Li Anxiang, Liu Yunwei, Liu Qi, Peng You, Liang Qingshun, Tao Yiming, Liu Yunyi, Cui Chongsong, Ren Qiqi, Zhou Yingling, Long Jieer, Fan Guanjie, Lu Qiyun, Liu Zhenjie
Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Nutr. 2024 Dec 18;11:1480707. doi: 10.3389/fnut.2024.1480707. eCollection 2024.
Metabolic disease has become a global health concern, and insulin resistance (IR) is a crucial underlying mechanism in various metabolic diseases. This study aims to compare the ability of seven anthropometric indicators in predicting IR in the Chinese population, and to find more sensitive and simple anthropometric indicator for early identification of IR.
This prospective cross-sectional study obtained participants' medical history, anthropometric indicators, and serum samples from three hospitals in China. Various anthropometric indicators were calculated, including body mass index (BMI), Waist-to-hip ratio (WHR), waist-to-height ratio (WtHR), conicity index (CI), A Body Shape Index (ABSI), body roundness index (BRI), abdominal volume index (AVI). The evaluation of IR is performed using the homeostasis model assessment-insulin resistance (HOMA-IR). Logistic regression analysis examined the relationship between indicators and HOMA-IR. The ability of the anthropometric indicators to predict IR was analyzed using the receiver operating characteristic (ROC) curve. Additionally, a stratified analysis was performed to evaluate the ability of the indicators in different age and gender groups.
The study included 1,592 adult subjects, with 531 in the non-IR group and 1,061 in the IR group. After adjusting for confounding factors, the anthropometric indicators showed a positive correlation with IR in the general population and across different genders and age groups (OR > 1, < 0.05), except for ABSI. In the ROC curve analysis, WtHR and BRI had the highest AUC values of 0.711 for detecting IR. The optimal cut-off value for WtHR to diagnose IR was 0.53, while for BRI, it was 4.00. In the gender-stratified and age-stratified analysis, BMI, WtHR, BRI, and AVI all had AUC values >0.700 in females and individuals below 60.
WtHR and BRI demonstrated a better ability to predict IR in the overall study population, making them preferred indicators for screening IR, and gender and age are important considerations. In the stratified analysis of different genders or age, BMI, WtHR, BRI, and AVI are also suitable for detecting IR in women or individuals under 60 years old in this study.
www.chictr.org.cn, ChiCTR2100054654.
代谢性疾病已成为全球关注的健康问题，胰岛素抵抗（IR）是各种代谢性疾病的关键潜在机制。本研究旨在比较七种人体测量指标预测中国人群IR的能力，并寻找更敏感、简单的人体测量指标用于早期识别IR。
这项前瞻性横断面研究收集了来自中国三家医院参与者的病史、人体测量指标和血清样本。计算了各种人体测量指标，包括体重指数（BMI）、腰臀比（WHR）、腰高比（WtHR）、锥度指数（CI）、体型指数（ABSI）、身体圆润度指数（BRI）、腹部容积指数（AVI）。使用稳态模型评估-胰岛素抵抗（HOMA-IR）来评估IR。逻辑回归分析检验了指标与HOMA-IR之间的关系。使用受试者工作特征（ROC）曲线分析人体测量指标预测IR的能力。此外，进行了分层分析以评估指标在不同年龄和性别组中的能力。
该研究纳入了1592名成年受试者，非IR组531人，IR组1061人。在调整混杂因素后，除ABSI外，人体测量指标在总体人群以及不同性别和年龄组中均与IR呈正相关（OR>1，<0.05）。在ROC曲线分析中，WtHR和BRI检测IR的AUC值最高，为0.711。WtHR诊断IR的最佳截断值为0.53，BRI为4.00。在性别分层和年龄分层分析中，BMI、WtHR、BRI和AVI在女性和60岁以下个体中的AUC值均>0.700。
在整个研究人群中，WtHR和BRI表现出更好的预测IR的能力，使其成为筛查IR的首选指标，性别和年龄是重要的考虑因素。在不同性别或年龄的分层分析中，BMI、WtHR、BRI和AVI在本研究中也适用于检测60岁以下女性或个体的IR。
