{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:49:46Z","timestamp":1791499786919,"version":"4.3.4"},"reference-count":48,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Apolipoprotein E \u03b54 (APOE-\u03b54), the strongest genetic risk factor for late-onset Alzheimer disease (AD), is associated with early neuromotor vulnerability that may precede measurable cognitive decline. Because speech integrates fine neuromotor processes, acoustic analysis could offer a sensitive, noninvasive marker of preclinical effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to determine if speech acoustics distinguish cognitively normal APOE-\u03b54 carriers from noncarriers, and to assess which speech tasks provide optimal classification performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this cross-sectional observational study, 80 cognitively normal adults aged 41 to 89 years (22 APOE-\u03b54 carriers and 58 noncarriers) completed sustained phonation, oral diadochokinetic syllable repetition, passage reading, and spontaneous-speech tasks. Speech samples were collected remotely using participants\u2019 personal devices and processed to extract 88 acoustic features from the extended Geneva Minimalistic Acoustic Parameter Set. Task-specific random forest classifiers were developed to distinguish APOE-\u03b54 carriers from noncarriers. A genetic algorithm was used to select informative features, and model performance was evaluated using leave-one-participant-out cross-validation. Performance metrics included balanced accuracy, accuracy, precision, sensitivity, specificity, F1-score, and receiver operating characteristic area under the curve (ROC-AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Spontaneous speech produced the strongest classification performance, with balanced accuracy of 0.84, accuracy of 0.89, sensitivity of 0.73, specificity of 0.95, F1-score of 0.78, and ROC-AUC of 0.75. Performance was lower for sustained phonation (F1-score=0.69), diadochokinetic syllable repetition (F1-score =0.70 for \/ba\/ and 0.64 for \/pa\/), and passage reading (F1-score=0.61). Combining recordings across tasks reduced performance (F1-score=0.53), indicating that task-specific acoustic patterns were more informative than pooled data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Automated analysis of task-specific acoustic speech features, particularly spontaneous speech, internally distinguished cognitively normal APOE-\u03b54 carriers from noncarriers. These findings support further validation of speech acoustics as a low-burden digital biomarker of preclinical AD risk and suggest that spontaneous-speech tasks may be especially well suited for future remote and longitudinal assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/89830","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T21:55:08Z","timestamp":1791496508000},"page":"e89830","source":"Crossref","is-referenced-by-count":0,"title":["Remote Identification of APOE-\u03b54 Carrier Status in Cognitively Normal Adults via Speech Acoustics: Cross-Sectional Observational 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