort | 531-689 | 1st trimester | 0.5-4 | BMIZ, central OB, skin folds | p,p′-DDT, p,p′-DDE, p,p′-DDD, ∑DDTs, α-HCH, β-HCH, γ-HCH, ∑HCHs | ▲ | HCB was associated with a higher BMIZ, central OB, and greater sum of skinfold thickness. |
| DDE exposure was associated with higher BMIZ and central OB. | |||||||||
| Agay-Shay (2015) [134] | Spain | Cohort | 470 | 1st trimester | 7 | BMIZ,OV | Maternal blood | ▲ | HCB, βHCH, PCB-138 and 180 were associated with increased BMI z scores. |
| DDE, HCB, βHCH, PCB-138, PCB-153, PCB-180 | HCB, βHCH, PCB-138, and DDE were associated with higher OV risk. | ||||||||
| Heggeseth (2015) [135] | US | Cohort | 249 | 3rd trimester and at birth | 2-7 | BMI trajectories | Maternal blood o,p′-DDT, p,p′-DDT, p,p′-DDE | ▲ | DDT and DDE are associated with a BMI growth pattern that is stable until age 5, then to increased growth up to age 9 in boys. |
| Warner (2017) [136] | US | Cohort | 240 | 2nd trimester and at delivery | 12 | BMIZ, WC, BF% | Maternal blood | ▲ | DDE and DDT were positively associated with BMIZ and WC at age 12 in boys. |
| o,p′-DDT, p,p′-DDT, p,p′-DDE | |||||||||
| Karlsen (2017) [107] | Denmark | Cohort | 444 | Postpartum 2 weeks | 1.5-5 | BMI | Maternal blood | ▲ | HCB was associated with increased BMI z scores and/or OV risk. |
| HCB, p,p’-DDE, PCB-138, PCB-153, PCB-180 | ↔ | No associations for PCBs, p,p’-DDE. | |||||||
| Lauritzen (2018) [109] | Norway and Sweden | Cohort | 412 | 2nd trimester | 5 | BMI, Fat mass, BF%, OV | Maternal blood | ▲ | PCB 153 was positively associated with BMIZ at age 5. |
| PCB 153, p,p’-DDE, HCB, β-HCH, t-NC | |||||||||
| Kalloo (2018) [137] | US | Cohort | 218 | 2nd trimester | 8 | BMIZ, WC, BF% | Maternal urine | ↔ | No associations with adiposity at the age of 8. |
| Triclosan | |||||||||
| Wang (2019) [138] | UK | Cohort | 339 girls | 1st and 2nd trimesters | 9 | BMI, BF%, FMI, Trunk Fat % | Maternal blood | ↔ | No associations with adiposity at age 9 in girls. |
| PCB-118, PCB-138, PCB-153, PCB-180 | |||||||||
| Tahir (2020) [139] | Canada | Cohort | 212 | At birth | 11 | BMI, FMI | Cord blood | ▲ | In girls, PCB 153 was associated with BMI and FMI. |
| PCB 153 | |||||||||
| Vrijheid (2020) [114] | Europe | Cohort | 1301 | various | 6-11 | BMIZ, WCZ, Skinfolds, Fat mass | Maternal blood | ▽ | DDE, HCB, and sum of PCBs were associated with lower BMI. |
| DDE, HCB, PCBs (118, 138, 153, 170, 180) | |||||||||
| Güil-Oumrait (2021) [140] | Spain | Cohort | 379 | At birth | 4-18 | BMIZ, WC, BF %, OV | Cord blood | ▲ | HCB was associated with higher BMIZ and elevated BF% at age 14. |
| p,p’-DDE, HCB, PCB-138, PCB-150, PCB-180 | DDT was associated with BMIZ. | ||||||||
| Berger (2021) [28] | US | Cohort | 309 | 1st and 3rd trimesters | 5 | BMIZ, WC, OV | Maternal blood | ↔ | No associations with adiposity at age 5. |
| Triclosan, 2,4 DCP, 2,5-DCP | |||||||||
| Cai (2023) [119] | Belgium | Cohort | 346 | At birth | Birth-8 | BMIZ, BMI trajectories | Cord blood | ▲ | PCB-153 was associated with ΔBMIZ from 0 to 2 years. |
| p,p’-DDE, HCB, PCB-138, PCB-150, PCB-180 | ↔ | No associations with BMI trajectories during childhood. | |||||||
| Rouxel (2023) [141] | France and Spain | Cohort | 1667 | 1st trimester | 12 | BMIZ, BF% | Maternal blood PCB 138, 153 and 180, p,p'-DDE, β-HCH, HCB | ▲ | HCB and β-HCH were associated with higher BMIZ and BF% at age 12 |
| Cano-Sancho (2023) [122] | Spain | Cohort | 1241 | 1st trimester | 7 | BMIZ, OV | Maternal blood | ▲ | HCB was positively associated with BMIZ and risk of OV. |
| DDE, HCB, βHCH, PCB-138, PCB-153, PCB-180 | βHCH was positively associated with risk of OV. | ||||||||
| PAHs | |||||||||
| Rundle (2012) [77] | US | Cohort | 702 | 3rd trimester | 5-7 | BMIZ, BF%, OB | Personal air monitoring; Sum of 8 HMW PAHs | ▲ | PAH exposures was associated with higher BMIZ and obesity at age 5-7. |
| Rundle (2019) [78] | US | Cohort | 505 | 3rd trimester | 5-14 | BMIZ trajectories | Personal air monitoring; Sum of 8 HMW PAHs | ▲ | PAH exposures were associated with higher BMIZ and obesity at age 5-7. |
| With age, the differences in BMIZ tends to be diminished. | |||||||||
| Kehm (2021) [142] | US | Cohort | 196 girls | 3rd trimester | 11-20 | BMIZ, WHtR, BF% | Personal air monitoring; Sum of 8 HMW PAHs | ↔ | No associations with adiposity at age 11-20. |
Direction of associations with adiposity: ▲, positive; ▽, negative; ↔, null.
Σ2-,3-OH-Flu, Σ2-,3-hydroxyfluorene; Σ2-,3-OH-Phe, Σ2-,3-hydroxyphenanthrene; 1-OH-Phe, 1-hydroxyphenanthrene; 1-OH-Pyr, 1-hydroxypyrene; 4-OH-Phe, 4-hydroxyphenanthrene; BF%, body fat percentage; BMIZ, body mass index z score; BPA, bisphenol A; DDE, dichlorodiphenyldichloroethane; EDCs, endocrine-disrupting chemicals; EtFOSAA, 2-(N-ethyl-perfluorooctane sulfonamide) acetate; FMI, fat mass index; HMW, high molecular weight; KR, Republic of Korea; LMW, low molecular weight; MBP, mono-n-butyl phthalate; MBZP, monobenzyl phthalate; MCINP, monocarboxyisonyl phthalate; MCIOP, mono-(4-methyl-7carboxyheptyl) phthalate; MCMHP, mono-[2-(carboxymethyl)hexyl] phthalate; MCNP, Mono(carboxy-isononyl) phthalate; MECPP mono-(2-ethyl-5-carboxypentyl) phthalate; MECPTP, Mono-2-ethyl-5-carboxypentyl terephthalate; MeFOSAA, 2-(N-methyl-perfluorooctane sulfonamide) acetate; MEHP, mono(2-ethylhexyl) phthalate; MEHHP, mono-(2-ethyl-5-hydroxylhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyhl) phthalate; MEP, monoethyl phthalate; MHINP, mono(4-methyl-7-hydroxyoctyl) phthalate; MOINCH, mono-oxo isononyl oxy carbonyl cyclohexane carboxylic acid; MOINP, mono-(4-methyl-7-oxo-octyl) phthalate; MPHHP, 6-hydroxy-monopropylheptyl phthalate; NHB, Non-Hispanic Blacks; NHW, Non-Hispanic Whites; OB, obesity; OV, overweight; PAHs, polycyclic aromatic hydrocarbons; PCB, polychlorinated biphenyl; PFAS, per- and polyfluoroalkyl substances; PFDA, perfluorodecanoic acid; PFDoA, perfluorododecanoic acid; PFHxS, perfluorohexane sulfonate; PFNA, perfluorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonate; PFTrDA, perfluorotridecanoic acid PFUdA, perfluoroundecanoic acid; SMI, skeletal muscle index; t-NC, trans-nonachlor; WC, waist circumference; WHtR, waist-to-height ratio.
Table 2.
Postnatal exposure to EDCs and childhood obesity in epidemiologic studies
| Study | Country | Study design |
Study population |
Outcome | Biomonitoring parameter | Direction of relationship | Findings | ||
|---|---|---|---|---|---|---|---|---|---|
| Sample size (n) | Time of sampling | Age at outcome (yr) | |||||||
| Bisphenols | |||||||||
| Trasande (2012) [143] | US | Cross-sectional | 2,338 | 6-19 | 6-19 | BMIZ, OB | Urine BPA | ▲ | Positive associations with BMIZ and odds of OB. |
| Wang (2012) [144] | China | Cross-sectional | 259 | 8-15 | 8-15 | BMI | Urine BPA | ▲ | Positive associations with BMIZ in girls aged 8-11 yr. |
| Eng (2013) [145] | US | Cross-sectional | 3,370 | 6-18 | 6-18 | OB, central OB | Urine BPA | ▲ | Positive associations with OB and central OB. |
| Li (2013) [146] | China | Cross-sectional | 1,326 | 9-18 | 9-18 | OV, central OB | Urine BPA | ▲ | Positive associations with OV and central OB in girls aged 9-12 yr. |
| Harley (2013) [83] | US | Cross-sectional | 311 | 5, 9 | 5, 9 | BMIZ, WC, OV, BF% | Urine BPA | ↔ | No associations with adiposity at age 5. |
| ▲ | Positive associations with adiposity at age 9. | ||||||||
| Vafeiadi (2016) [24] | Greece | Cross-sectional | 494 | 4 | 4 | BMIZ, WC, OB, Skinfolds | Urine BPA | ▲ | Positive associations with BMIZ, WC, and sum of skinfolds at age 4. |
| Li (2017) [147] | US | Cross-sectional | 1,860 | 8-19 | 8-19 | FMI, BF% | Urine BPA | ▲ | Positive associations with elevated FMI and BF% in girls. |
| Deierlein (2017) [31] | US | Cohort | 1,017 | 6-8 | 7-15 | BMI, WC, BF% | Urine BPA | ↔ | No associations with adiposity at ages 7-15. |
| Yang (2017) [26] | Mexico | Cross-sectional | 249 | 8-14 | 8-14 | BMI, WC, BF% | Urine BPA | ▲ | Positive associations with BMI z score only in girls. |
| Okubo (2019) [33] | US | Cross-sectional | 5,419 | 6-19 | 6-19 | BMIZ, OB | Urine BPA | ▲ | Children with higher urinary BPA concentrations had elevated odds of obesit y during 2003 to 2008, whereas these associations were inconsistent during 2009 to 2014. |
| ↔ | |||||||||
| Mustieles (2019) [148] | Spain | Cross-sectional | 298 Boys | 9-11 | 9-11 | BMIZ, WHtR, Fat mass, OV, central OB | Urine BPA | ▲ | BPA was associated with increased BMIZ and WHtR, as well as greater odds of OV and central OB. |
| Wu (2020) [149] | US | Cross-sectional | 2,372 | 6-19 | 6-19 | BMIZ, OB | Urine BPA | ▲ | In the WQS regression model for OB, the WQS index significantly correlated with the outcome. 2,5-DCP (0.41), BPA (0.17), and MEP (0.14) were notably weighted. |
| Gajjar (2022) [32] | US | Cohort | 212 | 8 | 12 | BMIZ, WC, BF% | Urine BPA, BPS | ↔ | No associations with adiposity at age 12. |
| Seo (2022) [34] | KR | Cross-sectional | 2,351 | 3-17 | 3-17 | BMIZ, OB | Urine BPA | ↔ | No associations with adiposity at ages 3-17. |
| Deodati (2023) [150] | Europe | Cross-sectional | 122 | 5-10 | 5-10 | BMI, OB | Urine BPA | ▲ | Positive association with OB in girls. |
| Phthalate | |||||||||
| Teitelbaum (2012) [151] | US | Cohort | 387 Hispanic and Black | 6-8 | After 1 year | BMI, WC | Urine MBP, MEP, MiBP, MCPP, MECPP, MEHHP, MEOHP, MEHP, MBzP | ▲ | Baseline MEP and ΣLMW phthalates were associated with an increase in adiposity over 1 year follow up. |
| Trasande (2013) [152] | US | Cross-sectional | 2,884 | 6-19 | 6-19 | BMIZ, OV, OB | Urine MBP, MEP, MiBP, MCPP, MECPP, MEHHP, MEOHP, MEHP, MBzP | ▲ | ΣLMW phthalates was associated with higher BMIZ and odds for OV and OB in NHB. |
| Wang (2013) [153] | China | Cross-sectional | 259 | 8-15 | 8-15 | BMI, WC | Urine MEHP, MEHHP, MEOHP, MCMHP, MECPP, MCHP, MBzP, MBP, MHBP, MEP, MiBP, MMP | ▲ | MEP and MEHP were positively associated with BMI and WC. |
| Zhang (2014) [154] | China | Cross-sectional | 493 | 8-13 | 8-13 | BMIZ, WC, BF%, skinfolds, OB | Urine MEHP, MEHHP, MEOHP, MBP, MMP, MEP | ▲ | MEP and ΣLMW phthalates were associated with higher odds for OB in boys. |
| ▽ | MEHP, MEHHP and ΣMEHP were associated with lower odds for OB in girls. | ||||||||
| Hou (2015) [155] | Taiwan | Cross-sectional | 270 | 6-15 | 6-15 | BMI, WC, WHtR, skinfolds | Urine MMP, MEP, MiBP, MnBP, MBzP, MEHP, MEOHP, MEHHP, MECPP | ▲ | MEP and DEHP metabolites were associated with adiposity. |
| Yang (2017) [26] | Mexico | Cross-sectional | 249 | 8-14 | 8-14 | BMI, WC, BF% | Urine MEHP, MEHHP, MEOHP, MECPP, MCPP, MBzP, MEP, MiBP, MBP | ▽ | MEHP was inversely associated with sum of skinfold thickness in boys. |
| Kim (2018) [156] | KR | Cross-sectional | 137 girls | 6-13 | 6-13 | BMIZ, WC, BF%, central OB | Urine MEHP, MEHHP, MEOHP, MECPP | ↔ | Percentage fraction of MEHHP among the DEHP metabolites was positively associated with central OB in prepubertal girls. |
| Amin (2018) [157] | Iran | Cross-sectional | 242 | 6-18 | 6-18 | BMI, WC | Urine MMP, MBP, MBzP, MEHP, MEHHP, MEOHP | ▲ | All of the measured phthalate metabolites were associated with higher BMI and WC. |
| Wu (2020) [149] | US | Cross-sectional | 2,372 | 6-19 | 6-19 | BMIZ, OB | Urine MBzP, MEP, MiBP | ▲ | In the WQS regression model for OB, the WQS index significantly correlated with the outcome. 2,5-DCP (0.41), BPA (0.17), and MEP (0.14) were notably weighted. |
| Dong (2022) [158] | China | Cross-sectional | 829 | 7-13 | 7-13 | OV | Urine MMP, MEP, MBP, MiBP, MEHP, MEOHP, MEHHP | ▲ | ΣPhthalates, MEP, MBP, MiBP, MEHP, and MEHHP were associated with increased odds for OV. |
| Seo (2022) [34] | KR | Cross-sectional | 2,351 | 3-17 | 3-17 | BMIZ, OB | Urine MnBP, MBzP, MCOP, MCNP, MCPP, MEHHP, MEOHP, MECPP | ▲ | MECPP was associated with higher odds for OB. |
| Wang (2023) [159] | China | Cross-sectional | 798 | 7-10 | 7-10 | WC, central OB | Urine MMP, MEP, MBP, MiBP, MEHP, MEOHP, MEHHP | ▲ | MEP and MiBP were associated with higher odds for central OB. |
| Li (2023) [160] | China | Cross-sectional | 240 | 6-8 | 6-8 | BMI, OB | Urine MMP, EP, MnBP, MiBP, MEHP, MEOHP, MEHHP, MCEPP, MCMHP | ↔ | No associations with OB at ages 6-8. |
| Deodati (2023) [150] | Europe | Cross-sectional | 122 | 5-10 | 5-10 | BMI, OB | Urine MEHP, MEOHP, MEHHP | ↔ | The first step of DEHP metabolic rate was significantly higher in obese girls. |
| PFAS | |||||||||
| Domazet (2016) [161] | Denmark | Cohort | 501 | 9-15 | 15-21 | BMI, WC, BF%, OV, skinfolds | Urine PFOS, PFOA | ▲ | PFOS was associated with elevated BMI, skinfolds, WC at ages 15-21. |
| Karsen (2017) [107] | Denmark | Cross-sectional | 444 | 5 | 5 | BMI | Urine PFOS, PFOA, PFHxS, PFNA, PFDA | ▽ | PFNA and PFDA were negatively associated with BMI at age 5. |
| Harris (2017) [162] | US | Cross-sectional | 653 | 6-10 | 6-10 | BMI | Blood PFOS, PFOA, PFHxS, Me-PFOSA-AcOH | ▽ | PFOA and PFDA were related with lower BMI. |
| Pinney (2019) [163] | US | Cohort | 704 girls | 6-8 | 18 | BMI, WHtR | Blood PFOA | ▽ | Baseline PFOA exposure was negatively associated with BMI and WHtR at age 18. |
| Fassler (2019) [164] | US | Cross-sectional | 353 girls | 6-8 | 6-8 | BMI, WHtR, BF% | Blood PFOA, PFOS, PFDA | ▽ | PFOA was negatively associated with BMIZ and BF%. |
| Scinicariello (2020) [165] | US | Cross-sectional | 600 | 3-11 | 3-11 | BMI | Blood PFOA, PFNA, PFHxS, PFOS | ▽ | In boys, PFHxS was associated with decreased BMIZ. |
| Vrijheid (2020) [114] | Europe | Cross-sectional | 1,301 | 6-11 | 6-11 | BMIZ, WCZ, skinfolds, fat mass | Blood PFOS, PFOA, PFHxS, PFNA, PFUnDA | ▽ | PFOA was negatively associated with BMIZ. |
| Domazet (2020) [166] | Denmark | Cross-sectional | 242 | 9 | 9 | BMI, BF%, fat mass | Blood PFOS, PFOA, PFNA, PFDA, PFHxS | ▽ | PFNA, PFDA, and PFHxS were associated with decreased fat mass at age 9. |
| Li (2021) [167] | China | Cross-sectional | 189 | 8-12 | 8-12 | BMI, OV | Blood PFHxA, PFHpA, PFBS, PFOA, PFBS, PFHxS, PFOS, PFHpA | ▽ | Obese/overweight children had lower levels of PFHpA, PFBS, and PFOS. |
| Papadopoulou (2021) [116] | UK, France, Spain, Lithu ania, Norway, Greece | Cross-sectional | 1,101 | 6-12 | 6-12 | WC | Blood PFOA, PFNA, PFHxS, PFOS, PFUnDA | ▽ | PFAS mixture was negatively associated with WC. |
| Geiger (2021) [168] | US | Cross-sectional | 2,473 | 12-18 | 12-18 | BMI, WC, OV | Blood PFOA, PFOS | ▲ | PFOA was associated with higher risk of OV. |
| Averina (2021) [169] | Norway | Cross-sectional | 940 | 15-19 | 15-19 | OB | Blood ΣPFAS, PFOS, PFOA, PFNA, PFDA, PFUnDA, PFHpS | ▲ | PFHxS and PFHpS were positively associated with OB. |
| Canova (2021) [170] | Italy | Cross-sectional | 6,669 | 8-11, 4-19 | 8-11, 4-19 | BMI | Blood PFOA, PFOS, PFNA | ▽ | PFAS mixture was negatively associated with BMI. |
| 2,693 | |||||||||
| Janis (2021) [61] | US | Cohort | 537 | 6-10 | 6-10, 11-16 | BMIZ, total FMI, truncal FMI, LMI | Blood PFOA, PFOS, PFDA, PFHxS, MeFOSAA, PFNA | ▽ | PFOS and PFHxS were related with less accrual of SQ fat mass. |
| ▲ | PFDA and PFNA were related with greater accrual of truncal fat mass. | ||||||||
| Li (2021) [115] | US | Cohort | 221 | 3, 8, 12 | 12 | WC, fat mass | Blood PFOS, PFHxS, PFOA, PFNA | ↔ | No associations with adiposity at age 12. |
| Thomsen (2021) [171] | Denmark | Cross-sectional | 109 | 10-14 | 10-14 | BF% | Blood PFOA, PFOS, PFDA, PFHxS | ▽ | PFOS and PFDA were related with less BF%. |
| Sevelsted (2022) [118] | Denmark | Cohort | 533 | 6-18 months | 6-10 | BMI, BF% | Blood PFOS, PFOA | ↔ | No associations with adiposity at age 6-10. |
| Schillemans (2023) [172] | Europe | Cross-sectional | 1,957 | 12-18 | 12-18 | BMIZ | Blood PFPeA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFUnDA, PFDoDA, PFBS, PFHxS, PFHpS, PFOS | ▽ | Most PFASs were negatively associated with BMI. |
| Organochlorine | |||||||||
| Dhooge (2010) [173] | Belgium | Cross-sectional | 1,679 | 14-15 | 14-15 | BMI | Blood PCB-118, 138, 153, 180, HCB, p,p-DDE, | ▽ | HCB and ΣPCBs were negatively associated with BMI. |
| ▲ | PCB-118 was positively associated with BMI. | ||||||||
| Buser (2014) [174] | US | Cross-sectional | 1,298 | 6-19 | 6-19 | BMIZ, WC, OB | Urine 2,4-DCP, 2,5-DCP, Triclosan | ▲ | 2,4-DCP and 2,5-DCP were positively associated with BMIZ, WC, and odds of OB among adolescents aged 12-19. |
| Li (2015) [175] | US | Cross-sectional | 2,898 | 6-19 | 6-19 | BMI, WC | Urine Triclosan | ▽ | Negatively associated with BMI and WC. |
| Tang-Péronard (2015) [176] | Denmark | Cohort | 509 | 8-10 | 8-10, 14-16, 20-22 | BMIZ, BF%, WC, OB | Urine PCBs-138, 153, 180, p,p-DDE, HCB | ↔ | No associations with subsequent adiposity after exposure. |
| Xue (2015) [177] | India | Cross-sectional | 103 | 2-14 | 2-14 | OB | Urine Triclosan | ↔ | No associations with OB. |
| Lee (2016) [178] | KR | Cohort | 158 | 7-9 | After 1 yr | BMIZ | Blood PCB (1, 3, 4, 15, 19, 28, 37, 77, 81, 104, 105, 114, 123, 126, 155, 157, 167, 169, 188, 189, 202, 205, 206, 208), ox ychlordane, chlordane, heptachlor, heptachlor epoxide, a-HCH, g-HCH, d-HCH, o, p’-DDT, p,p’-DDD, o,p’-DDD, o,p’-DDE | ↔ | No associations with ΔBMI after 1 year. |
| Deierlein (2017) [31] | US | Cohort | 1,017 | 6-8 | 7-15 | BMI, WC, BF% | Urine Enterolactone, 2,5-DCP, Triclosan | ▲ | Triclosan was positively associated with adiposity only among overweight girls. |
| Karlsen (2017) [107] | Denmark | Cross-sectional | 349 | 5 | 5 | BMIZ, WC | Serum PCB 138, 153, 180, HCB, p,p’-DDE | ▽ | Negatively associated with BMI and WC. |
| Parastar (2018) [179] | Iran | Cross-sectional | 242 | 6-18 | 6-18 | BMIZ, WC | Urine 2,4-DCP, 2,5-DCP, 2,4,5-TCP, 2,4,6-TCP | ▲ | Positively associated with adiposity at ages 6-18. |
| Kalloo (2018) [137] | US | Cohort | 218 | 1-8 | 8 | BMIZ, WC, BF% | Urine Triclosan | ↔ | No associations with adiposity at the age of 8. |
| Wu (2020) [149] | US | Cross-sectional | 2,372 | 6-19 | 6-19 | BMIZ, OB | Urine 2,4-DCP and 2,5-DCP | ▲ | In the WQS regression model for OB, the WQS index significantly correlated with the outcome. 2,5-DCP (0.41), BPA (0.17), and MEP (0.14) were notably weighted. |
| Seo (2021) [180] | KR | Cross-sectional | 165 girls | 7-8 | 7-8 | BMI, WC, WHtR | Urine 2,4-DCP, 2,5-DCP, 2,4,5-TCP, 2,4,6-TCP | ▲ | ΣChlorophenols was associated with WC and WHtR. |
| PAHs | |||||||||
| Scinicariello (2014) [80] | US | Cross-sectional | 3,189 | 6-19 | 6-19 | BMIZ, WC, OV | Urine 1-naphthol, 2-naphthol, 2-fluorene, 3-fluorene, 1-phenanthrene, 2-phenanthrene, 3-phenanthrene | ▲ | ΣPAHs and ΣNaphthalene metabolites were associated with risk of OV in ages 6-11. |
| Kim (2014) [79] | US | Cross-sectional | 1,985 | 6-18 | 6-18 | OB, central OB, WC | Urine 1-naphthol, 2-naphthol, 2-fluorene, 3-fluorene, 1-phenanthrene, 2-phenanthrene, 3-phenanthrene | ▲ | ΣPAHs was associated with higher BMI and WC. |
| Poursafa (2018) [179] | Iran | Cross-sectional | 186 | 6-18 | 6-18 | OB, Central OB, WC | Urine 1-naphthol, 2-naphthol, 1-hydroxypyrene, 9-phenanthrene | ▲ | ΣPAHs is associated with OB. |
| Bushnik (2020) [82] | Canada | Cross-sectional | 3,667 | 3-18 | 3-18 | BMI, WC, WHtR, central OB | Urine naphthalene, fluorene, phenanthrene, pyrene metabolites | ▲ | ΣPAHs and ΣNaphthalene metabolites were associated with WHtR in ages 3-5, and associated with BMI, WC, and WHtR in ages 6-18. |
| Uche (2020) [81] | US | Cross-sectional | 50,048 | 6-17 | 6-17 | BMI, WC, WHtR, central OB | Urine 2-naphthol, 9-fluorene, 1-phenan threne, 2-phenanthrene | ▲ | Phthalate metabolites were associated with obesity. |
| Kim (2023) [42] | KR | Cross-sectional | 2,286 | 3-17 | 3-17 | BMIZ, OV | Urine 2-naphthol, 2-hydroxyfluorene, 1-OH-phenanthrene, 1-hydroxypyrene | 2-naphthol was associated with higher BMIZ and odds of overweight. | |
Direction of associations with adiposity: ▲, positive; ▽, negative; ↔, null.
1-OH-Phe, 1-hydroxyphenanthrene; 1-OH-Pyr, 1-hydroxypyrene; 2-,3-OH-Flu, 2-,3-hydroxyfluorene; 2-,3-OH-Phe, 2-,3-hydroxyphenanthrene; 4-OH-Phe, 4-hydroxyphenanthrene; BF%, body fat percentage; BMIZ, body mass index z score; BPA, bisphenol A; BPS, bisphenol A; DDE, dichlorodiphenyldichloroethane; EDCs, endocrine-disrupting chemicals; EtFOSAA, 2-(N-ethyl-perfluorooctane sulfonamide) acetate; FMI, fat mass index; HMW, high molecular weight; KR, Republic of Korea; LMI, lean mass index; LMW, low molecular weight; MBP, mono-n-butyl phthalate; MBZP, monobenzyl phthalate; MCEPP, mono(2-ethyl-5-carboxypentyl) phthalate; MCINP, monocarboxyisonyl phthalate; MCIOP, mono-(4-methyl-7carboxyheptyl) phthalate; MCMHP, mono-[2-(carboxymethyl)hexyl] phthalate; MCNP, mono(carboxy-isononyl) phthalate; MECPP, mono-(2-ethyl-5-carboxypentyl) phthalate; MECPTP, mono-2-ethyl-5-carboxypentyl terephthalate; MeFOSAA, 2-(N-methyl-perfluorooctane sulfonamide) acetate; MEHHP, mono-(2-ethyl-5-hydroxylhexyl) phthalate; MEHP, mono(2-ethylhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyhl) phthalate; MEP, monoethyl phthalate; Me-PFOSA-AcOH, 2-(N-methyl-perfluorooctane sulfonamide) acetate MHBP, mono(4-hydroxybutyl) phthalate; MHINP, mono(4-methyl-7-hydroxyoctyl) phthalate; MOINP, mono-(4-methyl-7-oxo-octyl) phthalate; MOINCH, mono-oxo isononyl oxy carbonyl cyclohexane carboxylic acid; MPHHP, 6-hydroxy-monopropylheptyl phthalate; NHB, Non-Hispanic Blacks; NHW, Non-Hispanic Whites; OB, obesity; OV, overweight; PAHs, polycyclic aromatic hydrocarbons; PCB, polychlorinated biphenyl; PFAS, per- and polyfluoroalkyl substances; PFDA, perfluorodecanoic acid; PFDoA, perfluorododecanoic acid; PFHpS, perfluoroheptane sulfonate; PFHxS, perfluorohexane sulfonate; PFNA, perfluorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonate; PFTrDA, perfluorotridecanoic acid; PFUdA, perfluoroundecanoic acid; SMI, skeletal muscle index; SQ, subcutaneous; t-NC, trans-nonachlor; WC, waist circumference; WHtR, waist-to-height ratio.
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