ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2020
Metabolic Syndrome and 5-Year Incident Hyperuricemia Among Older Chinese Adults: A Community-Based Cohort Study.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Dietary pattern and risk of hyperuricemia: an updated systematic review and meta-analysis of observational studies.Frontiers in nutrition · 2024Review
- Metabolic Syndrome and Its Components Are Associated with New-Onset Hyperuricemia in a Large Taiwanese Population Follow-Up Study.Nutrients · 2023Observational
- Fatty liver index for hyperuricemia diagnosis: a community-based cohort study.BMC endocrine disorders · 2022Article
- The bidirectional relationship between metabolic syndrome and hyperuricemia in China: A longitudinal study from CHARLS.Endocrine · 2022Article
- Factors Related to Metabolic Syndrome Development and Recovery in Chinese Adults: A Prospective Longitudinal Study.Frontiers in endocrinology · 2022Article
- Metabolic Risk Factors Associated With Chronic Kidney Disease in a Middle-Aged and Elderly Taiwanese Population: A Cross-Sectional Study.Frontiers in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere was a lack of studies focusing on older adults about the longitudinal association between metabolic syndrome (MetS) and hyperuricemia (HUA). We aimed to assess the association of baseline MetS and incident HUA among older Chinese adults, with a special focus on the associations between different combinations of MetS components and HUA.
methodsData of 3247 Chinese adults aged 60 years or older included in a community-based longitudinal cohort study were analyzed. Anthropometric examinations and collection of blood sample were conducted both at baseline and follow-up. HUA was defined as 7 mg/dl or above for men and 6 mg/dl or greater for women. MetS was assessed based on the National Cholesterol Education Program-Adult Treatment Panel III, and older adults with the presence of at least three of MetS components were considered as having MetS.
resultsMetS and its components, including high blood pressure (BP), high body mass index, diabetes mellitus and high triglycerides, were significantly related to incident HUA. The association between high BP and incident HUA is strongest among the five MetS components. Among all combinations of MetS components, the group consisting of diabetes mellitus, high BP and high triglycerides had the highest odds for incident HUA (OR = 13.07, 95% CI = 4.95-34.54).
conclusionMetS and its components, except for low high-density lipoprotein cholesterol, could increase the risk of HUA among community-dwelling older adults, and high BP may be the most important determinant.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.