Early detection of predictors of cognitive impairment is crucial, particularly in Malaysia where the aging population is rapidly increasing. This study sought to determine baseline predictors of Montreal Cognitive Assessment (MoCA) scores at 36 months using data from the Long-Term Research Grant Scheme: Towards Useful Ageing (LRGS TUA) cohort. Hierarchical multiple linear regression was employed with predictors entered in three blocks: (i) sociodemographic variables, (ii) health and functional variables, and (iii) baseline cognitive test scores. Results showed that the sociodemographic variables accounted for 16.8% of the variance in MoCA score at 36 months (p < .001). The inclusion of health and functional variables increased the explained variance to 26.6%. The final model, incorporating baseline cognitive tests, explained 47.2% of the variance. Five cognitive domains (verbal learning, processing speed, working memory, and visual memory) emerged as significant predictors of 36 months MoCA performance. Education years, functional status, grip strength and diabetes were no longer significant after adjusting for baseline cognitive performance, suggesting that their effects may be mediated through cognitive functioning. However, family history of dementia remained as an independent predictor associating with lower MoCA scores. These findings underscore the importance of early identification of cognitive predictor to facilitate timely detection of cognitive decline among Malaysian older adults. Future research should explore culturally appropriate digital cognitive assessment tools to enhance early detection and longitudinal monitoring, thereby strengthening evidence-based approaches to early detection and management of cognitive impairment among Malaysian older adults.

Authors:
Nurul Syasya binti Mohd Ridzwan Goh, National University of Malaysia (UKM), Malaysia
Ponnusamy Subramaniam, National University of Malaysia (UKM), Malaysia
Suzana Shahar, National University of Malaysia (UKM), Malaysia
Devinder Kaur Ajit Singh, National University of Malaysia (UKM), Malaysia

About the Presenter(s)
Nurul Syasya Mohd Ridzwan Goh is a PhD postgraduate student developing digital cognitive assessment tools for early detection of cognitive decline. Her research uses longitudinal data to identify key predictors of dementia risk in ageing populations.

See this presentation on the full schedule – Saturday Schedule

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