A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project.

The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP). Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-022-00171-1.

Mobility Difficulties, Physical Activity, and All-cause Mortality Risk in a Nationally representative Sample of Older Adults.

Background: This study sought to determine the association between levels of physical activity and mobility difficulties and time to death while accounting for the effect of several confounders. We also examined the possible interaction between them and how various daily-life mobility difficulties could predict all-cause mortality. Methods: A nationally representative sample of 2,074 noninstitutionalized adults aged 60 years and older was analyzed. Vital status over a 3-year follow-up period was ascertained through national registers or by asking participants' relatives. Kaplan-Meier survival curves were stratified by levels of physical activity and mobility difficulties. Unadjusted and adjusted Cox proportional hazards regression models (by age, gender, marital status, years of education, multimorbidity, tobacco and alcohol consumption, depression, and memory function) were calculated, and interactions between the predictors and the covariates were explored. Results: There was a dose-gradient effect of physical activity on time to death, with high levels associated with a 51% lower risk of dying, compared with moderate physical activity. Each unit increase in mobility functioning was associated with a 2% drop in mortality. Difficulties in standing for long periods, getting where one wants to go or extending arms to reach objects, were also found to be strong predictors of all-cause mortality. Conclusions: Our results confirm the importance of older adults practicing moderate-to-vigorous physical activity. The assessment of self-reported difficulties in daily-life mobility activities, such as standing for long periods or not being able to move around, could be used in health settings as a screening for mortality risk.

Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: a prospective cohort study.

BACKGROUND: This study sought to identify multimorbidity patterns and determine the association between these latent classes with several outcomes, including health, functioning, disability, quality of life and use of services, at baseline and after 3 years of follow-up. METHODS: We analyzed data from a representative Spanish cohort of 3541 non-institutionalized people aged 50 years old and over. Measures were taken at baseline and after 3 years of follow-up. Latent Class Analysis (LCA) was conducted using eleven common chronic conditions. Generalized linear models were conducted to determine the adjusted association of multimorbidity latent classes with several outcomes. RESULTS: 63.8% of participants were assigned to the "healthy" class, with minimum disease, 30% were classified under the "metabolic/stroke" class and 6% were assigned to the "cardiorespiratory/mental/arthritis" class. Significant cross-sectional associations were found between membership of both multimorbidity classes and poorer memory, quality of life, greater burden and more use of services. After 3 years of follow-up, the "metabolic/stroke" class was a significant predictor of lower levels of verbal fluency while the two multimorbidity classes predicted poor quality of life, problems in independent living, higher risk of hospitalization and greater use of health services. CONCLUSIONS: Common chronic conditions in older people cluster together in broad categories. These broad clusters are qualitatively distinct and are important predictors of several health and functioning outcomes. Future studies are needed to understand underlying mechanisms and common risk factors for patterns of multimorbidity and to propose more effective treatments.