{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T03:04:14Z","timestamp":1790046254157,"version":"4.0.1"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Annals of Neurology"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The objective of this study was to examine whether machine learning has the capacity to prospectively identify and predict the emergence of Fragile X\u2010associated tremor\/ataxia syndrome (FXTAS) among male fragile X premutation carriers (PCs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We explored neuropsychological and motor evaluation metrics, brain magnetic resonance imaging (MRI), and health metrics in 103 male participants (72 PCs, mean\u2009=\u200960.4\u2009years at enrollment) and 31 healthy controls (HCs; mean\u2009=\u200957.8\u2009years at enrollment) across a total of 299 visits to identify optimal FXTAS risk markers. We compared different machine learning model and feature selection method combinations to identify the best features and models for (a) identifying patients with FXTAS and (b) for predicting which individuals were likely to later develop FXTAS in the study to date. Using an optimal set of features (including age, psychological symptoms, executive function and motor measures, IQ, body mass index (BMI), and structural brain measurements), we developed random forest binary classifiers for the 2 tasks. We split the dataset randomly into multiple different train and test splits and observed the average classification performance metrics across all the splits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The models showed promising ability to identify and pre\u2010emptively predict the emergence of FXTAS and achieved a reasonable balance between precision and recall. Accumulation of body fat (BMI), executive function weaknesses, slower reaction time and dexterity, and mental health changes, are clinical factors that may significantly increase a carrier's risk. Structural brain MRI measurements significantly added to the predictive power of the models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>These results suggest that machine learning has the potential to inform prediction of risk for FXTAS early, enabling better planning, timely interventions, and provision of necessary care. 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