Dataset Information

Systematic Evaluation of Rheumatoid Arthritis Risk by Integrating Lifestyle Factors and Genetic Risk Scores.

ABSTRACT:

Background

Effective identification of high-risk rheumatoid arthritis (RA) individuals is still a challenge. Whether the combined effects of multiple previously reported genetic loci together with lifestyle factors can improve the prediction of RA risk remains unclear.

Methods

Based on previously reported results and a large-scale Biobank dataset, we constructed a polygenic risk score (PRS) for RA to evaluate the combined effects of the previously identified genetic loci in both case-control and prospective cohorts. We then evaluated the relationships between several lifestyles and RA risk and determined healthy lifestyles. Then, the joint effects of healthy lifestyles and genetic risk on RA risk were evaluated.

Results

We found a positive association between PRS and RA risk (OR = 1.407, 95% confidence interval (CI) = 1.354~1.463; HR = 1.316, 95% CI = 1.257~1.377). Compared with the low genetic risk group, the group with intermediate or high genetic risk had a higher risk (OR = 1.347, 95% CI = 1.213~1.496; HR = 1.246, 95% CI = 1.108~1.400) (OR = 2.169, 95% CI = 1.946~2.417; HR = 1.762, 95% CI = 1.557~1.995). After adjusting for covariates, we found protective effects of three lifestyles (no current smoking, regular physical activity, and moderate body mass index) on RA risk and defined them as healthy lifestyles. Compared with the individuals with low genetic risks and favorable lifestyles, those with high genetic risks and unfavorable lifestyles had as high as OR of 4.637 (95%CI = 3.767~5.708) and HR of 3.532 (95%CI = 2.799~4.458).

Conclusions

In conclusion, the integration of PRS and lifestyles can improve the prediction of RA risk. High RA risk can be alleviated by adopting healthy lifestyles but aggravated by adopting unfavorable lifestyles.

SUBMITTER: Yu XH

PROVIDER: S-EPMC9299428 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRheumatoid arthritis (RA) shares genetic variants with other autoimmune conditions, but existing studies test the association between RA variants with a pre-defined set of phenotypes. The objective of this study was to perform a large-scale, systemic screen to determine phenotypes that share genetic architecture with RA to inform our understanding of shared pathways.MethodsIn the UK Biobank (UKB), we constructed RA genetic risk scores (GRS) incorporating human leukocyte antigen (HLA) and non-HLA risk alleles. Phenotypes were defined using groupings of International Classification of Diseases (ICD) codes. Patients with an RA code were excluded to mitigate the possibility of associations being driven by the diagnosis or management of RA. We performed a phenome-wide association study, testing the association between the RA GRS with phenotypes using multivariate generalized estimating equations that adjusted for age, sex, and first five principal components. Statistical significance was defined using Bonferroni correction. Results were replicated in an independent cohort and replicated phenotypes were validated using medical record review of patients.FindingsWe studied n = 316,166 subjects from UKB without evidence of RA and screened for association between the RA GRS and n = 1317 phenotypes. In the UKB, 20 phenotypes were significantly associated with the RA GRS, of which 13 (65%) were immune mediated conditions including polymyalgia rheumatica, granulomatosis with polyangiitis (GPA), type 1 diabetes, and multiple sclerosis. We further identified a novel association in Celiac disease where the HLA and non-HLA alleles had strong associations in opposite directions. Strikingly, we observed that the non-HLA GRS was exclusively associated with greater risk of the validated conditions, suggesting shared underlying pathways outside the HLA region.InterpretationThis study replicated and identified novel autoimmune phenotypes verified by medical record review that share immune pathways with RA and may inform opportunities for shared treatment targets, as well as risk assessment for conditions with a paucity of genomic data, such as GPA.FundingThis research was funded by the US National Institutes of Health (P30AR072577, R21AR078339, R35GM142879, T32AR007530) and the Harold and DuVal Bowen Fund.