Phthalate metabolites and sex steroid hormones in relation to obesity in US adults: NHANES 2013-2016

被引:4
|
作者
Zhang, Jiechang [3 ]
Gu, Wen [2 ]
Zhai, Shilei [2 ]
Liu, Yumeng [2 ]
Yang, Chengcheng [4 ]
Xiao, Lishun [2 ,5 ,6 ]
Chen, Ding [1 ]
机构
[1] Xuzhou Med Univ, Sch Med Informat & Engn, Xuzhou, Jiangsu, Peoples R China
[2] Xuzhou Med Univ, Sch Publ Hlth, Dept Biostat, Xuzhou 221004, Jiangsu, Peoples R China
[3] Jinan Univ, Zhuhai Peoples Hosp, Dept Cardiol, Zhuhai Clin Med Coll, Zhuhai, Guangdong, Peoples R China
[4] Sun Yat Sen Univ, Affiliated Hosp 5, Dept Ophthalmol, Zhuhai, Guangdong, Peoples R China
[5] Xuzhou Med Univ, Ctr Med Stat & Data Anal, Xuzhou, Jiangsu, Peoples R China
[6] Xuzhou Med Univ, Key Lab Human Genet & Environm Med, Xuzhou, Jiangsu, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
obesity; phthalates; sex hormones; machine learning; NHANES; DI(2-ETHYLHEXYL) PHTHALATE; URINARY CONCENTRATIONS; REPRODUCTIVE HORMONES; BINDING GLOBULIN; ASSOCIATIONS; CHILDHOOD; POPULATION; MECHANISMS; WOMEN; RISK;
D O I
10.3389/fendo.2024.1340664
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background Obesity and metabolic syndrome pose significant health challenges in the United States (US), with connections to disruptions in sex hormone regulation. The increasing prevalence of obesity and metabolic syndrome might be associated with exposure to phthalates (PAEs). Further exploration of the impact of PAEs on obesity is crucial, particularly from a sex hormone perspective.Methods A total of 7780 adult participants in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016 were included in the study. Principal component analysis (PCA) coupled with multinomial logistic regression was employed to elucidate the association between urinary PAEs metabolite concentrations and the likelihood of obesity. Weighted quartiles sum (WQS) regression was utilized to consolidate the impact of mixed PAEs exposure on sex hormone levels (total testosterone (TT), estradiol and sex hormone-binding globulin (SHBG)). We also delved into machine learning models to accurately discern obesity status and identify the key variables contributing most to these models.Results Principal Component 1 (PC1), characterized by mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) as major contributors, exhibited a negative association with obesity. Conversely, PC2, with monocarboxyononyl phthalate (MCNP), monocarboxyoctyl phthalate (MCOP), and mono(3-carboxypropyl) phthalate (MCPP) as major contributors, showed a positive association with obesity. Mixed exposure to PAEs was associated with decreased TT levels and increased estradiol and SHBG. During the exploration of the interrelations among obesity, sex hormones, and PAEs, models based on Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms demonstrated the best classification efficacy. In both models, sex hormones exhibited the highest variable importance, and certain phthalate metabolites made significant contributions to the model's performance.Conclusions Individuals with obesity exhibit lower levels of TT and SHBG, accompanied by elevated estradiol levels. Exposure to PAEs disrupts sex hormone levels, contributing to an increased risk of obesity in US adults. In the exploration of the interrelationships among these three factors, the RF and XGBoost algorithm models demonstrated superior performance, with sex hormones displaying higher variable importance.
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页数:15
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