A multimodal approach to distinguish multiple sclerosis phenotypes at diagnosis using biomarker profiles

Multiple sclerosis (MS) is a complex and heterogeneous disease characterized by variable clinical outcomes.

We aimed to develop a predictive model combining principal component analysis (PCA) and clustering techniques to identify biomarker sets associated with MS and characterize distinct phenotypes.

A monocentric, cross-sectional study on treatment naïve patients at the time of MS diagnosis.

Clinical, laboratory, and neuroimaging data were collected, including retinal layer measurements via optical coherence tomography and neurofilament light (NFL) chains levels.

The cohort included 71 MS patients with mean age 35.7 years (SD = 9.8). PCA yielded five components with eigenvalues >1.0, explaining 68.1% of total variance. Component 1 showed strong negative coefficients for retinal thickness (ganglion cell-inner plexiform layer: -0.82, peripapillary retinal nerve fiber layer (RNFL): -0.79, macular RNFL: -0.75) and moderate positive coefficient for serum NFL (0.45). Component 2 featured high positive coefficients for NFL in cerebrospinal fluid (0.88) and serum (0.56). K-means clustering identified two distinct groups: one (n = 33) with thicker retinal layers, better cognitive performance, and unexpectedly higher serum NFL levels compared to the other group (n = 38).

These findings suggest that MS may present with distinct phenotypic profiles even at diagnosis. Future longitudinal studies are needed to validate these early biomarkers and refine personalized treatment approaches.