Evidence map›Paper›PMID 42273684›Full record

ArticleFrontiers in immunology2026

Association of a data-driven nutrient risk score with incident rheumatoid arthritis in UK Biobank adults: a prospective cohort study.

Aimin Wang, Wenfeng Gao, Xinyu Yang, Yanxia Wang, Yonghua Ma, Na Sun, Suzhen Wang, Peizhong Peter Wang, Fuyan Shi

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Article in Frontiers in immunology, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Aimin Wang *School of Public Health, Shandong Second Medical University, Weifang, China.
Wenfeng Gao *Department of Rheumatology and Immunology, Affiliated Hospital of Shandong Second Medical University, Weifang, China.
Xinyu YangSchool of Public Health, Shandong Second Medical University, Weifang, China.
Yanxia WangSchool of Public Health, Shandong Second Medical University, Weifang, China.
Yonghua MaSchool of Public Health, Shandong Second Medical University, Weifang, China.
Na SunSchool of Public Health, Shandong Second Medical University, Weifang, China.
Suzhen WangSchool of Public Health, Shandong Second Medical University, Weifang, China.
Peizhong Peter WangSchool of Public Health, Shandong Second Medical University, Weifang, China.
Fuyan ShiSchool of Public Health, Shandong Second Medical University, Weifang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although specific nutrients have been linked to rheumatoid arthritis (RA), the impact of overall nutrient intake patterns remains unclear. Here, we aimed to explore the association between the nutrient risk score (NRS) and incident RA, as well as the modifying effect of genetic susceptibility and the potential mediating role of obesity. Methods: This prospective study included 161,725 participants from the UK Biobank. Nutrients were selected using a LASSO-Cox model. We used Cox proportional hazards models to investigate the association between NRS and the risk of RA. Generalized propensity score (GPS) and inverse probability weighting (IPW) methods were employed to control for potential confounding factors. Stratified analyses were conducted to assess effect heterogeneity across subgroups. Mediation analysis was used to estimate the mediating effect of obesity. Results: During a median follow-up of 13.4 years, 1,756 participants developed RA. Each unit increase in NRS was associated with a 96% higher risk of RA (HR: 1.96, 95% CI: 1.64-2.33) after adjusting for covariates. The association remained robust in analyses using GPS and IPW weighting. Stratified analyses showed a more pronounced association among individuals reporting short sleep duration (<7 hours) and low levels of physical activity. Furthermore, a gradient association between NRS and RA risk was observed across genetic risk strata. Specifically, compared with participants at low genetic risk with the lowest NRS (Q1), those at high genetic risk with the highest NRS (Q4) had nearly a threefold higher risk of incident RA (HR: 3.28, 95% CI: 2.49-4.32). In addition, several obesity indicators showed potential mediating effects, with the proportion mediated ranging from 3.81% to 13.57%. Conclusion: Higher NRS was associated with an increased risk of incident RA, especially among individuals with high genetic susceptibility. Obesity partially mediated the association, highlighting NRS as a potential tool for RA risk stratification and intervention.

Indexed as

Arthritis, RheumatoidNutrientsAdultAgedBiological Specimen BanksFemaleGenetic Predisposition to DiseaseHumansIncidenceMaleMiddle AgedObesityProspective StudiesRisk FactorsUK BiobankUnited Kingdomcohort studygenetic susceptibilitynutrient risk scorenutrientsobesityrheumatoid arthritis

Identifiers

PMID42273684
PMCPMC13246429

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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.