Insulin resistance: Risk factors, diagnostic approaches and mathematical models for clinical practice, epidemiological studies, and beyond

Abstract

Insulin resistance (IR) is a multifactorial metabolic disorder associated with the development of cardiometabolic syndrome, cardiovascular diseases and obesity. Factors such as inflammation, hyperinsulinemia, hyperglucagonemia, mitochondrial dysfunction, glucotoxicity and lipotoxicity contribute to the development of IR. Despite being extensively studied for over 60 years, assessing the incidence of IR, developing effective prevention strategies, and implementing appropriate therapeutic approaches remain challenging. This review explores the multifaceted nature of IR, including its association with various conditions such as obesity, primary hypertension, dyslipidemia, obstructive sleep apnea, Alzheimer's disease, non-alcoholic fatty liver disease, polycystic ovary syndrome, chronic kidney disease and cancer. Additionally, we discuss the complexity of diagnosing and quantifying IR, emphasizing the lack of absolute, common criteria for classification. We delve into the use of mathematical models in clinical and epidemiological studies, focusing on the choice between insulin, triglycerides, or waist-to-hip ratio as IR determinants. Furthermore, we highlight the importance of reliable input data and caution in interpreting results when utilizing mathematical models for IR assessment. This narrative review aims to provide insights into the challenges and considerations involved in conducting IR diagnostics, with implications for clinical practice, epidemiological research, and future advancements in this field.