Environmental exposure to perfluoroalkyl substances in early pregnancy, maternal glucose homeostasis and the risk of gestational diabetes: A prospective cohort study
Open accessAbstract
BACKGROUND: Humans are widely exposed to environmental perfluoroalkyl substances (PFAS), which may affect glucose homeostasis. However, research linking PFAS exposure to glucose homeostasis during pregnancy is limited and the results were inconsistent. We aimed to investigate the association between PFAS exposure and glucose homeostasis in pregnancy in a large prospective cohort. METHODS: A total of 2747 pregnant women who participated in the Shanghai Birth Cohort, had blood samples in early pregnancy and completed a 75 g oral glucose tolerance test (OGTT) at 24-28 gestational weeks were included. 10 PFAS were determined by high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS-MS) in the plasma samples in early pregnancy. Logistic regression was used to explore the associations between PFAS concentrations and gestational diabetes mellitus (GDM), while multiple linear regression was used to model the associations between PFAS and OGTT fasting, 1-h and 2-h glucose levels. Potential confounders were adjusted. Bayesian kernel machine regression (BKMR) and a quantile-based g-computation approach (qgcomp) were employed to explore the joint and independent effects of PFAS on glucose homeostasis. RESULTS: The incidence of GDM was 11.8%. One log-unit increment in plasma concentrations in early pregnancy was associated with an increased risk of GDM for perfluorobutane sulfonate (PFBS) (adjusted odd ratio (aOR) = 1.23, 95% confidence interval (95% CI): 1.05, 1.44) and perfluoroheptanoic acid (PFHpA) (aOR = 1.25, 95% CI: 1.07, 1.46). Perfluorooctane sulfonic acid (PFOS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluorohexanesulfonate (PFHxS) and PFHpA were positively correlated with 1-h and 2-h glucose levels. Results of the mixed exposure model showed that the joint effects of PFAS were significantly associated with abnormal glucose homeostasis; In the BKMR model, PFAS mixture exposure was positively associated with the GDM incidence, 1-h and 2-h glucose levels and negatively correlated with FBG level. A similar trend could be observed in qgcomp and the positive correlation between PFAS and 2-h glucose level was significant (β = 0.12, 95% CI: 0.04, 0.20). PFOS, PFNA and PFHpA may be the main contributors after controlling for other PFAS congeners. PFOS was significantly correlated with GDM incidence and 2-h glucose level, and PFHpA was significantly associated with FBG and 2-h glucose levels. The above associations were more prominent among women with a normal prepregnant BMI. CONCLUSIONS: Environmental exposure to PFAS may affect glucose homeostasis in pregnancy and increase the risk of GDM, especially in normal weight women.
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Yu et al. identified strong associations between higher levels of PFOS, PFOA, PFHxS, and Et-PFOSA-AcOH and increased HOMAβ, a marker of pancreatic β-cell function.
“Yu et al. [211] identified strong associations in cross-sectional data between higher levels of PFOS, PFOA, PFHxS, and Et-PFOSA-AcOH and increased HOMAβ , a marker of pancreatic β -cell function.”
From Industrial Applications, Environmental Fate, Human Exposure, and Health Effects of PFAS (2025) · cites this paper as [211]
Methods §2.1 identifies the data as coming from a longitudinal birth cohort, contrary to the claim that Yu et al. used cross-sectional data. Methods §2.2 lists the measured PFAS without Et-PFOSA-AcOH, and §2.1 describes glucose-tolerance outcomes rather than HOMAβ; our copy does not report the claimed HOMAβ associations. (LLM, conf=0.86)
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Cites
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Estimation of Average Concentration in the Presence of Nondetectable Values
Hornung, R. W., & Reed, L. D. (1990) · Applied Occupational and Environmental Hygiene · doi:10.1080/1047322x.1990.10389587 · Cited by 3,495 · see citing works
A Never-Ending Story of Per- and Polyfluoroalkyl Substances (PFASs)?
Wang, Z., DeWitt, J. C., Higgins, C. P., & Cousins, I. T. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.6b04806 · Cited by 1,921 · see citing works
The Pathophysiology of Gestational Diabetes Mellitus
Plows, J. F., Stanley, J. L., Baker, P. N., Reynolds, C. M., & Vickers, M. H. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19113342 · Cited by 1,874 · see citing works
McIntyre, H. D., Catalano, P., Zhang, C., Desoye, G., Mathiesen, E. R., & Damm, P. (2019) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0098-8 · Cited by 1,850 · see citing works
Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures
Bobb, J. F., Valeri, L., Henn, B. C., Christiani, D. C., Wright, R. O., Mazumdar, M., Godleski, J. J., & Coull, B. A. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu058 · Cited by 1,831 · see citing works
A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures
Keil, A. P., Buckley, J. P., O’Brien, K. M., Ferguson, K. K., Zhao, S., & White, A. J. (2020) · Environmental Health Perspectives · doi:10.1289/ehp5838 · Cited by 1,756 · see citing works
Sjöström, M., Ainsworth, B., Bauman, A. E., Bull, F., Hamilton-Craig, C., & Sallis, J. F. (2005) · Cited by 1,403 · see citing works
Reducing bias through directed acyclic graphs
Shrier, I., & Platt, R. W. (2008) · BMC Medical Research Methodology · doi:10.1186/1471-2288-8-70 · Cited by 1,394 · see citing works
The integrative biology of type 2 diabetes
Roden, M., & Shulman, G. I. (2019) · Nature · doi:10.1038/s41586-019-1797-8 · Cited by 1,167 · see citing works
Early Life Exposure to Endocrine Disrupting Chemicals and Childhood Obesity and Neurodevelopment
Braun, J. M. (2016) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2016.186 · Cited by 837 · see citing works
Gestational diabetes mellitus: mechanisms, treatment, and complications
Johns, E. C., Denison, F. C., Norman, J. E., & Reynolds, R. M. (2018) · Trends in Endocrinology & Metabolism · doi:10.1016/j.tem.2018.09.004 · Cited by 828 · see citing works
Longitudinal changes in insulin release and insulin resistance in nonobese pregnant women
Catalano, P. M., Tyzbir, E. D., Roman, N. M., Amini, S. B., & Sims, E. A. H. (1991) · American Journal of Obstetrics and Gynecology · doi:10.1016/0002-9378(91)90012-g · Cited by 709 · see citing works
Prevalence of gestational diabetes mellitus in mainland China: A systematic review and meta‐analysis
Gao, C., Sun, X., Lu, L., Liu, F., & Yuan, J. (2018) · Journal of Diabetes Investigation · doi:10.1111/jdi.12854 · Cited by 690 · see citing works
Domingo, J. L., & Nadal, M. (2019) · Environmental Research · doi:10.1016/j.envres.2019.108648 · Cited by 688 · see citing works
Dynamics of microglial activation: A confocal time-lapse analysis in hippocampal slices
Stence, N., Waite, M., & Dailey, M. E. (2001) · Glia · doi:10.1002/1098-1136(200103)33:3 · Cited by 617 · see citing works
Lowe, W. L., Scholtens, D. M., Kuang, A., Linder, B., Lawrence, J. M., Lebenthal, Y., McCance, D., Hamilton, J., Nodzenski, M., Talbot, O., et al. (2019) · Diabetes Care · doi:10.2337/dc18-1646 · Cited by 574 · see citing works
Gestational diabetes mellitus: an updated overview
CHIEFARI, E. D., Arcidiacono, B., FOTI, D. P., & Brunetti, A. (2017) · Journal of Endocrinological Investigation · doi:10.1007/s40618-016-0607-5 · Cited by 529 · see citing works
Liu, Y., & De, A. K. (2015) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2015.04.03.7 · Cited by 465 · see citing works
Takacs, M. L., & Abbott, B. D. (2007) · Toxicological Sciences · doi:10.1093/toxsci/kfl135 · Cited by 396 · see citing works
Distinct but complementary contributions of PPAR isotypes to energy homeostasis
Dubois, V., Eeckhoute, J., Lefèbvre, P., & Staels, B. (2017) · Journal of Clinical Investigation · doi:10.1172/jci88894 · Cited by 385 · see citing works
PFAS health effects database: protocol for a systematic evidence map
Pelch, K. E., Reade, A., Wolffe, T. A. M., & Kwiatkowski, C. F. (2019) · Environment International · doi:10.1016/j.envint.2019.05.045 · Cited by 273 · see citing works
Lin, C., Chen, P., Lin, Y.-C., & Lin, L. (2008) · Diabetes Care · doi:10.2337/dc08-1816 · Cited by 262 · see citing works
PPAR Agonists and Metabolic Syndrome: An Established Role?
Botta, M., Audano, M., Sahebkar, A., Sirtori, C. R., Mitro, N., & Ruscica, M. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19041197 · Cited by 253 · see citing works
Activation of Human Nuclear Receptors by Perfluoroalkylated Substances (PFAS)
Behr, A.-C., Plinsch, C., Braeuning, A., & Buhrke, T. (2020) · Toxicology in Vitro · doi:10.1016/j.tiv.2019.104700 · Cited by 235 · see citing works
PPARs Integrate the Mammalian Clock and Energy Metabolism
Chen, L., & Yang, G. (2014) · PPAR Research · doi:10.1155/2014/653017 · Cited by 223 · see citing works
Diet during early pregnancy and development of gestational diabetes
Radesky, J. S., Oken, E., Rifas‐Shiman, S. L., KLEINMAN, K. P., Rich-Edwards, J. W., & Gillman, M. W. (2007) · Paediatric and Perinatal Epidemiology · doi:10.1111/j.1365-3016.2007.00899.x · Cited by 211 · see citing works
Risk factors for gestational diabetes: An umbrella review of meta-analyses of observational studies
Giannakou, K., Εvangelou, Ε., Yiallouros, P. K., Christophi, C. A., Middleton, N., Papatheodorou, E., & Papatheodorou, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215372 · Cited by 195 · see citing works
Wang, Q., Tsui, M. M. P., Ruan, Y., Lin, H., Zhao, Z., Ku, J. P. H., Sun, H., & Lam, P. K. S. (2019) · Chemosphere · doi:10.1016/j.chemosphere.2019.05.162 · Cited by 190 · see citing works
Liu, Y., Colby, J. K., Zuo, X., Jaoude, J. C., Wei, D., & Shureiqi, I. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19113339 · Cited by 186 · see citing works
PPARs and Metabolic Disorders Associated with Challenged Adipose Tissue Plasticity
Corrales, P., Vidal‐Puig, A., & Medina‐Gómez, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19072124 · Cited by 185 · see citing works
Cohort profile: the Shanghai Birth Cohort
Zhang, J., Tian, Y., Wang, W., Ouyang, F., Xu, J., Yu, X., Luo, Z., Jiang, F., Huang, H. L., Shen, X., et al. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy277 · Cited by 183 · see citing works
Self-citationSun, Q., Zong, G., Valvi, D., Nielsen, F., Coull, B. A., & Grandjean, P. (2018) · Environmental Health Perspectives · doi:10.1289/ehp2619 · Cited by 175 · see citing works
Duan, Y., Sun, H., Yao, Y., Meng, Y., & Li, Y. (2020) · Environment International · doi:10.1016/j.envint.2019.105295 · Cited by 170 · see citing works
Zhou, W., Zhang, L., Tong, C., Fang, F., Zhao, S., Tian, Y., Tao, Y., Zhang, J., & Study, F. T. S. B. C. (2017) · Environmental Health Perspectives · doi:10.1289/ehp1203 · Cited by 159 · see citing works
Self-citationSantander, N. M., Valvi, D., López-Espinosa, M.-J., Manzano-Salgado, C. B., Ballester, F., Ibarluzea, J. M., Santa‐Marina, L., Schettgen, T., Guxens, M., Sunyer, J., et al. (2017) · Environmental Health Perspectives · doi:10.1289/ehp1062 · Cited by 159 · see citing works
Seo, S.-H., Son, M.-H., Choi, S., Lee, D., & Chang, Y. (2018) · Environment International · doi:10.1016/j.envint.2018.01.025 · Cited by 153 · see citing works
Cárdenas, A., Gold, D. R., Hauser, R. B., KLEINMAN, K. P., Hivert, M., Calafat, A. M., Ye, X., Webster, T. F., Horton, E. S., & Oken, E. (2017) · Environmental Health Perspectives · doi:10.1289/ehp1612 · Cited by 146 · see citing works
Zhang, H., Wang, Q., He, S., Wu, K., Ren, M., Dong, H., Di, J., Yu, Z., & Huang, C. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137349 · Cited by 143 · see citing works
Zhang, L., Liu, J., Hu, J., Liu, C., Guo, W., Wang, Q., & Wang, H. (2012) · Environmental Pollution · doi:10.1016/j.envpol.2011.09.001 · Cited by 142 · see citing works
Shapiro, G. D., Dodds, L. A., Arbuckle, T. E., Ashley‐Martin, J., Ettinger, A. S., Fisher, M. M., Taback, S. P., Bouchard, M. F., Monnier, P., Dallaire, R., et al. (2016) · Environmental Research · doi:10.1016/j.envres.2016.01.040 · Cited by 136 · see citing works
Zhang, C., Sundaram, R., Maisog, J., Calafat, A. M., Barr, D. B., & Buck Louis, G. M. (2015) · Fertility and Sterility · doi:10.1016/j.fertnstert.2014.10.001 · Cited by 135 · see citing works
Domazet, S. L., Grøntved, A., Timmermann, C. A. G., Nielsen, F., & Jensen, T. K. (2016) · Diabetes Care · doi:10.2337/dc16-0269 · Cited by 131 · see citing works
Rahman, M. L., Zhang, C., Smarr, M. M., Lee, S., Honda, M., Kannan, K., Ayele, F. T., & Louis, G. M. B. (2019) · Environment International · doi:10.1016/j.envint.2019.01.027 · Cited by 129 · see citing works
Circulating levels of perfluoroalkyl substances and prevalent diabetes in the elderly
Lind, L., Zethelius, B., Salihovic, S., van Bavel, B., & Lind, P. M. (2014) · Diabetologia · doi:10.1007/s00125-013-3126-3 · Cited by 128 · see citing works
Gestational diabetes and offspring birth size at elevated environmental pollutant exposures
Valvi, D., Oulhote, Y., Weihe, P., Dalgård, C., Bjerve, K. S., Steuerwald, U., & Grandjean, P. (2017) · Environment International · doi:10.1016/j.envint.2017.07.016 · Cited by 124 · see citing works
Risk Factors and Management of Gestational Diabetes
Chen, P., Wang, S., Ji, J., Ge, A., Chen, C., Zhu, Y., Xie, N., & Wang, Y. (2014) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-014-0248-2 · Cited by 119 · see citing works
Lee, J.-W., Lee, H.-K., Lim, J.-E., & Moon, H.-B. (2020) · Chemosphere · doi:10.1016/j.chemosphere.2020.126633 · Cited by 115 · see citing works
Wang, Q., Ruan, Y., Lin, H., & Lam, P. K. S. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139804 · Cited by 114 · see citing works
Yan, S., Zhang, H., Zheng, F., Sheng, N., Guo, X., & Dai, J. (2015) · Scientific Reports · doi:10.1038/srep11029 · Cited by 110 · see citing works
Blotsky, A., Rahme, E., Dahhou, M., Nakhla, M. M., & Dasgupta, K. (2019) · Canadian Medical Association Journal · doi:10.1503/cmaj.181001 · Cited by 89 · see citing works
Perfluoroalkyl substances and risk of type II diabetes: A prospective nested case-control study
Donat-Vargas, C., Bergdahl, I. A., Tornevi, A., Wennberg, M., Sommar, J., Kiviranta, H., Koponen, J., Rolandsson, O., & Åkesson, A. (2019) · Environment International · doi:10.1016/j.envint.2018.12.026 · Cited by 88 · see citing works
Xu, H., Zhou, Q., Zhang, J., Chen, X., Zhao, H., Lu, H., Ma, B., Wang, Z., Wu, C., Ying, C., et al. (2020) · Environment International · doi:10.1016/j.envint.2020.105952 · Cited by 87 · see citing works
Self-citationWang, X., Liu, L., Zhang, W., Zhang, J., Du, X., Huang, Q., Tian, M., & Shen, H. (2017) · Environmental Pollution · doi:10.1016/j.envpol.2017.04.086 · Cited by 82 · see citing works
Self-citationWang, Y., Zhang, L., Teng, Y., Zhang, J., Yang, L., Li, J., Lai, J., Zhao, Y., & Wu, Y. (2018) · Journal of Environmental Sciences · doi:10.1016/j.jes.2018.03.016 · Cited by 82 · see citing works
Self-citationAdiposity and Glycemic Control in Children Exposed to Perfluorinated Compounds
Timmermann, C. A. G., Rossing, L. I., Grøntved, A., Ried‐Larsen, M., Dalgård, C., Andersen, L. B., Grandjean, P., Nielsen, F., Svendsen, K. D., Scheike, T., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-3460 · Cited by 78 · see citing works
Perfluoroalkyl substances in older male anglers in Wisconsin.
Christensen, K., Raymond, M. R., Thompson, B. A., & Anderson, H. (2016) · Environment International · doi:10.1016/j.envint.2016.03.012 · Cited by 78 · see citing works
Wang, H., Yang, J., Du, H., Xu, L., Liu, S., Yi, J., Qian, X., Chen, Y., Jiang, Q., & He, G. (2018) · Environment International · doi:10.1016/j.envint.2018.01.027 · Cited by 78 · see citing works
Yang, Q., Guo, X., Sun, P., Chen, Y., Zhang, W., & Gao, A. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.10.074 · Cited by 77 · see citing works
Lebeaux, R. M., Doherty, B. T., Gallagher, L. G., Zoeller, R. T., Hoofnagle, A. N., Calafat, A. M., Karagas, M. R., Yolton, K. A., Chen, A., Lanphear, B. P., et al. (2020) · Environmental Research · doi:10.1016/j.envres.2020.109395 · Cited by 76 · see citing works
Perfluoroalkyl substances and beta cell deficient diabetes
Conway, B. N., Innes, K. E., & Long, D. M. (2016) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2016.05.001 · Cited by 75 · see citing works
Qin, W.-P., Cao, L.-Y., Li, C.-H., Guo, L.-H., Colbourne, J., & Ren, X.-M. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.9b07295 · Cited by 71 · see citing works
Basri, N. I., Mahdy, Z. A., Ahmad, S., Karim, A., Lim, P. S., Manaf, M. R. A., & Ismail, N. A. M. (2018) · Hormone Molecular Biology and Clinical Investigation · doi:10.1515/hmbci-2017-0077 · Cited by 66 · see citing works
Perfluoroalkyl substances and glycemic status in pregnant Danish women: The Odense Child Cohort
Jensen, R. C., Glintborg, D., Timmermann, C. A. G., Nielsen, F., Kyhl, H. B., Andersen, H. R., Grandjean, P., Jensen, T. K., & Andersen, M. (2018) · Environment International · doi:10.1016/j.envint.2018.04.010 · Cited by 61 · see citing works
Preston, E. V., Rifas‐Shiman, S. L., Hivert, M., Zota, A. R., Sagiv, S. K., Calafat, A. M., Oken, E., & James-Todd, T. M. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa328 · Cited by 58 · see citing works
Mancini, F. R., Rajaobelina, K., Praud, D., Dow, C., Antignac, J. P., Kvaskoff, M., Severi, G., Bonnet, F., Boutron-Ruault, M.-C., & Fagherazzi, G. (2018) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2018.07.007 · Cited by 55 · see citing works
Bodin, J., Groeng, E.-C., Andreassen, M., Dirven, H., & Nygaard, U. C. (2016) · Toxicology Reports · doi:10.1016/j.toxrep.2016.08.009 · Cited by 51 · see citing works
Huo, X., Huang, R., Gan, Y., Luo, K., Aimuzi, R., Nian, M., Ao, J., Feng, L., Tian, Y., Wang, W., et al. (2020) · Environment International · doi:10.1016/j.envint.2020.105656 · Cited by 49 · see citing works
Self-citationEnvironmental mixtures and children's health: identifying appropriate statistical approaches
Tanner, E. M., Lee, A., & Colicino, E. (2020) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000000877 · Cited by 45 · see citing works
Fang, X., Gao, G., Xue, H., Zhang, X., & Wang, H. (2012) · Toxicology · doi:10.1016/j.tox.2012.02.008 · Cited by 43 · see citing works
Perfluorooctane sulfonate-induced insulin resistance is mediated by protein kinase B pathway
Qiu, T., Chen, M., Sun, X., et al. (2016) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2016.06.135 · Cited by 39 · see citing works
Multitarget PPARγ Agonists as Innovative Modulators of the Metabolic Syndrome
Ammazzalorso, A., Maccallini, C., Amoia, P., & Amoroso, R. (2019) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2019.04.030 · Cited by 39 · see citing works
Mitro, S. D., Sagiv, S. K., Rifas‐Shiman, S. L., Calafat, A. M., Fleisch, A. F., Jaacks, L. M., Williams, P. L., Oken, E., & James-Todd, T. M. (2020) · Obesity · doi:10.1002/oby.22933 · Cited by 37 · see citing works
Reliability of perfluoroalkyl substances in plasma of 100 women in two consecutive pregnancies
Papadopoulou, E., Haug, L. S., Sabaredzovic, A., Eggesbø, M. Å., & Longnecker, M. P. (2015) · Environmental Research · doi:10.1016/j.envres.2015.04.022 · Cited by 36 · see citing works
Zheng, J., Wang, H., & Ren, M. (2017) · Journal of Endocrinological Investigation · doi:10.1007/s40618-017-0673-3 · Cited by 36 · see citing works
Mixture effects of 30 environmental contaminants on incident metabolic syndrome—A prospective study
Lind, L., Salihović, S., Lampa, E., & Lind, P. (2017) · Environment International · doi:10.1016/j.envint.2017.06.005 · Cited by 35 · see citing works
The Role and Future of FFA1 as a Therapeutic Target
Ghislain, J. J., & Poitout, V. (2016) · Handbook of experimental pharmacology · doi:10.1007/164_2016_51 · Cited by 25 · see citing works
Marks, K. J., Jeddy, Z., Flanders, W., Northstone, K., Fraser, A., Calafat, A., Kato, K., & Hartman, T. (2019) · Reproductive Toxicology · doi:10.1016/j.reprotox.2019.08.003 · Cited by 15 · see citing works
Xia, Y., Yan, H., Wang, L., et al. (2019) · PubMed · doi:10.3760/cma.j.issn.0254-6450.2019.06.004 · Cited by 6 · see citing works
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Zang, L., Liu, X., Xie, X., Zhou, X., Pan, Y., & Dai, J. (2023) · Environmental Pollution · doi:10.1016/j.envpol.2023.121504 · Cited by 42 · see citing works
Svoboda, L. K., Ishikawa, T., & Dolinoy, D. C. (2022) · Environmental Epigenetics · doi:10.1093/eep/dvac017 · Cited by 27 · see citing works
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Yang, M., Su, W., Li, H., Li, L., An, Z., Xiao, F., Liu, Y., Zhang, X., Liu, X., Guo, H., et al. (2023) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2023.115473 · Cited by 23 · see citing works
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“Similarly, Yu et al. [211] identified strong associations in cross-sectional data between higher levels of PFOS, PFOA, PFHxS, and Et-PFOSA-AcOH and increased HOMA-β , a marker of pancreatic β -cell function.”
- Authors
- Yu, G., Jin, M., Huang, Y., Aimuzi, R., Zheng, T., Nian, M., Tian, Y., Wang, W., Luo, Z., Shen, L., Wang, X., Du, Q., Xu, W., & Zhang, J.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Zhang, Jun
- Venue
- Environment International
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 156
- Pages
- 106621
- ISSN
- 0160-4120 (ISSN-L)
- OpenAlex
- W3161744331
- Cited by
- 141
- References
- 79
- Citations by year
- 2026: 27 · 2025: 33 · 2024: 29 · 2023: 29 · 2022: 20 · 2021: 3
- FWCI
- 7.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environment International (publishedVersion, cc-by-nc-nd)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Per- and polyfluoroalkyl substances research
- Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Birth, Development, and Health
- Keywords
- perfluorinated compounds, PFAS exposure, gestational diabetes mellitus, glucose homeostasis, early pregnancy, oral glucose tolerance test, PFBS, PFHpA, PFOS, perfluorononanoic acid, perfluorodecanoic acid, PFHxS, plasma PFAS concentrations, Shanghai Birth Cohort
- MeSH terms
- Bayes Theorem, China, Environmental Exposure, Environmental Pollutants, Female, Fluorocarbons, Glucose, Homeostasis, Humans, Pregnancy, Prospective Studies, Cohort Studies, Diabetes, Gestational, Alkanesulfonic Acids
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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