Objective: In multiple sclerosis (MS), cerebral gray matter (GM) atrophy correlates more strongly than white matter (WM) atrophy with disability. The corresponding relationships in the spinal cord (SC) are unknown due to technical limitations in assessing SC GM atrophy. Using phase-sensitive inversion recovery (PSIR) magnetic resonance imaging, we determined the association of the SC GM and SC WM areas with MS disability and disease type. Methods: A total of 113 MS patients and 20 healthy controls were examined at 3T with a PSIR sequence acquired at the C2/C3 disk level. Two independent, clinically masked readers measured the cord WM and GM areas. Correlations between cord areas and Expanded Disability Status Score (EDSS) were determined. Differences in areas between groups were assessed with age and sex as covariates. Results: Relapsing MS (RMS) patients showed smaller SC GM areas than age- and sex-matched controls (p = 0.008) without significant differences in SC WM areas. Progressive MS patients showed smaller SC GM and SC WM areas compared to RMS patients (all p ≤ 0.004). SC GM, SC WM, and whole cord areas inversely correlated with EDSS (rho: -0.60, -0.32, -0.42, respectively; all p ≤ 0.001). The SC GM area was the strongest correlate of disability in multivariate models including brain GM and WM volumes, fluid-attenuated inversion recovery lesion load, T1 lesion load, SC WM area, number of SC T2 lesions, age, sex, and disease duration. Brain and spinal GM independently contributed to EDSS. Interpretation: SC GM atrophy is detectable in vivo in the absence of WM atrophy in RMS. It is more pronounced in progressive MS than RMS and contributes more to patient disability than SC WM or brain GM atrophy.

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine - that is, the application of information technology to medicine - has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision making, and ultimately lead to improved outcomes.