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Epidemiology and Population Health

Organochlorine pesticides and obesity in a rural prediabetic population: exploring bidirectional pathways with metabolic indicators

International Journal of Obesity (2026) Cite this article

Subjects

Abstract

Objective

Given the potential of organochlorine pesticides (OCPs) to disrupt metabolic health, we aimed to explore their association with obesity and to explore the potential bidirectional mediating relationships involving metabolic health indicators among 894 rural Chinese adults with prediabetes.

Methods

A total of 894 individuals were included in this cross-sectional study. The associations of plasma OCPs on obesity and obese anthropometric measurements were assessed by generalized linear regression models for single exposure, and quantile g-computation (QGC) and LASSO regression for mixed exposure. The potential contributions of multiple health indicators to observed associations were assessed through mediation analysis. Exploratory bidirectional mediation analysis was employed to assess two potential pathways: (1) whether metabolic health indicators mediate the association between OCP exposure and obesity, and (2) whether obesity mediates the relationship between OCP exposure and metabolic health indicators.

Results

We discovered that β-Benzene hexachloride (β-BHC) and p,p’-Dichlorodiphenyldichloroethylene (p,p’-DDE) were related to obesity for single exposure. QGC and LASSO demonstrated that OCPs were positively correlated with a higher likelihood of obesity for mixed exposure, with β-BHC being the primary contributor. Exploratory mediation analysis found that obesity and metabolic-related indicators play a bidirectional mediating role in the association with OCPs, mainly involving systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglyceride (TG), high-density lipoprotein-Cholesterol (HDL-C), low-density lipoprotein-Cholesterol (LDL-C), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase/alanine aminotransferase (AST/ALT).

Conclusions

In this cross-sectional study, we found that OCPs exposure may increase obesity risk both directly and by disrupting metabolism, while obesity itself can worsen OCP-related metabolic damage, revealing a bidirectional environment-body interaction.

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Fig. 1: Associations between organochlorine pesticides and obese indicators (dichotomous) from generalized linear regression model.
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Fig. 2: Estimated risk and weighted values of organochlorine pesticides for obesity by quantile-based g computation models.
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Data availability

The individual-level data supporting the findings of this study are not publicly available due to privacy and ethical restrictions protecting the confidentiality of participants from “Henan rural cohort”. As a result, the data used in this research and related data can be obtained upon reasonable request from the corresponding author. The analytical code used in this study is available from the corresponding author upon reasonable request for academic and non-commercial purposes.

Code availability

The individual-level data supporting the findings of this study are not publicly available due to privacy and ethical restrictions protecting the confidentiality of participants from “Henan rural cohort”. As a result, the data used in this research and related data can be obtained upon reasonable request from the corresponding author. The analytical code used in this study is available from the corresponding author upon reasonable request for academic and non-commercial purposes.

References

  1. Wong MCS, Huang J, Wang J, Chan PSF, Lok V, Chen X, et al. Global, regional and time-trend prevalence of central obesity: a systematic review and meta-analysis of 13.2 million subjects. Eur J Epidemiol. 2020;35:673–83. https://doi.org/10.1007/s10654-020-00650-3.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Chen Y, Peng Q, Yang Y, Zheng S, Wang Y, Lu W. The prevalence and increasing trends of overweight, general obesity, and abdominal obesity among Chinese adults: a repeated cross-sectional study. BMC Public Health. 2019;19:1293. https://doi.org/10.1186/s12889-019-7633-0.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Tian H, Xie H, Song G, Zhang H, Hu G. Prevalence of overweight and obesity among 2.6 million rural Chinese adults. Prevent Med. 2009;48:59–63. https://doi.org/10.1016/j.ypmed.2008.10.020.

    Article  Google Scholar 

  4. Koenen M, Hill MA, Cohen P, Sowers JR. Obesity, adipose tissue and vascular dysfunction. Circul Res. 2021;128:951–68. https://doi.org/10.1161/circresaha.121.318093.

    Article  CAS  Google Scholar 

  5. Klein S, Gastaldelli A, Yki-Järvinen H, Scherer PE. Why does obesity cause diabetes?. Cell Metab. 2022;34:11–20. https://doi.org/10.1016/j.cmet.2021.12.012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Coral DE, Fernandez-Tajes J, Tsereteli N, Pomares-Millan H, Fitipaldi H, Mutie PM, et al. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes. Nat Metab. 2023;5:237–47. https://doi.org/10.1038/s42255-022-00731-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kadowaki T, Isendahl J, Khalid U, Lee SY, Nishida T, Ogawa W, et al. Semaglutide once a week in adults with overweight or obesity, with or without type 2 diabetes in an East Asian population (STEP 6): a randomised, double-blind, double-dummy, placebo-controlled, phase 3a trial. Lancet Diab Endocrinol. 2022;10:193–206. https://doi.org/10.1016/s2213-8587(22)00008-0.

    Article  CAS  Google Scholar 

  8. Lingvay I, Sumithran P, Cohen RV, le Roux CW. Obesity management as a primary treatment goal for type 2 diabetes: time to reframe the conversation. Lancet. 2022;399:394–405. https://doi.org/10.1016/s0140-6736(21)01919-x.

    Article  CAS  PubMed  Google Scholar 

  9. Echouffo-Tcheugui JB, Selvin E. Prediabetes and what it means: the epidemiological evidence. In: Fielding JE, editor. Annual Review of Public Health, Vol 42, 2021. Annual Review of Public Health. 422021. p. 59–77.

  10. Lee H-S, Lee J-C, Lee I-K, Moon H-B, Chang Y-S, Jacobs DR Jr, et al. Associations among organochlorine pesticides, methanobacteriales, and obesity in Korean women. Plos One. 2011;6:e27773 https://doi.org/10.1371/journal.pone.0027773.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Dirinck E, Jorens PG, Covaci A, Geens T, Roosens L, Neels H, et al. Obesity and persistent organic pollutants: possible obesogenic effect of organochlorine pesticides and polychlorinated biphenyls. Obesity. 2011;19:709–14. https://doi.org/10.1038/oby.2010.133.

    Article  CAS  PubMed  Google Scholar 

  12. Lee D-H, Steffes MW, Sjödin A, Jones RS, Needham LL, Jacobs DR Jr. Low dose organochlorine pesticides and polychlorinated biphenyls predict obesity, dyslipidemia, and insulin resistance among people free of diabetes. Plos One. 2011;6:15977 https://doi.org/10.1371/journal.pone.0015977.

    Article  CAS  Google Scholar 

  13. C FC, Kamalesh T, Senthil Kumar P, Rangasamy G. An insights of organochlorine pesticides categories, properties, eco-toxicity and new developments in bioremediation process*. Environ Pollut. 2023;333:122114 https://doi.org/10.1016/j.envpol.2023.122114.

    Article  CAS  PubMed  Google Scholar 

  14. Guida Y, Carvalho GO, Capella R, Pozo K, Lino AS, Azeredo A, et al. Atmospheric occurrence of organochlorine pesticides and inhalation cancer risk in Urban Areas at Southeast Brazil. Environ Pollut. 2021;271:116359 https://doi.org/10.1016/j.envpol.2020.116359.

    Article  CAS  PubMed  Google Scholar 

  15. Zhang A, Liu W, Yuan H, Zhou S, Su Y, Li YF. Spatial distribution of hexachlorocyclohexanes in agricultural soils in Zhejiang province, China, and correlations with elevation and temperature. Environ Sci Technol. 2011;45:6303–8. https://doi.org/10.1021/es200488n.

    Article  CAS  PubMed  Google Scholar 

  16. Qiu X, Zhu T, Li J, Pan H, Li Q, Miao G, et al. Organochlorine pesticides in the air around the Taihu Lake, China. Environ Sci Technol. 2004;38:1368–74. https://doi.org/10.1021/es035052d.

    Article  CAS  PubMed  Google Scholar 

  17. Wang L, Zhang Z-F, Liu L-Y, Zhu F-J, Ma W-L. National-scale monitoring of historic used organochlorine pesticides (OCPs) and current used pesticides (CUPs) in Chinese surface soil: Old topic and new story. J Hazard Mater. 2023;443:130285 https://doi.org/10.1016/j.jhazmat.2022.130285.

    Article  CAS  PubMed  Google Scholar 

  18. Chen M, Chen L, Huang P. Assessment, composition and possible source of organochlorine pesticides in surface soils from Urumqi, China. Pedosphere. 2015;25:888–900. https://doi.org/10.1016/s1002-0160(15)30069-2.

    Article  CAS  Google Scholar 

  19. Lee DH, Porta M, Jacobs DR, Vandenberg LN. Chlorinated persistent organic pollutants, obesity, and type 2 diabetes. Endocr Rev. 2014;35:557–601. https://doi.org/10.1210/er.2013-1084.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lee DH, Lee IK, Porta M, Steffes M, Jacobs DR Jr. Relationship between serum concentrations of persistent organic pollutants and the prevalence of metabolic syndrome among non-diabetic adults: results from the National Health and Nutrition Examination Survey 1999-2002. Diabetologia. 2007;50:1841–51. https://doi.org/10.1007/s00125-007-0755-4.

    Article  CAS  PubMed  Google Scholar 

  21. Lee DH, Lind PM, Jacobs DR, Salihovic S, van Bavel B, Lind L. Polychlorinated biphenyls and organochlorine pesticides in plasma predict development of type 2 diabetes in the elderly. Diab Care. 2011;34:1778–84. https://doi.org/10.2337/dc10-2116.

    Article  Google Scholar 

  22. Peng F-J, Lin C-A, Wada R, Bodinier B, Iglesias-González A, Palazzi P, et al. Association of hair polychlorinated biphenyls and multiclass pesticides with obesity, diabetes, hypertension and dyslipidemia in NESCAV study. J Hazard Mater. 2024;461:132637 https://doi.org/10.1016/j.jhazmat.2023.132637.

    Article  CAS  PubMed  Google Scholar 

  23. Aminov Z, Carpenter DO. Serum concentrations of persistent organic pollutants and the metabolic syndrome in Akwesasne Mohawks, a Native American community. Environ Pollut. 2020;260:114004 https://doi.org/10.1016/j.envpol.2020.114004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li J, Wang P, Shi S, Xue J. Background biomonitoring of residue levels of 137 pesticides in the blood plasma of the general population in Beijing. Environ Monit Assess. 2018;190:315. https://doi.org/10.1007/s10661-018-6694-3.

    Article  CAS  PubMed  Google Scholar 

  25. Fan G, Liu Q, Bi J, Fang Q, Qin X, Wu M, et al. Associations of polychlorinated biphenyl and organochlorine pesticide exposure with hyperuricemia: modification by lifestyle factors. Environ Sci Pollut Res Int. 2023;30:106562–70. https://doi.org/10.1007/s11356-023-29938-z.

    Article  CAS  PubMed  Google Scholar 

  26. Yin S, Sun Y, Yu J, Su Z, Tong M, Zhang Y, et al. Prenatal exposure to organochlorine pesticides is associated with increased risk for neural tube defects. Sci Total Environ. 2021;770:145284 https://doi.org/10.1016/j.scitotenv.2021.145284.

    Article  CAS  PubMed  Google Scholar 

  27. Zhang M, Wang L, Li X, Song L, Luo D, Li Q, et al. Individual and mixtures of polychlorinated biphenyls and organochlorine pesticides exposure in relation to metabolic syndrome among Chinese adults. Sci Total Environ. 2023;877:162935 https://doi.org/10.1016/j.scitotenv.2023.162935.

    Article  CAS  PubMed  Google Scholar 

  28. Lee H, Gao Y, Ko E, Lee J, Lee H-K, Lee S, et al. Nonmonotonic response of type 2 diabetes by low concentration organochlorine pesticide mixture: Findings from multi-omics in zebrafish. J Hazard Mater. 2021;416:125956 https://doi.org/10.1016/j.jhazmat.2021.125956.

    Article  CAS  PubMed  Google Scholar 

  29. Liu Q, Fan G, Bi J, Qin X, Fang Q, Wu M, et al. Associations of polychlorinated biphenyls and organochlorine pesticides with metabolic dysfunction-associated fatty liver disease among Chinese adults: effect modification by lifestyle. Environ Res. 2024;240:117507 https://doi.org/10.1016/j.envres.2023.117507.

    Article  CAS  PubMed  Google Scholar 

  30. Rouxel E, Costet N, Monfort C, Audouze K, Cirugeda L, Gaudreau E, et al. Prenatal exposure to multiple persistent organic pollutants in association with adiposity markers and blood pressure in preadolescents. Environ Int. 2023;178:108056 https://doi.org/10.1016/j.envint.2023.108056.

    Article  CAS  PubMed  Google Scholar 

  31. Cano-Sancho G, Warembourg C, Güil N, Stratakis N, Lertxundi A, Irizar A, et al. Nutritional modulation of associations between prenatal exposure to persistent organic pollutants and childhood obesity: a prospective cohort study. Environmental Health Perspectives. 2023;131. https://doi.org/10.1289/ehp11258.

  32. Svensson K, Gennings C, Lindh C, Kiviranta H, Rantakokko P, Wikström S, et al. Prenatal exposures to mixtures of endocrine disrupting chemicals and sex-specific associations with children’s BMI and overweight at 5.5 years of age in the SELMA study. Environ Int. 2023;179:108176 https://doi.org/10.1016/j.envint.2023.108176.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Shi J, Wei D, Wang L, Xu Q, Wang J, Ma C, et al. Serum progesterone is negatively associated with hypertension and blood pressure indicators among men and postmenopausal women. Horm Metab Res. 2023;55:273–83. https://doi.org/10.1055/a-2024-0708.

    Article  CAS  PubMed  Google Scholar 

  34. Shi J, Wei D, Ma C, Geng J, Zhao M, Hou J, et al. Combined effects of organochlorine pesticides on type 2 diabetes mellitus: Insights from endocrine disrupting effects of hormones. Environ Pollut (Barking, Essex 1987). 2024;341:122867. https://doi.org/10.1016/j.envpol.2023.122867.

    Article  CAS  Google Scholar 

  35. Wai CT, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conjeevaram HS, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38:518–26.

    Article  PubMed  Google Scholar 

  36. Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43:1317–25. https://doi.org/10.1002/hep.21178.

    Article  CAS  PubMed  Google Scholar 

  37. Alberti KGMM, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications Part 1: Diagnosis and classification of diabetes mellitus - Provisional report of a WHO consultation. Diabet Med. 1998;15:539–53.

    Article  CAS  PubMed  Google Scholar 

  38. Amer Diabet A. Diagnosis and classification of diabetes mellitus. Diab Care. 2008;31:S55–S60. https://doi.org/10.2337/dc08-S055.

    Article  CAS  Google Scholar 

  39. Zhou BF. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults—study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 2002;15:83–96.

    PubMed  Google Scholar 

  40. Tan Q, Yang S, Wang B, Wang M, Yu L, Liang R, et al. Gene-environment interaction in long-term effects of polychlorinated biphenyls exposure on glucose homeostasis and type 2 diabetes: the modifying effects of genetic risk and lifestyle. J Hazard Mater. 2023;457:131757. https://doi.org/10.1016/j.jhazmat.2023.131757.

    Article  CAS  PubMed  Google Scholar 

  41. Yu YJ, Li ZC, Tian JL, Hao CJ, Kuang HX, Dong CY, et al. Why do people gain belly fat in rural areas? A study of urinary Metal(loid)s and abdominal obesity in China. Environ Sci Technol. 2023;57:7938–49. https://doi.org/10.1021/acs.est.2c09464.

    Article  CAS  PubMed  Google Scholar 

  42. Schisterman EF, Whitcomb BW, Buck Louis GM, Louis TA. Lipid adjustment in the analysis of environmental contaminants and human health risks. Environ Health Perspect. 2005;113:853–7. https://doi.org/10.1289/ehp.7640.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Teixeira D, Pestana D, Santos C, Correia-Sá L, Marques C, Norberto S, et al. Inflammatory and cardiometabolic risk on obesity: role of environmental xenoestrogens. J Clin Endocrinol Metab. 2015;100:1792–801. https://doi.org/10.1210/jc.2014-4136.

    Article  CAS  PubMed  Google Scholar 

  44. Stratakis N, Rock S, La Merrill MA, Saez M, Robinson O, Fecht D, et al. Prenatal exposure to persistent organic pollutants and childhood obesity: a systematic review and meta-analysis of human studies. Obes Rev. 2022;23. https://doi.org/10.1111/obr.13383.

  45. Henríquez-Hernández LA, Luzardo OP, Valerón PF, Zumbado M, Serra-Majem L, Camacho M, et al. Persistent organic pollutants and risk of diabetes and obesity on healthy adults: Results from a cross-sectional study in Spain. Sci Total Environ. 2017;607-608:1096–102. https://doi.org/10.1016/j.scitotenv.2017.07.075.

    Article  CAS  PubMed  Google Scholar 

  46. Mustieles V, Fernández MF, Martin-Olmedo P, González-Alzaga B, Fontalba-Navas A, Hauser R, et al. Human adipose tissue levels of persistent organic pollutants and metabolic syndrome components: combining a cross-sectional with a 10-year longitudinal study using a multi-pollutant approach. Environ Int. 2017;104:48–57. https://doi.org/10.1016/j.envint.2017.04.002.

    Article  CAS  PubMed  Google Scholar 

  47. Arrebola JP, Ocaña-Riola R, Arrebola-Moreno AL, Fernández-Rodríguez M, Martin-Olmedo P, Fernández MF, et al. Associations of accumulated exposure to persistent organic pollutants with serum lipids and obesity in an adult cohort from Southern Spain. Environ Pollut. 2014;195:9–15. https://doi.org/10.1016/j.envpol.2014.08.003.

    Article  CAS  PubMed  Google Scholar 

  48. Vrijheid M, Fossati S, Maitre L, Márquez S, Roumeliotaki T, Agier L, et al. Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach. Environ Health Perspect. 2020;128:67009 https://doi.org/10.1289/ehp5975.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Akbar L, Zuk AM, Martin ID, Liberda EN, Tsuji LJS. Potential obesogenic effect of a complex contaminant mixture on Cree First Nations adults of Northern Quebec, Canada. Environ Res. 2021;192:110478 https://doi.org/10.1016/j.envres.2020.110478.

    Article  CAS  PubMed  Google Scholar 

  50. Chevrier J, Dewailly É, Ayotte P, Mauriège P, Després JP, Tremblay A. Body weight loss increases plasma and adipose tissue concentrations of potentially toxic pollutants in obese individuals. Int J Obes Relat Metab Disord. 2000;24:1272–8. https://doi.org/10.1038/sj.ijo.0801380.

    Article  CAS  PubMed  Google Scholar 

  51. Stubleski J, Lind L, Salihovic S, Lind PM, Kärrman A. Longitudinal changes in persistent organic pollutants (POPs) from 2001 to 2009 in a sample of elderly Swedish men and women. Environ Res. 2018;165:193–200. https://doi.org/10.1016/j.envres.2018.04.009.

    Article  CAS  PubMed  Google Scholar 

  52. Yang C, Fang J, Sun X, Zhang W, Li J, Chen X, et al. Prenatal exposure to organochlorine pesticides and infant growth: a longitudinal study. Environ Int. 2021;148:106374 https://doi.org/10.1016/j.envint.2020.106374.

    Article  CAS  PubMed  Google Scholar 

  53. La Merrill MA, Vandenberg LN, Smith MT, Goodson W, Browne P, Patisaul HB, et al. Consensus on the key characteristics of endocrine-disrupting chemicals as a basis for hazard identification. Nat Rev Endocrinol. 2020;16:45–57. https://doi.org/10.1038/s41574-019-0273-8.

    Article  CAS  PubMed  Google Scholar 

  54. Lee DH, Steffes MW, Sjödin A, Jones RS, Needham LL, Jacobs DR Jr. Low dose of some persistent organic pollutants predicts type 2 diabetes: a nested case-control study. Environ Health Perspect. 2010;118:1235–42. https://doi.org/10.1289/ehp.0901480.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wahlang B, Appana S, Falkner KC, McClain CJ, Brock G, Cave MC. Insecticide and metal exposures are associated with a surrogate biomarker for non-alcoholic fatty liver disease in the National Health and Nutrition Examination Survey 2003-2004. Environ Sci Pollut Res. 2020;27:6476–87. https://doi.org/10.1007/s11356-019-07066-x.

    Article  CAS  Google Scholar 

  56. Serdar B, LeBlanc WG, Norris JM, Dickinson LM. Potential effects of polychlorinated biphenyls (PCBs) and selected organochlorine pesticides (OCPs) on immune cells and blood biochemistry measures: a cross-sectional assessment of the NHANES 2003-2004 data. Environ Health. 2014;13:114. https://doi.org/10.1186/1476-069x-13-114.

    Article  PubMed  Google Scholar 

  57. Hakkarainen K, Rantakokko P, Koponen J, Ruokojärvi P, Korkalainen M, Salomaa V, et al. Persistent organic pollutants associate with liver disease in a Finnish general population sample. Liver Int. 2023;43:2177–85. https://doi.org/10.1111/liv.15645.

    Article  CAS  PubMed  Google Scholar 

  58. Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circul Res. 2015;116:991–1006. https://doi.org/10.1161/circresaha.116.305697.

    Article  CAS  Google Scholar 

  59. Bays HE, Toth PP, Kris-Etherton PM, Abate N, Aronne LJ, Brown WV, et al. Obesity, adiposity, and dyslipidemia: a consensus statement from the National Lipid Association. J Clin Lipidol. 2013;7:304–83. https://doi.org/10.1016/j.jacl.2013.04.001.

    Article  PubMed  Google Scholar 

  60. Kelishadi R, Hemati Z, Qorbani M, Motlagh ME, Djalalinia S, Ahadi Z, et al. Association of alanine aminotransferase with different metabolic phenotypes of obesity in children and adolescents: the CASPIAN-V study. Front Endocrinol. 2020;11. https://doi.org/10.3389/fendo.2020.00358.

  61. Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity, kidney dysfunction and hypertension: mechanistic links. Nat Rev Nephrol. 2019;15:367–85. https://doi.org/10.1038/s41581-019-0145-4.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79:379–84. https://doi.org/10.1093/ajcn/79.3.379.

    Article  CAS  PubMed  Google Scholar 

  63. Bell RA, Chen H, Saldana S, Bertoni AG, Effoe VS, Hairston KG, et al. Comparison of measures of adiposity and cardiovascular disease risk factors among African American Adults: the Jackson Heart Study. J Racial Ethn Health Disparities. 2018;5:1230–7. https://doi.org/10.1007/s40615-018-0469-y.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, Magni P, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol. 2020;16:177–89. https://doi.org/10.1038/s41574-019-0310-7.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors are grateful to everyone who took part in the study.

Funding

This research was supported by the National Key Research and Development Program of China (Grant No.: 2023YFC2506505), the National Natural Science Foundation of China (Grant No.: 42177415, 21806146), the Postdoctoral Science Foundation of China (Grant No.: 2020T130604, 2021M702934), the Science and Technique Foundation of Henan Province (Grant No. 232102411006, 232102310213, 212102310074), the Scientific and Technological Innovation of Colleges and Universities in Henan Province Talent Support Program (Grant No. 22HASTIT044), the Young Backbone Teachers Program of Colleges and Universities in Henan Province (Grant No. 2021GGJS015), and the Excellent Youth Development Foundation of Zhengzhou University (Grant No. 2021ZDGGJS057).

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Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China

    Jiayu Shi, Dandan Wei, Cuicui Ma, Jintian Geng, Mengzhen Zhao, Jian Hou, Chongjian Wang & Zhenxing Mao

  2. Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China

    Wenqian Huo

  3. School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China

    Tao Jing

  4. Henan Institute of Food and Salt Industry Inspection Technology, Zhengzhou, Henan, PR China

    Zhuo Chen & Shan Huang

  5. Department of Social Medicine, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China

    Xin Zeng

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Contributions

Investigation, data curation, methodology, formal analysis, visualization, writing-original draft: JYS; investigation, data curation, methodology, formal analysis: DDW; investigation, writing-review & editing: CCM, JTG, and MZZ; Investigation, validation, writing-review & editing: JH; investigation, data curation, writing-review & editing: WQH, TJ, ZC, SH, and XZ; conceptualization, methodology, supervision, writing-reviewing & editing: CJW; conceptualization, methodology, investigation, validation, supervision, funding acquisition, project administration, writing-original draft: ZXM.

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Correspondence to Zhenxing Mao.

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Shi, J., Wei, D., Ma, C. et al. Organochlorine pesticides and obesity in a rural prediabetic population: exploring bidirectional pathways with metabolic indicators. Int J Obes (2026). https://doi.org/10.1038/s41366-026-02036-z

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