Changes in employment status over time in multiple sclerosis following a first episode of central nervous system demyelination, a Markov multistate model study.

BACKGROUND AND PURPOSE: Understanding predictors of changes in employment status among people living with multiple sclerosis (MS) can assist health care providers to develop appropriate work retention/rehabilitation programs. We aimed to model longitudinal transitions of employment status in MS and estimate the probabilities of retaining employment status or losing or gaining employment over time in individuals with a first clinical diagnosis of central nervous system demyelination (FCD). METHODS: This prospective cohort study comprised adults (aged 18-59 years) diagnosed with FCD (n = 237) who were followed for more than 11 years. At each review, participants were assigned to one of three states: unemployed, part-time, or full-time employed. A Markov multistate model was used to examine the rate of state-to-state transitions. RESULTS: At the time of FCD, participants with full-time employment had an 89% chance of being in the same state over a 1-year period, but this decreased to 42% over the 10-year follow-up period. For unemployed participants, there was a 92% likelihood of remaining unemployed after 1 year, but this probability decreased to 53% over 10 years. Females, those who progressed to clinically definite MS, those with a higher relapse count, and those with a greater level of disability were at increased risk of transitioning to a deteriorated employment state. In addition, those who experienced clinically significant fatigue over the follow-up period were less likely to gain employment after being unemployed. CONCLUSIONS: In our FCD cohort, we found a considerable rate of employment transition during the early years post-diagnosis. Over more than a decade of follow-up post-FCD, we found that females and individuals with a greater disability and a higher relapse count are at higher risk of losing employment.

The association between disability progression, relapses, and treatment in early relapse onset MS: an observational, multi-centre, longitudinal cohort study.

The indirect contribution of multiple sclerosis (MS) relapses to disability worsening outcomes, and vice-versa, remains unclear. Disease modifying therapies (DMTs) are potential modulators of this association. Understanding how these endo-phenotypes interact may provide insights into disease pathogenesis and treatment practice in relapse-onset MS (ROMS). Utilising a unique, prospectively collected clinical data from a longitudinal cohort of 279 first demyelinating event cases followed for up to 15 years post-onset, we examined indirect associations between relapses and treatment and the risk of disability worsening, and vice-versa. Indirect association parameters were estimated using joint models for longitudinal and survival data. Early relapses within 2.5 years of MS onset predicted early disability worsening outcomes (HR = 3.45, C.I 2.29-3.61) per relapse, but did not contribute to long-term disability worsening thereinafter (HR = 0.21, C.I 0.15-0.28). Conversely, disability worsening outcomes significantly contributed to relapse risk each year (HR = 2.96, C.I 2.91-3.02), and persisted over time (HR = 3.34, C.I 2.90-3.86), regardless of DMT treatments. The duration of DMTs significantly reduced the hazards of relapses (1st-line DMTs: HR = 0.68, C.I 0.58-0.79; 3rd-line DMTs: HR = 0.37, C.I 0.32-0.44) and disability worsening events (1st-line DMTs: HR = 0.74, C.I 0.69-0.79; 3rd-line DMTs: HR = 0.90, C.I 0.85-0.95), respectively. Results from time-dynamic survival probabilities further revealed individuals having higher risk of future relapses and disability worsening outcomes, respectively. The study provided evidence that in ROMS, relapses accrued within 2.5 years of MS onset are strong indicators of disability worsening outcomes, but late relapses accrued 2.5 years post onset are not overt risk factors for further disability worsening. In contrast, disability worsening outcomes are strong positive predictors of current and subsequent relapse risk. Long-term DMT use and older age strongly influence the individual outcomes and their associations.

Genetically determined serum serine level has a novel causal effect on multiple sclerosis risk and predicts disability progression.

BACKGROUND: There are currently no specific biomarkers for multiple sclerosis (MS). Identifying robust biomarkers for MS is crucial to improve disease diagnosis and management. METHODS: This study first used six Mendelian randomisation methods to assess causal relationship of 174 metabolites with MS, incorporating data from European-ancestry metabolomics (n=8569-86 507) and MS (n=14 802 MS cases, 26 703 controls) genomewide association studies. Genetic scores for identified causal metabolite(s) were then computed to predict MS disability progression in an independent longitudinal cohort (AusLong study) of 203 MS cases with up to 15-year follow-up. RESULTS: We found a novel genetic causal effect of serine on MS onset (OR=1.67, 95% CI 1.51 to 1.84, p=1.73×10-20), such that individuals whose serine level is 1 SD above the population mean will have 1.67 times the risk of developing MS. This is robust across all sensitivity methods (OR ranges from 1.49 to 1.67). In an independent longitudinal MS cohort, we then constructed time-dynamic and time-fixed genetic scores based on serine genetic instrument single-nucleotide polymorphisms, where higher scores for raised serum serine level were associated with increased risk of disability worsening, especially in the time-dynamic model (RR=1.25, 95% CI 1.10 to 1.42, p=7.52×10-4). CONCLUSIONS: These findings support investigating serine as an important candidate biomarker for MS onset and disability progression.

Novel plasma and brain proteins that are implicated in multiple sclerosis.

Understanding how variations in the plasma and brain proteome contribute to multiple sclerosis susceptibility can provide important insights to guide drug repurposing and therapeutic development for the disease. However, the role of genetically predicted protein abundance in multiple sclerosis remains largely unknown. Integrating plasma proteomics (n = 3301) and brain proteomics (n = 376 discovery; n = 152 replication) into multiple sclerosis genome-wide association studies (n = 14 802 cases and 26 703 controls), we employed summary-based methods to identify candidate proteins involved in multiple sclerosis susceptibility. Next, we evaluated associations of the corresponding genes with multiple sclerosis at tissue-level using large gene expression quantitative trait data from whole-blood (n = 31 684) and brain (n = 1194) tissue. Further, to assess transcriptional profiles for candidate proteins at cell-level, we examined gene expression patterns in immune cell types (Dataset 1: n = 73 cases and 97 controls; Dataset 2: n = 31 cases and 31 controls) for identified plasma proteins, and in brain cell types (Dataset 1: n = 4 cases and 5 controls; Dataset 2: n = 5 cases and 3 controls) for identified brain proteins. In a longitudinal multiple sclerosis cohort (n = 203 cases followed up to 15 years), we also assessed the corresponding gene-level associations with the outcome of disability worsening. We identified 39 novel proteins associated with multiple sclerosis risk. Based on five identified plasma proteins, four available corresponding gene candidates showed consistent associations with multiple sclerosis risk in whole-blood, and we found TAPBPL upregulation in multiple sclerosis B cells, CD8+ T cells and natural killer cells compared with controls. Among the 34 candidate brain proteins, 18 were replicated in a smaller cohort and 14 of 21 available corresponding gene candidates also showed consistent associations with multiple sclerosis risk in brain tissue. In cell-specific analysis, six identified brain candidates showed consistent differential gene expression in neuron and oligodendrocyte cell clusters. Based on the 39 protein-coding genes, we found 23 genes that were associated with disability worsening in multiple sclerosis cases. The findings present a set of candidate protein biomarkers for multiple sclerosis, reinforced by high concordance in downstream transcriptomics findings at tissue-level. This study also highlights the heterogeneity of cell-specific transcriptional profiles for the identified proteins and that numerous candidates were also implicated in disease progression. Together, these findings can serve as an important anchor for future studies of disease mechanisms and therapeutic development.

Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with multiple sclerosis.

Limited studies have been conducted to identify and validate multiple sclerosis (MS) genetic loci associated with disability progression. We aimed to identify MS genetic loci associated with worsening of disability over time, and to develop and validate ensemble genetic learning model(s) to identify people with MS (PwMS) at risk of future worsening. We examined associations of 208 previously established MS genetic loci with the risk of worsening of disability; we learned ensemble genetic decision rules and validated the predictions in an external dataset. We found 7 genetic loci (rs7731626: HR 0.92, P = 2.4 × 10-5; rs12211604: HR 1.16, P = 3.2 × 10-7; rs55858457: HR 0.93, P = 3.7 × 10-7; rs10271373: HR 0.90, P = 1.1 × 10-7; rs11256593: HR 1.13, P = 5.1 × 10-57; rs12588969: HR = 1.10, P = 2.1 × 10-10; rs1465697: HR 1.09, P = 1.7 × 10-128) associated with risk worsening of disability; most of which were located near or tagged to 13 genomic regions enriched in peptide hormones and steroids biosynthesis pathways by positional and eQTL mapping. The derived ensembles produced a set of genetic decision rules that can be translated to provide additional prognostic values to existing clinical predictions, with the additional benefit of incorporating relevant genetic information into clinical decision making for PwMS. The present study extends our knowledge of MS progression genetics and provides the basis of future studies regarding the functional significance of the identified loci.

Developing a clinical-environmental-genotypic prognostic index for relapsing-onset multiple sclerosis and clinically isolated syndrome.

Our inability to reliably predict disease outcomes in multiple sclerosis remains an issue for clinicians and clinical trialists. This study aims to create, from available clinical, genetic and environmental factors; a clinical-environmental-genotypic prognostic index to predict the probability of new relapses and disability worsening. The analyses cohort included prospectively assessed multiple sclerosis cases (N = 253) with 2858 repeated observations measured over 10 years. N = 219 had been diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Genotype data were available for 199 genetic variants associated with multiple sclerosis risk. Penalized Cox regression models were used to select potential genetic variants and predict risk for relapses and/or worsening of disability. Multivariable Cox regression models with backward elimination were then used to construct clinical-environmental, genetic and clinical-environmental-genotypic prognostic index, respectively. Robust time-course predictions were obtained by Landmarking. To validate our models, Weibull calibration models were used, and the Chi-square statistics, Harrell's C-index and pseudo-R 2 were used to compare models. The predictive performance at diagnosis was evaluated using the Kullback-Leibler and Brier (dynamic) prediction error (reduction) curves. The combined index (clinical-environmental-genotypic) predicted a quadratic time-dynamic disease course in terms of worsening (HR = 2.74, CI: 2.00-3.76; pseudo-R 2=0.64; C-index = 0.76), relapses (HR = 2.16, CI: 1.74-2.68; pseudo-R 2 = 0.91; C-index = 0.85), or both (HR = 3.32, CI: 1.88-5.86; pseudo-R 2 = 0.72; C-index = 0.77). The Kullback-Leibler and Brier curves suggested that for short-term prognosis (≤5 years from diagnosis), the clinical-environmental components of disease were more relevant, whereas the genetic components reduced the prediction errors only in the long-term (≥5 years from diagnosis). The combined components performed slightly better than the individual ones, although their prognostic sensitivities were largely modulated by the clinical-environmental components. We have created a clinical-environmental-genotypic prognostic index using relevant clinical, environmental, and genetic predictors, and obtained robust dynamic predictions for the probability of developing new relapses and worsening of symptoms in multiple sclerosis. Our prognostic index provides reliable information that is relevant for long-term prognostication and may be used as a selection criterion and risk stratification tool for clinical trials. Further work to investigate component interactions is required and to validate the index in independent data sets.