
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)11919-6
10.1016/j.heliyon.2024.e35888
e35888
Research Article
Sex and obesity influence the relationship between perfluoroalkyl substances and lean body mass: NHANES 2011–2018
Jia Xue a1
Liu Wenhui b1
Ling Xiaomeng a
Li Juan c
Ji Jing a
Wang Baozhen bzhenw@sdu.edu.cn
a⁎⁎
Zhao Min zhaomin1986zm@126.com
a⁎
a Department of Nutrition and Food Hygiene, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
b Department of Informat and Data Anal Lab, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
c Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
⁎ Corresponding author. zhaomin1986zm@126.com
⁎⁎ Corresponding author. bzhenw@sdu.edu.cn
1 Xue Jia and Wenhui Liu are co-first authors and contributed equally to this work.

08 8 2024
15 9 2024
08 8 2024
10 17 e358885 3 2024
27 6 2024
6 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective

Polyfluoroalkyl substances (PFAS) are known endocrine disruptors, that have been the subject of limited research regarding their impact on human lean body mass. The aim of this study was to investigate the effects of PFAS exposure on lean body mass.

Methods

We performed a cross-sectional data analysis involving 1022 adolescents and 3274 adults from the National Health and Nutrition Examination Survey (NHANES) 2011–2018, whose lean body mass was measured by dual-energy X-ray absorptiometry. The lean mass index (LMI) was calculated as lean body mass dividing by the square of height. The association between PFAS and LMI was examined through a multivariate-adjusted weighted generalized linear model. Moreover, weighted quantile sum (WQS) regression models were employed to futher examine the relationship between the mixture of PFAS and LMI.

Results

Regression analyses revealed an inverse correlation between PFAS exposure and LMI after adjusting for potential covariates. Adults with higher serum PFAS concentrations manifested a reduction in whole LMI (β = −0.193, 95 % confidence interval (CI): −0.325 to −0.06). Notably, this correlation was particularly significant in adult females and individuals with obesity, and it was observed across diverse anatomical regions, including lower limbs, right arm, trunk, and whole lean body mass. In adult females, the association between PFAS and whole LMI was statistically significant (β = −0.294, 95 % CI: −0.495 to −0.094), and a similar trend was found in obese individuals (β = −0.512, 95 % CI: −0.762 to −0.261). WQS regression analyses supported the results obtained from weighted linear regression analyses.

Conclusions

Our study suggests that exposure to PFAS, whether individually or in combination, is associated with decreased lean body mass in specific body areas, with sex and obesity serving as major influencing factors.

Graphical abstract

Image 1

Highlights

• Higher PFAS exposure is associated with an increased risk of reduced lean body mass.

• Gender and obesity influence the role of PFAS on lean body mass.

• WQS regression results are consistent with linear regression results.

Keywords

Lean body mass
Obesity
Perfluoroalkyl and poly-fluoroalkyl substances
Weighted quantile sum
==== Body
pmcAbbreviations

ALMI Arm lean mass index

BCA Body composition analysis

BMI Body mass index

CDC Center for Disease Control

CI Confidence interval

DXA Dual-energy X-ray Absorptiometry

EPA Environmental Protection Agency

ER Estradiol receptor

GH-IGF Growth hormone-insulin-like growth factor

HLMI Head lean mass index

LALMI Left arm lean mass index

LLLMI Left leg lean mass index

LLMI Leg lean mass index

LMI Lean mass index

Me-PFOSA-AcOH 2-(N-Methyl-perfluorooctane sulfonamido) acetic acid

NCHS National Center for Health Statistics

NHANES National Health and Nutrition Examination Survey

PFAA Perfluoroalkyl acids

PFAS Polyfluoroalkyl substances

PFDeA Perfluorodecanoic acid

PFHxS Perfluorohexane sulfonic acid

PFNA Perfluorononanoic acid

PFOA Perfluorooctanoate

PFOS Perfluorooctane sulfonate

PFUA Perfluoroundecanoic acid

PPAR Peroxisome proliferator-activated receptor

RALMI Right arm lean mass index

RLLMI Right leg lean mass index

SPE-HPLC-TIS-MS/MS Solid phase extraction and high-performance liquid chromatography-turbo ion spray coupled ionization-tandem mass spectrometry

TLMI Trunk lean mass index

WHO World Health Organization

WLMI Whole lean mass index

WQS Weighted quantile sum

1 Introduction

Perfluoroalkyl substances (PFAS) are a group of synthetic chemicals characterized by the substitution of hydrogen atoms with fluorine atoms [1]. Due to their hydrophobic and oleophobic properties, PFAS are extensively employed in the manufacture of household and industrial goods [2], including surfactants, fire-fighting foams, and textile treatments. Non-occupationally exposed populations primarily encounter PFAS through consumption of contaminated food and drinking water [3]. PFAS are notorious for their environmental persistence and ability to accumulate in living organisms [4], leading to long-term retention in human tissues, with half-lives ranging from 3.8 to 7.3 years [[5], [6], [7]]. According to the National Health and Nutrition Examination Survey (NHANES) 2017–2018, perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS) and perfluorohexane sulfonic acid (PFHxS) were found in the blood over 98 % U.S. population. Among several PFAS present in human blood, PFOS has the highest median serum concentration at 4.3 μg/L.

PFAS, a group of typical endocrine-disrupting chemicals [8,9] has been shown to potentially decrease lean mass by reducing anabolic hormone levels both in human and mouse models. Lean body mass serves as a valuable metric for assessing muscle function and nutritional status, and its role in maintaining metabolic homeostasis is well established [10]. Studies have demonstrated that low lean body mass correlates with an elevated risk of metabolic syndrome, diabetes, and cardiovascular mortality [[11], [12], [13], [14]]. Furthermore, it has been demonstrated that higher levels of whole lean body mass gain can help mitigate the development of metabolic syndrome and cardiovascular disease [15].

However, research on PFAS and its impact on lean body mass in adolescents and adults has yielded conflicting results. A longitudinal child health cohort study [16] discovered that higher serum concentrations of PFAS were associated with a decrease in lean body mass accumulation from mid-childhood to early adolescence. This effect may be influenced by sex and race. Conversely, another prospective study [17] focusing on middle-aged individuals (aged 50 years) demonstrated a negative correlation between PFHxS and whole lean mass as well as leg lean mass in men but not in women. Additionally, a study using NHANES 2011–2018 found a positive association between PFHxS and lean body mass in adolescents aged 12–18 years [18].

Given the lack of established associations between PFAS exposure and human lean body mass, current research is limited by small sample sizes and varied assessment methods. Many studies have used bioelectrical impedance analysis (BIA) instead of the gold standard Dual-energy X-ray Absorptiometry (DXA). DXA is widely recognized for its accuracy, reproducibility, and discrete cutoff values of DXA, and existing studies have shown that BIA tends to overestimate lean body mass compared with DXA [19]. In addition, a significant portion of recent studies have focused on vulnerable populations [[20], [21], [22]], specifically pregnant women and infants, which limits the generalizability of findings. The limitations of the sample also make it challenging to conduct stratified analyses to investigate the potential differences in the health effects of PFAS exposure on lean body mass across different age groups, sexes, and obesity levels.

Therefore, this study utilized nationally representative data from NHANES 2011–2018 to investigate the association between PFAS exposure and lean body mass. The study employed stratified analysis by age, sex, and obesity level to explore the potential health risks associated with co-exposure to multiple PFAS. This research aim to contribute additional evidence to the existing body of knowledge.

2 Methods

2.1 Study population

NHANES is a regular cross-sectional and nationally representative survey of US population conducted by the National Center for Health Statistics (NCHS) since 1999. The survey protocol was approved by the Research Ethics Review Board of the NCHS. Detailed descriptions of NHANES including study design, protocol, data collection methods and data access are publicly available (https://www.cdc.gov/nchs/nhanes).

For this study, data from four cycles (NHANES 2011–2018) were utilized. Among the 39156 participants recruited during NHANES 2011–2018, 5074 subjects had both lean body mass data accessed by DXA and serum PFAS measurements. After excluding individuals with missing covariates, we finally included 1022 adolescents (aged 12–19 years) and 3274 adults (aged over 19 years) in the analysis. The screening procedure for the study population is summarized in Fig.A.1.

2.2 PFAS measurements

PFAS wwere quantitatively detected in a subsample of one-third of the population over a span of 12 years (from 2011 to 2018) in NHANES using online solid phase extraction and high-performance liquid chromatography-turbo ion spray coupled with ionization-tandem mass spectrometry (online SPE-HPLC-TIS-MS/MS). Serum samples were prepared, refrigerated, and transported to the lab of the National Center for Environmental Health for detection following a standard operation protocol. Seven PFAS congeners [Perfluorodecanoic acid (PFDeA), PFHxS, Perfluorononanoic acid (PFNA), 2-(N-methylperfluoroctanesulfonamido) acetic acid (Me-PFOSA-AcOH), PFOA, Perfluoroundecanoic acid (PFUA) and PFOS] that are available through 2011 to 2018 were included in our analysis. The lower limit of detection (LLOD) ranged from 0.08 to 0.2 ng/ml and those below the LLOD were assigned an amount equal to LLOD divided by square root of 2 (LLOD/sqrt (2)).

2.3 Lean body mass assessments

DXA is a reliable modality that utilizes X-rays to quantify bone mineral density and soft tissue composition in various anatomical regions, including the whole body, bilateral upper and lower limbs, trunk, and head. These measurements were obtained by certified and experienced radiologists using the Apex software program on a Hologic Discovery Model A densitometer (Hologic, Inc., Bedford, MA). NHANES employed multiple imputations to estimate DXA measures for missing data. As suggested, we excluded subjects with highly variable imputed DXA measures from our analysis. Additionally, the NHANES body composition analysis (BCA) option was enabled for body scan analysis, which adds 5 % of lean body mass to fat mass. For more details on working with sample weights and other analytical issues, please refer to the NHANES Survey Methods and Analytic Guidelinesand the online NHANES Tutorial.

2.4 Covariates

The selection of covariates was based on previous studies on PFAS [23,24]. Demographic variables, including age (in years), sex (male and female), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black and Other race), ratio of family income to poverty, parental educational attainment, and educational attainment, were obtained from the NHANES database. The ratio of family income to poverty was calculated by dividing household income by the poverty guidelines for a given survey year and served as an indicator of economic status. Parental educational attainment was used as a proxy for adolescents' educational attainment as it is often correlated with age. Additional information on smoking exposure, physical activity, energy intake, and protein intake were collected through questionnaires and examinations. Smoking exposure was assessed using two indicators: whether adolescents had used tobacco products in the past 5 days and whether they lived in a household with smokers. Adult smoking exposure was defined by whether individuals had smoked 100 cigarettes in their lifetime. The World Health Organization (WHO) recommends 60 minutes of physical activity per day for adolescents, which translates to 420 minutes per week. This recommendation was used as a cut-off to assess adolescent physical activity. For adults, the recommendation was 300 minutes per week [25]. Finally, daily energy and protein intake were assessed using the energy and protein supply ratio [26], which were used as indicators of energy and protein intake in relation to lean body mass.

2.5 Data analysis

The statistical analysis and data representation in this study utilized sophisticated weighting techniques. Categorical variables were presented as frequencies and percentages, while continuous variables were reported as means with standard deviations. To account for the complicated sampling design and the non-response phenomenon of NHANES when evaluating serum PFAS substances, the NCHS recommended applying perfluoroalkyl acids (PFAA)-specific subsample weights [27]. Additionally, a natural logarithm(ln) transformation was performed to improve the normality of the data for the convenience of statistical analysis due to its non-normal distribution.

The data were divided into groups based on sex and obesity level for descriptive analysis, considering the differences in lean body mass development by sex. The lean mass index (LMI) was utilized to reflect changes in lean mass due to the influence of lean body mass receptor type. Subgroup analyses based on body mass index (BMI) were also stratified and reiterated according to prior analysis. Adolescents were categorized into normal or overweight/obese according to the recommendations of the Data Table of BMI-for-age Charts by the Center for Disease Control (CDC), while adults were classified as underweight and normal (BMI <25.0 kg/m2) or overweight and obese (BMI≥25 kg/m2) according to CDC guidelines. The association between PFAS and lean body mass was systematically examined through a multivariate-adjusted weighted generalized linear model. Furthermore, a linear trend was tested after stratifying the subjects by sex and degrees of obesity. The original p values and 95 % confidence intervals (CI) for the results are provided. Subsequently, the weighted quantile sum (WQS) [28] mixed effect regression was performed separately for different sexes to evaluate both the overall effect of environmental exposure and the contribution of each component in the mixture to the overall effect. In addition, the impact of protein intake and serum albumin on body composition were considered in a sensitivity analysis to test the robustness of the relationship between PFAS and LMI as indicated in previous studies [29,30]. Finally, a WQS index was established based on the quartiles of the seven independent variables.

R 4.1.2 Statistical software was used for data analysis, and p < 0.05 was considered statistically significant.

3 Results

3.1 Descriptive statistics

Table 1 shows the socio-demographic characteristics, body composition-related indices and blood concentrations of PFAS for the study population, categorized by sex and age groups. The population consisted of 1022 adolescents (median age 15 years; 53.13 % boy) and 3274 adults (median age 40 years; 49.39 % male). As expected, adolescents had lower exposure to most PFAS compared to adults. The Spearman's rank correlation coefficients, which assess the relationship between the concentrations of seven chemicals, ranged from 0.06 to 0.93. These correlations should be considered when evaluating their potential association with health outcomes. After stratification, statistically significant differences were found in race, poverty level, dietary intake, PFAS exposure, and LMI among adolescents and adults. Furthermore, additional descriptive statistics revealed significant differences between male and female adolescents and adults, stratified by obesity status.Table 1 Characteristics of adolescents and adults with PFAS and lean mass in NHANES2011-2018.a

Table 1Characteristics	Adolescents	p	Adults	p	
Boys	Girls	Males	Females	
Number	543	479	0.01	1617	1657	0.34	
Age [yrs, mean (SD)]	15 (0.11)	15 (0.10)	0.30	39 (0.45)	40 (0.51)	0.15	
Race/ethnicity [n (%)]	
 Mexican	119 (21.92)	104 (21.71)	0.20	221 (13.67)	230 (13.88)	0.12	
 Other Hispanic	40 (7.37)	50 (10.44)		152 (9.40)	169 (10.20)		
 Non-Hispanic White	173 (31.86)	120 (25.05)		614 (37.97)	591 (35.67)		
 Non-Hispanic Black	137 (25.23)	116 (24.22)		337 (20.84)	391 (23.60)		
 Other Race	74 (13.62)	89 (18.58)		293 (18.12)	276 (16.65)		
Parents/Education levelb	
 Less than high school	131 (24.13)	115 (24.01)	>0.90	293 (18.12)	244 (14.73)	<0.001	
 High school/GED/AA	313 (57.64)	266 (55.53)		894 (55.29)	916 (55.28)		
 College graduate/above	99 (18.23)	98 (20.46)		430 (26.59)	497 (29.99)		
PIR[mean (SD)]	2.52 (0.12)	2.43 (0.12)	0.50	2.98 (0.06)	2.97 (0.06)	>0.9	
BMI (kg/m2)	24.08 (0.37)	24.56 (0.49)	0.60	29.15 (0.21)	29.16 (0.27)	0.04	
Energy (kcal)	2235 (54.96)	1678 (28.63)	<0.001	2519 (28.38)	1822 (17.22)	<0.001	
Protein (gm)	86.61 (2.67)	60.05 (1.28)	<0.001	98.83 (1.37)	70.48 (0.68)	<0.001	
Protein energy supply (%)	0.15 (0.00)	0.15 (0.00)	0.01	0.16 (0.00)	0.15 (0.00)	0.30	
Cigarette exposure status	
 Exposed	166 (30.57)	144 (30.06)	0.80	709 (43.85)	546 (32.95)	<0.001	
 Unexposed	377 (69.43)	335 (69.94)		908 (56.15)	1111 (67.05)		
Physically active	
 Inadequate	271 (49.91)	365 (76.20)	<0.001	835 (51.64)	1085 (65.48)	<0.001	
 Adequate	272 (50.09)	114 (23.80)		782 (48.36)	572 (34.52)		
Lean mass index (kg/m2)	
 Trunk	8.15 (0.08)	7.35 (0.10)	<0.001	9.90 (0.06)	8.53 (0.05)	<0.001	
 Whole	17.16 (0.18)	15.00 (0.20)	<0.001	19.94 (0.12)	16.62 (0.10)	<0.001	
Serum PFAS (ng/mL) [mean (SD)]c	
 PFHxS	1.96 (0.18)	1.28 (0.07)	<0.001	2.31 (0.08)	1.12 (0.05)	<0.001	
 Me-PFOSA-AcOH	0.20 (0.01)	0.17 (0.02)	<0.001	0.16 (0.00)	0.17 (0.00)	>0.90	
 PFNA	0.72 (0.04)	0.67 (0.04)	0.01	0.79 (0.02)	0.71 (0.02)	<0.001	
 PFDeA	0.16 (0.00)	0.16 (0.00)	0.80	0.25 (0.01)	0.25 (0.01)	0.07	
 PFUA	0.09 (0.00)	0.10 (0.01)	0.30	0.17 (0.01)	0.15 (0.01)	0.13	
 PFOA	1.80 (0.06)	1.49 (0.05)	<0.001	2.37 (0.06)	1.89 (0.15)	<0.001	
 PFOS	4.42 (0.17)	3.43 (0.13)	<0.001	7.89 (0.22)	4.72 (0.15)	<0.001	
 ∑PFAS	9.38 (0.37)	7.30 (0.25)	<0.001	13.94 (0.31)	9.04 (0.28)	<0.001	
a Statistics were weighted; adolescents (12–19 years) and adults (>19 years).

b Education level: The educational level of young people is directly related to their age, so parental educational level is used as a proxy.

c Only those with detection frequencies >40 % were included.

3.2 Relationship between PFAS and body composition indicators

The associations between PFAS metabolites and LMI by age group were demonstrated in Table 2 and Fig.A.2. Serum PFUA levels were found to be negatively correlated with LMI of left arm, upper limbs, and trunk. Specifically, for each unit increase in ln-transformed PFUA concentration, there was a change of −0.031 (95 % CI: −0.059 to −0.002) in upper limbs LMI and a change of −0.103 (95 % CI: −0.198 to −0.007) in trunk LMI among adolescents. On the other hand, teenagers with higher levels of PFHxS had higher LMI in the upper limbs, lower limbs, trunk and whole regions (p < 0.05). Additionally, a significant positive association was observed between head LMI and PFOA, PFOS (p < 0.05). However, there was no significant correlation between LMI in different areas of the body and serum concentrations of other PFAS, only weak correlation tendencies were discovered.Table 2 Association between PFAS and lean mass index by age status groups in all participants in NHANES 2011–2018.

Table 2PFAS metabolites	HLMI	ALMI	LLMI	TLMI	WLMI	
β (95%CI)	β (95%CI)	β (95%CI)	β (95%CI)	β (95%CI)	
Adolescent	
 PFHxS	0.004 (−0.006, 0.015)	0.025* (-0.004, 0.053)	0.083* (0.009, 0.158)	0.137* (0.043, 0.231)	0.250* (0.048, 0.452)	
 Me-PFOSA-AcOH	0.001 (−0.012, 0.015)	0.024 (−0.005, 0.053)	0.014 (−0.055, 0.084)	0.016 (−0.071, 0.104)	0.044 (−0.138, 0.226)	
 PFNA	0.006 (−0.004, 0.017)	−0.013 (−0.045, 0.019)	−0.010 (−0.089, 0.068)	−0.037 (−0.136, 0.062)	−0.047 (−0.245, 0.150)	
 PFDeA	0.005 (−0.008, 0.018)	−0.013 (−0.053, 0.027)	−0.019 (−0.140, 0.103)	−0.053 (−0.174, 0.069)	−0.086 (−0.354, 0.183)	
 PFUA	0.001 (−0.018, 0.019)	−0.031* (-0.059, -0.002)	−0.067 (−0.164, 0.030)	−0.103* (-0.198, -0.007)	−0.187* (-0.370, -0.003)	
 PFOA	0.019* (0.003, 0.035)	0.010 (−0.048, 0.067)	0.023 (−0.133, 0.179)	−0.004 (−0.177, 0.169)	0.045 (−0.334, 0.423)	
 PFOS	0.014* (0.002, 0.025)	0.012 (−0.027, 0.051)	0.074 (−0.057, 0.205)	0.026 (−0.117, 0.169)	0.158 (−0.158, 0.473)	
 ∑PFAS	0.013 (−0.001, 0.026)	0.016 (−0.030, 0.063)	0.085 (−0.056, 0.227)	0.065 (−0.094, 0.225)	0.208 (−0.143, 0.559)	
Adults	
 PFHxS	−0.004 (−0.011, 0.003)	0.002 (−0.014, 0.018)	0.002 (−0.034, 0.038)	−0.047 (−0.110, 0.017)	−0.061 (−0.164, 0.041)	
 Me-PFOSA-AcOH	−0.001 (−0.008, 0.005)	−0.007 (−0.024, 0.009)	−0.017 (−0.060, 0.027)	−0.016 (−0.077, 0.046)	−0.071 (−0.169, 0.027)	
 PFNA	0.004 (−0.004, 0.012)	−0.001 (−0.021, 0.019)	−0.039* (−0.074, −0.003)	−0.065 (−0.141, 0.011)	−0.117* (-0.239, 0.004)	
 PFDeA	0.001 (−0.006, 0.009)	−0.011 (−0.033, 0.011)	−0.056* (−0.097, −0.015)	−0.091* (-0.163, -0.018)	−0.151* (-0.274, -0.027)	
 PFUA	0.008* (0.0001, 0.016)	−0.0001 (−0.021, 0.021)	0.001 (−0.040, 0.041)	−0.016 (−0.074, 0.042)	−0.026 (-0.136, 0.085)	
 PFOA	0.003 (−0.012, 0.006)	−0.006 (−0.032, 0.020)	−0.043 (−0.095, 0.008)	−0.087* (-0.177, 0.003)	−0.138* (-0.279, 0.004)	
 PFOS	−0.003 (−0.010, 0.005)	−0.009 (−0.029, 0.011)	−0.036* (-0.076, 0.003)	−0.117* (-0.184, -0.050)	−0.172* (-0.275, -0.070)	
 ∑PFAS	0.003 (−0.012, 0.007)	−0.010 (−0.035, 0.015)	−0.045* (-0.093, 0.003)	−0.121* (-0.207, -0.035)	−0.193* (-0.325, -0.060)	
Notes: adolescents (12–19 years) and adult (>19 years). Estimates were presented as coefficients and 95 % confidence intervals (CIs) and were adjusted for age (continuous), gender (categorical), race/ethnicity (categorical), education (categorical), BMI (categorical), ratio of family income to poverty (categorical), smoke expose (categorical), Energy (continuous), Protein energy supply ratio(continuous), physically active(categorical). HLMI: Head lean mass index; ALMI: Arm lean mass index; LLMI: leg lean mass index; TLMI: trunk lean mass index; WLMI: whole lean mass index. *p < 0.05.

In our study involving adult participants, we observed a negative correlation between serum levels of PFDeA, PFOA, total PFAS and both trunk and whole LMI (Table 2 and Fig.A.3). The correlation coefficients between PFDeA, PFOA, PFOS, total PFAS concentrations and trunk LMI were −0.091 (95 % CI: −0.163 to −0.018), −0.087 (95 % CI: −0.177 to 0.003), −0.117 (95 % CI: −0.184 to −0.050), and −0.121 (95 % CI: −0.207 to −0.035), respectively. Additionally, we observed a significant negative correlation between PFNA and LMI of left leg, lower limbs, as well as between PFDeA and left leg, right leg, and lower limb lean mass (p < 0.05).

3.3 Relationship between PFAS and body composition indicators by sex

In the analysis of male adolescents, PFAS showed rare associations with LMI, except for PFHS which consistently correlated positively with trunk and whole LMI (Table A.1 and Fig.A.2). Among female adolescents, PFOA and PFOS showed positive associations with the head LMI while PFUA was negatively linked with LMI of the head, upper limbs, lower limbs, trunk, and whole body significantly.

For adults, the stratified assessment revealed that only PFOA, PFOS, total PFAS, and trunk LMI were adversely associated in continuous modeling for men (Table A.2 and Fig.A.3). However, most PFAS demonstrated relationships in females. The whole lean mass showed a negative correlation with PFOA, PFOS, PFDeA, Me-PFOSA-AcOH, and total PFAS (β = −0.189, 95%CI: −0.365 to −0.012; β = −0.259, 95%CI: −0.417 to −0.102; β = −0.220, 95%CI: −0.411 to −0.029; β = −0.159, 95%CI: −0.299 to −0.020; β = −0.294, 95%CI: −0.495 to −0.094). Additionally, there were apparent negative correlations between Me-PFOSA-AcOH and right arm LMI, and total PFAS, PFOA, PFDeA, and PFOS with lower limbs LMI (p < 0.05). Furthermore, right arm LMI showed a negative connection with PFDeA and PFOS, and other PFAS that did not demonstrate a connection also displayed a negative correlation trend in certain regions.

3.4 Relationship between PFAS and body composition indicators by obesity level

Stratified analyses were conducted to examine the impact of obesity on the association between PFAS and lean body mass. Specifically, it was found that PFHxS had a positive correlation with all lean mass components in the normal weight adolescent population, while this correlation was not observed in the adolescent obese population (Table A.3 and Fig.A.4). Additionally, positive correlations were observed between head lean mass and both PFNA (β = 0.014, 95%CI: 0.003 to 0.025) and PFOA (β = 0.031, 95 % CI: 0.009 to 0.053) in normal weight adolescents.

However, in the subgroup of obese adults, a more pronounced negative association between PFAS and LMI was evident. Specifically, PFOA, PFOS, PFDeA, PFNA, PFUA and total PFAS showed a significant negative association with LMI. The correlation coefficients for whole LMI were as follows: β = −0.444, 95 % CI: −0.694 to −0.194 for PFOA; β = −0.405, 95 % CI: −0.623 to −0.187 for PFOS; β = −0.068, 95 % CI: −0.120 to −0.016 for PFDeA; β = −0.277, 95 % CI: 0.481 to −0.073 for PFNA; β = −0.381, 95 % CI: −0.626 to −0.135 for PFUA. Moreover, serum levels of PFUA, PFOA, PFOS, total PFAS and LMI in left leg, right leg, lower limbs, trunk, as well as whole LMI were also significantly negatively correlated (p < 0.05). Furthermore, PFNA and PFDeA exhibited unfavorable correlations with the right leg and lower limbs. Finally, a negative correlation was observed between left arm, leg, whole LMI and Me-PFOSA-AcOH in the normal population (Table A.4 and Fig. 1). However, no correlation was found between LMI and Me-PFOSA-AcOH in obese adults.Fig. 1 Associations of quartiles of PFAS with HLMI, LALMI, LLLMI, RALMI, RLLLMI, ALMI, LLMI, TWWI, WLMI in obesity-age groups in participants of >19 years old in NHANES 2011–2018. Obesity status: adults were defined as “normal” if BMI≤25 kg/m2, otherwise adults' status was defined as “obese”. Estimates were adjusted for age (continuous), gender (categorical), race/ethnicity (categorical), education (categorical), BMI category, ratio of family income to poverty (categorical), smoke expose (categorical), Energy (continuous), Protein energy supply ratio(continuous), physically active(categorical). HLMI: Head lean mass index; LALMI: left arm lean mass index; LLLMI: left leg lean mass index; RALMI: right arm lean mass index; RLLMI: right leg lean mass index; TLMI: trunk lean mass index; WLMI: whole lean mass index; CI: confidence interval.

Fig. 1

3.5 Sensitivity analysis

Multivariate-adjusted weighted generalized linear models were re-run in this study after including serum and urine albumin as covariates (Table A.5-A.7). The outcomes derived from the sensitivity analysis demonstrated the robustness of most findings. Specifically, the associations and linear trends between PFDeA, PFUA, PFOS, total PFAS and LMI in obese adult groups, as well as PFDeA, PFOS, total PFAS and LMI in female groups were consistent with our primary results (Table A.7).

3.6 Relationship between WQS indices and body composition for perfluorinated mixtures

The WQS analysis confirmed the findings of the linear regression model. Specifically, in obese teenagers, an inverse association was found between the WQS index and the LMI of right leg (β = −0.077, 95%CI: −0.128 to −0.026) when the WQS negative model was stratified by sex and degree of obesity (Table A.9).

In the WQS analysis of adults, the WQS index was found to be negatively associated with women head LMI (β = −0.003, 95%CI: −0.005 to 0.000) (Table A.10). Conversely, among adult males, both the leg and trunk LMI were inversely correlated with the WQS index. Further investigation of obesity stratification revealed that in the normal population, only the whole LMI was correlated, while in the obese individuals, negative correlations were found with the left leg, right arm, trunk, subtotal, and whole LMI (Table 3). Additionally, a negative trend was found between PFAS and lower LMI in obese people.Table 3 Associations between PFAS and lean mass index by WQS regression model by BMI status groups in >19-year-old participants in NHANES 2011–2018.

Table 3Body lean mass index	β	95%CI	p	
Normal	
 HLMI	−0.004	(−0.003, 0.002)	0.7944	
 LALMI	−0.002	(−0.008, 0.003)	0.5251	
 LLLMI	0.006	(−0.004, 0.018)	0.3168	
 RALMI	−0.003	(−0.009, 0.002)	0.3346	
 RLLMI	−0.011	(−0.025, 0.002)	0.1805	
 ALMI	−0.004	(−0.012, 0.002)	0.3186	
 LLMI	0.001	(−0.023, 0.027)	0.9001	
 TLMI	−0.023	(−0.051, 0.004)	0.1596	
 WLMI	−0.084	(−0.155, −0.014)	0.0486	
Obese	
 HLMI	−0.002	(−0.006, 0.001)	0.2496	
 LALMI	−0.005	(−0.012, 0.000)	0.1598	
 LLLMI	−0.035	(−0.054, −0.015)	0.0031	
 RALMI	−0.012	(−0.018, −0.006)	0.0011	
 RLLMI	−0.016	(−0.035, −0.005)	0.1586	
 ALMI	−0.014	(−0.027, −0.001)	0.0707	
 LLMI	−0.031	(−0.063, −0.000)	0.0962	
 TLMI	−0.099	(−0.142, −0.055)	0.0001	
 WLMI	−0.235	(−0.345, −0.126)	0.0004	
Notes: Estimates were obtained in WQS regression after adjusting for age (continuous), gender (categorical), race/ethnicity (categorical), education (categorical), BMI (categorical), ratio of family income to poverty (categorical), smoke expose (categorical), Energy (continuous), Protein energy supply ratio(continuous), physically active(categorical). Obesity status was defined as “normal” if BMI≤25 kg/m2, otherwise adults' status was defined as “obese”. HLMI: Head lean mass index; LALMI: left arm lean mass index; LLLMI: left leg lean mass index; RALMI: right arm lean mass index; RLLMI: right leg lean mass index; TLMI: trunk lean mass index; WLMI: whole lean mass index; CI: confidence interval.

4 Discussion

This population-based study using data from NHANES aimed to investigate the relationship between serum concentrations of PFAS and lean body mass in adolescents and adults. The study findings showed that individuals with high serum concentrations of PFAS generally had lower lean mass in their whole body, trunk, and limbs. Moreover, when analyzing the data by sex, the study found a strong inverse correlation between fractional PFAS and lean body mass in females during both adolescence and adulthood, while males showed minimal association. Additionally, the study observed that obese adults were more likely to have a negative correlation between PFAS and lean body mass, although this association tended to be less pronounced in the normal weight group.

To date, a small number of epidemiological researches [16,17,21,31,32] have confirmed the association between lean body mass and PFAS. Lind [17] used POEM data to explore the relationship between PFAS and both body fat and lean body mass in a middle-aged population. They found a negative correlation between overall lean mass and serum PFHxS levels, but only among males. Another study from the Project Viva cohort [16] presented similar findings, showing that children with higher concentrations of PFOA, PFOS, PFDeA, and PFHxS had reduced lean body mass. A recent prospective study [21] in the United States found that PFNA affects lean body mass and thus bone health, suggesting a direct impact of PFNA on lean body mass. Collectivel, these studies highlight the negative effects of PFAS on lean body mass, which aligns with our findings. However, a cross-sectional study [18] involving 1067 adolescents aged 12–18 years presented a contradictory conclusion, revealing that higher PFHxS exposure was associated with lower body fat mass and higher lean body mass. Although our study also showed some positive correlations for PFHxS in the adolescent group, no such correlations were observed in the subsequent adult groups, suggesting potentially different responses to PFAS exposure across different age groups. Furthermore, it is worth noting that another cross-sectional study did not observe any correlation between PFOS, PFOA, PFNA, PFDeA and lean body mass [17]. This absence of association may be attributed to the limited sample size, population variations, the inadequate consideration of other potential confounding factors (such as diet and exercise patterns), and the varying half-lives and synergistic effect of different PFAS on health. Therefore, our study conducted a subgroup analysis of adolescents and adults to explore the impact of PFAS on people of different ages, sexes and obesity levels in more details. Our research aims to provide a comprehensive comparison of the physiological responses exhibited by distinct populations to PFAS exposure.

Given the sex dependence of PFAS exposure and elimination in the body [33,34], there is a potential impact on sex hormone levels. Our analysis of sex subgroups revealed that PFAS has a more pronounced harmful effect on female groups, particularly adults. Specifically, we observed a significant decrease in whole lean body mass associated with PFDeA (β = −0.220, 95 % CI: −0.411 to −0.029), PFOS (β = −0.259, 95 % CI: −0.417 to −0.102) and total PFAS (β = −0.294, 95 % CI: −0.495 to −0.094), which aligns with findings from prior studies [[35], [36], [37]]. Therefore, our results underscore the importance of implementing additional protective measures to reduce PFAS exposure, especially in women. It is worth noting that PFAS are considered to have “obesogenic” properties and exposure to PFAS may cause changes in the lipid profile of human cells, ultimately affecting the biological processes of fat cells [38,39]. Additionally, studies have shown that PFAS may induce lipid metabolism disorders and oxidative stress [40,41], thereby increasing the risk of obesity. Metabolic disorders and hormonal abnormalities resulting from obesity have detrimental effects on lean body mass [42]. Although there is limited existing research on the impact of obesity on lean mass, and our analysis showed that obese adults exposed to PFAS have lower lean body mass compared to normal-weight adults, especially in the lower limbs, trunk, and whole body. A similar trend was observed among adolescent obesity groups. Hence, it is important to evaluate the risks associated with varying levels of obesity. Finally, significant differences exist in the effects of PFAS on the hormone and metabolic systems of adolescents and adults [43,44]. In adolescents, PFAS may disrupt the dynamic balance of sex and growth hormones, interfering with reproductive development and altering body composition [45]. Conversely, in adults, PFAS may directly interfere [46,47] with sex hormones and thyroid hormones [48], thereby affecting metabolism, fat, and energy balance. Therefore, further research is required to investigate how PFAS interact with age, gender, and obesity in different populations.

It has been illustrated that the binding affinity of PFAS [[49], [50], [51]] varies by their functional groups and carbon-chain length. Our study highlights the significant impact of PFOA, PFDeA, and PFOS onlean body mass. PFOA and PFDeA fall into the category of protracted perfluorinated carboxylic acids, while PFOS belongs to perfluorinated sulfonic acids. Hence, further investigation is necessary to elucidate the effects of different PFAS characteristics on lean body mass loss. Additionally, establishing precise recommended thresholds for various PFAS is critical for effective protection of public health and promotion of environmental sustainability.

Finally, sensitivity analyses revealed that serum albumin and albuminuria had minimal impact on our results, especially in women and obese groups. These findings further strengthen the evidence for a negative association between PFAS and LMI. However, further in-depth studies and exploration of possible mechanistic associations are necessary.

Our study provides evidence supporting an association between certain PFAS and lean body mass loss. This relationship may be attributed to a physiological link between low lean body mass and high blood concentrations of PFAS. Firstly, previous studies have proven that PFOA affects glucose and lipid metabolism through inhibiting AMPK, activating mTOR, increasing P70S6K increase, and upregulating FASN and ACACA (key enzyme for de novo fatty acid synthesis) [52,53], thereby affecting muscle energy production and supply. Recent studies have also demonstrated that exposure to PFAS leads to increased expression of peroxisome proliferator-activated receptor-α (PPARα) [54] in skeletal muscle, disrupting signaling and subsequently affecting muscle mass. Secondly, sex is an important factor in determining the toxic effects of environmental pollutants [55]. Some studies have found that PFAS can competitively bind to the estradiol receptor (ER), thereby affecting the transcriptional activity of the ER. PFOS and PFOA, in particular, can activate the ER and upregulate the expression of ERα [[56], [57], [58], [59]], leading to changes in estrogen and progesterone levels or inhibiting hormone activity [60]. This, in turn, results in reduced lean body mass and alterations in metabolic function [61], specifically in women. Additionally, PFAS may interfere with the androgen or estrogen pathways by affecting the expression of genes involved in steroid hormone metabolism, such as those related to the growth hormone-insulin-like growth factor (GH-IGF) axis and the steroid hormone axis [62,63]. Thirdly, obesity serves as an important indicator [64] for examining environmental toxicity. PFAS can interfere with hormones such as sex hormone [65,66], testosterone and thyroid hormone [[67], [68], [69]]which are typically expressed at lower levels in obese individuals. Furthermore, PFAS exacerbates metabolic disturbances and reduces muscle mass by worsening insulin resistance [70], increasing insulin secretion and impairing leptin receptor sensitivity [71,72]. In addition, studies showed that PFAS has also been found to contribute to obesity through inflammation [[73], [74], [75]]. Obesity, in turn, leads to increased immune cell infiltration and pro-inflammatory activation in adipose tissue surrounding muscle cells [76]. This results in an increase in pro-inflammatory markers and a decrease in anti-inflammatory markers [[77], [78], [79], [80]]. Whereas more research is urgently needed to fully understand the molecular mechanism and potential toxicological effects of PFAS on human lean body mass.

Nonetheless, our study has several limitations that need to be considered. Firstly, given the cross-sectional design of this study, causal inferences cannot be established. Secondly, the data of PFAS only represents a subsample of 1/3 and the narrow range and limited specimens of PFUA and PFHxS may introduce bias and potentially promote specific results. To ensure accuracy, future studies should include larger sample populations for verification. Additionally, despite adjusting for potential confounding variables, the complexity, volatility of muscle accumulation, as well as individual variations hampered a thorough investigation of the underlying biological mechanisms, such as human albumin levels, glomerular filtration, and other unmeasured biases. Thus, future studies should strive for more comprehensive mechanistic research. Lastly, it is essential to note that stratifying research into adolescents versus adults may introduce heterogeneity, requiring careful interpretation and extrapolation of previous findings. Notwithstanding these limitations, our study used a nationally representative and sufficiently large sample to enhance the plausibility and validity of the extrapolation of the results.

5 Conclusions

Exposure to certain PFAS may be related to adverse health effects on body composition. We detected a significant negative relationship between serum PFAS concentrations and lean body mass, with this correlation being more prominent in women and individuals who are obese. These findings align with the results obtained from the WQS model, indicating that reducing PFAS levels may help mitigate their potential impacts on human health.

Funding

This work was supported by the 10.13039/501100001809 National Natural Science Foundation of China 82373598 .

Ethics declarations

Review and/or approval by an ethics committee was not needed for this study because all of our data were derived from published public databases.

Data availability statement

Data comes from the public database NHANES and will be made available on request.

CRediT authorship contribution statement

Xue Jia: Writing – original draft, Methodology, Formal analysis, Data curation. Wenhui Liu: Visualization, Validation, Software. Xiaomeng Ling: Resources, Project administration. Juan Li: Validation, Supervision. Jing Ji: Validation, Supervision. Baozhen Wang: Writing – review & editing, Project administration. Min Zhao: Writing – review & editing, Funding acquisition.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Min Zhao reports financial support was provided by 10.13039/501100001809 National Natural Science Foundation of China . If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Acknowledgements

We are thankful to the National Health and Nutrition Examination Survey.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e35888.
==== Refs
References

1 Pelch K.E. Reade A. Wolffe T.A.M. Kwiatkowski C.F. PFAS health effects database: protocol for a systematic evidence map Environ. Int. 130 2019 104851 10.1016/j.envint.2019.05.045
2 Glüge J. Scheringer M. Cousins I.T. DeWitt J.C. Goldenman G. Herzke D. Lohmann R. Ng C.A. Trier X. Wang Z. An overview of the uses of per- and polyfluoroalkyl substances (PFAS) Environ Sci Process Impacts 22 2020 2345 2373 10.1039/d0em00291g 33125022
3 Sunderland E.M. Hu X.C. Dassuncao C. Tokranov A.K. Wagner C.C. Allen J.G. A review of the pathways of human exposure to poly- and perfluoroalkyl substances (PFASs) and present understanding of health effects J. Expo. Sci. Environ. Epidemiol. 29 2019 131 147 10.1038/s41370-018-0094-1 30470793
4 Buck R.C. Franklin J. Berger U. Conder J.M. Cousins I.T. de Voogt P. Jensen A.A. Kannan K. Mabury S.A. van Leeuwen S.P.J. Perfluoroalkyl and polyfluoroalkyl substances in the environment: terminology, classification, and origins Integrated Environ. Assess. Manag. 7 2011 513 541 10.1002/ieam.258
5 Olsen G.W. Burris J.M. Ehresman D.J. Froehlich J.W. Seacat A.M. Butenhoff J.L. Zobel L.R. Half-life of serum elimination of perfluorooctanesulfonate,perfluorohexanesulfonate, and perfluorooctanoate in retired fluorochemical production workers Environ. Health Perspect. 115 2007 1298 1305 10.1289/ehp.10009 17805419
6 Jackson-Browne M.S. Eliot M. Patti M. Spanier A.J. Braun J.M. PFAS (per- and polyfluoroalkyl substances) and asthma in young children: NHANES 2013–2014 Int. J. Hyg Environ. Health 229 2020 113565 10.1016/j.ijheh.2020.113565
7 Ye X. Kato K. Wong L.-Y. Jia T. Kalathil A. Latremouille J. Calafat A.M. Per- and polyfluoroalkyl substances in sera from children 3 to 11 years of age participating in the National Health and Nutrition Examination Survey 2013-2014 Int. J. Hyg Environ. Health 221 2018 9 16 10.1016/j.ijheh.2017.09.011 28993126
8 Jensen A.A. Leffers H. Emerging endocrine disrupters: perfluoroalkylated substances Int. J. Androl. 31 2008 161 169 10.1111/j.1365-2605.2008.00870.x 18315716
9 Tarapore P. Ouyang B. Perfluoroalkyl chemicals and male reproductive health: do PFOA and PFOS increase risk for male infertility? Int. J. Environ. Res. Publ. Health 18 2021 3794 10.3390/ijerph18073794
10 Willoughby D. Hewlings S. Kalman D. Body composition changes in weight loss: strategies and supplementation for maintaining lean body mass, a brief review Nutrients 10 2018 1876 10.3390/nu10121876 30513859
11 Zhang H. Lin S. Gao T. Zhong F. Cai J. Sun Y. Ma A. Association between sarcopenia and metabolic syndrome in middle-aged and older non-obese adults: a systematic review and meta-analysis Nutrients 10 2018 364 10.3390/nu10030364 29547573
12 Han J.M. Lee M.-Y. Lee K.-B. Kim H. Hyun Y.Y. Low relative skeletal muscle mass predicts incident hypertension in Korean men: a prospective cohort study J. Hypertens. 38 2020 2223 2229 10.1097/HJH.0000000000002530 32618894
13 Kim K. Park S.M. Association of muscle mass and fat mass with insulin resistance and the prevalence of metabolic syndrome in Korean adults: a cross-sectional study Sci. Rep. 8 2018 2703 10.1038/s41598-018-21168-5 29426839
14 Lagacé J.-C. Brochu M. Dionne I.J. A counterintuitive perspective for the role of fat-free mass in metabolic health J Cachexia Sarcopenia Muscle 11 2020 343 347 10.1002/jcsm.12520 31999082
15 He H. Pan L. Du J. Jin Y. Wang L. Jia P. Shan G. Effect of fat mass index, fat free mass index and body mass index on childhood blood pressure: a cross-sectional study in south China Transl. Pediatr. 10 2021 541 551 10.21037/tp-20-325 33850812
16 Janis J.A. Rifas-Shiman S.L. Seshasayee S.M. Sagiv S. Calafat A.M. Gold D.R. Coull B.A. Rosen C.J. Oken E. Fleisch A.F. Plasma concentrations of per- and polyfluoroalkyl substances and body composition from mid-childhood to early adolescence J. Clin. Endocrinol. Metab. 106 2021 e3760 e3770 10.1210/clinem/dgab187 33740056
17 Lind P.M. Lind L. Salihovic S. Ahlström H. Michaelsson K. Kullberg J. Strand R. Serum levels of perfluoroalkyl substances (PFAS) and body composition – a cross-sectional study in a middle-aged population Environ. Res. 209 2022 112677 10.1016/j.envres.2022.112677
18 Lin L.-Z. Cai L. Liu Z.-Y. Gao J. Zhou Y. Zeng X.-Y. Ou Y. Dong G.-P. Dong P.-X. Wu Q.-Z. Chu C. Wu L.-Y. Liang L.-X. Qin S.-J. Zeng X.-W. Zhao X. Dong G.-H. Exposure to per- and polyfluoroalkyl substances and body composition in US adolescents aged 12-18 years: an analysis of data from the National Health and Nutrition Examination Surveys 2011-2018 Hygiene and Environmental Health Advances 3 2022 100009 10.1016/j.heha.2022.100009
19 Cheng K.Y.-K. Chow S.K.-H. Hung V.W.-Y. Wong C.H.-W. Wong R.M.-Y. Tsang C.S.-L. Kwok T. Cheung W.-H. Diagnosis of sarcopenia by evaluating skeletal muscle mass by adjusted bioimpedance analysis validated with dual-energy X-ray absorptiometry J Cachexia Sarcopenia Muscle 12 2021 2163 2173 10.1002/jcsm.12825 34609065
20 Zhang S. Lei X. Zhang Y. Shi R. Zhang Q. Gao Y. Yuan T. Li J. Tian Y. Prenatal exposure to per- and polyfluoroalkyl substances and childhood adiposity at 7 years of age Chemosphere 307 2022 136077 10.1016/j.chemosphere.2022.136077
21 Buckley J.P. Kuiper J.R. Lanphear B.P. Calafat A.M. Cecil K.M. Chen A. Xu Y. Yolton K. Kalkwarf H.J. Braun J.M. Associations of maternal serum perfluoroalkyl substances concentrations with early adolescent bone mineral content and density: the health outcomes and measures of the environment (HOME) study Environ. Health Perspect. 129 2021 97011 10.1289/EHP9424
22 Bloom M.S. Commodore S. Ferguson P.L. Neelon B. Pearce J.L. Baumer A. Newman R.B. Grobman W. Tita A. Roberts J. Skupski D. Palomares K. Nageotte M. Kannan K. Zhang C. Wapner R. Vena J.E. Hunt K.J. Association between gestational PFAS exposure and Children's adiposity in a diverse population Environ. Res. 203 2022 111820 10.1016/j.envres.2021.111820
23 Rogeri P.S. Zanella R. Martins G.L. Garcia M.D.A. Leite G. Lugaresi R. Gasparini S.O. Sperandio G.A. Ferreira L.H.B. Souza-Junior T.P. Lancha A.H. Strategies to prevent sarcopenia in the aging process: role of protein intake and exercise Nutrients 14 2021 52 10.3390/nu14010052 35010928
24 Kim J.W. Kim R. Choi H. Lee S.-J. Bae G.-U. Understanding of sarcopenia: from definition to therapeutic strategies Arch Pharm. Res. (Seoul) 44 2021 876 889 10.1007/s12272-021-01349-z
25 Bull F.C. Al-Ansari S.S. Biddle S. Borodulin K. Buman M.P. Cardon G. Carty C. Chaput J.-P. Chastin S. Chou R. Dempsey P.C. DiPietro L. Ekelund U. Firth J. Friedenreich C.M. Garcia L. Gichu M. Jago R. Katzmarzyk P.T. Lambert E. Leitzmann M. Milton K. Ortega F.B. Ranasinghe C. Stamatakis E. Tiedemann A. Troiano R.P. van der Ploeg H.P. Wari V. Willumsen J.F. World Health Organization 2020 guidelines on physical activity and sedentary behaviour Br. J. Sports Med. 54 2020 1451 1462 10.1136/bjsports-2020-102955 33239350
26 Deshpande S.B. Rao K.S. Mandal M.B. Saxena I.D. Protein energy ratio as a critical factor in determining growth & glycogen content of muscles in rats Indian J. Med. Res. 90 1989 328 334 2516837
27 Akinbam L. Chen T.-C. Davy O. Ogden C. Fink S. Clark J. Riddles M. Mohadjer L. National Health and Nutrition Examination Survey, 2017–March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines 2022 National Center for Health Statistics (U.S.) 10.15620/cdc:115434
28 C. Gennings, Gennings, C. Analysis of Environmental Chemical Mixtures Using Weighted Quantile Sum (WQS) Regression.(https://www.healthandenvironment.org/assets/images/Gennigs_Presentation_5-20-19.pdf), (n.d.).
29 Reijnierse E.M. Trappenburg M.C. Leter M.J. Sipilä S. Stenroth L. Narici M.V. Hogrel J.Y. Butler-Browne G. McPhee J.S. Pääsuke M. Gapeyeva H. Meskers C.G.M. Maier A.B. Serum albumin and muscle measures in a cohort of healthy young and old participants Age (Dordr) 37 2015 88 10.1007/s11357-015-9825-6 26310888
30 Jain R.B. Ducatman A. Perfluoroalkyl acids serum concentrations and their relationship to biomarkers of renal failure: serum and urine albumin, creatinine, and albumin creatinine ratios across the spectrum of glomerular function among US adults Environ. Res. 174 2019 143 151 10.1016/j.envres.2019.04.034 31077989
31 Schillemans T. Iszatt N. Remy S. Schoeters G. Fernández M.F. D'Cruz S.C. Desalegn A. Haug L.S. Lignell S. Lindroos A.K. Fábelová L. Murinova L.P. Kosjek T. Tkalec Ž. Gabriel C. Sarigiannis D. Pedraza-Díaz S. Esteban-López M. Castaño A. Rambaud L. Riou M. Pauwels S. Vanlarebeke N. Kolossa-Gehring M. Vogel N. Uhl M. Govarts E. Åkesson A. Cross-sectional associations between exposure to per- and polyfluoroalkyl substances and body mass index among European teenagers in the HBM4EU aligned studies Environ Pollut 316 2023 120566 10.1016/j.envpol.2022.120566
32 Liu G. Dhana K. Furtado J.D. Rood J. Zong G. Liang L. Qi L. Bray G.A. DeJonge L. Coull B. Grandjean P. Sun Q. Perfluoroalkyl substances and changes in body weight and resting metabolic rate in response to weight-loss diets: a prospective study PLoS Med. 15 2018 e1002502 10.1371/journal.pmed.1002502
33 Choi G.-W. Choi E.-J. Kim J.H. Kang D.W. Lee Y.-B. Cho H.-Y. Gender differences in pharmacokinetics of perfluoropentanoic acid using non-linear mixed-effect modeling in rats Arch. Toxicol. 94 2020 1601 1612 10.1007/s00204-020-02705-6 32170342
34 Fassler C.S. Pinney S.E. Xie C. Biro F.M. Pinney S.M. Complex relationships between perfluorooctanoate, body mass index, insulin resistance and serum lipids in young girls Environ. Res. 176 2019 108558 10.1016/j.envres.2019.108558
35 Pinney S.M. Windham G.C. Xie C. Herrick R.L. Calafat A.M. McWhorter K. Fassler C.S. Hiatt R.A. Kushi L.H. Biro F.M. Breast cancer and the environment research program, perfluorooctanoate and changes in anthropometric parameters with age in young girls in the greater Cincinnati and San Francisco bay area Int. J. Hyg Environ. Health 222 2019 1038 1046 10.1016/j.ijheh.2019.07.002 31300293
36 Tian Y.-P. Zeng X.-W. Bloom M.S. Lin S. Wang S.-Q. Yim S.H.L. Yang M. Chu C. Gurram N. Hu L.-W. Liu K.-K. Yang B.-Y. Feng D. Liu R.-Q. Nian M. Dong G.-H. Isomers of perfluoroalkyl substances and overweight status among Chinese by sex status: isomers of C8 Health Project in China Environ. Int. 124 2019 130 138 10.1016/j.envint.2019.01.006 30641256
37 Thomsen M.L. Henriksen L.S. Tinggaard J. Nielsen F. Jensen T.K. Main K.M. Associations between exposure to perfluoroalkyl substances and body fat evaluated by DXA and MRI in 109 adolescent boys Environ. Health 20 2021 73 10.1186/s12940-021-00758-3 34187491
38 Sadrabadi F. Alarcan J. Sprenger H. Braeuning A. Buhrke T. Impact of perfluoroalkyl substances (PFAS) and PFAS mixtures on lipid metabolism in differentiated HepaRG cells as a model for human hepatocytes Arch. Toxicol. 98 2024 507 524 10.1007/s00204-023-03649-3 38117326
39 Kn F. F K. Ba F. Ia M. Sw C. Integrative physiology of human adipose tissue Int. J. Obes. Relat. Metab. Disord. : Journal of the International Association for the Study of Obesity 27 2003 10.1038/sj.ijo.0802326
40 Pfohl M. Ingram L. Marques E. Auclair A. Barlock B. Jamwal R. Anderson D. Cummings B.S. Slitt A.L. Perfluorooctanesulfonic acid and perfluorohexanesulfonic acid alter the blood lipidome and the hepatic proteome in a murine model of diet-induced obesity Toxicol. Sci. 178 2020 311 324 10.1093/toxsci/kfaa148 32991729
41 Sant K.E. Annunziato K. Conlin S. Teicher G. Chen P. Venezia O. Downes G.B. Park Y. Timme-Laragy A.R. Developmental exposures to perfluorooctanesulfonic acid (PFOS) impact embryonic nutrition, pancreatic morphology, and adiposity in the zebrafish, Danio rerio Environ Pollut 275 2021 116644 10.1016/j.envpol.2021.116644
42 Mengeste A.M. Rustan A.C. Lund J. Skeletal muscle energy metabolism in obesity Obesity 29 2021 1582 1595 10.1002/oby.23227 34464025
43 González-Alvarez M.E. Keating A.F. Hepatic and ovarian effects of perfluorooctanoic acid exposure differ in lean and obese adult female mice Toxicol. Appl. Pharmacol. 474 2023 116614 10.1016/j.taap.2023.116614
44 Freire C. Vela-Soria F. Castiello F. Salamanca-Fernández E. Quesada-Jiménez R. López-Alados M.C. Fernandez M.F. Olea N. Exposure to perfluoroalkyl substances (PFAS) and association with thyroid hormones in adolescent males Int. J. Hyg Environ. Health 252 2023 114219 10.1016/j.ijheh.2023.114219
45 Guo J. Huang S. Yang L. Zhou J. Xu X. Lin S. Li H. Xie X. Wu S. Association between polyfluoroalkyl substances exposure and sex steroids in adolescents: the mediating role of serum albumin Ecotoxicol. Environ. Saf. 253 2023 114687 10.1016/j.ecoenv.2023.114687
46 Haug L.S. Thomsen C. Becher G. Time trends and the influence of age and gender on serum concentrations of perfluorinated compounds in archived human samples Environ. Sci. Technol. 43 2009 2131 2136 10.1021/es802827u 19368225
47 Manore M.M. Larson-Meyer D.E. Lindsay A.R. Hongu N. Houtkooper L. Dynamic energy balance: an integrated framework for discussing diet and physical activity in obesity prevention-is it more than eating less and exercising more? Nutrients 9 2017 905 10.3390/nu9080905 28825615
48 Xie X. Weng X. Liu S. Chen J. Guo X. Gao X. Fei Q. Hao G. Jing C. Feng L. Perfluoroalkyl and Polyfluoroalkyl substance exposure and association with sex hormone concentrations: results from the NHANES 2015-2016 Environ. Sci. Eur. 33 2021 69 10.1186/s12302-021-00508-9 36061407
49 Wolf C.J. Takacs M.L. Schmid J.E. Lau C. Abbott B.D. Activation of mouse and human peroxisome proliferator-activated receptor alpha by perfluoroalkyl acids of different functional groups and chain lengths Toxicol. Sci. 106 2008 162 171 10.1093/toxsci/kfn166 18713766
50 Wolf C.J. Schmid J.E. Lau C. Abbott B.D. Activation of mouse and human peroxisome proliferator-activated receptor-alpha (PPARα) by perfluoroalkyl acids (PFAAs): further investigation of C4-C12 compounds Reprod. Toxicol. 33 2012 546 551 10.1016/j.reprotox.2011.09.009 22107727
51 Zhang L. Ren X.-M. Wan B. Guo L.-H. Structure-dependent binding and activation of perfluorinated compounds on human peroxisome proliferator-activated receptor γ Toxicol. Appl. Pharmacol. 279 2014 275 283 10.1016/j.taap.2014.06.020 24998974
52 Zhang X. Ren X. Sun W. Griffin N. Wang L. Liu H. PFOA exposure induces aberrant glucose and lipid metabolism in the rat liver through the AMPK/mTOR pathway Toxicology 493 2023 153551 10.1016/j.tox.2023.153551
53 Loveless S.E. Finlay C. Everds N.E. Frame S.R. Gillies P.J. O'Connor J.C. Powley C.R. Kennedy G.L. Comparative responses of rats and mice exposed to linear/branched, linear, or branched ammonium perfluorooctanoate (APFO) Toxicology 220 2006 203 217 10.1016/j.tox.2006.01.003 16448737
54 Solan M.E. Koperski C.P. Senthilkumar S. Lavado R. Short-chain per- and polyfluoralkyl substances (PFAS) effects on oxidative stress biomarkers in human liver, kidney, muscle, and microglia cell lines Environ. Res. 223 2023 115424 10.1016/j.envres.2023.115424
55 Pavic D. A review of environmental and occupational toxins in relation to sex ratio at birth Early Hum. Dev. 141 2020 104873 10.1016/j.earlhumdev.2019.104873
56 Benninghoff A.D. Bisson W.H. Koch D.C. Ehresman D.J. Kolluri S.K. Williams D.E. Estrogen-like activity of perfluoroalkyl acids in vivo and interaction with human and rainbow trout estrogen receptors in vitro Toxicol. Sci. 120 2011 42 58 10.1093/toxsci/kfq379 21163906
57 Henry N.D. Fair P.A. Comparison of in vitro cytotoxicity, estrogenicity and anti-estrogenicity of triclosan, perfluorooctane sulfonate and perfluorooctanoic acid J. Appl. Toxicol. 33 2013 265 272 10.1002/jat.1736 21935973
58 Maras M. Vanparys C. Muylle F. Robbens J. Berger U. Barber J.L. Blust R. De Coen W. Estrogen-like properties of fluorotelomer alcohols as revealed by mcf-7 breast cancer cell proliferation Environ. Health Perspect. 114 2006 100 105 10.1289/ehp.8149 16393665
59 Zhao Y. Tan Y.S. Haslam S.Z. Yang C. Perfluorooctanoic acid effects on steroid hormone and growth factor levels mediate stimulation of peripubertal mammary gland development in C57BL/6 mice Toxicol. Sci. 115 2010 214 224 10.1093/toxsci/kfq030 20118188
60 Cook J.C. Murray S.M. Frame S.R. Hurtt M.E. Induction of Leydig cell adenomas by ammonium perfluorooctanoate: a possible endocrine-related mechanism Toxicol. Appl. Pharmacol. 113 1992 209 217 10.1016/0041-008x(92)90116-a 1561629
61 Barrett E.S. Chen C. Thurston S.W. Haug L.S. Sabaredzovic A. Fjeldheim F.N. Frydenberg H. Lipson S.F. Ellison P.T. Thune I. Perfluoroalkyl substances and ovarian hormone concentrations in naturally cycling women Fertil. Steril. 103 2015 1261 1270.e3 10.1016/j.fertnstert.2015.02.001 25747128
62 Rosen M.B. Thibodeaux J.R. Wood C.R. Zehr R.D. Schmid J.E. Lau C. Gene expression profiling in the lung and liver of PFOA-exposed mouse fetuses Toxicology 239 2007 15 33 10.1016/j.tox.2007.06.095 17681415
63 Spachmo B. Arukwe A. Endocrine and developmental effects in Atlantic salmon (Salmo salar) exposed to perfluorooctane sulfonic or perfluorooctane carboxylic acids Aquat. Toxicol. 108 2012 112 124 10.1016/j.aquatox.2011.07.018 22265611
64 Hu W. Dong T. Wang L. Guan Q. Song L. Chen D. Zhou Z. Chen M. Xia Y. Wang X. Obesity aggravates toxic effect of BPA on spermatogenesis Environ. Int. 105 2017 56 65 10.1016/j.envint.2017.04.014 28501790
65 Ding N. Harlow S.D. Randolph J.F. Loch-Caruso R. Park S.K. Perfluoroalkyl and polyfluoroalkyl substances (PFAS) and their effects on the ovary Hum. Reprod. Update 26 2020 724 752 10.1093/humupd/dmaa018 32476019
66 Bhasin S. Woodhouse L. Casaburi R. Singh A.B. Bhasin D. Berman N. Chen X. Yarasheski K.E. Magliano L. Dzekov C. Dzekov J. Bross R. Phillips J. Sinha-Hikim I. Shen R. Storer T.W. Testosterone dose-response relationships in healthy young men Am. J. Physiol. Endocrinol. Metab. 281 2001 E1172 E1181 10.1152/ajpendo.2001.281.6.E1172 11701431
67 Behnisch P.A. Besselink H. Weber R. Willand W. Huang J. Brouwer A. Developing potency factors for thyroid hormone disruption by PFASs using TTR-TRβ CALUX® bioassay and assessment of PFASs mixtures in technical products Environ. Int. 157 2021 106791 10.1016/j.envint.2021.106791
68 Li Y. Xu Y. Fletcher T. Scott K. Nielsen C. Pineda D. Lindh C.H. Olsson D.S. Andersson E.M. Jakobsson K. Associations between perfluoroalkyl substances and thyroid hormones after high exposure through drinking water Environ. Res. 194 2021 110647 10.1016/j.envres.2020.110647
69 Walczak K. Sieminska L. Obesity and thyroid Axis Int. J. Environ. Res. Publ. Health 18 2021 9434 10.3390/ijerph18189434
70 Dagar M. Kumari P. Mirza A.M.W. Singh S. Ain N.U. Munir Z. Javed T. Virk M.F.I. Javed S. Qizilbash F.H. Kc A. Ekhator C. Bellegarde S.B. The hidden threat: endocrine disruptors and their impact on insulin resistance Cureus 15 2023 e47282 10.7759/cureus.47282
71 Ding N. Karvonen-Gutierrez C.A. Herman W.H. Calafat A.M. Mukherjee B. Park S.K. Associations of perfluoroalkyl and polyfluoroalkyl substances (PFAS) and PFAS mixtures with adipokines in midlife women Int. J. Hyg Environ. Health 235 2021 113777 10.1016/j.ijheh.2021.113777
72 Kim T.N. Park M.S. Lim K.I. Choi H.Y. Yang S.J. Yoo H.J. Kang H.J. Song W. Choi H. Baik S.H. Choi D.S. Choi K.M. Relationships between sarcopenic obesity and insulin resistance, inflammation, and vitamin D status: the Korean Sarcopenic Obesity Study Clin. Endocrinol. 78 2013 525 532 10.1111/j.1365-2265.2012.04433.x
73 Papadopoulou E. Stratakis N. Basagaña X. Brantsæter A.L. Casas M. Fossati S. Gražulevičienė R. Småstuen Haug L. Heude B. Maitre L. McEachan R.R.C. Robinson O. Roumeliotaki T. Sabidó E. Borràs E. Urquiza J. Vafeiadi M. Zhao Y. Slama R. Wright J. Conti D.V. Vrijheid M. Chatzi L. Prenatal and postnatal exposure to PFAS and cardiometabolic factors and inflammation status in children from six European cohorts Environ. Int. 157 2021 106853 10.1016/j.envint.2021.106853
74 Omoike O.E. Pack R.P. Mamudu H.M. Liu Y. Strasser S. Zheng S. Okoro J. Wang L. Association between per and polyfluoroalkyl substances and markers of inflammation and oxidative stress Environ. Res. 196 2021 110361 10.1016/j.envres.2020.110361
75 Pîrsean C. Neguț C. Stefan-van Staden R.-I. Dinu-Pirvu C.E. Armean P. Udeanu D.I. The salivary levels of leptin and interleukin-6 as potential inflammatory markers in children obesity PLoS One 14 2019 e0210288 10.1371/journal.pone.0210288
76 Wu H. Ballantyne C.M. Skeletal muscle inflammation and insulin resistance in obesity J. Clin. Invest. 127 2017 43 54 10.1172/JCI88880 28045398
77 Reyna S.M. Ghosh S. Tantiwong P. Meka C.S.R. Eagan P. Jenkinson C.P. Cersosimo E. Defronzo R.A. Coletta D.K. Sriwijitkamol A. Musi N. Elevated toll-like receptor 4 expression and signaling in muscle from insulin-resistant subjects Diabetes 57 2008 2595 2602 10.2337/db08-0038 18633101
78 Corpeleijn E. Saris W.H.M. Jansen E.H.J.M. Roekaerts P.M.H.J. Feskens E.J.M. Blaak E.E. Postprandial interleukin-6 release from skeletal muscle in men with impaired glucose tolerance can be reduced by weight loss J. Clin. Endocrinol. Metab. 90 2005 5819 5824 10.1210/jc.2005-0668 16030153
79 Nguyen M.T.A. Favelyukis S. Nguyen A.-K. Reichart D. Scott P.A. Jenn A. Liu-Bryan R. Glass C.K. Neels J.G. Olefsky J.M. A subpopulation of macrophages infiltrates hypertrophic adipose tissue and is activated by free fatty acids via Toll-like receptors 2 and 4 and JNK-dependent pathways J. Biol. Chem. 282 2007 35279 35292 10.1074/jbc.M706762200 17916553
80 Hong E.-G. Ko H.J. Cho Y.-R. Kim H.-J. Ma Z. Yu T.Y. Friedline R.H. Kurt-Jones E. Finberg R. Fischer M.A. Granger E.L. Norbury C.C. Hauschka S.D. Philbrick W.M. Lee C.-G. Elias J.A. Kim J.K. Interleukin-10 prevents diet-induced insulin resistance by attenuating macrophage and cytokine response in skeletal muscle Diabetes 58 2009 2525 2535 10.2337/db08-1261 19690064
