Machine learning approaches to racial/ethnic differences in social determinants of mild cognitive impairment and its progression to dementia in the All of Us Research Program.
Objective This study examines how social determinants of health (SDOH) influence mild cognitive impairment (MCI) and its progression to dementia across racial/ethnic groups, identifying disparities and key predictors using machine learning approaches. Methods We analyzed data from 83,180 participant...
| Publicado en: | Journals of Gerontology Series B: Psychological Sciences & Social Sciences Vol. 80; no. 12; pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Dec2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=189866944&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 189866944 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10795014 JGB jtl: Journals of Gerontology Series B: Psychological Sciences & Social Sciences issn: 10795014 maglogo: N pubinfo: dt: Dec2025 vid: 80 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 189866944 10.1093/geronb/gbaf179 ppf: 1 ppct: 12 formats: tig: atl: Machine learning approaches to racial/ethnic differences in social determinants of mild cognitive impairment and its progression to dementia in the All of Us Research Program. aug: au: Dong, Qianyu Wu, Wenbo Jiang, Yanping Sui, Junyu Tan, Chenxin Qi, Xiang affil: Department of Statistics, University of California, Santa Cruz, Baskin School of Engineering, Santa Cruz, California, United States Departments of Population Health and Medicine, NYU Grossman School of Medicine, New York, New York, United States Department of Family Medicine and Community Health, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, United States Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, Arizona, United StatesRory Meyers College of Nursing, New York University, New York, New York, United States Rory Meyers College of Nursing, New York University, New York, New York, United States su: United States Ethnic groups Social determinants of health African Americans Hispanic Americans Artificial intelligence White people Sex discrimination Race Psychological stress Spirituality Health equity Social support Discrimination (Sociology) Psychosocial factors Active aging Disease risk factors Dementia risk factors Risk assessment Random forest algorithms Mild cognitive impairment Research funding Prediction models Receiver operating characteristic curves T-test (Statistics) Questionnaires Logistic regression analysis Descriptive statistics Multivariate analysis Chi-squared test Machine learning Comparative studies Data analysis software Disease progression Nosology Nonparametric statistics sug: subj: Ethnic groups Social determinants of health African Americans Hispanic Americans Artificial intelligence White people Sex discrimination Race Psychological stress Spirituality Health equity Social support Discrimination (Sociology) Psychosocial factors Active aging Disease risk factors United States Other Individual and Family Services Dementia risk factors Risk assessment Random forest algorithms Mild cognitive impairment Research funding Prediction models Receiver operating characteristic curves T-test (Statistics) Questionnaires Logistic regression analysis Descriptive statistics Multivariate analysis Chi-squared test Machine learning Comparative studies Data analysis software Disease progression Nosology Nonparametric statistics keyword: Alzheimer's disease Artificial Intelligence Cognition Health disparities Healthy aging Alzheimer's disease Artificial Intelligence Cognition Health disparities Healthy aging ab: Objective This study examines how social determinants of health (SDOH) influence mild cognitive impairment (MCI) and its progression to dementia across racial/ethnic groups, identifying disparities and key predictors using machine learning approaches. Methods We analyzed data from 83,180 participants aged 50+ in the All of Us Research Program (65,582 White, 6,207 Black, 4,170 Hispanic, 7,221 Other). The sample had mean ages ranging from 62.4 (Hispanic) to 68.1 (White) years, with significant gender disparities (70.9% Black females vs. 46.0% Other females). We developed machine learning classification models to predict MCI and its progression to dementia across the four racial/ethnic groups using 18 SDOH, along with key sociodemographic variables. We then applied SHapley Additive exPlanations (SHAP) to quantify each factor's contribution and interpret its risk and protective effects on individual predictions. Results MCI prevalence was comparable across groups (7.5%–8.0%), but progression to dementia varied (9.4% Black vs. 11.4% Other). Perceived stress was the strongest predictor of MCI across all groups, with SHAP values of 15.1% (White), 13.5% (Black), 17.4% (Other), and 19.3% (Hispanic). Predictors of progression to dementia varied by groups: perceived stress (7.0%) for Whites, instrumental social support (14.2%) for Hispanics, daily spiritual experience (34.0%) for Blacks, and everyday discrimination (11.2%) for other groups. Discussion The findings underscore the need for group-specific interventions addressing stress mitigation for MCI prevention and culturally-tailored support systems to delay dementia progression. This machine learning approach reveals complex SDOH interactions that traditional methods might overlook, particularly for racial/ethnic underrepresented populations. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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