Evidence map›Paper›PMID 29216328›Full record

ArticlePloS one2017

Comparative analysis of IDF, ATPIII and CDS in the diagnosis of metabolic syndrome among adult inhabitants in Jiangxi Province, China.

Lengmei Cheng, Wei Yan, Liping Zhu, Yiying Chen, Jie Liu, Yan Xu, Lu Ji, Junrong He

Open access · goldAbstract readComparative Study
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 23% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Lengmei ChengSchool of Public Health, Nanchang University, Nanchang, P.R. China.
Wei YanJiangxi Provincial Center for Disease Control and Prevention, Nanchang, P.R. China.
Liping ZhuSchool of Public Health, Nanchang University, Nanchang, P.R. China.ORCID http://orcid.org/0000-0002-9015-4825
Yiying ChenJiangxi Provincial Center for Disease Control and Prevention, Nanchang, P.R. China.
Jie LiuJiangxi Provincial Center for Disease Control and Prevention, Nanchang, P.R. China.
Yan XuJiangxi Provincial Center for Disease Control and Prevention, Nanchang, P.R. China.
Lu JiJiangxi Provincial Center for Disease Control and Prevention, Nanchang, P.R. China.
Junrong HeGanzhou People's Hospital, Ganzhou, P.R. China.
Nanchang Center for Disease Control and Prevention · CNNanchang University · CNGanzhou People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrently, the prevalence of metabolic syndrome (MS) has attracted widespread public attention. However, there is a war regarding the applicability of the diagnosis in different populations regarding the distinct criteria for the diagnosis of MS. Data about the prevalence rate of MS and its components in Jiangxi Province are limited. Thus, our goals were to compare the consistency rates and applicability of three criteria, i.e., those of the International Diabetes Federation (IDF), the National Cholesterol Education Program Adult Treatment PanelⅢ (ATPⅢ), and the Chinese Diabetes Society (CDS).

methodsFrom September 2013 to March 2014, 5959 residents (age≥18 years) from Jiangxi Province were selected by multistage stratified cluster random sampling methods. The prevalence rate of MS and its components were calculated according to the IDF, ATPⅢ and CDS criteria, and the protocols of the different criteria were measured in terms of consistency with the kappa statistic and Youden's index. Receiver operator characteristic (ROC) curve analysis was used to explore the optimal cut-off points of body mass index (BMI) and waist circumference (WC).

resultsThe mean age of the participants was 50.52±13.92 years among the total of 5959 individuals (2451 male and 3508 female). The standardized prevalence rate of MS was 19.85%, 24.77% and 9.95% according to the IDF, ATPⅢ and CDS criteria, respectively. The order of the prevalence rates of the different components of MS according to the IDF or ATPⅢ criteria from high to low were as follows: elevated blood pressure, central obesity, reduced high density lipoprotein cholesterol (HDL-C), hyperglycemia, and hypertriglyceridemia. The most prevalent component of MS, according to the CDS criterion, was being overweight or obese, followed by elevated blood pressure, dyslipidemia and hyperglycemia. The Youden's index in IDF criterion was higher than which in CDS criterion (0.79 for IDF vs. 0.38 for CDS) referring to the ATPⅢ criterion. The agreement between the IDF and ATPⅢ criteria was good (kappa = 0.85), whereas the agreement of the CDS with the IDF and ATPⅢ criteria was moderate (kappa = 0.46 and 0.46, respectively). The ability to predict MS risk factors clusters was superior when the BMI cut-off point was 24/24kg/m2 (male/female), and the WC cut-off point was 87/80cm (male/female). Among the 18~59 years old male group, BMI was superior to WC in predicting clusters of risk factors for MS; while in the 60 years and above male group and all-age female group, WC was superior to BMI.

conclusionOur results revealed that the prevalence rate of metabolic syndrome among adults was high in Jiangxi Province. BMI and WC had different ability to predict clusters of risk factors for MS in different age groups and gender. Among the three criteria for MS, there was bigger difference in applicability for the adults of Jiangxi Province. The choice of the appropriate criteria should be based on the actual conditions of the site.

Indexed as

Societies, MedicalAdultChinaFemaleHumansMaleMetabolic SyndromeMiddle AgedPractice Guidelines as TopicPrevalence

Identifiers

PMID29216328
PMCPMC5720703
OpenAlexW2771500038

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.