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Background: Psoriatic arthritis (PsA) is a chronic inflammatory arthritis associated with psoriasis, affecting approximately 0.36% and 0.11% of the UK and global adult population, respectively [1]. Around 30% of individuals with psoriasis develop PsA during their lifetime. PsA can lead to joint damage, increased disability, reduced quality of life, work absenteeism, and a higher risk of cardiovascular mortality [2]. Tumour necrosis factor inhibitor (TNFi) therapy has revolutionised the management of psoriatic arthritis (PsA); however, approximately 40% of patients experience an inadequate response [3]. Previous studies were not hypothesis-free, and although those have demonstrated the potential of genetics to affect response to treatment, if and how that relates to clinical factors is uncertain.
Objectives: The objective of the study was to identify genetic and non-genetic biomarkers of treatment response to TNFi in PsA.
Methods: Patients were recruited to the OUTPASS study, a prospective observational study of patients commencing a biologic or small molecule inhibitor. Patients were seen at baseline (pre-drug) and followed up for 12 months. To be eligible for this analysis, patients had to be commencing a TNFi. Samples were genotyped using the Illumina Core-Exome array, and single-nucleotide polymorphisms (SNPs) were imputed following standard quality control for genetic data. Treatment response was assessed at six months using the Psoriatic Arthritis Response Criteria (PsARC) and the EULAR response criteria, where PsARC was missing. Comorbidities such as depression, hypertension, diabetes, cardiovascular, respiratory, kidney, liver, thyroid diseases and malignancy were recorded; missing or unknown data were considered absent. A genome-wide association study, adjusted for clinical covariates, was conducted to identify genetic loci associated with TNFi response.
Results: After stringent quality control, >7.5 million SNPs and 329 patients of European ancestry were available for analysis. Baseline higher body-mass-index was associated with poor treatment response (OR= 0.95, 95% CI= 0.91, 0.99, p-value = 0.028. The presence of any comorbidities was associated with lower response rates (73.8% vs 86.3% in those without, p=0.024), underlining the influence of comorbidities on therapeutic effectiveness. Age, sex, smoking, disease duration, use of NSAIDs, baseline DAS-28 and serum CRP level were not associated with treatment response. The GWAS identified 28 SNPs with suggestive evidence for association with non-response (P < 5 × 10 −5 ), including the top hit rs2658890 in the XKR4 gene at chromosome 8 (OR=0.127; P=2.82x10 -6 ). None of the identified loci have been previously reported to be associated with TNFi response in PsA or other inflammatory arthritides, nor with higher BMI or metabolic syndrome.
Conclusions: This study supports that high BMI and comorbidities predict poor TNFi response and identifies novel putative genetic loci associated with treatment failure after adjustment, which now require independent replication. Future studies on genetic predictors of biomarkers of treatment response in PsA should adjust for clinical factors.
Summary of baseline characteristics of OUTPASS cohort genotyped (N=329). *csDMARD such as methotrexate (MTX), sulfasalazine, leflunomide, hydroxychloroquine, cyclosporine, and azathioprine, either alone or in any combination alongside TNFi. MTX was most frequently prescribed. SD= standard deviation, IQR= Interquartile range
| Clinical characteristic | Missing/ Unknown
| Summary of the observed data | |||
|---|---|---|---|---|---|
| Age at baseline (years) | 3 (0.91) | Mean= 48.47 (SD= 12.22) | |||
| Sex, n (%) | 4 (1.22) | Female= 194 (59.69) | |||
| Body Mass Index (BMI) kg/m 2 | 29 (8.81) | Median= 29.07, (IQR=25.83 - 33.80) | |||
| Comorbidity count, n (%) | One | 0 (0) | 107 (32.52) | ||
| Two | 57 (17.33) | ||||
| Three | 19 (5.78) | ||||
| More than three | 8 (2.43) | ||||
| Top three comorbidities, n (%) | Depression | 0 (0) | 89 (27.05) | ||
| Hypertension | 82 (24.92) | ||||
| Diabetes mellitus | 18 (5.47) | ||||
| Smoking status, n (%) | 16 (04.86) | 163 (52.08) Current or former smokers | |||
| Disease duration at baseline in years | 4 (1.22) | Median= 5 (IQR= 2.0 - 9.0) | |||
| csDMARD* | Single csDMARD | 151 (45.90) | |||
| Double csDMARDs | 82 (24.92) | ||||
| Triple csDMARDs | 5 (1.52) | ||||
| NSAID co-therapy, n (%) | Daily used | 5 (1.52) | 125 (38.58) | ||
| Used when needed | 66 (20.37) | ||||
| Any Steroids co-therapy, n (%) | 129 (39.21) | 75 (37.50) | |||
| CASPAR Criteria, n (%) | 18 (5.47) | 303 (97.4) | |||
| TNFi subclass, n (%) | Adalimumab | 0 (0) | 148 (44.99) | ||
| Etanercept | 137 (41.64) | ||||
| Golimumab | 26 (7.90) | ||||
| Certolizumab | 16 (4.86) | ||||
| Infliximab | 2 (0.61) | ||||
| Baseline DAS28 score | 30 (9.12) | Median= 4.89 (IQR= 4.19 - 5.55) | |||
| Baseline serum CRP level in mg/dl | 28 (8.51) | Median= 6.4 (IQR= 4.0 - 16.0) | |||
Top 10 Single Nucleotide Polymorphisms (SNP) of adjusted GWAS of treatment response to TNFi. Adjusted for age, sex, types of response measures, BMI and presence of any comorbidity. EA = Effective Allele, EAF = Effective Allele Frequency
| Chromosome | SNP | Gene/Locus | EA | EAF | Odds Ratio (95% CI) | P value | Functional annotation |
|---|---|---|---|---|---|---|---|
| 1 | rs1288592 | A | 0.14 | 0.18 (0.08, 0.39) | 1.59E-05 | ||
| 1 | rs61840345 | A | 0.29 | 0.23 (0.12, 0.43) | 6.31E-06 | ||
| 2 | rs17557755 | T | 0.07 | 0.20 (0.09, 0.40) | 9.27E-06 | ||
| 3 | rs9809725 | FRMD4B | C | 0.47 | 0.26 (0.14, 0.48) | 1.81E-05 | Upstream |
| 5 | rs78215119 | ADGRV1 or GPR98 | A | 0.04 | 0.05 (0.01, 0.20) | 1.17E-05 | Downstream |
| 6 | rs502096 | LOC105374910 | A | 0.22 | 0.23 (0.12, 0.44) | 1.36E-05 | Intron |
| 7 | rs73263870 | LINC01162 | G | 0.22 | 0.23 (0.12, 0.46) | 2.49E-05 | Intron |
| 7 | rs138860455 | COPG2 | T | 0.18 | 0.25 (0.13, 0.47) | 2.02E-05 | Intron |
| 8 | rs2658890 | XKR4 | C | 0.09 | 0.13 (0.05, 0.30) | 2.82E-06 | Intron |
| 15 | rs55731407 | LOC105370775 | G | 0.21 | 0.26 (0.14, 0.49) | 2.60E-05 | Intron |
| 20 | rs12625993 | A | 0.08 | 0.13 (0.05, 0.32) | 1.35E-05 |
REFERENCES: [1] Lembke S, Macfarlane GJ, Jones GT. The worldwide prevalence of psoriatic arthritis-a systematic review and meta-analysis. Rheumatology (Oxford). 2024;63(12):3211-20.
[2] Druce KL. The epidemiology of psoriatic arthritis in the UK: a health intelligence analysis of UK Primary Care Electronic Health Records 1991–2020. Rheumatology (Oxford). 2023:Kead586.
[3] Curry PDK, Morris AP, Barton A, Bluett J. Do genetics contribute to TNF inhibitor response prediction in Psoriatic Arthritis? Pharmacogenomics J. 2023;23(1):1-7.
Acknowledgments: NIL.
Disclosure of Interests: Moshiur Rahman Khasru MK received a speaker fee from Novartis in 2022, Michael Stadler: None declared, Hector Chinoy HC has received grant support from Pfizer, Meghna Jani: None declared, Pauline Ho PH attended educational meetings and conferences sponsored by Johnson and Johnson, UCB and Novartis, speaker fees from UCB, Novartis., John Bowes: None declared, Nisha Nair: None declared, Anne Barton: None declared, James Bluett JB reports a research grant award from Pfizer and travel/conference fees in the last 3 years from Fresenius Kabi and Novartis.