迄今为止,预测高血压的最佳肥胖相关指标(ORI),血脂异常,2型糖尿病(T2DM)和多发病仍然存在争议。这项研究评估了17个ORI的能力[体重指数(BMI),体脂百分比(BF%),c指数,Clínica大学纳瓦拉身体脂肪估计器(CUN-BAE),身体形状指数(ABSI),身体肥胖指数(BAI),腰围(WC),腰臀比(WHR),腰围与身高比(WHtR),身体圆度指数(BRI),腹部容积指数(AVI),甘油三酯葡萄糖指数(TYG),脂质积累产物(LAP),内脏肥胖指数(VAI),中国内脏肥胖指数(CVAI),腰围甘油三酯指数(WTI)和心脏代谢指数(CMI)]预测高血压,血脂异常,T2DM,和40-69岁人群的多发病率。从2017年11月到2022年12月,10,432名合规居民参加了这项研究。受试者工作特征曲线用于评估ORI预测整个人群和性别目标疾病的能力。DeLong检验用于分析曲线下面积(AUC)的异质性。多变量logistic回归分析ORI与高血压的关系,血脂异常,T2DM,和多重性。高血压的患病率,血脂异常,T2DM,多发病率为67.46%,39.36%,12.54%和63.58%,分别。排除与目标疾病成分相关的ORI后,在整个人口中,CVAI(AUC=0.656),BMI(AUC=0.655,与WC和AVI没有显着差异),CVAI(AUC=0.645,与LAP无显著差异,CMI,WHR,和WTI),TYG(AUC=0.740)是高血压的最佳预测因子,血脂异常,T2DM,和多发病率,(均P<0.05)。在男性人口中,BF%(AUC=0.677),BMI(AUC=0.698),CMI(AUC=0.648,与LAP和CVAI没有显着差异),TYG(AUC=0.741)是最佳预测因子(均P<0.05)。在女性人口中,CVAI(AUC=0.677),CUN-BAE(AUC=0.523,与BF%无显著差异,WC,WHR,WHtR,BRI和BMI),CVAI(AUC=0.657,与WHR没有显着差异),TYG(AUC=0.740)是最佳预测因子(均P<0.05)。在调整所有协变量后,所有ORI都与高血压显著相关,血脂异常,T2DM,和多发病率(均P<0.05),除了ABSI和高血压以及BAI和T2DM,这是微不足道的。最终,在考虑了不同种群之间ORI预测的异质性后,对于高血压,BF%是男性的最佳指标,而CVAI是其他人群的最佳指标。血脂异常的最佳预测因子,T2DM,多发病率是BMI,CVAI和TYG,分别。与这些因素相结合的常见慢性疾病筛查可能有助于提高有效性。
To date, the best obesity-related indicators (ORIs) for predicting hypertension, dyslipidaemia, Type 2 diabetes mellitus (T2DM) and multimorbidity are still controversial. This study assessed the ability of 17 ORIs [body mass index (BMI), body fat percentage (BF%), c-index, Clínica Universidad de Navarra-Body
Adiposity Estimator (CUN-BAE), a body shape index (ABSI), body
adiposity index (BAI), waist circumference (WC), waist-hip ratio (WHR), waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), triglyceride glucose index (TYG), lipid accumulation product (LAP), visceral
adiposity index (VAI), Chinese visceral
adiposity index (CVAI), waist triglyceride index (WTI) and cardiometabolic index (CMI)] to predict hypertension, dyslipidemia, T2DM, and multimorbidity in populations aged 40-69 years. From November 2017 to December 2022, 10,432 compliant residents participated in this study. Receiver operating characteristic curves were used to assess the ability of ORIs to predict target diseases across the whole population and genders. The DeLong test was used to analyse the heterogeneity of area under curves (AUCs). Multivariable logistic regression was used to analyse the association of ORIs with hypertension, dyslipidaemia, T2DM, and multimorbidity. The prevalence of hypertension, dyslipidaemia, T2DM, and multimorbidity was 67.46%, 39.36%, 12.54% and 63.58%, respectively. After excluding ORIs associated with the target disease components, in the whole population, CVAI (AUC = 0.656), BMI (AUC = 0.655, not significantly different from WC and AVI), CVAI (AUC = 0.645, not significantly different from LAP, CMI, WHR, and WTI), and TYG (AUC = 0.740) were the best predictor of hypertension, dyslipidemia, T2DM, and multimorbidity, respectively (all P < 0.05). In the male population, BF% (AUC = 0.677), BMI (AUC = 0.698), CMI (AUC = 0.648, not significantly different from LAP and CVAI), and TYG (AUC = 0.741) were the best predictors (all P < 0.05). In the female population, CVAI (AUC = 0.677), CUN-BAE (AUC = 0.623, not significantly different from BF%, WC, WHR, WHtR, BRI and BMI), CVAI (AUC = 0.657, not significantly different from WHR), TYG (AUC = 0.740) were the best predictors (all P < 0.05). After adjusting for all covariates, all ORIs were significantly associated with hypertension, dyslipidaemia, T2DM, and multimorbidity (all P < 0.05), except for ABSI and hypertension and BAI and T2DM, which were insignificant. Ultimately, after considering the heterogeneity of prediction of ORIs among different populations, for hypertension, BF% was the best indicator for men and CVAI for the rest of the population. The best predictors of dyslipidaemia, T2DM, and multimorbidity were BMI, CVAI and TYG, respectively. Screening for common chronic diseases in combination with these factors may help to improve the effectiveness.