Evidence map›Paper›PMID 21181218›Full record

ArticleClinical rheumatology2011

Gender and age impacts on the correlations between hyperuricemia and metabolic syndrome in Chinese.

Qing Zhang, Shanshan Lou, Zhaowei Meng, Xiaojun Ren

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Article in Clinical rheumatology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 2 pooled it
–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

50 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2021
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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

4 authors.

Qing ZhangDepartment of Health Management, Tianjin Medical University General Hospital, Tianjin, People's Republic of China. bohan92@vip.sina.com
Shanshan Lou
Zhaowei Meng
Xiaojun Ren

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prevalence of both metabolic syndrome (MS) and hyperuricemia are increasing. However, findings regarding their relationships are inconsistent. We aimed to explore correlations between MS and hyperuricemia in a large Chinese population, emphasizing the impacts from gender and age. Data analyses were performed in 17,762 subjects randomly recruited from Tianjin municipality in China. Hyperuricemia was defined as serum uric acid (SUA) >420 μmol/L for men, >360 μmol/L for women. MS was diagnosed by the consensus criterion released in 2009 from a joint collaboration between American Heart Association and other organizations. MS was also diagnosed by Chinese Diabetes Society (CDS) criterion. Total hyperuricemic prevalence was 12.16%, with male significantly higher than female. Total MS prevalence by consensus criterion was much higher than by CDS criterion (25.56% versus 14.09%). Correlation coefficients were much greater in women than in men. SUA was significantly positively related with body mass index and waist circumference. Generally, binary logistic regression models disclosed females with high SUA were twice likely to suffer from MS than males. Young females (≤44 years old) with hyperuricemia had the highest odd ratio of 7.857 by consensus criterion; and after further adjustment by body mass index, this odd ratio was 3.040. SUA and MS were much more closely related in females than in males. Young women with hyperuricemia had the highest risk of MS.

Indexed as

Asian PeopleSex CharacteristicsAdultAgingBody Mass IndexChinaCross-Sectional StudiesFemaleHumansHyperuricemiaLogistic ModelsMaleMetabolic SyndromeMiddle AgedPrevalenceRetrospective StudiesUric Acid

Identifiers

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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.