Digital Clock Drawing as an Alzheimer's Disease Susceptibility Biomarker: Associations with Genetic Risk Score and APOE in Older Adults.

BACKGROUND: Alzheimer's disease (AD) is the leading cause of dementia in older adults, but most people are not diagnosed until significant neuronal loss has likely occurred along with a decline in cognition. Non-invasive and cost-effective digital biomarkers for AD have the potential to improve early detection. OBJECTIVE: We examined the validity of DCTclockTM (a digitized clock drawing task) as an AD susceptibility biomarker. DESIGN: We used two primary independent variables, Apolipoprotein E (APOE) ε4 allele carrier status and polygenic risk score (PRS). We examined APOE and PRS associations with DCTclockTM composite scores as dependent measures. SETTING: We used existing data from the Framingham Heart Study (FHS), a community-based study with the largest dataset of digital clock drawing data to date. PARTICIPANTS: The sample consisted of 2,398 older adults ages 60-94 with DCTclockTM data (mean age of 72.3, 55% female and 92% White). MEASUREMENTS: PRS was calculated using 38 variants identified in a recent large genome-wide association study (GWAS) and meta-analysis of late-onset AD (LOAD). RESULTS: Results showed that DCTclockTM performance decreased with advancing age, lower education, and the presence of one or more copies of APOE ε4. Lower DCTclockTM Total Score as well as lower composite scores for Information Processing Speed (both command and copy conditions) and Drawing Efficiency (command condition) were significantly associated with higher PRS levels and more copies of APOE ε4. APOE and PRS associations displayed similar effect sizes in both men and women. CONCLUSIONS: Our results indicate that higher AD genetic risk is associated with poorer DCTclockTM performance in older adults without dementia. This is the first study to demonstrate significant differences in clock drawing performance on the basis of APOE status or PRS.

Cognitive Digital Biomarkers from Automated Transcription of Spoken Language.

BACKGROUND: Although patients with Alzheimer's disease and other cognitive-related neurodegenerative disorders may benefit from early detection, development of a reliable diagnostic test has remained elusive. The penetration of digital voice-recording technologies and multiple cognitive processes deployed when constructing spoken responses might offer an opportunity to predict cognitive status. OBJECTIVE: To determine whether cognitive status might be predicted from voice recordings of neuropsychological testing. DESIGN: Comparison of acoustic and (para)linguistic variables from low-quality automated transcriptions of neuropsychological testing (n = 200) versus variables from high-quality manual transcriptions (n = 127). We trained a logistic regression classifier to predict cognitive status, which was tested against actual diagnoses. SETTING: Observational cohort study. PARTICIPANTS: 146 participants in the Framingham Heart Study. MEASUREMENTS: Acoustic and either paralinguistic variables (e.g., speaking time) from automated transcriptions or linguistic variables (e.g., phrase complexity) from manual transcriptions. RESULTS: Models based on demographic features alone were not robust (area under the receiver-operator characteristic curve [AUROC] 0.60). Addition of clinical and standard acoustic features boosted the AUROC to 0.81. Additional inclusion of transcription-related features yielded an AUROC of 0.90. CONCLUSIONS: The use of voice-based digital biomarkers derived from automated processing methods, combined with standard patient screening, might constitute a scalable way to enable early detection of dementia.