OBJECTIVES To analyze the health care utilization and risk profiles of patients with metabolic syndrome (MS) based on subgroups identified by a latent class analysis. METHODS Utilizing the medical record database from GE Healthcare, we estimate a set of regressions to take a closer look at the effect of metabolic syndrome (MS) on patients' hospital admission, office visit, and the risk of developing one particular type of cardiovascular disease, coronary heart disease (CHD). Patients are identified as having MS by using the guidelines proposed by the National Cholesterol Education Program Adult Treatment Panel III. We conduct the analysis in this study in the following steps; first, we adopt negative binominal regression models to estimate the effect of MS on hospital admission and office visit; second, Probit models are applied to examine the relationship between MS and CHD; and third, the results from the Latent Class Analysis, which identifies five subgroups of MS patients by risk factors, are integrated into the regression estimations. RESULTS We find 1) that patients with MS have higher number of hospital admissions and office visits, and are more likely to develop CHD than those without MS, and 2) that MS patients with abnormal blood pressure are more likely to be hospitalized than MS patients with normal blood pressure. Strikingly, we find, by integrating the results from a latent class analysis, that MS patients with the simultaneous presence of three risk factors, abnormal HDL, waist circumference, and blood pressure, are more likely to develop CHD than other types of MS patients. CONCLUSIONS LCA offers a useful method to help better classify MS patients into sub groups by shared group-specific characteristics that would be hard to measure otherwise. Based on the LCA study, we confirm that MS patients with multiple risk factors are more likely to develop CHD conditions.