Positive association of exposure to per- and polyfluoroalkyl substances with arterial stiffness parameters and its interaction with sex hormone levels

doi:10.12122/j.issn.1673-4254.2026.10.16

Abstract

Abstract:

Objective To examine the associations of per- and polyfluoroalkyl substances (PFAS) with estimated pulse wave velocity (ePWV) and assess their interactions with estradiol and testosterone. Methods Based on the data of healthy adults with measurements of 5 PFAS from NHANES 2013-2016, we assessed linear and nonlinear associations of exposure to a single PFAS with ePWV using weighted linear regression and restricted cubic spline (RCS) models; weighted quantile sum (WQS) regression was used to estimate the association of PFAS mixture exposure with ePWV with additional age-stratified WQS analyses, and ePWVs between high and low mixture exposure groups were compared using doubly robust estimation. Additive and multiplicative interactions between PFAS and sex hormones were evaluated. Results After covariate adjustment, all the 5 PFAS were positively associated with ePWV. RCS analyses indicated that only PFOS had a significant nonlinear association with ePWV. WQS regression showed a positive association between PFAS mixture exposure and ePWV. Perfluorooctane sulfonate (PFOS) and perfluorohexane sulfonate (PFHS) contributed the most to the WQS index with weights of 47.5% and 24.3%, respectively. Age-stratified WQS analyses showed that positive association was mainly observed in middle-aged and older adults, with PFOS and PFHS retaining high weights across different age groups. In doubly robust analysis, ePWV was 1.284 m/s higher in the high- than in low-mixture exposure group (95%CI:1.078-1.490; P<0.001). Multiplicative interaction analyses suggested weaker positive PFAS-ePWV associations in participants with high estradiol or testosterone levels. Both of the sex hormones showed negative interactions with PFHS; estradiol also interacted negatively with PFOS and PFNA, whereas testosterone interacted negatively with PFOA. Conclusion PFAS exposure is associated with an elevated ePWV, with PFOS and PFHS as the key driving components. The positive association between PFAS and ePWV is relatively weaker in individuals with high levels of sex hormones.

Key words: per- and polyfluoroalkyl substances, arterial stiffness, sex hormones, interaction effect

Chaoyuan ZHENG, Yinglei YU, Haoyan ZHANG, Ying LI, Bing GUO. Positive association of exposure to per- and polyfluoroalkyl substances with arterial stiffness parameters and its interaction with sex hormone levels[J]. Journal of Southern Medical University, 2026, 46(10): 2471-2481.

Figures/Tables 10

References 35

[1]	Kwiatkowski CF, Andrews DQ, Birnbaum LS, et al. Scientific basis for managing PFAS as a chemical class[J]. Environ Sci Technol Lett, 2020, 7(8): 532-43. doi：10.1021/acs.estlett.0c00255
[2]	Evich MG, Davis MJB, McCord JP, et al. Per- and polyfluoroalkyl substances in the environment[J]. Science, 2022, 375(6580): eabg9065. doi：10.1126/science.abg9065
[3]	Zhou L, Andersson EM, Harari F, et al. Exposure to high levels of perfluoroalkyl substances through drinking water and risk of cardiovascular morbidity and mortality in a Swedish register-based study[J]. Environ Res, 2025, 286: 122765. doi：10.1016/j.envres.2025.122765
[4]	Meneguzzi A, Fava C, Castelli M, et al. Exposure to perfluoroalkyl chemicals and cardiovascular disease: experimental and epidemiological evidence[J]. Front Endocrinol, 2021, 12: 706352. doi：10.3389/fendo.2021.706352
[5]	Averina M, Brox J, Huber S, et al. Exposure to perfluoroalkyl substances (PFAS) and dyslipidemia, hypertension and obesity in adolescents. The Fit Futures study[J]. Environ Res, 2021, 195: 110740. doi：10.1016/j.envres.2021.110740
[6]	Kang N, Chen W, Osazuwa N, et al. Longitudinal associations of PFAS exposure with insulin sensitivity and β‑cell function among hispanic women with a history of gestational diabetes mellitus[J]. Diabetes Care, 2025, 48(4): 564-8. doi：10.2337/dc24-2056
[7]	Wittkopp S, Wu F, Windheim J, et al. Vascular endothelium as a target for perfluroalkyl substances (PFAs)[J]. Environ Res, 2022, 212: 113339. doi：10.1016/j.envres.2022.113339
[8]	Gump BB, Hill DT, Robinson M, et al. Perfluoroalkyl substances (PFAS) and lead (Pb) as "cardiovascular disruptors" in 9-11-year-old children living in Syracuse, New York, United States[J]. Environ Res, 2023, 236: 116758. doi：10.1016/j.envres.2023.116758
[9]	Connelly PJ, Casey H, Montezano AC, et al. Sex steroids receptors, hypertension, and vascular ageing[J]. J Hum Hypertens, 2022, 36(2): 120-5. doi：10.1038/s41371-021-00576-7
[10]	Wu B, Sheng N, Li Z, et al. Positive associations of perfluoroalkyl and polyfluoroalkyl substances with hypertension may be attenuated by endogenous sex hormones: a nationally representative cross-sectional study[J]. Hypertension, 2024, 81(8): 1799-810. doi：10.1161/hypertensionaha.123.22127
[11]	Heffernan KS, Stoner L, London AS, et al. Estimated pulse wave velocity as a measure of vascular aging[J]. PLoS One, 2023, 18(1): e0280896. doi：10.1371/journal.pone.0280896
[12]	Greve SV, Laurent S, Olsen MH. Estimated pulse wave velocity calculated from age and mean arterial blood pressure[J]. Pulse, 2017, 4(4): 175-9. doi：10.1159/000453073
[13]	Cheng WK, Kong FL, Pan HC, et al. Superior predictive value of estimated pulse wave velocity for all-cause and cardiovascular disease mortality risk in U.S. general adults[J]. BMC Public Health, 2024, 24(1): 600. doi：10.1186/s12889-024-18071-2
[14]	Greve SV, Blicher MK, Kruger R, et al. Estimated carotid-femoral pulse wave velocity has similar predictive value as measured carotid-femoral pulse wave velocity[J]. J Hypertens, 2016, 34(7): 1279-89. doi：10.1097/hjh.0000000000000935
[15]	Wu ML, Zhu ZW, Wan RJ, et al. Exposure to per- and polyfluoroalkyl substance and metabolic syndrome: a nationally representative cross-sectional study from NHANES, 2003-2018[J]. Environ Pollut, 2024, 346: 123615. doi：10.1016/j.envpol.2024.123615
[16]	Lai TJ, Liang YY, Guan FL, et al. Association between remnant cholesterol and arterial stiffness: Evidence from NHANES 1999-2018[J]. Nutr Metab Cardiovasc Dis, 2025, 35(8): 104013. doi：10.1016/j.numecd.2025.104013
[17]	Santos S, Maitre L, Warembourg C, et al. Applying the exposome concept in birth cohort research: a review of statistical approaches[J]. Eur J Epidemiol, 2020, 35(3): 193-204. doi：10.1007/s10654-020-00625-4
[18]	Zong SL, Wang L, Wang ST, et al. Exposure to per- and polyfluoroalkyl substances is associated with impaired cardiovascular health: a cross-sectional study[J]. Front Public Health, 2024, 12: 1418134. doi：10.3389/fpubh.2024.1418134
[19]	Zhu L, Liu BK, Hu Y, et al. Per- and polyfluoroalkyl substances, apolipoproteins and the risk of coronary heart disease in US men and women[J]. Environ Health, 2024, 23(1): 108. doi：10.1186/s12940-024-01147-2
[20]	Schlezinger JJ, Gokce N. Perfluoroalkyl/polyfluoroalkyl substances: links to cardiovascular disease risk[J]. Circ Res, 2024, 134(9): 1136-59. doi：10.1161/circresaha.124.323697
[21]	Wen ZJ, Wei YJ, Zhang YF, et al. A review of cardiovascular effects and underlying mechanisms of legacy and emerging per- and polyfluoroalkyl substances (PFAS)[J]. Arch Toxicol, 2023, 97(5): 1195-245. doi：10.1007/s00204-023-03477-5
[22]	Connolly JC, Ishihara Y, Sawaya E, et al. Per- and polyfluoroalkyl substances (PFAS) enhance cholesterol accumulation and dysregulate inflammatory responses in macrophages[J]. Cardiovasc Toxicol, 2025, 25(10): 1455-70. doi：10.1007/s12012-025-10048-w
[23]	Vajeethaveesin N, Kanitwithayanun J, Suriyo T, et al. Perfluorooctane sulfonic acid: a possible risk factor of endothelial dysfunction based on in silico and in vitro studies[J]. Arch Toxicol, 2025, 99(7): 2975-91. doi：10.1007/s00204-025-04047-7
[24]	Juhasz AL, Kastury F, Jones R, et al. PFOA, PFOS and PFHxS toxicokinetic considerations for the development of an in vivo approach for assessing PFAS relative bioavailability in soil[J]. Environ Int, 2025, 195: 109232. doi：10.1016/j.envint.2024.109232
[25]	Fischer FC, Ludtke S, Thackray C, et al. Binding of per- and polyfluoroalkyl substances (PFAS) to serum proteins: implications for toxicokinetics in humans[J]. Environ Sci Technol, 2024, 58(2): 1055-63. doi：10.1021/acs.est.3c07415
[26]	Nevzati E, Shafighi M, Bakhtian KD, et al. Estrogen induces nitric oxide production via nitric oxide synthase activation in endothelial cells[M]//Neurovascular Events After Subarachnoid Hemorrhage: Towards Experimental and Clinical Standardisation. Cham: Springer International Publishing, 2015: : 141-5. doi：10.1007/978-3-319-04981-6_24
[27]	Perusquía M, Stallone JN. Do androgens play a beneficial role in the regulation of vascular tone? Nongenomic vascular effects of testosterone metabolites[J]. Am J Physiol Heart Circ Physiol, 2010, 298(5): H1301-7. doi：10.1152/ajpheart.00753.2009
[28]	He YJ, Liao HL, Yang G, et al. Perfluorohexanesulfonic acid (PFHxS) impairs lipid homeostasis in zebrafish larvae through activation of PPARα[J]. Environ Sci Technol, 2024, 58(37): 16258-68. doi：10.1021/acs.est.4c03053
[29]	Han R, Hu MX, Zhong Q, et al. Perfluorooctane sulphonate induces oxidative hepatic damage via mitochondria-dependent and NF‑κB/TNF-α-mediated pathway[J]. Chemosphere, 2018, 191: 1056-64. doi：10.1016/j.chemosphere.2017.08.070
[30]	Moreau KL. Modulatory influence of sex hormones on vascular aging[J]. Am J Physiol Heart Circ Physiol, 2019, 316(3): H522-6. doi：10.1152/ajpheart.00745.2017
[31]	Benninghoff AD, Bisson WH, Koch DC, et al. Estrogen-like activity of perfluoroalkyl acids in vivo and interaction with human and rainbow trout estrogen receptors in vitro [J]. Toxicol Sci, 2011, 120(1): 42-58. doi：10.1093/toxsci/kfq379
[32]	Kudo N, Katakura M, Sato Y, et al. Sex hormone-regulated renal transport of perfluorooctanoic acid[J]. Chem Biol Interact, 2002, 139(3): 301-16. doi：10.1016/s0009-2797(02)00006-6
[33]	VanderWeele TJ. Explanation in causal inference: developments in mediation and interaction[J]. Int J Epidemiol, 2016: dyw277. doi：10.1093/ije/dyw277
[34]	An ZW, Mei YY, Tan ZZ, et al. Association between per- and polyfluoroalkyl substances exposure and blood pressure: From epidemiological evidence to mechanistic insights[J]. Ecotoxicol Environ Saf, 2025, 303: 118888. doi：10.1016/j.ecoenv.2025.118888
[35]	Shi YM, Yu C, Zhou W, et al. Estimated pulse wave velocity as a predictor of all-cause and cardiovascular mortality in patients with hypertension in China: a prospective cohort study[J]. Front Cardiovasc Med, 2024, 11: 1365344. doi：10.3389/fcvm.2024.1365344

Variable	Overall (n=1869)	2013-2014 (n=918)	2015-2016 (n=951)	P
Age (years)	42.4±15.5	42.5±15.4	42.2±-15.6	0.582
Gender				0.917
Male	864 (46.2%)	426 (46.4%)	438 (46.1%)
Female	1005 (53.8%)	492 (53.6%)	513 (53.9%)
Race/ethnicity				<0.001
Mexican american	289 (15.5%)	125 (13.6%)	164 (17.2%)
Other hispanic	219 (11.7%)	86 (9.4%)	133 (14.0%)
Non-hispanic white	675 (36.1%)	377 (41.1%)	298 (31.3%)
Non-hispanic black	373 (20.0%)	170 (18.5%)	203 (21.3%)
Other	313 (16.7%)	160 (17.4%)	153 (16.1%)
Education level				0.247
Low	364 (19.5%)	189 (20.6%)	175 (18.4%)
High	1503 (80.5%)	727 (79.4%)	776 (81.6%)
Missing	2	2	0
BMI category				0.459
Underweight	31 (1.7%)	17 (1.9%)	14 (1.5%)
Normal	558 (30.0%)	269 (29.5%)	289 (30.5%)
Overweight	616 (33.1%)	316 (34.6%)	300 (31.6%)
Obese	657 (35.3%)	311 (34.1%)	346 (36.5%)
Missing	7	5	2
Smoking status				0.919
Never	1158 (62.1%)	565(61.6%)	593 (62.5%)
Former	322 (17.3%)	161(17.6%)	161 (17.0%)
Current	386 (20.7%)	191(20.8%)	195 (20.5%)
Missing	3	1	2
Alcohol drinking				0.078
No	,043 (67.9%)	494 (65.7%)	549 (70.0%)
Yes	493 (32.1%)	258 (34.3%)	235 (30.0%)
Missing	333	166	167
Physical activity				0.168
Inactive	240 (16.7%)	127 (18.1%)	113 (15.3%)
Active	1199 (83.3%)	573 (81.9%)	626 (84.7%)
Missing	430	218	212
Menopausal status				0.560
Postmenopausal	304 (17.2%)	142 (16.3%)	162 (18.0%)
Premenopausal	602 (34.0%)	304 (34.9%)	298 (33.2%)
Missing	99 (5.3%)	46 (5.0%)	53 (5.6%)
Ever used female hormone therapy				0.978
No	798 (45.1%)	391 (44.9%)	407 (45.3%)
Yes	108 (6.1%)	54 (6.2%)	54 (6.0%)
Missing	99 (5.3%)	47 (5.1%)	52 (5.5%)
Dietary types				0.265
Normal	1532 (87.0%)	761 (88.2%)	771 (86.0%)
Low calorie diet	146 (8.3%)	66 (7.6%)	80 (8.9%)
Low fat diet	32 (1.8%)	12 (1.4%)	20 (2.2%)
Low salt diet	11 (0.6%)	3 (0.3%)	8 (0.9%)
Other special diet	39 (2.3%)	21 (2.4%)	18 (2.0%)
Missing	109 (5.8%)	55 (6.0%)	54 (5.7%)
Ever thyroid problem				0.711
No	1717 (92.0%)	846 (92.3%)	871 (91.7%)
Yes	150 (8.0%)	71 (7.7%)	79 (8.3%)
Missing	2 (0.1%)	1 (0.1%)	1 (0.1%)
Decreased kidney function or albuminuria				0.253
No	1712 (91.6%)	834 (90.8%)	878 (92.4%)
Yes	156 (8.4%)	84 (9.2%)	72 (7.6%)
Missing	1 (0.1%)	0 (0.0%)	1 (0.1%)
Elevated triglycerides				0.208
No	1686 (90.3%)	820 (89.3%)	866 (91.2%)
es	182 (9.7%)	98 (10.7%)	84 (8.8%)
Missing	1 (0.1%)	0 (0.0%)	1 (0.1%)
High total cholesterol				0.510
No	1204 (64.5%)	599 (65.3%)	605 (63.7%)
Yes	664 (35.5%)	319 (34.7%)	345 (36.3%)
Missing	1 (0.1%)	0 (0.0%)	1 (0.1%)
Fasting plasma glucose (mg/dL)	98.5 (93.0, 106.0)	97.0 (91.0, 103.0)	99.5 (93.0, 107.0)	<0.001
Missing	939 (50.2%)	460 (50.1%)	479 (50.4%)
Estimated pulse wave velocity (m/s)	7.4±1.6	7.4±1.6	7.4±1.6	0.654
Perfluorooctanoic acid (ng/mL)	1.8 (1.1, 2.6)	1.9 (1.3, 2.8)	1.6 (1.0, 2.4)	<0.001
Perfluorooctane sulfonate (ng/mL)	4.8 (2.8, 8.2)	5.1 (3.1, 8.5)	4.6 (2.7, 7.8)	0.004
Perfluorononanoic acid (ng/mL)	0.6 (0.4, 1.0)	0.7 (0.5, 1.0)	0.6 (0.4, 0.9)	<0.001
Perfluorohexane sulfonate (ng/mL)	1.2 (0.6, 2.1)	1.3 (0.7, 2.3)	1.1 (0.6, 1.9)	<0.001
Perfluorodecanoic acid (ng/mL)	0.2 (0.1, 0.3)	0.2 (0.1, 0.3)	0.1 (0.1, 0.3)	<0.001
Estradiol (pg/mL)	25.8 (16.0, 50.0)	26.1 (15.5, 50.6)	25.6 (16.4, 48.2)	0.676
Testosterone (ng/mL)	45.0 (19.9, 398.0)	46.1 (19.9, 395.0)	44.1 (20.0, 405.0)	0.860

Variable	Overall (n=1869)	2013-2014 (n=918)	2015-2016 (n=951)	P
Age (years)	42.4±15.5	42.5±15.4	42.2±-15.6	0.582
Gender				0.917
Male	864 (46.2%)	426 (46.4%)	438 (46.1%)
Female	1005 (53.8%)	492 (53.6%)	513 (53.9%)
Race/ethnicity				<0.001
Mexican american	289 (15.5%)	125 (13.6%)	164 (17.2%)
Other hispanic	219 (11.7%)	86 (9.4%)	133 (14.0%)
Non-hispanic white	675 (36.1%)	377 (41.1%)	298 (31.3%)
Non-hispanic black	373 (20.0%)	170 (18.5%)	203 (21.3%)
Other	313 (16.7%)	160 (17.4%)	153 (16.1%)
Education level				0.247
Low	364 (19.5%)	189 (20.6%)	175 (18.4%)
High	1503 (80.5%)	727 (79.4%)	776 (81.6%)
Missing	2	2	0
BMI category				0.459
Underweight	31 (1.7%)	17 (1.9%)	14 (1.5%)
Normal	558 (30.0%)	269 (29.5%)	289 (30.5%)
Overweight	616 (33.1%)	316 (34.6%)	300 (31.6%)
Obese	657 (35.3%)	311 (34.1%)	346 (36.5%)
Missing	7	5	2
Smoking status				0.919
Never	1158 (62.1%)	565(61.6%)	593 (62.5%)
Former	322 (17.3%)	161(17.6%)	161 (17.0%)
Current	386 (20.7%)	191(20.8%)	195 (20.5%)
Missing	3	1	2
Alcohol drinking				0.078
No	,043 (67.9%)	494 (65.7%)	549 (70.0%)
Yes	493 (32.1%)	258 (34.3%)	235 (30.0%)
Missing	333	166	167
Physical activity				0.168
Inactive	240 (16.7%)	127 (18.1%)	113 (15.3%)
Active	1199 (83.3%)	573 (81.9%)	626 (84.7%)
Missing	430	218	212
Menopausal status				0.560
Postmenopausal	304 (17.2%)	142 (16.3%)	162 (18.0%)
Premenopausal	602 (34.0%)	304 (34.9%)	298 (33.2%)
Missing	99 (5.3%)	46 (5.0%)	53 (5.6%)
Ever used female hormone therapy				0.978
No	798 (45.1%)	391 (44.9%)	407 (45.3%)
Yes	108 (6.1%)	54 (6.2%)	54 (6.0%)
Missing	99 (5.3%)	47 (5.1%)	52 (5.5%)
Dietary types				0.265
Normal	1532 (87.0%)	761 (88.2%)	771 (86.0%)
Low calorie diet	146 (8.3%)	66 (7.6%)	80 (8.9%)
Low fat diet	32 (1.8%)	12 (1.4%)	20 (2.2%)
Low salt diet	11 (0.6%)	3 (0.3%)	8 (0.9%)
Other special diet	39 (2.3%)	21 (2.4%)	18 (2.0%)
Missing	109 (5.8%)	55 (6.0%)	54 (5.7%)
Ever thyroid problem				0.711
No	1717 (92.0%)	846 (92.3%)	871 (91.7%)
Yes	150 (8.0%)	71 (7.7%)	79 (8.3%)
Missing	2 (0.1%)	1 (0.1%)	1 (0.1%)
Decreased kidney function or albuminuria				0.253
No	1712 (91.6%)	834 (90.8%)	878 (92.4%)
Yes	156 (8.4%)	84 (9.2%)	72 (7.6%)
Missing	1 (0.1%)	0 (0.0%)	1 (0.1%)
Elevated triglycerides				0.208
No	1686 (90.3%)	820 (89.3%)	866 (91.2%)
es	182 (9.7%)	98 (10.7%)	84 (8.8%)
Missing	1 (0.1%)	0 (0.0%)	1 (0.1%)
High total cholesterol				0.510
No	1204 (64.5%)	599 (65.3%)	605 (63.7%)
Yes	664 (35.5%)	319 (34.7%)	345 (36.3%)
Missing	1 (0.1%)	0 (0.0%)	1 (0.1%)
Fasting plasma glucose (mg/dL)	98.5 (93.0, 106.0)	97.0 (91.0, 103.0)	99.5 (93.0, 107.0)	<0.001
Missing	939 (50.2%)	460 (50.1%)	479 (50.4%)
Estimated pulse wave velocity (m/s)	7.4±1.6	7.4±1.6	7.4±1.6	0.654
Perfluorooctanoic acid (ng/mL)	1.8 (1.1, 2.6)	1.9 (1.3, 2.8)	1.6 (1.0, 2.4)	<0.001
Perfluorooctane sulfonate (ng/mL)	4.8 (2.8, 8.2)	5.1 (3.1, 8.5)	4.6 (2.7, 7.8)	0.004
Perfluorononanoic acid (ng/mL)	0.6 (0.4, 1.0)	0.7 (0.5, 1.0)	0.6 (0.4, 0.9)	<0.001
Perfluorohexane sulfonate (ng/mL)	1.2 (0.6, 2.1)	1.3 (0.7, 2.3)	1.1 (0.6, 1.9)	<0.001
Perfluorodecanoic acid (ng/mL)	0.2 (0.1, 0.3)	0.2 (0.1, 0.3)	0.1 (0.1, 0.3)	<0.001
Estradiol (pg/mL)	25.8 (16.0, 50.0)	26.1 (15.5, 50.6)	25.6 (16.4, 48.2)	0.676
Testosterone (ng/mL)	45.0 (19.9, 398.0)	46.1 (19.9, 395.0)	44.1 (20.0, 405.0)	0.860

Hormone	Hormone level	β (95% CI)	P interaction
Estradiol	Low	0.891 (0.751, 1.032)	<0.01
	Moderate	0.670 (0.558, 0.783)
	High	0.428 (0.291, 0.565)
Testosterone	Low	0.814 (0.659, 0.970)	<0.001
	Moderate	0.432 (0.335, 0.529)
	High	0.193 (0.099, 0.287)

Hormone	Hormone level	β (95% CI)	P interaction
Estradiol	Low	0.891 (0.751, 1.032)	<0.01
	Moderate	0.670 (0.558, 0.783)
	High	0.428 (0.291, 0.565)
Testosterone	Low	0.814 (0.659, 0.970)	<0.001
	Moderate	0.432 (0.335, 0.529)
	High	0.193 (0.099, 0.287)

E2				T
PFAS	Hormone level	β (95% CI)	P interaction	PFAS	Hormone level	β (95% CI)	P interaction
PFOA			0.134	PFOA			<0.001
	Low	0.59 (0.36, 0.82)			Low	0.87 (0.68, 1.07)
	Moderate	0.29 (0.14, 0.44)			Moderate	0.57 (0.43, 0.71)
	High	0.21 (0.10, 0.32)			High	0.19 (-0.06, 0.41)
PFOS			<0.001	PFOS			0.092
	Low	0.81 (0.63, 0.99)			Low	0.79 (0.63, 0.95)
	Moderate	0.53 (0.41, 0.65)			Moderate	0.63 (0.52, 0.74)
	High	0.14 (0.05, 0.23)			High	0.50 (0.31, 0.69)
PFNA			<0.01	PFNA			0.278
	Low	0.75 (0.52, 0.98)			Low	0.79 (0.59, 0.98)
	Moderate	0.45 (0.32, 0.59)			Moderate	0.62 (0.48, 0.76)
	High	0.19 (0.09, 0.30)			High	0.50 (0.28, 0.71)
PFHS			<0.001	PFHS			<0.001
	Low	0.69 (0.50, 0.88)			Low	0.85 (0.70, 1.01)
	Moderate	0.24 (0.12, 0.35)			Moderate	0.42 (0.31, 0.53)
	High	0.10 (0.01, 0.19)			High	0.23 (0.04, 0.42)
PFDA			0.780	PFDA			0.884
	Low	0.38 (0.18, 0.59)			Low	0.44 (0.24, 0.63)
	Moderate	0.28 (0.17, 0.40)			Moderate	0.36 (0.24, 0.48)
	High	0.20 (0.11, 0.30)			High	0.28 (0.10, 0.46)