Evidence map›Paper›PMID 42042896›Full record

ArticleMetabolites2026

Serum Uric Acid as a Biomarker for Incident Type 2 Diabetes Mellitus: A 6-Year Cohort Study in Qatar.

Alan Saeed, Yamane Chawa, Samer Kaspo, Hassan Ibrahim, Aisha Al Adab, Anas Kalfah

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Article in Metabolites, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Alan SaeedPrimary Health Care Corporation, Doha P.O. Box 26555, Qatar.ORCID 0000-0003-4003-4216
Yamane ChawaHamad Medical Corporation, Doha P.O. Box 3050, Qatar.
Samer KaspoSaint Francis Hospital, Evanston, IL 60202, USA.
Hassan IbrahimHamad Medical Corporation, Doha P.O. Box 3050, Qatar.
Aisha Al AdabHamad Medical Corporation, Doha P.O. Box 3050, Qatar.ORCID 0000-0003-2149-7493
Anas KalfahPrimary Health Care Corporation, Doha P.O. Box 26555, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSerum uric acid (SUA) may predict incident type 2 diabetes mellitus (T2DM), but longitudinal evidence from Middle Eastern populations remains limited.

methodsWe conducted a retrospective cohort study using electronic health records from Qatar's Primary Health Care Corporation over a six-year period (2018-2023). Adults aged ≥18 years with at least one valid serum uric acid (SUA) measurement and no prior diabetes at baseline were eligible. All eligible participants were retained; no propensity score matching was performed. Baseline SUA was defined at the first valid measurement, and repeated-measure exposures included current SUA, cumulative-average SUA, and landmark time-weighted average (TWA) SUA. Sex-specific SUA categories were low <208, normal 208-428, and high >428 µmol/L in males and low <149, normal 149-357, and high >357 µmol/L in females. Sex-stratified Cox models, restricted cubic spline analyses, prespecified sensitivity analyses, and complementary explainable boosting machine (EBM) models were used to evaluate associations with incident type 2 diabetes mellitus (T2DM).

resultsThe cohort included 169,876 adults (85,361 males and 84,515 females) and 18,714 incident T2DM events. In fully adjusted baseline Cox models, high baseline SUA was associated with higher T2DM hazard in females (hazard ratio [HR]: 1.44; 95% CI: 1.36-1.53), whereas low baseline SUA was associated with higher hazard in males (HR: 1.60; 95% CI: 1.44-1.78), and high SUA was not. In women, positive SUA-T2DM associations persisted in time-varying and landmark analyses, including current high- versus- normal SUA (HR: 1.50; 95% CI: 1.41-1.58) and 2-measurement landmark TWA SUA per 1 mg/dL (HR: 1.17; 95% CI: 1.13-1.20). In men, unlagged whole-cohort analyses showed inverse continuous associations, but lagged and repeated-measure analyses shifted toward positive associations, including 365-day lagged high- versus- normal baseline SUA (HR: 1.19; 95% CI: 1.11-1.28) and 2-measurement landmark TWA SUA per 1 mg/dL (HR: 1.06; 95% CI: 1.03-1.09). Restricted cubic splines showed a steadily rising risk gradient in females above approximately 262 µmol/L and a J-shaped pattern in males, with the lowest risk near 374 µmol/L. In EBM models, TWA SUA ranked third in women and fifth in men in the 2-measurement landmark cohorts.

conclusionsIn this large Qatar cohort, longitudinal SUA was associated with incident T2DM in a sex-specific manner, with consistent positive associations in females and exposure-definition-dependent patterns in males. Repeated SUA measurements may improve diabetes risk stratification, but causal and therapeutic implications require further study.

Indexed as

Cox regressionexplainable boosting machinehyperuricemiaQatarrestricted cubic splinesretrospective cohortserum uric acidsex differencestype 2 diabetes

Identifiers

PMID42042896
PMCPMC13117883

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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.