OBJECTIVE: To identify subgroups of metabolic syndrome patients who would be more homogeneous in terms of metabolic risk factors. METHODS: The electronic medical record database from GE Healthcare was used for this study. The database comprises de-identified longitudinal medical records of nationally representative patients attending general practitioners. All adult patients were assessed for the presence of metabolic syndrome using the guidelines proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III). A latent class analysis was conducted based on the abnormality of patients' metabolic risk factors including triglycerides (TG; ≥150 mg/dL), high-density lipoprotein cholesterol (HDL-C; men: <40 mg/dL; women: <50 mg/dL), blood pressure (BP; systolic ≥130 mmHg, diastolic ≥85 mmHg, or drug treatment for hypertension), fasting plasma glucose (≥100 mg/dL or drug treatment for diabetes mellitus), and waist circumference (WC; men: ≥102cm; women: ≥88cm). The final model was selected based on model fit indices including Akaike's information criterion, Bayesian information criterion (BIC), and sample-size adjusted BIC. RESULTS: Metabolic syndrome was present in 19,251 individuals. The prevalence of high blood pressure was 96.4%. All the fit indices in the LCA modeling pointed to a 5-class solution. In addition to high blood pressure, members of Class 1 (6.5%) had abnormal TG and HDL-C; members of Class 2 (4.6%) had abnormal HDL-C and WC; members of Class 3 (19.7%) had abnormal TG and WC and the majority also had abnormal HDL-C (probability: 68.7%); members of Class 4 (8.2%) had abnormal plasma glucose and WC; and members of Class 5 (61.1%) had high probability of abnormality in all metabolic risk factors (range: 79.9-100%). CONCLUSIONS: LCA represents a new method for casemix study of patients with metabolic syndrome. The subgroups of metabolic syndrome identified in this study need to be further studied for its usefulness in clinical and health services research.