Evidence map›Paper›PMID 35739406›Full record

ArticleClinical rheumatology2022

Risk factors of urinary calculi in men with gout.

Yu Cao, Xinxin Han, Xiaoxue Wang, Yun Zhang, He Xiao, Xuejun Zeng

Abstract read
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In one paragraph

Article in Clinical rheumatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 4 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

6 authors at 1 institution in 1 country.

Yu CaoDepartment of Family Medicine and Division of General Internal Medicine, Department of Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, State Key Laboratory of Complex Severe and Rare Diseases (Peking Union Medical College Hospital), Shuaifuyuan No.1, Dongcheng District, Beijing, 100730, China.
Xinxin HanDepartment of Family Medicine and Division of General Internal Medicine, Department of Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, State Key Laboratory of Complex Severe and Rare Diseases (Peking Union Medical College Hospital), Shuaifuyuan No.1, Dongcheng District, Beijing, 100730, China.
Xiaoxue WangDepartment of Family Medicine and Division of General Internal Medicine, Department of Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, State Key Laboratory of Complex Severe and Rare Diseases (Peking Union Medical College Hospital), Shuaifuyuan No.1, Dongcheng District, Beijing, 100730, China.
Yun ZhangDepartment of Family Medicine and Division of General Internal Medicine, Department of Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, State Key Laboratory of Complex Severe and Rare Diseases (Peking Union Medical College Hospital), Shuaifuyuan No.1, Dongcheng District, Beijing, 100730, China.
He XiaoDepartment of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, State Key Laboratory of Complex Severe and Rare Diseases (Peking Union Medical College Hospital), Beijing, 100730, China.
Xuejun ZengDepartment of Family Medicine and Division of General Internal Medicine, Department of Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, State Key Laboratory of Complex Severe and Rare Diseases (Peking Union Medical College Hospital), Shuaifuyuan No.1, Dongcheng District, Beijing, 100730, China. zxjpumch@126.com.ORCID http://orcid.org/0000-0002-6426-7586
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate risk factors of urinary calculi in men with gout.

methodA study was conducted at a tertiary hospital in Beijing from October 2016 to January 2020. Male gouty patients were included. Logistic regression was used to establish the association between patients' characteristics and urinary stone disease. A cluster analysis was performed to identify distinct phenotypes of gout based on nine common comorbidities.

resultThe prevalence of calculi was 23.6% among 454 patients. Longer disease courses and comorbid dyslipidemia were associated with an increased risk of urinary calculi, with an OR of 2.76 (95% CI 1.40-5.64) (p = 0.004) for patients with a disease course of 10 years or longer compared with those with a disease course of gout shorter than 1 year, and an OR of 1.71 (95% CI 1.04-2.88) (p = 0.039) for patients with dyslipidemia. Three clinical phenotypes were recognized. Cluster C1 consists of patients with large body mass index, dyslipidemia, and abnormal liver function. The prevalence of stone was 23.2%. Patients in cluster C2 had fewer comorbidities and the lowest stone risk (13.5%). Cluster C3 had more cardiovascular comorbidities and the highest prevalence of stone (47.1%).

conclusionLonger disease course and dyslipidemia contribute to stone risk in men with gout. Three distinct phenotypes represent distinct pathophysiological mechanisms underlying stone occurrence, which would help us to optimize gout management. Key points • Patients with gout have higher risks for urinary stones. This study found that longer disease courses and comorbid dyslipidemia were associated with an increased risk of urinary calculi in largely untreated young men with uncontrolled gout. • This study reveals three distinct phenotypes of gout based on comorbidities. Among all, the group with more cardiovascular comorbidities has the highest prevalence of stones.

Indexed as

DyslipidemiasGoutUrinary CalculiHumansMalePrevalenceRisk FactorsCluster analysisComorbiditiesGoutUrinary calculi

Identifiers

PMID35739406
OpenAlexW4283362515

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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.