In this cross-sectional study, our results indicated that Cl-PFESAs exposure was associated with overweight/obesity status. We found that serum concentrations of 6:2 Cl-PFESA and 8:2 Cl-PFESA in the second quartile had higher BMI compared with these in the lowest quartile, but not in the higher quartiles, suggesting the non-monotonic relationship between exposure to Cl-PFESAs and overweight/obesity incidence. Cl-PFESAs displayed an inverse U-shape association with the prevalence of overweight/obesity. Our study also demonstrated that exposure to PFOA was linearly positively associated with each outcome, and PFOS was only positively associated waist circumference. This is the first study to report the non-monotonic associations between Cl-PFESAs exposure and overweight status in human populations.
Among 1275 Chinese adults’ serum, 6:2 Cl-PFESA, PFOA, and PFOS were all detected and 8:2 Cl-PFESA was detected in 70% of participants. A recent review summarized Cl-PFESAs concentrations in China, the means of which are 4.20, 102, 941 ng/mL in the general population, high fish consumers, and metal plating workers, respectively (Brase et al. 2021). In Shandong province of China, the 6:2 Cl-PFESA level of 977 residents living near a fluorochemical plant was 2.311 ng/mL (Yao et al. 2020). These were slightly higher than our results. But the median value of 6:2 Cl-PFESA concentrations was 0.34 ng/mL that lower than ours among 519 pregnant women in Shanxi, China (X Liu et al. 2020). The concentrations of Cl-PFESAs varied regionally (Chen et al. 2017; Chu et al. 2020; X Liu et al. 2020; Pan et al. 2017). The serum PFOA and PFOS concentrations found by the current study were higher than those reported in most other national studies (Schulz et al. 2020).
To date, there are no human studies available to explore the association between the levels of Cl-PEFSA and overweight/obesity status, but several studies have reported the associations of exposure to PFOS and PFOA with obesity outcomes (Averina et al. 2021; Chen et al. 2019; Christensen et al. 2016; Eriksen et al. 2011; Geiger et al. 2021; Jain 2014; Lin et al. 2009; Liu et al. 2018; Nelson et al. 2010; Timmermann et al. 2014). We summarize them in Table S7. In line with our results, Geiger et al (Geiger et al. 2021) reported greater PFOA exposure was associated with higher risk of overweight/obesity in US children during 1999-2012 (ORQ3 vs. Q1 = 2.22, 95%CI: 1.20, 4.13; ORQ4 vs. Q1 = 2.73, 95%CI: 1.10, 6.74). Also consistent with our studies, increased PFOA levels measured in 5591 US people aged 12 and older was associated with increases in BMI (P = 0.038) (Jain 2014). However, the relationship of human exposure and overweight/ obesity status is still controversial, given concerns about reverse causality and effective dose (Jain 2020). Due to different modeling approaches and inconsistent confounding variables, the results of some studies were not consistent with ours (Averina et al. 2021; Chen et al. 2019; Christensen et al. 2016; Eriksen et al. 2011; Lin et al. 2009; Timmermann et al. 2014). For example, a cross-sectional study about the associations between PFAS concentrations in Norwegian adolescents and obesity, without adjustment for socioeconomic status, showed null associations for PFOS and PFOA (Averina et al. 2021).
Though there is no evidence that showed Cl-PFESAs might be potential obesogens in human beings, our previous epidemiologic studies found serum Cl-PFESAs concentrations were significantly positively associated with serum lipids (Cong et al. 2021) and the risk of metabolic syndrome (MetS) (Yu et al. 2021). In addition, Yao et al (Yao et al. 2020) reported multivariate linear regression coefficients of 9.80% (95% CI: 6.09, 13.63) for cholesterol (mmol/L), 9.59% (95% CI: 5.29, 14.07) for low-density lipoprotein cholesterol (mmol/L), 12.34% (95% CI: 2.80, 2.88) for triglycerides (mmol/L), higher 6:2 Cl-PFESA per ln-ng/mL concentrations, respectively, which suggests potential elevated lipid parameters from exposure to 6:2 Cl-PFESA. Furthermore, F-53B has a stronger metabolism-disrupting effect than PFOS in fertilized zebrafish embryos, affecting both metabolic transcription and organismal metabolic phenotype (Tu et al. 2019). A study in vitro indicated that Cl-PFESAs elevated the relative triglyceride content in mouse 3T3-L1 preadipocyte with adipogenesis promotion potency greater than PFOS (Li et al. 2018).
Potential biological effects were described in previous studies that could reveal the hazard of Cl-PFESAs exposure, although the mechanisms promoting adiposity risk by Cl-PFESAs are not clear. 6:2 Cl-PFESA displayed toxic effects on human liver HL-7702 cell, and significantly up-regulated gene Cd36 expression regulated long-chain fatty acids transportation through the adipocyte plasma membrane(Sheng et al. 2018). Cl-PFESAs also affected osteogenic differentiation in human bone mesenchymal stem cells (hBMSCs) related to obesity and metabolic diseases (Pan et al. 2019). In several in vivo and in vitro investigations, Cl-PFESAs showed agonistic activity toward the peroxisome proliferator-activated receptors (PPARs) pathways related lipid metabolism (Li et al. 2018; Sheng et al. 2018; Shi et al. 2019). Cl-PFESAs also have the characteristics of endocrine disruptors, which might be associated with glucocorticoids and progestogens synthesis in neonates (H Liu et al. 2020), sex hormone disorders in adult men (Cui et al. 2020), and induced estrogenic effects in zebrafish (Xin et al. 2020). The endocrine system is important for energy balance, fat distribution and fat deposition. For example, sex hormones affect food intake and alter the balance of glucose and insulin, lipogenesis and lipolysis to cause obesity (Heindel and Blumberg 2019). Endocrine disrupting chemicals (EDCs) are considered as obesogens promoting obesity in humans or animals (Nadal et al. 2017). However, we need more data before identifying Cl-PFESAs as an obesogen.
Interestingly, Cl-PFESAs displayed an inverted U-shaped relationship with the prevalence of overweight/obesity in our study. This unconventional dose-response relationship called non-monotonic dose-response (NMDR) relationship is normal in studies investigating the effects of EDCs (Lagarde et al. 2015; Vassilopoulou et al. 2017). Although there is less data about NMDR of Cl-PFESAs, several studies had reported that NMDR relationships occurred between PFAS and health outcomes. For example, Liao et al found a J-shaped relationship of PFOA and PFNA with the risk of hypertension among adults in US (Liao et al. 2020). PFOS and progesterone levels displayed an inverse U-shape dose-response relationship in neonates (H Liu et al. 2020). NMDR relationship indicates the possibility of Cl-PFESAs with endocrine disrupting characteristics, and this exposure-outcome association is a challenge for risk assessment of Cl-PFESAs.
Our results were based on a community-based cross-sectional study with a relatively large population, which could reduce the occurrence of random errors. Moreover, we enrolled a comprehensive panel of potential confounders, consistent with previous studies, exploring the association between exposure to PFAS and overweight/obesity, including sociodemographic and behavior factors. Finally, we accounted for non-monotonic association of PFAS with the prevalence of overweight/obesity using a restricted cubic spline regression analysis and 2-piecewise binary logistic regression for statistical analyses.
However, several limitations should not be ignored in this study. First, there may be reverse causality in our cross-sectional study design. However, it is difficult for PFAS to accumulate in adipose tissue with lipophobic properties (Aas et al. 2014; Fabrega et al. 2014; Wu et al. 2019). Lipid mobilization and not fat could effect PFAS concentrations in blood and other tissues (Aas et al. 2014). Second, although we included key confounding variables in the models, we did not adjust for diets, which could change PFAS exposure due to different PFAS-containing food consumption, dietary quality, and energy intake (Christensen et al. 2016; Eriksen et al. 2011). Third, we did not consider the cocktail effect of various pollutants that may affect overweight/obesity, such as polychlorinated biphenyls (PCBs), phthalates and other POPs, as EDCs, which might act in a synergistic or antagonistic manner to impact PFAS metabolic disorder effects (Chamorro-Garcia and Veiga-Lopez 2021; Choi et al. 2021; Egusquiza and Blumberg 2020). Bayesian kernel machine regression (BKMR) models performed to analyze multiple-chemical exposures will be necessary for further research in a larger population. Fourth, we conducted a common but controversial approach that imputed values below detection limits by LOD/√2 (Huynh et al. 2014; Richardson and Ciampi 2003; Schisterman et al. 2006), due to the detection proportion of 8:2 Cl-PFESA being 70.12%. However, our results were similar by using multiple imputing values below LOD for a repeat analysis (Table S10), although modestly stronger for the second quartile of 8:2 Cl-PFESA. Fifth, the population in this study was in China, hence future research conducted in other areas is needed.