2021 CMSC Annual Meeting

Predicting Stress Levels and Response to a Self-Management Program Using Participant Demographic, Clinical, and Biomarker Data: A Secondary Analysis


Background: Stress management techniques are actively being pursued for multiple sclerosis (MS); the utility of biomarkers for predicting response to stress management is unknown. Objectives: This exploratory pilot explored relationships between biomarkers and stress in MS. Methods: Women (n=14) who completed an 8-week pilot stress reduction program were included in this secondary analysis if they provided plasma and self-reported perceived stress scale (PSS) at two time points. Samples were shipped on dry ice to AssayGate, Inc. (Ijamsville, MD) and biomarkers (IL-1Ra, IL-6, Cortisol, and Melatonin) batch assayed in triplicate. Google Sheets, R, and Python were used to clean and analyse data. Spearman’s rank-order correlations were used to test for collinearity (Rho ? 0.8). Multiple and logistic regressions were planned to (1) associate biomarkers (IL-1ra, IL-6, Cortisol, and Melatonin) with PSS scores at both time points, and (2) use biomarkers to predict reduction in PSS. Regression assumptions were not met, even with logarithmic transformation. Thus, correlation analysis was used to address the research questions. Results: Biomarkers were not significantly significant correlates of T1 PSS, T2 PSS, or PSS change. However, IL-1ra levels were moderately negatively correlated (r = -0.602) with Stress at T1 (P = .05021). Cortisol levels moderately negatively correlated (r = -0.560) with Stress at T2 (P = .07351). Conclusions: This pilot was underpowered to detect statistically significant relationships between the biomarkers and PSS. Other factors (e.g. clinical realities of longstanding MS; changes in medication; response bias) may have confounded the results of this study. The moderately negatively correlated biomarkers should be further explored in larger samples.