Familial Aggregation of component traits of Metabolic Syndrome: The GENNID study
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Introduction: Metabolic Syndrome (MetS) is defined as a constellation of cardiovascular and metabolic risk factors (elevated blood pressure, lower plasma HDL cholesterol, elevated plasma triglycerides [TG], higher blood glucose and abdominal obesity). The clustering of MetS components among individuals suggests shared etiologies and underlying pathophysiologic mechanisms, including genetic susceptibility factors. Despite strong evidence for genetic determinants of MetS and its components, studies have been unable to conclusively find genetic variations that account for significantly higher risk of MetS. Understanding aggregation patterns of traits will enhance the effort to find underlying genes contributing to the clustering of traits. Objective: The objective of the study was to investigate the within and across family aggregation of MetS traits in families with type 2 diabetes across four ethnic groups. Methods: The study was conducted among a total of 1701 subjects which included 758 Caucasian Americans (CA), 569 Mexican Americans (MA), 253 African Americans (AA), and 121 Japanese Americans (JA) from the Genetics of Non-Insulin Dependent Diabetes (GENNID) study. The National Cholesterol Education Program's Adult Treatment Panel III (NCEP ATP III) criteria was used to define the presence of MetS among subjects. Aggregation of MetS within (number of MetS affected individuals with a given trio of traits in the family divided by the number of MetS affected family members) and across families (number of families with at least half of the family members affected by a given MetS trait trio divided by the total number of MetS affected families in that ethnic group) was examined by evaluating prevalence estimates of individual traits and all possible three trait clusters of MetS. Results: The most prevalent trait trio in the within family analysis was high glucose-abdominal obesity -low HDL cholesterol. However, the prevalence of this trait trio was significantly different across the different populations (prevalence of 54.4%, 69.2%, 43.8% and 70.7% among AA, CA, JA and MA, respectively). The most prevalent trio of MetS traits across families in AA, CA and MA was high glucose-abdominal obesity -low HDL cholesterol (prevalence of 58.9%, 60.2% and 63.3% among AA, CA, and MA, respectively). Among JA families, high glucose -hypertriglyceridemia-low HDL cholesterol was the most prevalent (prevalence of 46.8%). Conclusion: We observed variation in the clustering patterns of MetS traits both within and across families. Understanding the underlying clustering of MetS traits may be helpful in future genetic studies for the identification of genes that are involved in the etiology of MetS.