METHODS: Data from four population-based National Health and Morbidity Surveys conducted in 1996, 2006, 2010, and 2015 were pooled. Hierarchical Age-Period-Cohort (HAPC) analysis explored the trajectories of BMI and waist circumference across the life course and birth cohorts by sex and ethnicity. These models assumed no period effect.
RESULTS: Generally, BMI and waist circumference trajectories increased across age and birth cohorts. These trajectories varied by sex and ethnicity. Females have more profound increasing BMI and waist circumference trajectories than their male counterparts as they age and as cohort recency increases. Chinese have less profound BMI and waist circumference increases across the life course and birth cohorts than other ethnic groups.
CONCLUSIONS: The profound increasing cohort trajectories of obesity, regardless of sex and ethnicity, are alarming. Future studies should focus on identifying factors associated with the less profound cohort effect among the Chinese to reduce the magnitude of trajectories in obesity, particularly among future generations.
METHODS: Cross-sectional analysis of the Malaysian Elders Longitudinal Research (MELoR) study involving community-dwelling individuals aged >55 years was conducted. Information on sociodemographic factors, medical history, and lifestyle were obtained by computer-assisted interviews in participants' homes. Cognitive performance was assessed with the Montreal Cognitive Assessment (MoCA) tool during subsequent hospital-based health checks. Hierarchical multiple linear regression analyses were conducted with continuous MoCA scores as the dependent variable.
RESULTS: Data were available for 1,140 participants, mean (standard deviation [SD]) = 68.48 (7.23) years, comprising 377 (33.1%) ethnic Malays, 414 (36.3%) Chinese, and 349 (30.6%) Indians. Mean (SD) MoCA scores were 20.44 (4.92), 23.97 (4.03), and 22.04 (4.83) for Malays, Chinese, and Indians, respectively (p = 0.01). Age >75 years, <12 years of education, and low functional ability were common risk factors for low cognitive performance across all three ethnic groups. Cognitive performance was positively associated with social engagement among the ethnic Chinese (β [95% CI] = 0.06 [0.01, 0.11]) and Indians (β [95% CI] = 0.16 [0.09, 0.23]) and with lower depression scores (β [(95% CI] = -0.08 [-0.15, -0.01]) among the ethnic Indians.
CONCLUSION: Common factors associated with cognitive performance include age, education, and functional ability, and ethnic-specific factors were social engagement and depression. Interethnic comparisons of risk factors may form the basis for identification of ethnic-specific modifiable risk factors for cognitive decline and provision of culturally acceptable prevention measures.