Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two most common rheumatic diseases worldwide, causing pain and disability. Both conditions are highly heterogeneous, and their onset occurs insidiously with non-specific symptoms, so they are not always distinguishable from other arthritis during the initial stages. This makes early diagnosis difficult and resource-demanding in clinical environments. Here, we estimated its diagnostic performance in classifying ATR-FTIR spectra obtained from serum samples from OA patients, RA patients, and healthy controls. Altogether, 334 serum samples were obtained from 100 OA patients, 134 RA patients, and 100 healthy controls. The infrared spectral acquisition was performed on air-dried 1 μl of serum with a diamond-ATR-FTIR spectrometer. Machine learning models combining Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) were trained to binary classify preprocessed ATR-FTIR spectra as follows: controls vs. OA, controls vs. RA, and OA vs. RA. For a separated test dataset and the validation dataset, the overall model performance was better in classifying OA and RA patients, followed by the RA and controls, and lastly, between OA and controls, with corresponding AUC-ROC values: 0.84, 0.76, 0.72 (test dataset) and 0.94, 0.92, 79 (validation dataset). In conclusion, this study reports robust binary classifier models to differentiate blood serum from the two most common rheumatic diseases, showing the potential of ATR-FTIR as an effective aid in rheumatic disease classification.
Competing Interest StatementThe authors have declared no competing interest.
Funding StatementThis study did not receive any funding.
Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
All samples were obtained from a local Biopank (Biobank Borealis of Northern Finland), governed by the Finnish Biobank Act 688/2021. The biobank approved study approval requests with a detailed study protocol and a sample request and did not require a statement from the local ethics committee.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data AvailabilityAll data produced in the present study are available upon reasonable request to the authors.
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