Lifestyle‐Associated Metabolic Signature Predicts the Risk of Amyotrophic Lateral Sclerosis
Year: 2026
Type: article
Study design: Observational Study
Abstract: INTRODUCTION/AIMS: Although the association between certain lifestyle factors and the risk of amyotrophic lateral sclerosis (ALS) has been recognized, the potential mechanism underlying it remains unexplored. This study aimed to identify the metabolic signature indicative of lifestyle factors related to ALS, to examine its association with ALS risk, and to evaluate the mediating effects of muscle strength underlying these associations. METHODS: This study used UK Biobank data, including ALS diagnoses, potential ALS-related factors, metabolomics, and hand grip strength. A total protective factor score was calculated from lifestyle data. Multivariable Cox regression analyzed associations between the score, its components, and ALS risk. ALS-related metabolic signatures were identified via elastic net regression. ALS risk across signature levels was compared by log-rank tests. Mediation analysis assessed the role of baseline hand grip strength. RESULTS: 248,222 participants were included in this study. Among lifestyle factors, the total protective factor score, no military service, and higher body mass index were significantly associated with reduced ALS risk. The metabolic signature derived from 163 metabolites indicative of potential ALS-associated protective factors was identified and found to be associated with lower ALS risk. Baseline hand grip strength of both hands was also associated with reduced ALS risk. Mediation analysis revealed that right-hand grip strength significantly mediated the associations between the total protective factor score, the metabolic signature, and ALS risk. DISCUSSION: Our study highlights the potential of the metabolic signature as a biomarker for early disease identification and underscores the importance of lifestyle-based prevention strategies.
Source: Muscle & Nerve
DOI: 10.1002/mus.70166
Authors: Jiatong Li, Dongqing Zhu, Wenbo Yang, Tianshu Guan, Liu Yang, Weikang Gong, Xiaoni Liu, Jia You, Jianfeng Feng, Xiangjun Chen
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Award: G2898550202
OpenAlex ID: W7128384991
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