Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors

We found significant associations between CF and older age, female sex, lower education level, certain clinical characteristics (i.e., peripheral vascular disease, osteoarthritis, osteoporosis, depression, and the number of prescription medications), ADL, PF-M for physical function limitations, SARC-F for sarcopenia, ABC for balance confidence, motor capacity (sit-to-stand, TUG test time, and TUG test time ≥10 seconds), fall characteristics (individual’s fall experience, number of falls, concerns about falling, and their ability to safely cross a street before the traffic light turns red), health-related QoL (EQ-5D and VAS), depression, discomfort in chewing and pronunciation, MNA score, and malnutritional status. The machine learning-based model, incorporating six optimal features (TUG test time, education level, PF-M, MNA score, ABC, and K-ADL score), exhibited robust predictive performance with an AUC >80% (an AUC of 80–90% is considered excellent^29,30,31).

Age, sex, and education in CF

The significant associations found between older age, sex, and education level with CF in this study align with the results of previous research, indicating that CF as a transitional stage between normal aging and dementia, often linked to PF⁴. The heightened CF risk likely results from the cumulative effects of cognitive and physical decline with age⁷. These findings emphasize the importance of early detection and targeted interventions. Consistent with previous studies, females were more affected by CF than males, with frailty and cognitive decline being more common among older women¹³, likely due to biological and social factors, including postmenopausal hormonal changes¹⁴. The association between education and CF supports the cognitive reserve hypothesis, where higher educational attainment increases resilience against cognitive decline³². Lower educational levels, often associated with poorer health outcomes, may reflect lower socioeconomic status, health literacy, and healthcare access³³.

Although this study primarily focused on health-related predictors of CF, education level may also reflect broader socioeconomic context. Individuals with lower education levels may experience greater barriers in accessing health information, utilizing healthcare services, and maintaining healthy lifestyle behaviors. While our dataset did not include other indicators such as income or occupation, future research could explore these social and contextual pathways more directly to better understand their contribution to CF.

Clinical characteristics in CF

Peripheral vascular disease, osteoarthritis, osteoporosis, depression, and the number of prescription medications were found to be significantly associated with CF, consistent with the findings of previous literature. Peripheral vascular disease impairs cerebral blood flow, accelerating CI by limiting the oxygen supply essential for neuronal function³⁴. This vascular insufficiency exacerbates both CI and PF, supporting the vascular CI theory³⁵, which links small vessel disease to cognitive decline and PF. Osteoarthritis, characterized by chronic pain and reduced mobility, contributes to PF and impairs cognition by reducing physical activity, increasing social isolation, and promoting systemic inflammation^36,37. Osteoporosis further restricts mobility, increasing fall and fracture risk, and compounding PF by diminishing functional capacity³⁶. Depression is also linked to cognitive decline and PF, likely due to neuroinflammatory pathways and impaired neuroplasticity, which are increasingly recognized as shared mechanisms underlying both depression and neurodegenerative processes^38,39. Depression accelerates CF progression by promoting social withdrawal, reduced physical activity, and decreased cognitive engagement⁴⁰. Furthermore, polypharmacy increases the risk of adverse drug reactions and interactions and medication non-adherence, negatively impacting cognitive and physical functions^41,42,43. Additionally, the use of five or more medications significantly increases CF risk⁴².

Health status characteristics in CF

ADL, physical function limitation, sarcopenia, balance confidence, motor capacity, fall characteristics, health-related QoL, depression, discomfort in chewing and pronunciation, and malnutritional status were found to be significantly associated with CF. A decline in ADL was closely related to CF, as CIs impair one’s ability to plan and complete daily tasks, while PF limits strength and mobility⁴⁴. This reciprocal relationship between cognitive and physical decline is well-documented¹³. Limitations in physical function emphasize the interplay between motor and cognitive systems, with mobility impairments, such as slower gait speed, being strong detectors of cognitive decline⁴⁵. Reduced physical activity diminishes neurotrophic stimulation, accelerating both physical and cognitive deterioration⁴⁶.

Sarcopenia is linked to shared pathophysiological mechanisms such as inflammation and oxidative stress, which degrade both muscle and brain function^47,48. Chronic inflammation and oxidative stress contribute to muscle loss and cognitive decline, leading to decreased physical activity and further exacerbation of PF and CI^47,48,49. Balance confidence and motor capacity were also significantly associated with CF. Poor balance and reduced motor capacity, both key PF indicators, were strongly linked to CI^13,50. Additionally, limitations in balance and motor skills restrict physical activity, reducing neurotrophic stimulation and further contributing to cognitive decline^13,50. Fall characteristics, including fall experience, fear of falling, and the ability to cross traffic lights, were also significantly associated with CF. Fear of falling restricts physical activity, leading to muscle atrophy and worsening cognitive decline⁵¹.

CF was significantly associated with poor health-related QoL by limiting participation in meaningful activities and reducing perceived well-being⁹. Both PF and CIs independently contribute to reduced mobility, psychological distress, and social isolation, collectively affecting QoL⁵². Additionally, CF was significantly associated with poor mental health, emphasizing its psychological dimension. Psychological distress and physical inactivity intensify CF and PF⁵³.

CF was also significantly associated with nutritional health, assessed by MNA. Poor nutritional status worsens CF by depriving individuals of essential nutrients needed for cognitive function and neuroprotection⁵⁴. Chewing discomfort exacerbates malnutrition, accelerating cognitive decline. This finding aligns with evidence linking poor oral health, reduced dietary intake, and CF exacerbation in older adults⁵⁵.

Machine learning-based model performance

The machine learning model developed in this study employed a logistic regression algorithm, enhanced by recursive feature elimination and bootstrapping, to improve predictive accuracy. Six key features, the TUG test time, education level, PF-M, MNA, ABC, and K-ADL score, were identified as being essential for optimal model performance. The model demonstrated strong discriminatory power, achieving an AUC of 84.34%, indicating excellent differentiation between individuals with and without CF. The model’s sensitivity reached 75.12%, reflecting its ability to correctly identify individuals with CF, while its specificity was 80.87%, indicating its accuracy in excluding individuals without frailty. Overall, the model achieved 79.51% accuracy, underscoring its robust predictive performance in nearly 80% of cases.

By integrating the motor capacity of TUG test time, education level, physical function limitations (PF-M), nutritional status (MNA), balance confidence (ABC), and ADL, the model captures the interplay between cognitive and physical health. These findings emphasize the importance of comprehensive health assessments in prediction models, providing valuable insights for early detection and timely intervention to slow or prevent CF progression in vulnerable older adults.

In addition to informing public health planning, the proposed model has practical utility in primary care and clinical settings. Its reliance on simple, easily measurable indicators allows for rapid screening and early identification of individuals at risk, thereby facilitating timely referrals and personalized intervention planning. Furthermore, the model may serve as a valuable tool for community-based health programs to prioritize outreach, and for health administrators to allocate resources based on population-level risk stratification. It also holds potential for integration into digital health platforms, empowering older adults and caregivers with accessible, evidence-based risk insights.

The six features selected for the final machine learning model were drawn from variables that showed significant group differences in the univariate logistic regression analysis. This sequential approach ensured that only statistically relevant predictors were considered for model development, enhancing both performance and interpretability.

Our findings complement previous studies that have highlighted the importance of multidomain risk assessment for CF^23,24,25. By combining clinical, psychological, nutritional, and functional variables within a machine learning framework, this study contributes to building more context-specific predictive models for aging populations, particularly in underrepresented settings.

In summary, this study identified six key predictors of CF through a machine learning-based analysis of multidomain health characteristics. The resulting model demonstrated excellent predictive accuracy and practical utility, serving as a valuable tool for early detection and risk stratification among older adults. These findings demonstrate the potential for integrating simple, scalable assessments into routine clinical and community-based care to proactively and efficiently manage CF.

Study limitations and future work

While this study offers significant findings and a robust prediction model, four limitations should be acknowledged: First, the use of retrospective data from the KFACS may have introduced recall bias from self-reported measures. Additionally, this study’s cross-sectional design limited us from drawing causal inferences between the identified risk factors and CF; hence, longitudinal studies are needed to establish temporal relationships. Second, the study’s population, limited to older Korean adults, may affect generalizability due to demographic homogeneity. Cultural, environmental, and healthcare systems influence aging and CF; therefore, the model needs to be validated in more diverse populations. Cross-cultural studies could provide further insights into how socio-environmental factors and lifestyle behaviors shape CF. Third, this study did not include biological markers. Future research should include these variables to better explore the neurobiological mechanisms underlying CF and enhance the accuracy of predictive models. Fourth, cognitive impairment in this study was evaluated using the MMSE, a well-established and widely used tool in geriatric populations. While the MMSE has been validated in large-scale studies and provides strong clinical utility, its use alone may not fully capture the multifaceted nature of cognitive decline. Specifically, the MMSE has limited sensitivity in detecting impairments in executive function, attention, and visuospatial abilities, which are essential components of CF^56,57. Given the complexity of CF, future research may benefit from incorporating multiple cognitive assessment tools to more comprehensively reflect the breadth of cognitive functioning, particularly in domains beyond memory and orientation.