XingWang Zhu, HaiPing Wang, HongLong Zhang, Guole Nie, Jun Yan, Xun Li
{"title":"基于全国健康与营养调查的代谢相关指标对非肥胖人群非酒精性脂肪肝的影响","authors":"XingWang Zhu, HaiPing Wang, HongLong Zhang, Guole Nie, Jun Yan, Xun Li","doi":"10.1155/2024/7081486","DOIUrl":null,"url":null,"abstract":"<p><i>Objective</i>. Nonalcoholic fatty liver disease (NAFLD) is becoming more prevalent in the nonobese population. The aim of this study was to investigate the combined effects of metabolism-related mixtures on NAFLD subjects in nonobese populations using four statistical models. <i>Study Design.</i> This was a retrospective observational study. <i>Methods.</i> Our study included 904 nonobese patients who had taken part in the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used logistic regression models, Bayesian kernel machine regression (BKMR), and the weighted quantile sum (WQS) regression model to estimate the association between metabolism-related indicators and NAFLD in the nonobese population. Finally, we included several indicators to create nomograms to predict the risk of NAFLD occurrence in the nonobese population. <i>Results</i>. Among the 904 participants, 116 (12.83%) had NAFLD. The logistic regression model found that the waist-to-hip ratio (WHR), HDL-c, triglyceride (TG), and HbA1c were positively associated with the outcomes. The WQS regression model showed that the WQS index was significantly associated with the occurrence of NAFLD in the nonobese population (OR: 5.789, 95% CI: 3.933–8.520), and WHR, TC, and TG had the largest weight. The BKMR model’s WHR and TG increased from the 25th percentile to the 75th percentile (other metabolite exposure remained fixed at the 75th percentile) and the risk of developing NAFLD increased in the nonobese people. The significant predictors mentioned above were introduced to construct the nomogram. The calibration curve, DCA, and AUROC (0.796) (95% CI: 0.743–0.843) all indicated that the model had a good potential clinical performance. <i>Conclusions</i>. By comparing the results of the four models together, WHR and TG were identified as important factors associated with NAFLD in the nonobese population. Further research is warranted to investigate the risk factors and pathogeny of NAFLD in nonobese populations.</p>","PeriodicalId":13782,"journal":{"name":"International Journal of Clinical Practice","volume":"2024 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effects of Metabolism-Related Indicators on Nonalcoholic Fatty Liver Disease in Nonobese Population Based on the National Health and Nutrition Examination Survey\",\"authors\":\"XingWang Zhu, HaiPing Wang, HongLong Zhang, Guole Nie, Jun Yan, Xun Li\",\"doi\":\"10.1155/2024/7081486\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><i>Objective</i>. Nonalcoholic fatty liver disease (NAFLD) is becoming more prevalent in the nonobese population. The aim of this study was to investigate the combined effects of metabolism-related mixtures on NAFLD subjects in nonobese populations using four statistical models. <i>Study Design.</i> This was a retrospective observational study. <i>Methods.</i> Our study included 904 nonobese patients who had taken part in the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used logistic regression models, Bayesian kernel machine regression (BKMR), and the weighted quantile sum (WQS) regression model to estimate the association between metabolism-related indicators and NAFLD in the nonobese population. Finally, we included several indicators to create nomograms to predict the risk of NAFLD occurrence in the nonobese population. <i>Results</i>. Among the 904 participants, 116 (12.83%) had NAFLD. The logistic regression model found that the waist-to-hip ratio (WHR), HDL-c, triglyceride (TG), and HbA1c were positively associated with the outcomes. The WQS regression model showed that the WQS index was significantly associated with the occurrence of NAFLD in the nonobese population (OR: 5.789, 95% CI: 3.933–8.520), and WHR, TC, and TG had the largest weight. The BKMR model’s WHR and TG increased from the 25th percentile to the 75th percentile (other metabolite exposure remained fixed at the 75th percentile) and the risk of developing NAFLD increased in the nonobese people. The significant predictors mentioned above were introduced to construct the nomogram. The calibration curve, DCA, and AUROC (0.796) (95% CI: 0.743–0.843) all indicated that the model had a good potential clinical performance. <i>Conclusions</i>. By comparing the results of the four models together, WHR and TG were identified as important factors associated with NAFLD in the nonobese population. Further research is warranted to investigate the risk factors and pathogeny of NAFLD in nonobese populations.</p>\",\"PeriodicalId\":13782,\"journal\":{\"name\":\"International Journal of Clinical Practice\",\"volume\":\"2024 1\",\"pages\":\"\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Clinical Practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/7081486\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Clinical Practice","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/7081486","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Effects of Metabolism-Related Indicators on Nonalcoholic Fatty Liver Disease in Nonobese Population Based on the National Health and Nutrition Examination Survey
Objective. Nonalcoholic fatty liver disease (NAFLD) is becoming more prevalent in the nonobese population. The aim of this study was to investigate the combined effects of metabolism-related mixtures on NAFLD subjects in nonobese populations using four statistical models. Study Design. This was a retrospective observational study. Methods. Our study included 904 nonobese patients who had taken part in the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used logistic regression models, Bayesian kernel machine regression (BKMR), and the weighted quantile sum (WQS) regression model to estimate the association between metabolism-related indicators and NAFLD in the nonobese population. Finally, we included several indicators to create nomograms to predict the risk of NAFLD occurrence in the nonobese population. Results. Among the 904 participants, 116 (12.83%) had NAFLD. The logistic regression model found that the waist-to-hip ratio (WHR), HDL-c, triglyceride (TG), and HbA1c were positively associated with the outcomes. The WQS regression model showed that the WQS index was significantly associated with the occurrence of NAFLD in the nonobese population (OR: 5.789, 95% CI: 3.933–8.520), and WHR, TC, and TG had the largest weight. The BKMR model’s WHR and TG increased from the 25th percentile to the 75th percentile (other metabolite exposure remained fixed at the 75th percentile) and the risk of developing NAFLD increased in the nonobese people. The significant predictors mentioned above were introduced to construct the nomogram. The calibration curve, DCA, and AUROC (0.796) (95% CI: 0.743–0.843) all indicated that the model had a good potential clinical performance. Conclusions. By comparing the results of the four models together, WHR and TG were identified as important factors associated with NAFLD in the nonobese population. Further research is warranted to investigate the risk factors and pathogeny of NAFLD in nonobese populations.
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