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Hum, Ryan M., Sharma, Seema D., Stadler, Michael, Viatte, Sebastien, Ho, Pauline, Nair, Nisha, Shi, Chenfu, Yap, Chuan Fu, Soomro, Mehreen, Plant, Darren, Humphreys, Jenny H., MacGregor, Alexander 
ORCID: https://orcid.org/0000-0003-2163-2325, Yates, Max ORCID: https://orcid.org/0000-0003-3977-8920, Verstappen, Suzanne, Barton, Anne and Bowes, John
and
on behalf of all NOAR collaborators
(2023)
Using polygenic risk scores to aid diagnosis of patients with early inflammatory arthritis: Results from the Norfolk Arthritis Register.
Arthritis & Rheumatology.
ISSN 2326-5191
Abstract
Objective: There is growing evidence that genetic data are of benefit in the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G-PROB) has been developed to aid diagnosis has not yet been tested in a real-world setting. Our aim was to assess whether G-PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a prospective observational cohort of patients presenting with early inflammatory arthritis. Methods: Genotypes and clinician diagnoses were obtained from patients from NOAR. Six G-probabilities (0%–100%) were created for each patient based on known disease-associated odds ratios of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, spondyloarthropathy, gout, or “other diseases.” Performance of the G-probabilities compared with clinician diagnosis was assessed. Results: We tested G-PROB on 1,047 patients. Calibration of G-probabilities with clinician diagnosis was high, with regression coefficients of 1.047, where 1.00 is ideal. G-probabilities discriminated clinician diagnosis with pooled areas under the curve (95% confidence interval) of 0.85 (0.84–0.86). G-probabilities <5% corresponded to a negative predictive value of 96.0%, for which it was possible to suggest >2 unlikely diseases for 94% of patients and >3 for 53.7% of patients. G-probabilities >50% corresponded to a positive predictive value of 70.4%. In 55.7% of patients, the disease with the highest G-probability corresponded to clinician diagnosis. Conclusion: G-PROB converts complex genetic information into meaningful and interpretable conditional probabilities, which may be especially helpful at eliminating unlikely diagnoses in the rheumatology outpatient setting.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding information: Supported by Arthritis Research UK (core program grant 20385) and by the NIHR Manchester Biomedical Research Centre. Supported by the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement 101007757, HIPPOCRATES). The Innovative Medicines Initiative 2 receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations. Drs. Hum and Sharma's work was supported by the NIHR as holders of Academic Clinical Fellowships funded by the Integrated Academic Training program. Dr. Viatte was supported by the Swiss Foundation for Medical-Biological Scholarships, managed by the Swiss National Science Foundation (research grant PASMP3_134380). |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging Faculty of Medicine and Health Sciences > Research Groups > Nutrition and Preventive Medicine Faculty of Medicine and Health Sciences > Research Groups > Musculoskeletal Medicine Faculty of Medicine and Health Sciences > Research Groups > Epidemiology and Public Health Faculty of Medicine and Health Sciences > Research Centres > Population Health |
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| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Mar 2024 18:27 |
| Last Modified: | 04 Mar 2024 18:27 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94448 |
| DOI: | 10.1002/art.42760 |
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