Evidence map›Paper›PMID 40551883›Full record

ArticleFrontiers in endocrinology2025

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

Ting Yin, Jing Wang, XueQing Lan, Jiaxing Zhang, Qingan Wang, Jiangwei Qiu, Tao Ma, Yi Zhao, Yuhong Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ting Yin *Department of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China.
Jing Wang *Department of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China.
XueQing LanDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China.
Jiaxing ZhangDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China.
Qingan WangDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China.
Jiangwei QiuDepartment of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China.
Tao MaPeking University First Hospital Ningxia Women and Children's Hospital (Ningxia Hui Autonomous Region Maternal and Child Health Hospital), Ningxia, China.
Yi Zhao *Key Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, China.
Yuhong Zhang *Department of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Diabetes MellitusDyslipidemiasHypertensionObesityAdultAgedBody Mass IndexChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedRural Populationdiabetesdyslipidemiahypertensionobesity indicatorsrural residents

Identifiers

PMID40551883
PMCPMC12183022

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.