Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Dec 18:11:1480707.
doi: 10.3389/fnut.2024.1480707. eCollection 2024.

Waist-to-height ratio and body roundness index: superior predictors of insulin resistance in Chinese adults and take gender and age into consideration

Anxiang Li #  1   2   3 , Yunwei Liu #  3 , Qi Liu  1   2   3 , You Peng  3 , Qingshun Liang  1   2   3 , Yiming Tao  1   2   3 , Yunyi Liu  4 , Chongsong Cui  3 , Qiqi Ren  3 , Yingling Zhou  3 , Jieer Long  1   2   3 , Guanjie Fan  1   2   3 , Qiyun Lu  1   2   3 , Zhenjie Liu  1   2   3
Affiliations

Waist-to-height ratio and body roundness index: superior predictors of insulin resistance in Chinese adults and take gender and age into consideration

Anxiang Li et al. Front Nutr. .

Abstract

Background and objectives: 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.

Methods: 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.

Results: 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, p < 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.

Conclusion: 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.

Clinical trial registration: www.chictr.org.cn, ChiCTR2100054654.

Keywords: anthropometric indicators; body roundness index; insulin resistance; obesity; risk prediction; waist-to-height ratio.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Participant flow chart.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Lemieux I, Després J. Metabolic syndrome: past, present and future. Nutrients. (2020) 12:3501. doi: 10.3390/nu12113501, PMID: - DOI - PMC - PubMed
    1. Chen B, Zeng J, Qin M, Xu W, Zhang Z, Li X, et al. . The association between plant-based diet indices and obesity and metabolic diseases in Chinese adults: longitudinal analyses from the China health and nutrition survey. Front Nutr. (2022) 9:881901. doi: 10.3389/fnut.2022.881901, PMID: - DOI - PMC - PubMed
    1. Zhang P, Sun X, Jin H, Zhang FL, Guo ZN, Yang Y. Association between obesity type and common vascular and metabolic diseases: a cross-sectional study. Front Endocrinol. (2019) 10:900. doi: 10.3389/fendo.2019.00900, PMID: - DOI - PMC - PubMed
    1. Chinese SOE. Guideline for chronic weight management and clinical practice of anti-obesity medications (2024 version). Chinese J Endocrinol Metabol. (2024) 4:545–64. doi: 10.3760/cma.j.cn311282-20240412-00149 - DOI
    1. Chinese SD. Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition). Chinese J Diabet. (2021) 4:315–409. doi: 10.3760/cma.j.cn115791-20210221-00095 - DOI

LinkOut - more resources