METHODS: This was an analytical cross sectional study. Ethics approval was obtained and informed consent was given by all participants. Anthropometric measurements, blood pressure, fasting blood glucose and lipid profile were taken following standard protocols.
RESULTS: Metabolic Syndrome was diagnosed in 41.4% and 38.2% participants using the modified NCEP and IDF criteria respectively. Among those diagnosed with Metabolic Syndrome by modified NCEP, 7.6% were missed by the IDF criteria. Participants diagnosed by the modified NCEP criteria had lower BMI and waist circumference but had higher cardiometabolic risks than those diagnosed with both criteria. Their blood pressure, glucose, total cholesterol and triglyceride were more adverse than the IDF group. This demonstrated that central obesity may not be a prerequisite for the development of increased cardiometabolic risks within this Malay cohort.
CONCLUSION: Metabolic syndrome is common in this Malay cohort regardless of the criterion used. The modified NCEP ATP III criteria may be more suitable in diagnosis of metabolic syndrome for this Malay cohort.
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.
CONCLUSIONS: The prevalence of MONO was high, and participants with BMI ≥23.0 kg/m(2) had significantly higher odds of metabolic syndrome. Healthcare professionals and physicians should start to screen non-obese individuals for metabolic risk factors to facilitate early targeted intervention.
METHODS: A total of 509 patients with MetS were recruited. All were diagnosed by clinicians with ultrasonography-confirmed whether they were patients with NAFLD. Patients were randomly divided into derivation (n=400) and validation (n=109) cohort. To develop the risk score, clinical risk indicators measured at the time of recruitment were built by logistic regression. Regression coefficients were transformed into item scores and added up to a total score. A risk scoring scheme was developed from clinical predictors: BMI ≥25, AST/ALT ≥1, ALT ≥40, type 2 diabetes mellitus and central obesity. The scoring scheme was applied in validation cohort to test the performance.
RESULTS: The scheme explained, by area under the receiver operating characteristic curve (AuROC), 76.8% of being NAFLD with good calibration (Hosmer-Lemeshow χ2 =4.35; P=.629). The positive likelihood ratio of NAFLD in patients with low risk (scores below 3) and high risk (scores 5 and over) were 2.32 (95% CI: 1.90-2.82) and 7.77 (95% CI: 2.47-24.47) respectively. When applied in validation cohort, the score showed good performance with AuROC 76.7%, and illustrated 84%, and 100% certainty in low- and high-risk groups respectively.
CONCLUSIONS: A simple and non-invasive scoring scheme of five predictors provides good prediction indices for NAFLD in MetS patients. This scheme may help clinicians in order to take further appropriate action.