LB: A Multimodal CNS Burden Index for Myotonic Dystrophy Type 1 Across the Lifespan: A Multicenter Study

Topic:

Clinical Trials

Poster Number: 473 LBT

Author(s):

Tahereh Kamali, PhD, Stanford University School of Medicine, Katharine Hagerman, PhD, Stanford, Rajanikant Panda, UCSF, George Lin, PhD, Stanford, Meng Yao, BS, Stanford, Nathan Hageman, MD, Ph.D., Stanford, Kelvin Lim, University of Minnesota, Bryon Mueller, University of Minnesota, Gayle Deutsch, Stanford, Jeffrey Wozniak, University of Minnesota, Jacinda Sampson, MD, PhD, Stanford, John Day, MD, PhD, Stanford

Background: Central nervous system (CNS) involvement is increasingly recognized as a major contributor to morbidity in myotonic dystrophy type 1 (DM1), yet CNS outcomes are not routinely incorporated into clinical trials due to the absence of standardized, trial-ready biomarkers. Prior studies have typically examined isolated imaging or cognitive measures, focused on limited age ranges or single sites, and lacked harmonized analytic approaches suitable for multicenter deployment. Objective: The objective of this work was to develop and evaluate a multimodal CNS Burden Index integrating structural and functional brain network measures with neurocognitive outcomes across the lifespan in DM1, and to assess its suitability as a multicenter, trial-relevant CNS endpoint. Results: We collected and analyzed harmonized multicenter data from 250 participants, including individuals with DM1 and unaffected controls, spanning pediatric and adult age ranges. A composite CNS Burden Index was derived by integrating diffusion-based white matter measures, functional network metrics, and domain-specific neurocognitive performance using a prespecified pipeline. Higher index values were consistently associated with impairments in executive function and processing speed, independent of site-related variability. Multimodal integration improved sensitivity to CNS involvement compared with single-modality measures, supporting its potential utility for individual-level stratification. We observed convergent, system-level coupling between white-matter microstructure, functional network organization, and neurocognitive performance. This structure–function–cognition coupling supports a unified CNS phenotype in DM1 rather than isolated modality-specific abnormalities. Conclusion: This study presents the first multimodal, multicenter CNS Burden Index for DM1 spanning the lifespan, enabled by recently harmonized datasets comprising 250 participants. The findings demonstrate the feasibility of a single, interpretable CNS composite measure that captures clinically meaningful brain involvement in adult and pediatric DM1. Study results represent a promising trial-ready endpoint for CNS outcome assessment, patient stratification, and enrichment in future DM1 therapeutic studies.