Methods: Thirty-five male New Zealand white rabbits were divided into seven groups (n=5). Group CH was fed 1% cholesterol diet only, group C1 was fed 1% cholesterol diet and 0.5 ml/kg/day B. angulata WF juice, group C2 was fed 1% cholesterol diet and 1.0 ml/kg/day B. angulata WF juice, group C3 was fed 1% cholesterol diet and 1.5 ml/kg/day B. angulata WF juice, group N was fed standard pellet only, group N1 was fed standard pellet and 0.5 ml/kg/day B. angulata WF juice, and group N2 was fed standard pellet and 1.0 ml/kg/day B. angulata WF juice for 12 weeks.
Results: The administration of the various juices reduced the concentrations of induced serum inflammatory biomarkers.
Conclusion: This protective effect of B. angulata fruit against cardiovascular risk might be due to its polyphenol content.
Methods: Data from the National Health and Morbidity Survey (NHMS) 2018 was analysed. This survey applied a multistage stratified cluster sampling design to ensure national representativeness. Malnutrition was identified using a validated Mini Nutrition Assessment-Short Form (MNA-SF). Variables on sociodemographic, health status, and dietary practices were also obtained. The complex sampling analysis was used to determine the prevalence and associated factors of at-risk or malnutrition among the elderly.
Result: A total of 3,977 elderly completed the MNA-SF. The prevalence of malnutrition and at-risk of malnutrition was 7.3% and 23.5%, respectively. Complex sample multiple logistic regression found that the elderly who lived in a rural area, with no formal or primary level of education, had depression, Instrumental Activity of Daily Living (IADL) dependency, and low quality of life (QoL), were underweight, and had food insecurity and inadequate plain water intake were at a significant risk of malnutrition (malnutrition and at-risk), while Chinese, Bumiputra Sarawak, and BMI more than 25 kgm-2 were found to be protective.
Conclusions: Currently, three out of ten elderly in Malaysia were at-risk or malnutrition. The elderly in a rural area, low education level, depression, IADL dependency, low QoL, underweight, food insecurity, and inadequate plain water intake were at risk of malnutrition in Malaysia. The multiagency approach is needed to tackle the issue of malnutrition among the elderly by considering all predictors identified from this study.
Materials and Methods: This cross-sectional study was done among 349 staff of a public university in Sarawak. Data were collected using questionnaire, blood sampling, and anthropometric and blood pressure measurement. Data were analyzed using IBM SPSS version 20.
Results: A total of 349 respondents participated with majority females (66.8%), aged 38.5 ± 7.82 years. Nearly 80% of the respondents were overweight and obese, 87.1% with high and very high body fat, and 46.9% with abnormal visceral fat. For AIP category, 8.9% were found to be in intermediate and 16.4% were at high risk. Elevated lipid profile showed that total cholesterol (TC) is 15.5%, low density lipoprotein (LDL) is 16.1%, and triglyceride (TG) is 10.6%. AIP was significantly correlated with body mass index (r=0.25), visceral fat (r=0.37), TC (r=0.22), LDL (0.24), HDL (r=-0.72), TG (r=0.84), glucose (r=0.32), systolic blood pressure (r=0.22), and diastolic blood pressure (r=0.28).
Conclusion: It indicated that AIP is associated with other CVD risk factors. Modification of lifestyle is strongly recommended.
Methods: This was a cross-sectional study conducted in male and female adults living in the urban area of Yogyakarta, Indonesia. Adiposity was determined based on body weight, body mass index (BMI), percent body fat, and waist and hip circumference. Data on coffee consumption and other dietary components were collected using a semiquantitative food frequency questionnaire along with other caffeine-containing beverages such as tea, chocolate, and other beverages. The -866 G/A UCP2 gene variation was analyzed using polymerase chain reaction-restriction fragment length polymorphism. The correlation between coffee intake and adiposity was tested using linear regression test with adjustment for sex, age, energy intake, table sugar intake, and total caffeine intake.
Results: In all subjects, coffee intake was inversely correlated with body weight (β = -0.122, p=0.028), BMI (β = -0.157, p=0.005), and body fat (β = -0.135, p=0.009). In subjects with AA + GA genotypes, coffee intake was inversely correlated with body weight (β = -0.155, p=0.027), BMI (β = -0.179, p=0.010), and body fat (β = -0.148, p=0.021). By contrast, in subjects with GG genotype, coffee intake was not correlated with body weight (β = -0.017, p=0.822), BMI (β = -0.068, p=0.377), and body fat (β = -0.047, p=0.504).
Conclusion: We showed that coffee intake was negatively correlated with adiposity, and this was independent of total caffeine intake. Additionally, we showed that the -866 G/A UCP2 gene variation influences the relationship between coffee intake and adiposity.