METHODOLOGY: We recruited 175 subjects, aged 7 to 18 years old, referred for obesity. We studied their demography (age, gender, ethnicity, family background), performed clinical/auxological examinations [weight, height, body mass index (BMI), waist circumference (WC), blood pressure (BP)], and analyzed their biochemical risks associated with metabolic syndrome [fasting plasma glucose (FPG), fasting lipid profile (FLP), fasting insulin, liver function tests (LFT)]. MetS was identified according to the criteria proposed by the International Diabetes Federation (IDF) for pediatric obesity. Multiple logistic regression models were used to examine the associations between risk variables and MetS.

RESULTS: The prevalence of metabolic syndrome among children with obesity was 56% (95% CI: 48.6 to 63.4%), with a mean age of 11.3 ± 2.73 years. Multiple logistic regression analysis showed age [adjusted odds ratio (OR) 1.27, 95% CI: 1.15 to 1.45] and sedentary lifestyle (adjusted OR 3.57, 95% CI: 1.48 to 8.59) were the significant factors associated with metabolic syndrome among obese children.

METHODS: We conducted a cross-sectional study involving teachers recruited via multi-stage sampling from the state of Melaka, Malaysia. MONO was defined as individuals with BMI 18.5-29.9 kg/m(2) and metabolic syndrome. Metabolic syndrome was diagnosed based on the Harmonization criteria. Participants completed self-reported questionnaires that assessed alcohol intake, sleep duration, smoking, physical activity, and fruit and vegetable consumption.

RESULTS: A total of 1168 teachers were included in the analysis. The prevalence of MONO was 17.7% (95% confidence interval [CI], 15.3-20.4). Prevalence of metabolic syndrome among the normal weight and overweight participants was 8.3% (95% CI, 5.8-11.8) and 29.9% (95% CI, 26.3-33.7), respectively. MONO prevalence was higher among males, Indians, and older participants and inversely associated with sleep duration. Metabolic syndrome was also more prevalent among those with central obesity, regardless of whether they were normal or overweight. The odds of metabolic syndrome increased exponentially from 1.9 (for those with BMI 23.0-24.9 kg/m(2)) to 11.5 (for those with BMI 27.5-29.9 kg/m(2)) compared to those with BMI 18.5-22.9 kg/m(2) after adjustment for confounders.

METHODS: We recruited 48 peers (median age: 46 (IQR = 11) years old) into four peer groups, who underwent 3 months of PERSUADE, followed by 3 months of follow-up period. Statistical analyses were conducted at post-intervention and post-follow-up to assess the changes in nutrition intake, anthropometry, and metabolic parameters.

RESULTS: Although there were significant overall increases in total carbohydrate intake and glycemic load (both p < 0.001), we noted significant reductions in the intakes of total energy and fat (both p < 0.001). Physical activity (total METS/week) also showed a significant improvement (p < 0.001). Overall, significant but marginal improvements in anthropometric and vital metabolic parameters were also observed.

METHODS: Patients with confirmed CRC based on colonoscopy findings and cancer free controls from five local hospitals were assessed for MetS according to the International Diabetes Federation (IDF) definition. Each index case was matched for age, gender and ethnicity with two controls (140: 280).

RESULTS: MetS among cases was highly prevalent (70.7%), especially among women (68.7%). MetS as an entity increased CRC risk by almost three fold independently (OR=2.61, 95%CI=1.53-4.47). In men MetS increased the risk of CRC by two fold (OR=2.01, 95%CI, 1.43-4.56), demonstrating an increasing trend in risk with the number of Mets components observed.