{"title":"体重正常的腹部肥胖:高血压和心脏代谢失调的危险因素","authors":"Jin-Yu Sun, Q. Qu, Yue Yuan, Guozhen Sun, X. Kong, Wei Sun","doi":"10.1097/CD9.0000000000000034","DOIUrl":null,"url":null,"abstract":"Abstract Objective: This study aimed to examine the associations of waist circumference with hypertension and cardiometabolic dysregulation among normal-weight adults. Methods: This cross-sectional study included 8795 normal-weight participants aged 20 to 79 years from the 2009–2018 US National Health and Nutrition Examination Survey. The demographic characteristics and cardiometabolic risk factors across waist circumference quartiles were summarized. We used adjusted multivariate logistic regression models, subgroup analysis, and restricted cubic spline to analyze the association between waist circumference and the prevalence of hypertension. Thereafter, we used the random forest supervised machine learning method, together with least absolute shrinkage and selection operator regression, to select hypertension-related features and created a predictive model based on regression analysis to identify hypertension in normal-weight individuals. Results: Waist circumference was positively correlated with hypertension in the non-adjusted, minimally adjusted, and fully adjusted models, with odds ratios (95% confidence interval) of 2.28 (2.14–2.44), 1.27 (1.12–1.44), and 1.27 (1.12–1.44), respectively. In the fully adjusted model, participants in the highest waist circumference quartile had a higher risk of hypertension relative to those in the lowest quartile, with an odds ratio (95% confidence interval) of 3.87 (1.59–10.34). Sensitivity analysis demonstrated the robustness of the association. In the testing set, the predictive model exhibited good performance, with an area under the curve of 0.803, sensitivity of 0.72, specificity of 0.76, and negative predictive value of 0.84. Conclusions: Measuring waist circumference may improve the evaluation of the risk of hypertension and help to manage cardiometabolic risk in normal-weight individuals.","PeriodicalId":72524,"journal":{"name":"Cardiology discovery","volume":"2 1","pages":"13 - 21"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Normal-Weight Abdominal Obesity: A Risk Factor for Hypertension and Cardiometabolic Dysregulation\",\"authors\":\"Jin-Yu Sun, Q. Qu, Yue Yuan, Guozhen Sun, X. Kong, Wei Sun\",\"doi\":\"10.1097/CD9.0000000000000034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Objective: This study aimed to examine the associations of waist circumference with hypertension and cardiometabolic dysregulation among normal-weight adults. Methods: This cross-sectional study included 8795 normal-weight participants aged 20 to 79 years from the 2009–2018 US National Health and Nutrition Examination Survey. The demographic characteristics and cardiometabolic risk factors across waist circumference quartiles were summarized. We used adjusted multivariate logistic regression models, subgroup analysis, and restricted cubic spline to analyze the association between waist circumference and the prevalence of hypertension. Thereafter, we used the random forest supervised machine learning method, together with least absolute shrinkage and selection operator regression, to select hypertension-related features and created a predictive model based on regression analysis to identify hypertension in normal-weight individuals. Results: Waist circumference was positively correlated with hypertension in the non-adjusted, minimally adjusted, and fully adjusted models, with odds ratios (95% confidence interval) of 2.28 (2.14–2.44), 1.27 (1.12–1.44), and 1.27 (1.12–1.44), respectively. In the fully adjusted model, participants in the highest waist circumference quartile had a higher risk of hypertension relative to those in the lowest quartile, with an odds ratio (95% confidence interval) of 3.87 (1.59–10.34). Sensitivity analysis demonstrated the robustness of the association. In the testing set, the predictive model exhibited good performance, with an area under the curve of 0.803, sensitivity of 0.72, specificity of 0.76, and negative predictive value of 0.84. Conclusions: Measuring waist circumference may improve the evaluation of the risk of hypertension and help to manage cardiometabolic risk in normal-weight individuals.\",\"PeriodicalId\":72524,\"journal\":{\"name\":\"Cardiology discovery\",\"volume\":\"2 1\",\"pages\":\"13 - 21\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cardiology discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1097/CD9.0000000000000034\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiology discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/CD9.0000000000000034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Normal-Weight Abdominal Obesity: A Risk Factor for Hypertension and Cardiometabolic Dysregulation
Abstract Objective: This study aimed to examine the associations of waist circumference with hypertension and cardiometabolic dysregulation among normal-weight adults. Methods: This cross-sectional study included 8795 normal-weight participants aged 20 to 79 years from the 2009–2018 US National Health and Nutrition Examination Survey. The demographic characteristics and cardiometabolic risk factors across waist circumference quartiles were summarized. We used adjusted multivariate logistic regression models, subgroup analysis, and restricted cubic spline to analyze the association between waist circumference and the prevalence of hypertension. Thereafter, we used the random forest supervised machine learning method, together with least absolute shrinkage and selection operator regression, to select hypertension-related features and created a predictive model based on regression analysis to identify hypertension in normal-weight individuals. Results: Waist circumference was positively correlated with hypertension in the non-adjusted, minimally adjusted, and fully adjusted models, with odds ratios (95% confidence interval) of 2.28 (2.14–2.44), 1.27 (1.12–1.44), and 1.27 (1.12–1.44), respectively. In the fully adjusted model, participants in the highest waist circumference quartile had a higher risk of hypertension relative to those in the lowest quartile, with an odds ratio (95% confidence interval) of 3.87 (1.59–10.34). Sensitivity analysis demonstrated the robustness of the association. In the testing set, the predictive model exhibited good performance, with an area under the curve of 0.803, sensitivity of 0.72, specificity of 0.76, and negative predictive value of 0.84. Conclusions: Measuring waist circumference may improve the evaluation of the risk of hypertension and help to manage cardiometabolic risk in normal-weight individuals.