Evidence map›Paper›PMID 34960027›Full record

ArticleNutrients2021

Prevalence and Influencing Factors of Metabolic Syndrome among Adults in China from 2015 to 2017.

Fan Yao, Yacong Bo, Liyun Zhao, Yaru Li, Lahong Ju, Hongyun Fang, Wei Piao, Dongmei Yu, Xiangqian Lao

Open access · goldAbstract read
In one paragraph

Article in Nutrients, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
122citing papers in PubMed, 6 pooled it
18.5field-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

122 citing papers in PubMed, 6 syntheses or guidelines pooled it, 197 citations in OpenAlex.

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62 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

9 authors at 3 institutions in 1 country.

Fan YaoKey Laboratory of Trace Element Nutrition of National Health Commission, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Yacong BoJockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong 999077, China.
Liyun ZhaoKey Laboratory of Trace Element Nutrition of National Health Commission, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Yaru LiBeijing Friendship Hospital, Beijing 100050, China.
Lahong JuKey Laboratory of Trace Element Nutrition of National Health Commission, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Hongyun FangKey Laboratory of Trace Element Nutrition of National Health Commission, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Wei PiaoKey Laboratory of Trace Element Nutrition of National Health Commission, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Dongmei YuKey Laboratory of Trace Element Nutrition of National Health Commission, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Xiangqian LaoJockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong 999077, China.
National Health and Family Planning Commission · CNChinese University of Hong Kong · CNBeijing Friendship Hospital · CN

Funding

National Health Commission of the People's Republic of China Medical Reform Major Program: China National Chronic Diseases and Nutrition Surveillance of Adults (2015-2017) none
6 · The paper itself

Abstract

The prevalence and influencing factors of metabolic syndrome (MetS) in Chinese residents aged 20 or older were investigated. The data were collected from China Nutrition and Health Surveillance (2015-2017), which used a stratified, multistage, random sampling method. A total of 130,018 residents aged 20 years or older from 31 provinces were included in this study. The National Cholesterol Education Programme Adult Treatment Panel III (NCEP ATP III) criteria were used to define MetS. The standardised prevalence of high waist circumference, high blood pressure and low high-density lipoprotein cholesterol were 40.8%, 49.4% and 41.1%, respectively. The following factors were associated with a higher prevalence of MetS: female [odds ratio (OR) = 1.773, 95% CI = 1.709-1.840]; older age (OR = 1.037, 95% CI = 1.036-1.039); living in north China (OR = 1.087, 95% CI = 1.058-1.117); high body mass index (OR = 1.402, 95% CI = 1.395-1.408); higher income [OR (95% CI): 1.044 (1.007-1.083), 1.083 (1.044-1.124) and 1.123 (1.078-1.170) for moderate, high, and very high income, respectively]; family history of hypertension (OR = 1.237, 95% CI = 1.203-1.273); family history of diabetes (OR = 1.491, 95% CI = 1.426-1.558) and current smoking status (OR = 1.143, 95% CI = 1.098-1.191). Living in the countryside (OR = 0.960, 95% CI = 0.932-0.988), moderate alcohol consumption (OR = 0.917, 95% CI = 0.889-0.946) and being physically active (OR = 0.887, 95% CI = 0.862-0.913) were associated with a lower prevalence of MetS. The prevalence of MetS among residents aged 20 years or older in China is increasing, especially among women, people aged 45 years or older and urban residents. Preventive efforts, such as quitting smoking and engaging in physical activity, are recommended to reduce the risk of MetS.

Indexed as

AdultChinaFemaleHumansLogistic ModelsMaleMetabolic SyndromeMiddle AgedMultivariate AnalysisPopulation SurveillancePrevalenceRetrospective StudiesRisk FactorsSurveys and QuestionnairesYoung Adultadultsinfluencing factorsmetabolic syndromeprevalence

Identifiers

PMID34960027
PMCPMC8705649
OpenAlexW4200209517

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.