Previous studies have utilized various machine learning algorithms and clinical variables to identify metabolic subgroups among individuals with obesity or MetS; for example, a multicenter study applied k-means and two-step clustering techniques using three clinical indicators—the area under the curve (AUC) for glucose and insulin during an oral glucose tolerance test (OGTT), and serum uric acid levels—to define distinct metabolic phenotypes10. This evidence concerns the gene INS and metabolic syndrome.