Evidence map›Paper›PMID 36875382›Full record

ArticleFrontiers in public health2023

Obesity- and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study.

Jiaofeng Gui, Yuqing Li, Haiyang Liu, Lei-Lei Guo, Jinlong Li, Yunxiao Lei, Xiaoping Li, Lu Sun, Liu Yang, Ting Yuan and 7 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed
22.9field-weighted citation impact, top 1% of its field
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

71 citing papers in PubMed, 114 citations in OpenAlex.

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11 more citing papers are in PubMed but not listed here.

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

17 authors at 3 institutions in 1 country.

Jiaofeng GuiDepartment of Graduate School, Wannan Medical College, Wuhu, Anhui, China.
Yuqing LiDepartment of Graduate School, Wannan Medical College, Wuhu, Anhui, China.
Haiyang LiuStudent Health Center, Wannan Medical College, Wuhu, Anhui, China.
Lei-Lei GuoDepartment of Surgical Nursing, School of Nursing, Jinzhou Medical University, Jinzhou, Liaoning, China.
Jinlong LiDepartment of Occupational and Environmental Health, Key Laboratory of Occupational Health and Safety for Coal Industry in Hebei Province, School of Public Health, North China University of Science and Technology, Tangshan, Hebei, China.
Yunxiao LeiObstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Xiaoping LiDepartment of Emergency and Critical Care Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Lu SunDepartment of Emergency and Critical Care Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Liu YangDepartment of Internal Medicine Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Ting YuanObstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Congzhi WangDepartment of Internal Medicine Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Dongmei ZhangDepartment of Pediatric Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Huanhuan WeiObstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Jing LiDepartment of Surgical Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Mingming LiuDepartment of Surgical Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Ying HuaRehabilitation Nursing, School of Nursing, Wanna Medical College, Wuhu, Anhui, China.
Lin ZhangDepartment of Internal Medicine Nursing, School of Nursing, Wannan Medical College, Wuhu, Anhui, China.
Wannan Medical College · CNJinzhou Medical University · CNNorth China University of Science and Technology · CN

Funding

China Health and Retirement Longitudinal StudyR01AG037031 · NIA · PEKING UNIVERSITY · PI STRAUSS, JOHN A, WANG, YAFENG · 2010 to 2024
$15.1M
Studies of health, retirement and living arrangements of the aging population inR03TW008358 · FIC · PEKING UNIVERSITY · PI STRAUSS, JOHN A, ZHAO, YAOHUI · 2009 to 2011
$152k
FIC NIH HHS R03 TW008358NIA NIH HHS R01 AG037031
6 · The paper itself

Abstract

Objective: Metabolic syndrome is a common condition among middle-aged and elderly people. Recent studies have reported the association between obesity- and lipid-related indices and metabolic syndrome, but whether those conditions could predict metabolic syndrome is still inconsistent in a few longitudinal studies. In our study, we aimed to predict metabolic syndrome by obesity- and lipid-related indices in middle-aged and elderly Chinese adults. Method: A national cohort study that consisted of 3,640 adults (≥45 years) was conducted. A total of 13 obesity- and lipid-related indices, including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), conicity index (CI), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), and triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-WC, and TyG-WHtR), were recorded. Metabolic syndrome (MetS) was defined based on the criteria of the National Cholesterol Education Program Adult Treatment Panel III (2005). Participants were categorized into two groups according to the different sex. Binary logistic regression analyses were used to evaluate the associations between the 13 obesity- and lipid-related indices and MetS. Receiver operating characteristic (ROC) curve studies were used to identify the best predictor of MetS. Results: A total of 13 obesity- and lipid-related indices were independently associated with MetS risk, even after adjustment for age, sex, educational status, marital status, current residence, history of drinking, history of smoking, taking activities, having regular exercises, and chronic diseases. The ROC analysis revealed that the 12 obesity- and lipid-related indices included in the study were able to discriminate MetS [area under the ROC curves (AUC > 0.6, Conclusion: Among middle-aged and older adults, all obesity- and lipid-related indices, except ABSI, were able to predict MetS. In addition, in men, TyG-BMI is the best indicator to indicate MetS, and in women, CVAI is considered the best hand to indicate MetS. At the same time, TyG-BMI, TyG-WC, and TyG-WHtR performed better than BMI, WC, and WHtR in predicting MetS in both men and women. Therefore, the lipid-related index outperforms the obesity-related index in predicting MetS. In addition to CVAI, LAP showed a good predictive correlation, even more closely than lipid-related factors in predicting MetS in women. It is worth noting that ABSI performed poorly, was not statistically significant in either men or women, and was not predictive of MetS.

Indexed as

Metabolic SyndromeObesityAgedAsian PeopleCohort StudiesFemaleGlucoseHumansLipidsMaleMiddle AgedGlucoseLipidslipidsmetabolic syndromemiddle-aged and elderly Chinesenational cohort studyobesity

Identifiers

PMID36875382
PMCPMC9980350
OpenAlexW4321079920

What OpenQuestion holds

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LicenceCC BY
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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.