A Blood Test for Fibromyalgia Moves Closer

A Blood Test for Fibromyalgia Moves Closer

One of the most persistent frustrations in fibromyalgia is that there is no test. Diagnosis rests on symptoms, a clinical examination, and ruling out other conditions. In September 2026, researchers published proof-of-concept work on a different approach: a blood-based molecular profile built from infrared spectroscopy combined with metabolomics.

The study is exploratory, and it is not a diagnostic test you can access yet. But it demonstrates that the analytical route is technically feasible.

The Two Techniques

The researchers combined two methods that read the blood in complementary ways:

• Fourier-transform infrared (FTIR) spectroscopy shines infrared light through a sample. Different molecules absorb different wavelengths, producing a spectral "fingerprint" of the sample's overall chemical composition.
• Mass spectrometry (MS) metabolomics separates and identifies individual small molecules, giving a precise inventory of metabolites.

Blood was collected using volumetric absorptive microsampling (VAMS) on Mitra tips — a technique that needs only a small, dried blood volume, which is easier to collect than a full venous draw. The team tested several extraction solvents and found that methanol and a methanol/1-butanol mix gave the broadest metabolite coverage.

The Cohort

The study compared 40 people with fibromyalgia, 20 with rheumatoid arthritis, and 10 healthy controls. Including a rheumatoid arthritis group matters: the clinically difficult question is rarely "is anything wrong?" but "is this fibromyalgia or an inflammatory arthritis?"

The Results

Using partial least squares discriminant analysis with covariate adjustment, the model distinguished fibromyalgia from rheumatoid arthritis with an internally cross-validated AUC of 0.84 (sensitivity 0.84, specificity 0.78, balanced accuracy 0.81).

An AUC of 0.84 is a reasonable signal for an exploratory study, and it is not close to a clinically deployable test. What makes the result more interesting than a single number is that the two techniques agreed with each other. Regression models showed strong correlations (R² ≥ 0.70) between specific FTIR spectral features and the mass spectrometry intensities of the same molecules.

Which Molecules Stood Out

The metabolomic analysis flagged several candidate markers, including oligopeptides, inosine monophosphate, and signaling lipid molecules. These point toward pathways involving oxidative stress and inflammatory signalling. Individual molecules that correlated across both methods included inosine monophosphate, N-acylethanolamines (NAE), monoacylglycerols (MAG), fructosyl phenylalanine, fructosyl isoleucine, the dipeptide Ser-Phe, and 3,4,5-trihydroxypentanoylcarnitine.

What This Does and Does Not Mean

This study establishes feasibility, not a product. Several limitations matter:

• The sample is small. Seventy participants total, across three groups. Small cohorts produce optimistic performance estimates.
• Fibromyalgia versus rheumatoid arthritis is one comparison. Distinguishing fibromyalgia from healthy controls, or from ME/CFS, small fiber neuropathy, or lupus, is untested here.
• The validation was internal. Cross-validation within the same dataset is not the same as testing on a new, independent group of patients.
• The markers are hypotheses, not confirmed biomarkers. They align with existing theories about oxidative stress and inflammation, which is encouraging, but they need replication.

The authors describe the work as laying the groundwork for larger independent validation studies and a possible future FTIR-based screening assay.