Three-year trajectories in functional limitations and cognitive decline among Dutch 75+ year olds, using nine-month intervals

Risk and protective factors for cognitive decline in Brazilian lower educated older adults: A 15-year follow-up study using group-based trajectory modelling

BACKGROUND

Patterns of cognitive change and modifiable factors for cognitive decline versus stable cognitive trajectories have rarely been described in lower-educated older adults.

OBJECTIVES

We aimed to identify long-term trajectories of cognitive functioning and possible factors associated with cognitive decline.

DESIGN AND PARTICIPANTS

We used data from 1,042 adults aged ≥ 60 participating in the Health, Welfare and Aging Study (SABE), São Paulo, Brazil, without cognitive impairment at baseline. Data were collected across four waves (2000-2015). Group-based trajectory modelling was used to identify cognitive trajectories. Associations with socioeconomic variables, childhood background, lifestyle, and cardiovascular risk factors were explored using weighted multinomial logistic regressions.

MEASUREMENTS

The abbreviated Mini-Mental State Examination was used to measure cognition.

RESULTS

Three cognitive trajectories were identified: stable (n= 754, 68.6%), mild-decline (n= 183, 20.8%), and strong-decline (n= 105, 10.7%). At baseline, respondents in the strong-decline group were more likely to be older than those with stable and mild-decline trajectories. Furthermore, participants in both the mild and strong-decline groups were more likely to have no schooling, be divorced/separated, receive less than 4 monthly wages, and be underweight (BMI < 18.5) compared to the stable group. Finally, the mild-decline group was more likely to have lived in rural areas during childhood than participants located in a stable trajectory.

CONCLUSIONS

Our findings suggest that interventions to reduce cognitive decline for low-educated older adults might include strategies addressing inequalities and improving modifiable risk factor burden.

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis

OBJECTIVE

This study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China.

METHODS

Longitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix.

RESULTS

Two physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group.

LIMITATION

This study didn't carry out external validation.

CONCLUSIONS

This study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.