Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest China
ObjectiveThis 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...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1346193/full |
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| author | Ting Yin Ting Yin Jing Wang Jing Wang XueQing Lan Jiaxing Zhang Jiaxing Zhang Qingan Wang Qingan Wang Jiangwei Qiu Tao Ma Yi Zhao Yi Zhao Yuhong Zhang Yuhong Zhang |
| author_facet | Ting Yin Ting Yin Jing Wang Jing Wang XueQing Lan Jiaxing Zhang Jiaxing Zhang Qingan Wang Qingan Wang Jiangwei Qiu Tao Ma Yi Zhao Yi Zhao Yuhong Zhang Yuhong Zhang |
| author_sort | Ting Yin |
| collection | DOAJ |
| description | ObjectiveThis 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.MethodsThe 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.ResultsThe 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).ConclusionWaist 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. |
| format | Article |
| id | doaj-art-0a33b5dc24f742d8bc97d1a3fd6d33ab |
| institution | OA Journals |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-0a33b5dc24f742d8bc97d1a3fd6d33ab2025-08-20T02:05:17ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-06-011610.3389/fendo.2025.13461931346193Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest ChinaTing Yin0Ting Yin1Jing Wang2Jing Wang3XueQing Lan4Jiaxing Zhang5Jiaxing Zhang6Qingan Wang7Qingan Wang8Jiangwei Qiu9Tao Ma10Yi Zhao11Yi Zhao12Yuhong Zhang13Yuhong Zhang14Department of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaKey Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaGeneral Hospital of Ningxia Medical University, Public Health School, Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaKey Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaKey Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaPeking University First Hospital Ningxia Women and Children's Hospital (Ningxia Hui Autonomous Region Maternal and Child Health Hospital), Ningxia, ChinaKey Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Nutrition and Food Hygiene, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, ChinaKey Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, ChinaObjectiveThis 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.MethodsThe 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.ResultsThe 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).ConclusionWaist 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.https://www.frontiersin.org/articles/10.3389/fendo.2025.1346193/fullobesity indicatorshypertensiondiabetesdyslipidemiarural residents |
| spellingShingle | Ting Yin Ting Yin Jing Wang Jing Wang XueQing Lan Jiaxing Zhang Jiaxing Zhang Qingan Wang Qingan Wang Jiangwei Qiu Tao Ma Yi Zhao Yi Zhao Yuhong Zhang Yuhong Zhang Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest China Frontiers in Endocrinology obesity indicators hypertension diabetes dyslipidemia rural residents |
| title | Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest China |
| title_full | Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest China |
| title_fullStr | Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest China |
| title_full_unstemmed | Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest China |
| title_short | Different obesity indicators and their correlation with hypertension, diabetes, and dyslipidemia in 35–74 years rural residents in Northwest China |
| title_sort | different obesity indicators and their correlation with hypertension diabetes and dyslipidemia in 35 74 years rural residents in northwest china |
| topic | obesity indicators hypertension diabetes dyslipidemia rural residents |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1346193/full |
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