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
. 2025 Jun 9:16:1346193.
doi: 10.3389/fendo.2025.1346193. eCollection 2025.

Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35-74 years rural residents in Northwest China

Ting Yin #  1   2 , Jing Wang #  1   3 , XueQing Lan  1 , Jiaxing Zhang  1   2 , Qingan Wang  1   2 , Jiangwei Qiu  1 , Tao Ma  4 , Yi Zhao #  2   5 , Yuhong Zhang #  1   2
Affiliations

Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35-74 years rural residents in Northwest China

Ting Yin et al. Front Endocrinol (Lausanne). .

Abstract

Objective: This study aimed to explore the cut-off value of 10 obesity indicators, including BF% (Body Fat Ratio, BF%), BMI (Body Mass Index, BMI), WHR (Waist-to-Hip Ratio, WHR), WHtR (Waist-to-Height Ratio, WHtR), BAI (Body Adiposity Index, BAI), OBD (Obesity Degree, OBD), CI (Conicity Index,CI), AVI (Abdominal Volume Index, AVI), ABSI (A Body Shape Index, ABSI) and BRI (Body Roundness Index, BRI), and investigate their relationship between different anthropometric indices of obesity indicators and their correlation to hypertension, diabetes, and dyslipidemia in rural residents aged 35-74 years in Ningxia, an autonomous region of northwest China.

Methods: The study participants were interviewed by questionnaire (including demographic characteristics such as age, education status, economic status, and lifestyle variables such as exercise frequency, smoke, alcohol, tea, spice, and vinegar consumption), bio-impedance body composition analysis, and blood laboratory test. The t-test and chi-square test were used to compare the characteristics of different groups, and the receiver operating characteristic curve was used to analyze the correlation of different indicators and explore their cut-off values.

Results: The study comprised 14,926 participants, of whom 39.80% (5948/14,926) were male, and the mean age of the study population was 56.75 ± 9.74 years. The waist circumference had the greatest influence on obesity indicators, and BMI, AVI, and BRI are most susceptible to anthropometric indicators. WHtR had the largest AUC (Area Under the ROC Curves, AUC) for predicting obesity in both male and female. In addition, we provided a recommended cut-off value of BMI, WHR, WHtR, BAI, OBD, CI, AVI, ABSI and BRI. WHtR had the largest AUC for predicting diabetes, hypertension, and dyslipidemia, while WHtR served as a good predictive indicator (all P<0.001).

Conclusion: Waist circumference is closely related to obesity. Therefore, there is a great significance to carry out long-term health management education among the population, change the unhealthy lifestyle and promote the metabolic health for the primary prevention of cardiovascular diseases.

Keywords: diabetes; dyslipidemia; hypertension; obesity indicators; rural residents.

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
Respondent screening process for the participants.
See this image and copyright information in PMC

References

    1. Piché ME, Tchernof A, Després JP. Obesity phenotypes, diabetes, and cardiovascular diseases. Circ Res. (2020) 126:1477–500. doi: 10.1161/CIRCRESAHA.120.316101 - DOI - PubMed
    1. Franzago M, Pilenzi L, Di Rado S, Vitacolonna E, Stuppia L. The epigenetic aging, obesity, and lifestyle. Front Cell Dev Biol. (2022) 10:985274. doi: 10.3389/fcell.2022.985274 - DOI - PMC - PubMed
    1. Malandrino N, Bhat SZ, Alfaraidhy M, Grewal RS, Kalyani RR. Obesity and aging. Endocrinol Metab Clin North Am. (2023) 52:317–39. doi: 10.1016/j.ecl.2022.10.001 - DOI - PubMed
    1. Li J, Shi Q, Gao Q, Pan XF, Zhao L, He Y, et al. Obesity pandemic in China: epidemiology, burden, challenges, and opportunities. Chin Med J (Engl). (2022) 135:1328–30. doi: 10.1097/CM9.0000000000002189 - DOI - PMC - PubMed
    1. Li Y, Wang N, Ge W, Ding B, Wang J, Hong Z, et al. Association study between cereal intake and the risk of overweight and obesity among adult residents in China. Food Nutri China (Chinese). (2025) 31(4):88–93. doi: 10.19870/j.cnki.11-3716/ts.2025.04.001 - DOI

LinkOut - more resources