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Objective To investigate the relationship between metabolomic profiles, genome-wide polygenic risk scores (PRSs) and risk of rheumatoid arthritis (RA).Methods 143 nuclear magnetic resonance-based plasma metabolic biomarkers were measured among 93 800 participants in the UK Biobank. The Cox regression model was used to assess the associations between these metabolic biomarkers and RA risk, and genetic correlation and Mendelian randomisation analyses were performed to reveal their causal relationships. Subsequently, a metabolic risk score (MRS) comprised of the weighted sum of 17 clinically validated metabolic markers was constructed. A PRS was derived by assigning weights to genetic variants that exhibited significant associations with RA at a genome-wide level.Results A total of 620 incident RA cases were recorded during a median follow-up time of 8.2 years. We determined that 30 metabolic biomarkers were potentially associated with RA, while no further significant causal associations were found. Individuals in the top decile of MRS had an increased risk of RA (HR 3.52, 95% CI: 2.80 to 4.43) compared with those below the median of MRS. Further, significant gradient associations between MRS and RA risk were observed across genetic risk strata. Specifically, compared with the low genetic risk and favourable MRS group, the risk of incident RA in the high genetic risk and unfavourable MRS group has almost elevated by fivefold (HR 6.10, 95% CI: 4.06 to 9.14).Conclusion Our findings suggested the metabolic profiles comprising multiple metabolic biomarkers contribute to capturing an elevated risk of RA, and the integration of genome-wide PRSs further improved risk stratification.

Objectives: The present study aimed to discover novel susceptibility loci associated with risk of rheumatoid arthritis (RA).Methods: We performed a new genome-wide association study (GWAS) in Chinese subjects (1027 RA cases and 2879 controls) and further conducted an expanded meta-analysis with previous GWAS summary data and replication studies. The functional roles of the associated loci were interrogated using publicly available databases. Dual-luciferase reporter and cytokine assay were also used for exploring variant function.Results: We identified five new susceptibility loci (IL12RB2, BOLL-PLCL1, CCR2, TCF7 and IQGAP1; pmeta <5.00E-08) with same effect direction in each study cohort. The sensitivity analyses showed that the genetic association of at least three loci was reliable and robust. All these lead variants are expression quantitative trait loci and overlapped with epigenetic marks in immune cells. Furthermore, genes within the five loci are genetically associated with risk of other autoimmune diseases, and genes within four loci are known functional players in autoimmunity, which supports the validity of our findings. The reporter assay showed that the risk allele of rs8030390 in IQGAP1 have significantly increased reporter activity in HEK293T cells. In addition, the cytokine assay found that the risk allele of rs244672 in TCF7 was most significantly associated with increased plasma IL-17A levels in healthy controls. Finally, identified likely causal genes in these loci significantly interacted with RA drug targets.Conclusion: This study identified novel RA risk loci and highlighted that comprehensive genetic study can provide important information for RA pathogenesis and drug therapy. [ABSTRACT FROM AUTHOR]