METHODS: We used data from health and demographic surveillance conducted by the South East Asia Community Observatory in Segamat, Malaysia. Analyses included 9207 individuals (4806 children, 2570 mothers and 1831 fathers). Child obesity was defined based on the World Health Organization 2007 reference. We assessed the relation between parental anthropometric (overweight, obesity and central obesity) and cardiometabolic (systolic hypertension, diastolic hypertension and hyperglycaemia) risk factors and child obesity, using mixed effects Poisson regression models with robust standard errors.
RESULTS: We found a high burden of overweight and obesity among children in this population (30% overweight or obese). Children of one or more obese parents had a 2-fold greater risk of being obese compared with children of non-obese parents. Sequential adjustment for parental and child characteristics did not materially affect estimates (fully adjusted relative risk for obesity in both parents: 2.39, 95% confidence interval: 1.82, 3.10, P
METHODS: The setting was the University of Kuala Lumpur. Thirty-four Malay, 35 Chinese and 34 Indian normal pregnant middle-class women were studied longitudinally by monthly ultrasound scans for 18 to 38 weeks of gestation. The data were subjected to regression analysis; the quadratic curve was found to be the most adequate. Dummy variables were used to determine any effects by gender, parity as well as ethnicity on the length of limb growth. There was no difference in birth weights of the three ethnic groups studied, nor in gender or parity.
RESULTS: There were found to be significant differences in limb lengths of the Indians (longer) when compared with the Malays and Chinese. Parity seems to affect only Indians in whom the multiparous fetuses have shorter limb lengths than the primaparous. There appears to be no effect by gender.
CONCLUSION: There appear to be definite differences in growth of limb length between the different Malaysian ethnic groups and this should be taken into account when growth charts are used and when fetal weight formulas are calculated using limb lengths. The limitation of this study was that the numbers of subjects studied were small. Larger studies will be able to confirm or refute the findings.
Methods: A total of 413 individuals (163 men and 250 women) aged 30-60 years were selected by stratified random sampling. The participants had safe alcohol consumption habits (<2 drinks/day) and no symptoms of hepatitis B and C. NAFLD was diagnosed through ultrasound. Blood pressure, anthropometric, and body composition measurements were made and liver function tests were conducted. Biochemical assessments, including the measurement of fasting blood sugar (FBS) and ferritin levels, as well as lipid profile tests were also performed. Metabolic syndrome was evaluated according to the International Diabetes Federation (IDF) criteria.
Results: The overall prevalence of ultrasound-diagnosed NAFLD was 39.3%. The results indicated a significantly higher prevalence of NAFLD in men than in women (42.3% vs 30.4%; P < 0.05). Binary logistic regression analysis was performed to determine the significant variables as NAFLD predictors. Overall, male gender, high body mass index (BMI), high alanine aminotransferase (ALT), high FBS, and high ferritin were identified as the predictors of NAFLD. The only significant predictors of NAFLD among men were high BMI and high FBS. These predictors were high BMI, high FBS, and high ferritin in women (P < 0.05 for all variables).
Conclusions: The metabolic profile can be used for predicting NAFLD among men and women. BMI, FBS, ALT, and ferritin are the efficient predictors of NAFLD and can be used for NAFLD screening before liver biopsy.
METHODS: A population-based cross-sectional study was conducted in Singapore. Participants wore an accelerometer for 7 days to measure physical activity (PA). Demographic, anthropometric and psychological data were also collected. Psychological variables included PA guideline knowledge, motivational profile for PA self-regulation (5 subscales), perceived barriers to PA (4 subscales) and perceived social support for PA. Regression models with adjustment for socio-demographic variables were fitted.
RESULTS: External regulation (b = - 13.03, 95% CI - 34.55; - 1.50) and perceived daily life barriers (b = - 12.63, 95% CI - 24.95; - 0.32) were significantly associated with fewer weekly MVPA minutes. A significant interaction between perceived social support and age (p = 0.046) was found. Social support was significantly negative associated with MVPA minutes in younger (
Methods: The study was completed in 2016 and the baseline data were gathered from four groups in a school-based randomized community trial among Year Five students from primary schools in Kota Bharu, Kelantan, Malaysia. Participants completed anthropometry assessment, three-day dietary record, and Physical Activity Questionnaire for Older Children (PAQ-C).
Results: The prevalence of obesity was higher among the boys (52.5%). Mean energy intake was significantly higher among boys as compared to the girls (P=0.003). Twenty-five percent of the participants had exceeded the recommended nutrient intakes (RNI) of energy recommended. The calcium, thiamine, riboflavin, and niacin were also significantly higher among boys as compared to the girls (P<0.05). Boys also exhibited a significantly higher score on performance of physical activity (mean=2.68; SD=0.60) as compared to the girls (mean=2.38; SD=0.51) however it is still in the category of moderately active. Approximately 14.4% of children had a very low physical activity level.
Conclusion: Overweight and obese boys had higher energy and fat intakes but were more physically active as compared to the girls. These findings might be useful in planning appropriate intervention strategies to be designed and delivered especially for this cohort.
Methods: A cross-sectional study of government officers and their family members attending a health screening at a public healthcare facility was conducted. All subjects underwent clinical evaluation, biochemical testing, anthropometry, ultrasound carotid Doppler, and Fibroscan examination.
Results: Data for 251 subjects were analyzed (mean age 47.1 ± 12.4 years, 74.1% male). Prevalence of NAFLD and advanced fibrosis were 57.4 and 17.5%, respectively. Independent factors associated with NAFLD were waist circumference (odds ratio [OR] = 1.077, 95% confidence interval [CI] 1.038-1.118, P < 0.001) and serum alanine aminotransferase (ALT) (OR = 1.039, 95% CI 1.005-1.074, P = 0.024). Independent factors associated with advanced fibrosis were male gender (OR = 4.847, 95% CI 1.369-17.155, P = 0.014) and serum aspartate aminotransferase (AST) (OR = 1.057, 95% CI 1.003-1.113, P = 0.036). Prevalence of increased CIMT was 29.0%. Independent factor associated with increased CIMT was older age (OR = 1.146, 95% CI 1.067-1.231, P < 0.001). Of the subjects, 34.5% with NAFLD had increased CIMT compared to 19.1% of the subjects without NAFLD (P = 0.063). Advanced fibrosis was not associated with increased CIMT.
Conclusions: Prevalence of NAFLD, advanced liver fibrosis, and increased CIMT were high. NAFLD and advanced liver fibrosis appeared not to be associated with increased CIMT. However, a larger sample size is needed to demonstrate whether there is any association.
Methods: In 2015, a cross-sectional study was conducted among adults visiting an outpatient clinic in Northeast Malaysia. Face-to-face interviews were conducted using Malay and English versions of the Malaysia Non-Communicable Disease surveillance questionnaire. This instrument captured information about sociodemographic, lifestyle status, and anthropometric data. Blood pressure was measured three times with a sphygmomanometer, the first measurement value was discarded, and an average of blood pressure from the second two readings was recorded for further data analysis. Logistic regression was performed to analyse factors associated with prehypertension.
Result: A total 151 adults participated in the study, and the prevalence of prehypertension was 37.1% (95% confidence interval [CI]: 29.29, 44.69). Factors associated with prehypertension in this study were age (adjusted odds ratio [aOR] = 1.06 95% CI: 1.02, 1.11; p = 0.007), male sex (aOR = 4.44 95% CI: 1.58, 12.44; p = 0.005), and abnormal waist circumference (aOR = 31.65 95% CI: 11.25, 89.02; p