Beyond Missing Data: Questionnaire Uncertainty Responses as Early Digital Biomarkers of Cognitive Decline and Neurodegenerative Diseases
By: Yukun Lu, Bingjie Li, Zhigang Yao
Identifying preclinical biomarkers of neurodegenerative diseases remains a major challenge in aging research. In this study, we demonstrate that frequent "Don't know/can't remember" (DK) responses, often treated as missing data in touchscreen questionnaires, serve as a novel digital behavioral biomarker of early cognitive vulnerability and neurodegenerative disease risk. Using data from 502,234 UK Biobank participants, we stratified individuals based on DK response frequency (0-1, 2-4, 5-7, >7) and observed a robust, dose-dependent association with an increased risk of Alzheimer's disease (HR = 1.64, 95% CI: 1.26-2.14) and vascular dementia (HR = 1.93, 95% CI: 1.37-2.72), independent of established risk factors. As DK response frequency increased, participants exhibited higher BMI, reduced physical activity, higher smoking rates, and a higher prevalence of chronic diseases, particularly hypertension, diabetes, and depression. Further analysis revealed a dose-dependent relationship between DK response frequency and the risk of Alzheimer's disease and vascular dementia, with high DK responders showing early neurodegenerative changes, marked by elevated levels of Abeta40, Abeta42, NFL, and pTau-181. Metabolomic analysis also revealed lipid metabolism abnormalities, which may mediate this relationship. Together, these findings reframe DK response patterns as clinically meaningful signals of multidimensional neurobiological alterations, offering a scalable, low-cost, non-invasive tool for early risk identification and prevention at the population level.
Similar Papers
Interpretable Machine Learning for Cognitive Aging: Handling Missing Data and Uncovering Social Determinant
Machine Learning (CS)
Finds early Alzheimer's signs from life factors.
Early Prediction of Alzheimer's and Related Dementias: A Machine Learning Approach Utilizing Social Determinants of Health Data
Quantitative Methods
Helps stop memory loss by fixing social problems.
The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span
CV and Pattern Recognition
Creates realistic brain scans to predict Alzheimer's.