MATERIALS AND METHODS: One hundred thirty-five nAMD patients and 135 controls were recruited to determine the association of the -460 C/T, the -2549 I/D, and the +405 G/C polymorphisms with the VEGF gene. Genotyping was conducted using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) approach, and association analyses were conducted using chi-square analysis and logistic regression analysis.
RESULTS: A significant association was observed between nAMD and the VEGF +405 G/C genotypes (p = 0.002) and alleles (odds ratio = 1.36, 95% confidence interval = 1.12-1.62, p = < 0.001) compared with the controls. This association was confirmed by logistic regression analyses, using two different genetic models (additive and dominant) resulting in p-values of p = 0.001 and p
METHODS: A total of 1319 Malaysian adults participated in this cross-sectional online survey. Information on anthropometric data including body weight and height, and lifestyle behaviours including eating pattern, physical activity, and sleep pattern were self-reported by the respondents. A multivariable generalised linear mixed model was used to assess the associations between lifestyle behaviours and body weight changes with adjustment of confounding factors; namely, age, sex, ethnicity, and body weight status before MCO.
RESULTS: During MCO, 41.2% of the respondents perceived that their eating patterns were healthier, but 36.3% reduced their physical activities, and 25.7% had a poorer sleep quality. Further, the proportion of adults who reported having lose weight (32.2%) was almost similar to those who reported having gained weight (30.7%). Lifestyle behaviours including less frequent practice of healthy cooking methods and lunch skipping were associated with weight gain, while less frequent consumption of high fat foods, more frequent physical activity, and good sleep latency were associated with lower risk of weight gain. In contrast, practicing healthy eating concept, skipped lunch, and more frequent physical activity were significantly associated with weight loss.
CONCLUSION: Lifestyle behaviours were associated with body weight changes during MCO. While the COVID-19 pandemic lockdown is necessary to prevent further spread of the disease, promoting healthy lifestyle practices during lockdown should be implemented for a healthy weight and better health.
METHODS AND STUDY DESIGN: A cross-sectional study was conducted among 184 Malaysian HD patients. Anthropometric measurements and handgrip strength (HGS) were obtained using standardized protocols. Relevant biochemical indicators were retrieved from patients' medical records. Nutritional status was assessed using the dialysis malnutrition score. The sleep quality of patients was determined using the Pittsburgh Sleep Quality Index questionnaire on both dialysis and non-dialysis days.
RESULTS: Slightly more than half of the HD patients were poor sleepers, with approximately two-third of them having a sleep duration of <7 hours per day. Sleep latency (1.5±1.2) had the highest sleep component score, whereas sleep medicine use (0.1±0.6) had the lowest score. Significantly longer sleep latency and shorter sleep duration were observed in the poor sleepers, regardless of whether it was a dialysis day or not (p<0.001). Poor sleep quality was associated with male sex, old age, small triceps skinfold, hypoproteinemia, hyperkalemia, hyperphosphatemia, and poorer nutritional status. In a multivariate analysis model, serum potassium (β=1.41, p=0.010), male sex (β=2.15, p=0.003), and HGS (β=-0.088, p=0.021) were found as independent predictors of sleep quality.
CONCLUSIONS: Poor sleep quality was evident among the HD patients in Malaysia. The sleep quality of the HD patients was associated with nutritional parameters. Routine assessment of sleep quality and nutritional parameters indicated that poor sleepers have a risk of malnutrition and may benefit from appropriate interventions.
Methods: A total of 279 older adults aged 60 years and above were randomly selected. Respondents were classified as non-frail (<2 criteria) or frail (≥3 criteria) based on the 'phenotype of frailty'. A binary logistic regression was used to determine predictors of frailty.
Results: The prevalence of frailty was 18.3%. The frail older adults were positively associated with advanced age, being unmarried, hospitalisation in the previous year, poor self-rated health, and lower body mass index.
Discussion: These results give an overview on underlying effects and guiding actions for prevention programmes functioning to reverse and minimise the adverse effects of frailty syndrome.
DESIGN: Cross-sectional. Setting, participants, and outcome measures: We used data from the National Health and Morbidity Survey 2018, a nationwide community-based study. This study was conducted using a two-stage stratified cluster sampling design. Older persons were defined as persons aged 60 years and above. SRH was assessed using the question "How do you rate your general health?" and the answers were "very good", "good", "moderate", "not good", and "very bad". SRH was then grouped into two categories; "Good" (very good and good) and "Poor" (moderate, not good, and very bad). Descriptive and logistic regression analyses were conducted using SPSS version 25.0.
RESULTS: The prevalence of poor SRH among older persons was 32.6%. Poor SRH was significantly related to physical inactivity, depression, and limitations in activities of daily living (ADLs). Multiple logistic regression revealed that poor SRH was positively associated with those who had depression (aOR 2.92, 95% CI:2.01,4.24), limitations in ADLs (aOR 1.82, 95% CI: 1.31, 2.54), low individual income (aOR 1.66, 95% CI:1.22, 2.26), physical inactivity (aOR 1.40, 95% CI:1.08, 1.82), and hypertension (aOR 1.23, 95% CI:1.02, 1.49).
CONCLUSIONS: Older persons with depression, limitations in ADLs, low income, physical inactivity, and hypertension were significantly associated with poor SRH. These findings provide information to aid health personnel and policymakers in the development and implementation of health promotion and disease prevention programs, as well as adequate evidence in planning different levels of care for the older population.