Temporal trends of multiple sclerosis disease activity: Electronic health records indicators

Association between serum multi-protein biomarker profile and real-world disability in multiple sclerosis

Few studies examined blood biomarkers informative of patient-reported outcome (PRO) of disability in people with multiple sclerosis (MS). We examined the associations between serum multi-protein biomarker profiles and patient-reported MS disability. In this cross-sectional study (2017-2020), adults with diagnosis of MS (or precursors) from two independent clinic-based cohorts were divided into a training and test set. For predictors, we examined seven clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying therapy [DMT], and time interval between sample collection and closest PRO assessment) and 19 serum protein biomarkers potentially associated with MS disease activity endpoints identified from prior studies. We trained machine learning (ML) models (Least Absolute Shrinkage and Selection Operator regression [LASSO], Random Forest, Extreme Gradient Boosting, Support Vector Machines, stacking ensemble learning, and stacking classification) for predicting Patient Determined Disease Steps (PDDS) score as the primary endpoint and reported model performance using the held-out test set. The study included 431 participants (mean age 49 years, 81% women, 94% non-Hispanic White). For binary PDDS score, combined feature input of routine clinical factors and the 19 proteins consistently outperformed base models (comprising clinical features alone or clinical features plus one single protein at a time) in predicting severe (PDDS ≥ 4) versus mild/moderate (PDDS < 4) disability across multiple machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS = 0.91) and other metrics. For ordinal PDDS score, LASSO model comprising combined clinical factors and 19 proteins as feature input (R2PDDS = 0.31) again outperformed base models. The two best-performing LASSO models (i.e., binary and ordinal PDDS score) shared six clinical features (age, sex, race/ethnicity, disease subtype, disease duration, DMT efficacy) and nine proteins (cluster of differentiation 6, CUB-domain-containing protein 1, contactin-2, interleukin-12 subunit-beta, neurofilament light chain [NfL], protogenin, serpin family A member 9, tumor necrosis factor superfamily member 13B, versican). By comparison, LASSO models with clinical features plus one single protein at a time as feature input did not select either NfL or glial fibrillary acidic protein (GFAP) as a final feature. Forcing either NfL or GFAP as a single protein feature into models did not improve performance beyond clinical features alone. Stacking classification model using five functional pathways to represent multiple proteins as meta-features implicated those involved in neuroaxonal integrity as significant contributors to predictive performance. Thus, serum multi-protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus single protein biomarker, reaching clinically actionable performance.

Multiple sclerosis incidence temporal trend in the Northeast of Iran: Using the Empirical Bayesian method

BACKGROUND

In recent years dramatic changes in multiple sclerosis (MS) incidence have been reported in different provinces in Iran. This study was conducted to assess MS incidence temporal trends from March 21, 2005, to March 20, 2020, and provide a forecast until the end of 2025 in Shahroud county.

METHODS

This longitudinal study was carried out based on the data obtained from the MS registration system in Shahroud county. First, the annual incidence rates were calculated based on the year of diagnosis and smoothed using the Empirical Bayesian Method. Then temporal trends and annual percent change (APC) of MS incidence were analyzed using Joinpoint (JP) regression. Finally, the univariate time series model analysis was used to estimate the MS incidence trend until the end of 2025.

RESULTS

A total of 234 newly diagnosed cases (60 [25.64%] males and 174 [74.36.4%] females) were examined in this study. The mean age of patients at the time of diagnosis was 31.40 ± 3.78. It was 32.01 ± 6.35 and 30.66 ± 4.27 years for males and females, respectively (P<0.22). The mean annual MS incidence was 5.99 ± 1.46, 3.03 ± 0.21, and 8.98 ± 2.79 per 100,000 in overall, males and females respectively. The MS incidence increased significantly from 5.67 (95% CI: 3.63-7.99) in 2005 to 7.58 (95% CI: 5.17-10.28) in 2020 with an APC of 4.5 (2.8 - 6.1). The MS incidence had a non-linear time trend in the study period and the best time trend fitted to the annual MS incidence trend was the non-linear quadratic curve. Based on this model, the annual MS incidence is expected to increase until the end of 2025.

CONCLUSION

Shahroud county is one of the high-risk areas for MS and the increasing trend of MS incidence in it is similar to regional and global changes. This study, also, showed that MS incidence in Shahroud county will be increasing in the coming years.