journal articles
PREDICTING LOW PREMORBID COGNITIVE ABILITY WITH SOCIAL DETERMINANTS: A MACHINE LEARNING APPROACH
Lubnaa Badriyyah Abdullah, Ibshar Khandakar, Ashley Douglas, Robert Nance, Zhengyang Zhou, James Hall, Sid O’Bryant, HABS-HD Study Team
J Aging Res & Lifestyle 2026;15
BACKGROUND: Social determinants of health and biological processes are shaped by the exposome, which provides a framework for understanding how social adversity drives molecular and cellular mechanisms underlying Alzheimer’s disease risk. Individuals with low premorbid intellectual ability (pIQ ≤70) may be particularly vulnerable to adverse social determinants of health due to reduced cognitive reserve, yet this relationship is understudied.
METHODS: Data from the Health and Aging Brain Study–Health Disparities (n = 2691) were analyzed. Participants were classified as low pIQ (IQ ≤70) or average pIQ (IQ 90–100) via word reading scores. Using a machine learning approach, an XGBoost model evaluated education, income, Area Deprivation Index (ADI), social support, stress, health status, and worry in prediction of pIQ grouping.
RESULTS: The model achieved and AUC of 0.72 [0.64, 0.81]. Top predictors included worry, ADI, income, high school completion, and tangible support. Low pIQ was associated with greater neighborhood deprivation, lower income, and reduced support resources.
CONCLUSION: Low pIQ, when combined with SDoH factors reflects a vulnerable psychosocial-cognitive phenotype that may accelerate pathways to cognitive decline potentially through inflammatory mechanisms.
CITATION:
Lubnaa Badriyyah Abdullah ; Ibshar Khandakar ; Ashley Douglas ; Robert Nance ; Zhengyang Zhou ; James Hall ; Sid O’Bryant ; HABS-HD Study Team (2026): Predicting low premorbid cognitive ability with social determinants: A machine learning approach. The Journal of Aging and Lifestyle (JARLife). https://doi.org/10.1016/j.jarlif.2026.100062