Predicting low premorbid cognitive ability with social determinants: A machine learning approach - 08/02/26
, Ibshar Khandakar b, Ashley Douglas c, Robert Nance c, Zhengyang Zhou c, James Hall b, Sid O’Bryant bHABS-HD Study Team
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Highlights |
• | Using an XGBoost model, social determinants of health (SDoH) alone predicted low vs. average pIQ with an AUC of 0.72, showing that broader environmental factors play a strong role in pIQ beyond personal factors like health, stress, or age. |
• | Worry, Area Deprivation Index (ADI) and income emerged as the most important features |
• | In clinical contexts, traditional support metrics that weigh emotional, social, or appraisal support may be less appropriate to estimate social risk in low pIQ groups, where tangible support needs (e.g., access to healthcare) may be more relevant. |
Abstract |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Alzheimer’s disease, Machine learning, Social determinants of health (SDoH), Premorbid intellectual ability, Area deprivation index, Inflammation, Intellectual disability
Plan
Vol 15
Article 100062- 2026 Retour au numéro
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