已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Body adiposity index and other indexes of body composition in predicting cardiovascular disease in the Chinese population: A Cohort study

医学 体质指数 腰高比 腰围 肥胖 内科学 人口 逻辑回归 人口学 队列 环境卫生 社会学
作者
Wen-Shu Luo,Yi Ding,Zhening Guo
出处
期刊:Perfusion [SAGE]
卷期号:40 (6): 1397-1404 被引量:1
标识
DOI:10.1177/02676591241300973
摘要

Objective The purpose of this study was to compare the ability of four obesity indicators, including waist circumference (WC), body mass index (BMI), body adiposity index (BAI), and waist-to-height ratio (WHtR) on prediction for incident cardiovascular disease (CVD) in Chinese Han population. Methods We analyzed data from a prospective population cohort of 3598 participants aged 35 to 74 years from Jiangsu China. The logistic regression model was used to analyze the association between four obesity indicators and CVD risk. The ROC curve was used to assess and compare the diagnostic performance of four obesity indicators. Results During 8 years (median 6.3 years) of follow-up time, 82 CVD endpoints were collected during follow up (36 men and 46 women). After adjustment for age, smoking status, alcohol consumption and family history of CVD, in men, WC, BMI, and BAI were associated with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and hypertension. In women, WC, BMI and WHtR were associated with TG, HDL-C, hyperglycemia and hypertension, BAI was only associated with HDL-C, hyperglycemia, and hypertension. ROC curve analysis indicated that BAI have the highest area under the curve to identify CVD, and BMI has the lowest area under the curve to identify CVD in Chinese males. WHtR has the highest area under the curve to identify CVD, and BMI has the lowest area under the curve to identify CVD in Chinese females. Conclusions CVD risk was more consistently correlated with BAI in Chinese men and more consistently correlated with WHtR and WC in Chinese women.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
建议保存本图,每天支付宝扫一扫(相册选取)领红包
实时播报
jtksbf完成签到 ,获得积分10
2秒前
夜曲完成签到,获得积分20
2秒前
诺索发布了新的文献求助10
3秒前
ding应助枣点睡觉采纳,获得10
3秒前
zuyin完成签到 ,获得积分10
5秒前
5秒前
玛卡巴卡完成签到,获得积分10
7秒前
空2完成签到 ,获得积分0
8秒前
8秒前
9秒前
早上好章鱼哥完成签到 ,获得积分10
10秒前
激动的55完成签到 ,获得积分10
10秒前
12秒前
Tobby发布了新的文献求助10
13秒前
完美羿完成签到 ,获得积分10
13秒前
13秒前
蜡笔小z完成签到 ,获得积分10
14秒前
15秒前
15秒前
Denmark完成签到 ,获得积分10
16秒前
大模型应助诺索采纳,获得10
16秒前
慕青应助123采纳,获得10
16秒前
朴实觅夏发布了新的文献求助10
17秒前
泽2011完成签到 ,获得积分10
18秒前
李健的小迷弟应助Amy采纳,获得10
19秒前
发发发布了新的文献求助10
20秒前
Tobby完成签到,获得积分10
20秒前
21秒前
朴实的小萱完成签到 ,获得积分10
21秒前
23秒前
共享精神应助炙热安彤采纳,获得10
24秒前
Cast_Lappland发布了新的文献求助10
27秒前
朴实觅夏完成签到 ,获得积分10
27秒前
28秒前
GingerF应助科研通管家采纳,获得50
29秒前
小二郎应助科研通管家采纳,获得10
29秒前
Momomo应助科研通管家采纳,获得10
29秒前
浮游应助科研通管家采纳,获得10
29秒前
29秒前
脑洞疼应助科研通管家采纳,获得10
30秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1041
Mentoring for Wellbeing in Schools 1000
Binary Alloy Phase Diagrams, 2nd Edition 600
Atlas of Liver Pathology: A Pattern-Based Approach 500
A Technologist’s Guide to Performing Sleep Studies 500
EEG in Childhood Epilepsy: Initial Presentation & Long-Term Follow-Up 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5493501
求助须知:如何正确求助?哪些是违规求助? 4591594
关于积分的说明 14434178
捐赠科研通 4524033
什么是DOI,文献DOI怎么找? 2478548
邀请新用户注册赠送积分活动 1463537
关于科研通互助平台的介绍 1436387