Evidence map›Paper›PMID 39448988›Full record

ArticleBMC endocrine disorders2024

Developing a risk model for early diagnosis of metabolic syndrome in Chinese adults aged 40 years and above based on BMI/HDL-C: a cross-sectional study.

Yu Liu, Xixiang Wang, Jie Mu, Yiyao Gu, Shaobo Zhou, Xiaojun Ma, Jingjing Xu, Lu Liu, Xiuwen Ren, Zhi Duan and 2 more

Abstract read
In one paragraph

Article in BMC endocrine disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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. Article
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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

12 authors.

Yu Liu *School of Public Health, Capital Medical University, Beijing, 100069, P.R. China.
Xixiang Wang *School of Public Health, Capital Medical University, Beijing, 100069, P.R. China.
Jie MuSuzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China.
Yiyao GuSuzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China.
Shaobo ZhouSchool of Science, Faculty of Engineering and Science, University of Greenwich, Central Avenue, Chatham, ME4 4TB, UK.
Xiaojun MaSchool of Public Health, Capital Medical University, Beijing, 100069, P.R. China.
Jingjing XuSchool of Public Health, Capital Medical University, Beijing, 100069, P.R. China.
Lu LiuSchool of Public Health, Capital Medical University, Beijing, 100069, P.R. China.
Xiuwen RenSchool of Public Health, Capital Medical University, Beijing, 100069, P.R. China.
Zhi DuanSuzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China.
Linhong YuanSchool of Public Health, Capital Medical University, Beijing, 100069, P.R. China. ylhmedu@126.com.
Ying WangSuzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China. qqhewangying@163.com.

Funding

Beijing High-level Public Health Technical Personnel Training Program 2022-3-032National Natural Science Foundation of China 82173508; 81973027Suzhou Science and Technology City Hospital Talent Introduction Project Yj202208
6 · The paper itself

Abstract

backgroundThis study aimed to compare the diagnostic accuracy of four indicators, including waist-to-height ratio (WHTR), vascular adiposity index (VAI), TG/HDL-C, and BMI/HDL-C for metabolic syndrome (MS) in Chinese adults aged 40 years and above. Additionally, the study aimed to develop an efficient diagnostic model displayed by a nomogram based on individual's BMI and circulating HDL-C level.

methodsA cross-sectional study was conducted on 699 participants aged 40 years and above. Quartiles of BMI/HDL-C, TG/HDL-C, VAI, and WHTR were used as independent variables, and metabolic syndrome was used as the dependent variable. Logistic regression was conducted to explore the impact of each parameter on the risk of MS. The areas under the receiver operating characteristics were compared to determine the accuracy of the indicators in diagnosing MS in the participants. Logistic regression was run to construct the nomograms, and the performance of the nomogram was assessed by a calibration curve.

resultsMS subjects had higher levels of BMI, BFM, PBF, VFA, AMC, WC, SCR, TG, and insulin, but lower LDH and HDL-C levels than the subjects without MS. The BMI/HDL-C ratio was positively correlated with the prevalence of MS and its components. The final diagnostic model included five variables: gender, BFM, WC, TG, and BMI/HDL-C. The model showed good calibration and discrimination power with an AUC of 0.780. The cut-off value for the nomogram was 0.623 for diagnosing MS.

conclusionsBMI/HDL-C ratio was an independent risk factor for MS in Chinese adults. BMI/HDL-C was significantly correlated with MS and its components. BMI/HDL-C was the most powerful diagnostic indicator compared to other indicators, including TG/HDL-C, VAI and WHTR for diagnosing MS. The nomogram drawn based on the diagnostic model provided a practical tool for diagnosing MS in Chinese adults.

Indexed as

Body Mass IndexCholesterol, HDLEarly DiagnosisMetabolic SyndromeAdultAgedBiomarkersChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedNomogramsRisk FactorsBiomarkersCholesterol, HDLBMI/HDL-C ratioDiagnostic modelMetabolic syndromeNomogram

Identifiers

PMID39448988
PMCPMC11515612

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.