Systematic Review of Metabolic Syndrome Biomarkers: A Panel for Early Detection, Management, and Risk Stratification in the West Virginian Population

Introduction: Metabolic syndrome represents a cluster of related metabolic abnormalities, including central obesity, hypertension, dyslipidemia, hyperglycemia, and insulin resistance, with central obesity and insulin resistance in particular recognized as causative factors. These metabolic derangements present significant risk factors for cardiovascular disease, which is commonly recognized as the primary clinical outcome, although other outcomes are possible. Metabolic syndrome is a progressive condition that encompasses a wide array of disorders with specific metabolic abnormalities presenting at different times. These abnormalities can be detected and monitored via serum biomarkers. This review will compile a list of promising biomarkers that are associated with metabolic syndrome and this panel can aid in early detection and management of metabolic syndrome in high risk populations, such as in West Virginia. Methods: A literature review was conducted using PubMed, Science Direct, and Google Scholar to search for markers related to metabolic syndrome. Biomarkers searched included adipokines (leptin, adiponectin), neuropeptides (ghrelin), pro-inflammatory cytokines (IL-6, TNF-alpha), anti-inflammatory cytokines (IL-10), markers of antioxidant status (OxLDL, PON-1, uric acid), and prothrombic factors (PAI-1). Results: According to the literature, the concentrations of pro-inflammatory cytokines (IL-6, TNF-alpha), markers of pro-oxidant status (OxLDL, uric acid), and prothrombic factors (PAI-1) were elevated in metabolic syndrome. Additionally, leptin concentrations were found to be elevated in metabolic syndrome as well, likely due to leptin resistance. In contrast, concentrations of anti-inflammatory cytokines (IL-10), ghrelin, adiponectin, and antioxidant factors (PON-1) were decreased in metabolic syndrome, and these decreases also correlated with specific disorders within the cluster. Conclusion: Based on the evidence presented within the literature, the aforementioned biomarkers correlate significantly with metabolic syndrome and could provide a minimally-invasive means for early detection and specific treatment of these disorders. Further research is encouraged to determine the efficacy of applying these biomarkers to diagnosis and treatment in a clinical setting.