METHODOLOGY: Data for the study, consisting of 2553 older adults aged 60 years and older, were drawn from a nationwide household survey entitled "Determinants of Wellness among Older Malaysians: A Health Promotion Perspective" conducted in 2010.
RESULTS: Current smokers had lower rates of cognitive impairment compared to never smokers (17.4% vs 25.9%), while cognitive function in former or ex-smokers was almost similar to that of the never smokers. Findings from multiple logistic regression analysis showed that current smokers were 37% less likely to be cognitively impaired, compared to the never smokers (odds ratio [OR] = .63; 95% confidence interval [CI]: .46-.86) while controlling for potential confounders. No difference in cognitive function was observed between former smokers and never smokers (OR = .94; 95% CI: .71-1.25).
CONCLUSION: Although the findings indicated a negative association between cigarette smoking and cognitive impairment, we are unable to conclude whether this relationship is causal or affected by other unmeasured confounding factors, especially survival bias.
METHODS: Subjects aged 55 years and above from the Malaysian Elders Longitudinal Research (MELoR) study with available information on vision and Montreal Cognitive Assessment (MoCA) scores were included. Data were obtained through a home-based interview and hospital-based health check by trained researchers. Visual acuity (VA) was assessed with logMAR score with vision impairment defined as VA 6/18 or worse in the better-seeing eye. Cognition was evaluated using the MoCA-Blind scoring procedure. Those with a MoCA-Blind score of <19/22 were considered to have cognitive impairment.
RESULTS: Data was available for 1144 participants, mean (SD) age = 68.57 (±7.23) years. Vision impairment was present in 143 (12.5 %) and 758 (66.3 %) had MoCA-Blind score of <19. Subjects with vision impairment were less likely to have a MoCA-Blind score of ≥19 (16.8 % vs 36.2 %, p < 0.001). Vision impairment was associated with poorer MoCA-Blind scores after adjustments for age, gender, and ethnicity (β = 2.064; 95 % CI, -1.282 to 3.320; P = 0.003). In those who had > 6 years of education attainment, vision impairment was associated with a significant reduction of cognitive function and remained so after adjustment for age and gender (β = 1.863; 95 % CI, 1.081-3.209; P = 0.025).
CONCLUSION: Our results suggest that vision impairment correlates with cognitive decline. Therefore, maintaining good vision is an important interventional strategy for preventing cognitive decline in older adults.
METHODS: Data from TUA cohort study involving 1366 older adults (aged 60 years and above) categorized as low-income were analysed, for risk of MCR syndrome based on defined criteria. Chi-square analysis and independent t test were employed to examine differences in socioeconomic, demographic, chronic diseases and lifestyle factors between MCR and non-MCR groups. Risk factors of MCR syndrome were determined using hierarchical logistic regression.
RESULTS: A total of 3.4% of participants fulfilled the criteria of MCR syndrome. Majority of them were female (74.5%, p = 0.001), single/widow/widower/divorced (55.3%, p = 0.002), living in rural area (72.3%, p = 0.011), older age (72.74 ± 7.08 year old, p
METHODS: A total of 2406 Malaysian children aged 5 to 12 years, who had participated in the South East Asian Nutrition Surveys (SEANUTS), were included in this study. Cognitive performance [non-verbal intelligence quotient (IQ)] was measured using Raven's Progressive Matrices, while socioeconomic characteristics were determined using parent-report questionnaires. Body mass index (BMI) was calculated using measured weight and height, while BMI-for-age Z-score (BAZ) and height-for-age Z-score (HAZ) were determined using WHO 2007 growth reference.
RESULTS: Overall, about a third (35.0%) of the children had above average non-verbal IQ (high average: 110-119; superior: ≥120 and above), while only 12.2% were categorized as having low/borderline IQ ( 3SD), children from very low household income families and children whose parents had only up to primary level education had the highest prevalence of low/borderline non-verbal IQ, compared to their non-obese and higher socioeconomic counterparts. Parental lack of education was associated with low/borderline/below average IQ [paternal, OR = 2.38 (95%CI 1.22, 4.62); maternal, OR = 2.64 (95%CI 1.32, 5.30)]. Children from the lowest income group were twice as likely to have low/borderline/below average IQ [OR = 2.01 (95%CI 1.16, 3.49)]. Children with severe obesity were twice as likely to have poor non-verbal IQ than children with normal BMI [OR = 2.28 (95%CI 1.23, 4.24)].
CONCLUSIONS: Children from disadvantaged backgrounds (that is those from very low income families and those whose parents had primary education or lower) and children with severe obesity are more likely to have poor non-verbal IQ. Further studies to investigate the social and environmental factors linked to cognitive performance will provide deeper insights into the measures that can be taken to improve the cognitive performance of Malaysian children.
CONCLUSION: In this review, we will discuss the possible mechanisms which may relate the association between MetS and cognitive decline which include vascular damages, elevation of reactive oxygen species (ROS), insulin resistance and low-grade inflammation.