Evidence map›Paper›PMID 31767029›Full record

ArticleJournal of translational medicine2019

Nonlinear relationship between serum uric acid and body mass index: a cross-sectional study of a general population in coastal China.

Hui Zhou, Zhen Liu, Zhong Chao, Yeqing Chao, Lidan Ma, Xiaoyu Cheng, Yangang Wang, Changgui Li, Ying Chen

Abstract read
In one paragraph

Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

9 authors.

Hui ZhouHeze Medical College, Heze, Shandong, China.
Zhen LiuShandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China.
Zhong ChaoDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
Yeqing ChaoHeze Medical College, Heze, Shandong, China.
Lidan MaDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
Xiaoyu ChengDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
Yangang WangDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
Changgui LiDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
Ying ChenDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China. qdchenying@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConflicting evidence exists on the relationship between body mass index (BMI) and serum uric acid (SUA). Therefore, we aimed to evaluate the SUA-BMI relationship in a large-scale epidemiological survey in coastal China.

methodsThis survey was conducted among the general population in the coastal region of China from September 2014 to January 2015. SUA Levels were measured by the automatic Sysmex Chemix-180 biochemical analyzer.

resultsA total of 6098 men (BMI: 24.58 ± 3.74 kg/m

conclusionsA U-shaped relationship between BMI and SUA was found for both men and women; the association was stronger for men than women.

Indexed as

Body Mass IndexEcosystemNonlinear DynamicsChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedMultivariate AnalysisRegression AnalysisUric AcidUric AcidBody mass indexChinaCross-sectional studiesEpidemiologySerumUric acid

Identifiers

PMID31767029
PMCPMC6878643

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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.