LAUSR

Although accurate diagnosis of Alzheimer’s Disease (AD) remains a priority for research, even more research interest currently focuses on the prediction of the disease years or decades before its onset. Because the neurodegeneration caused by the disease is likely irreversible, a better treatment strategy would be to identify those undergoing the early changes linked to eventual disease onset and to administer a mitigating treatment (yet to be developed) at that time. One biomarker of intense interest is naturalistic language samples, as they are easy to acquire, completely noninvasive, and, compared to most neuropsychological assessments, easily repeated on a regular basis without practice effects. However, the analysis is complicated, laborious, and potentially subjective. In recent years, advances in machine learning and natural language processing have been applied to language samples for the detection of dementia, and researchers have achieved considerable success in distinguishing the speech of individuals with and without dementia [1 3]. Despite these advances, the predictive power of language samples is largely unproven, given that very few studies have been able to examine participants years before an eventual diagnosis of AD, to compare the language output of those who do and do not go on to develop the disease [4]. A prospective study of this topic would require a very large sample, take many years to complete, and would have a relatively low yield of positive findings for the effort. Fortunately, as the potential value of speech and other cognitive measures as a biomarker has come to increased attention, large-scale prospective studies of health in general have begun to include them in their assessment batteries. As published in EClinicalMedicine, Elif Eyigoz and colleagues present an analysis [5] of written language samples collected in the Framingham Heart Study (FHS), one of the world’s