Evidence map›Paper›PMID 39319305›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2024

Comparison of Incidence of Metabolic Syndrome and Five Obesity- and Lipid-Linked Indicators for Predicting Metabolic Syndrome Among Normal-Weight and Overweight Adults.

Jiahua Wu, Xihua Lin, Xueyao Yin, Zhiye Xu, Nan Wu, Ziyi Zhang, Jiaqiang Zhou, Hong Li

Abstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

8 authors.

Jiahua WuDepartment of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.ORCID 0000-0001-5436-2515
Xihua LinDepartment of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.ORCID 0000-0002-8537-4479
Xueyao YinDepartment of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Zhiye XuDepartment of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Nan WuDepartment of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Ziyi ZhangDepartment of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Jiaqiang Zhou *Department of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Hong Li *Department of Endocrinology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Metabolic syndrome (MetS) is an increasingly prevalent issue in China's public health landscape. Few studies have investigated the metabolic syndrome (MetS) in overweight people. We proposed to analyze and contrast the occurrence of MetS in normal-weight and overweight individuals and identify potential indicators for forecasting MetS in adults in Zhejiang Province. Methods: This cohort study included 359 adults aged 40-65 years and followed up for five years in Zhejiang Province. The study assessed the predictive capabilities of five indicators linked to obesity and lipid levels, namely body mass index (BMI), waist-to-height ratio (WHtR), triglyceride-glucose index (TyGi), and their combined indices (TyG-BMI, TyG-WHtR). The evaluation was done employing the area under the Receiver Operating Characteristic (ROC) Curve (AUC). DeLong test was applied to compare area under different ROC curves.We evaluated the relationships between five variables and MetS using multivariate logistic regression. Results: In normal-weight individuals, the five-year cumulative incidence of MetS was 21.85%, but in overweight people, it was 60.33%. After adjusting for confounding factors, BMI, WHtR, TyGi, TyG-BMI, and TyG-WHtR were independently linked to MetS in normal-weight individuals, while BMI, TyGi, TyG-BMI, and TyG-WHtR were independently linked to MetS in overweight individuals. In normal-weight individuals, the WHtR (AUC=0.738 and optimal threshold value =0.469) and TyG-WHtR (AUC=0.731 and optimal threshold value =4.121) had the larger AUC, which was significantly greater than that of the different three indicators. The TyG-BMI (AUC=0.769 and optimal threshold value = 211.099) was the best predictor of MetS in overweight individuals. Conclusion: The five-year cumulative incidence of MetS in overweight people was approximately triple that of normal-weight people in Zhejiang Province. In the overweight population, the TyG-BMI performed better than the other indices in predicting MetS. WHtR and TyG-WHtR outperformed BMI, TyGi, and TyG-BMI in anticipating MetS in a normal-weight population.

Indexed as

BMImetabolic syndromenormal weightoverweightTyGWHtR

Identifiers

PMID39319305
PMCPMC11421436

What Socratic holds

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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 Socratic graph.