Causal discovery identifies pathways linking physical activity to dementia risk in the UK BioBank
Authors: Wasif Khan, Panayiotis V. Benos, Joshua K. Wong, Ruogu Fang
Organizations: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA · Department of Epidemiology, University of Florida, Gainesville, FL, USA · Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA · Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA · Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
Physical Activity (PA) is consistently associated with lower risk of dementia, yet the mechanism linking PA to dementia prevention remain incomopletely understood. Here, we integrate large language model (LLM)-guided causal discovery with mediation analysis in 42,293 older adults aged 60 years or older from the UK Biobank to systematically identify pathways connecting objectively measured moderate-to-vigorous physical activity (MVPA) to dementia risk. Across behavioral, psychological, functional, and clinical domains, causal discovery consistently identified interconnected pathways linking higher MVPA to lower dementia risk through depression, functional capacity, smoking behavior, hypertension, cardiovascular disease, chronic kidney disease, and brain injury. Chain mediation analyses further identified depression as a central pathway, accounting for 15.1% of the overall association between MVPA and dementia risk. Sex-stratified analyses revealed distinct mechanistic patterns, with females exhibiting predominantly metabolic and functional pathways, while males showed behavioral and cardiometabolic cascades involving smoking and cardiovascular disease. Together, these findings suggest that the protective association between PA and dementia is mediated through interconnected behavioral, psychological, and cardiometabolic processes rather than a single pathway. Depression emerged as a prominent and potentially modifiable pathway, highlighting opportunities for integrating dementia prevention strategies that combine PA promotion with mental health and cardiovascular risk management.
Figures & tables
Figure 1: Causal pathways linking moderate-to-vigorous physical activity (MVPA) to dementia risk. (A). Causal graph derived from the PC causal discovery algorithm illustrating inferred pathways linking MVPA to dementia risk in older adults from the UK Biobank. Demographic, socioeconomic, behavioral, and clinical variables were incorporated into the causal discovery framework under domain-informed structural constraints. The resulting network identified both direct and indirect pathways connecting MVPA with dementia risk, including behavioral, functional, and psychological intermediates. Highlighted pathways indicate key mediation chains involving walking pace and depression. (B). Chain mediation analyses quantifying direct and indirect effects of MVPA on dementia risk through depression pathway. (C). Sensitivity analyses examining mediation effects across progressively longer exclusion windows (1-, 2-, 4-, and 5-year exclusions) to minimize potential reverse causation. Longer exclusion periods identified additional pathways involving hypertension, heart disease, brain injury, and chronic kidney disease (CKD). (D). Sex-specific causal pathways linking MVPA to dementia risk. Female-specific pathways primarily involved metabolic-functional mechanisms, including triglycerides, waist circumference, walking pace, depression, heart disease, and CKD, whereas male-specific pathways additionally involved smoking, PM10 exposure, hearing impairment, and a direct depression-mediated pathway. COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; TG, triglycerides.
Figure 2: Chain mediation analyses examining the indirect pathways linking moderate-to-vigorous physical activity (MVPA) with dementia risk through depression and walking pace. The left panels represent the overall population, the middle panels represent females, and the right panels represent males. Upper panels illustrate the mediation pathway of MVPA → depression → dementia in the overall cohort and stratified by sex (females and males). Lower panels present the extended chain mediation pathway incorporating walking pace, modeled as MVPA → walking pace → depression → dementia. Standardized regression coefficients ( β ) and corresponding p-values are shown for each pathway. Models were adjusted for age, sex, ethnicity, education, deprivation index, smoking, and alcohol use.
Figure 3: Overview of the variable selection and causal mediation framework. Candidate variables from the UK Biobank were first filtered through a five-stage selection pipeline consisting of LLM-based preselection, multi-model relevance scoring, expert reviewer agents across multiple domains, census ranking and aggregation, and expert curation with data preprocessing. The final curated variables were then used for causal discovery by integrating domain knowledge, the PC algorithm, and bootstrapping to estimate a directed acyclic graph (DAG) representing pathways linking moderate-to-vigorous physical activity (MVPA) to dementia. Identified causal pathways were subsequently evaluated using chain mediation analysis to quantify direct, indirect, and total effects.
Characteristic
Total ( N = 42,293)
Female ( n = 22,526)
Male ( n = 19,767)
p -value
Age (years)
67.0±4.1
66.7±4.1
67.3±4.2
< 0.001
TDI
−2.0±2.7
−1.9±2.7
−2.0±2.6
< 0.001
Ethnicity
0.677
Others
913 (2.2%)
493 (2.2%)
420 (2.1%)
White
41,380 (97.8%)
22,033 (97.8%)
19,347 (97.9%)
Qualifications
< 0.001
Table 1: Baseline characteristics of study participants stratified by sex.
To build a dementia classification model for middle-aged and elderly Brazilians, implemented in Python, combining variable selection and multivariable analysis, using low-cost variables with modification potential. Observational study with a predictive modeling approach using a cross-sectional design, aimed at estimating the chances of developing dementia, using data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil), involving 9,412 participants. Dementia was determined based on neuropsychological assessment and informant-based cognitive function. Analyses were performed using Random Forest (RF) and multivariable logistic regression to estimate the risk of dementia in the middle-aged and elderly populations of Brazil. The prevalence of dementia was 9.6%. The highest odds of dementia were observed in illiterate individuals (Odds Ratio (OR) = 7.42), individuals aged 90 years or older (OR = 11.00), low weight (OR = 2.11), low handgrip strength (OR = 2.50), self-reported black skin color (OR = 1.47), physical inactivity (OR = 1.61), self-reported hearing loss (OR = 1.65), and presence of depressive symptoms (OR = 1.72). Higher education (OR=0.44), greater life satisfaction (OR=0.72), and being employed (OR=0.78) were protective factors. The RF model outperformed logistic regression, achieving an area under the ROC curve of 0.776, with a sensitivity of 0.708, a specificity of 0.702, an F1-score of 0.311, a G-means of 0.705, and an accuracy of 0.703. Conclusion: The findings reinforce the multidimensional nature of dementia and the importance of accessible factors for identifying vulnerable individuals. Strengthening public policies focused on promoting brain health can contribute significantly to the efficient allocation of resources in primary care and dementia prevention in Brazil
F. S. Menezes, M. C. F. G. Barretto, E. Q. C. Garcia +2
Instituto de Medicina Integral Professor Fernando Figueira (IMIP) · Future Tech · Universidade Federal Rural de Pernambuco (UFRPE)
Physical activity (PA) is widely recognized as protective against mental distress, yet whether this benefit varies systematically across population subgroups remains poorly understood. Using pooled data from ten consecutive annual waves of the U.S. Behavioral Risk Factor Surveillance System (2015-2024; n = 3,242,218), we investigate heterogeneity in the association between leisure-time PA and frequent mental distress (FMD, >=14 days/month) across age groups. Survey-weighted logistic regression reveals a striking age gradient: the adjusted odds ratio for PA ranges from 0.89 among young adults (18-24) to 0.50 among adults aged 55-64, with the protective association strengthening monotonically with age. Temporal analysis across all ten years shows that the young-adult PA effect has been eroding over the past decade, with the 18-24 OR reaching 1.01 (null) in both 2018 and 2024 -- paralleling the deepening youth mental health crisis. Causal Forest via Double Machine Learning independently identifies age as the dominant driver of treatment effect heterogeneity (feature importance = 0.39, 2.5x the next predictor). E-value sensitivity analysis, propensity score overlap checks, placebo tests, and imputation comparisons confirm the robustness of the findings. These results suggest that the well-documented exercise--mental health link may not generalize to the youngest adult population, whose distress appears increasingly driven by stressors that PA alone cannot mitigate.
Yuan Shan
Department of Statistical Science, Duke University
Background: Existing dementia risk scores rely on fixed, pre-selected features and linear models, or ignore bias from informative missingness in cohort data. Here we introduce the Istanbul Technical University Dementia Risk Score (ITU-DESC), an explainable machine-learning score for incident all-cause dementia that combines broad feature selection across roughly 8,000 candidate features with explicit control of missingness bias. Results: We studied approximately 500,000 UK Biobank participants with a median follow-up of 16.6 years. We withheld missingness patterns from the model using unconditional random hot-deck imputation. We stabilized feature selection by repeating BorutaSHAP-based feature selection with ten random-imputation seeds and selecting predictors chosen in at least eight of the ten seeds, which yielded 38 predictors. We trained a CatBoost gradient-boosted decision-tree model and evaluated it on a holdout test set of 50,041 participants. For all-cause incident dementia, ITU-DESC achieved an average precision of 0.138 (95% confidence interval 0.123-0.156) and an area under the receiver operating characteristic curve (ROC AUC) of 0.853 (0.844-0.863), significantly outperforming other risk scores, including the strongest comparator, the UK Biobank Dementia Risk Prediction model (0.118 and 0.841). We identified dementia risk predictors absent from established scores, including leukocyte telomere length and liver enzymes. Conclusions: ITU-DESC is the first dementia risk score to combine broad feature selection with explicit control of missingness bias.
Abdullah Kavaklı, Faik Boray Tek
Department of Artificial Intelligence and Data Engineering, Istanbul Technical University, Istanbul, Turkey.