Rates of non-communicable diseases (NCDs), such as obesity, diabetes, cardiovascular events and cancer, continue to rise worldwide, which require objective instruments for preventive and management actions. Diverse anthropometric and biochemical markers have been used to qualitatively evaluate degrees of disease, metabolic traits and evolution of nutritional status. The aim of this study was to integrate and assess the interactions between an anthropometric measurement, such as waist circumference (WC), and biochemical data, such as the triglyceride glucose index (TyG), in order to individually characterize metabolic syndrome (MetS) features considering the hypertriglyceridemic waist phenotype as a marker. An ancillary cross-sectional study was conducted using anthropometric measurements, such as weight, height, waist and hip circumferences, as well as fasting biochemical data of 314 participants. Different indices based on WC (WC, WC*TG and WC*TyG) were estimated to compute MetS components and accompanying comorbidities. ROC curves were fitted to define the strength of the analyses and the validity of the relationships. Associations were confirmed between anthropometric, biochemical and combined indices with some chronic disease manifestations, including hyperglycemia, hypertension and dyslipidemia. Both WC*TG and WC*TyG indices showed similar performance in diagnosing MetS (area under the ROC curve = 0.81). Interestingly, when participants were categorized according to a reference value of the WC*TyG index (842.7 cm*mg/dl), our results evidenced that subjects classified over this limit presented statistically higher prevalence of MetS and accompanying individual components with clinical relevance for interventions. These results revealed that WC*TyG mirrors the hypertriglyceridemic phenotype, which suggests may serve as a good indicator to define the metabolic syndrome phenotype and a suitable, sensitive, and simple proxy to complement others. A reference point was proposed with a good clinical performance and maximized sensitivity and specificity values.