Description

This study aims to investigate the significance of measuring fasting insulin and the homeostatic model assessment of insulin resistance (HOMA-IR) in identifying metabolic health across various demographic and clinical factors. Specifically, the study will examine the influence of age, sex, race/ethnicity, BMI, and polycystic ovary syndrome (PCOS) diagnosis on insulin levels and insulin resistance as essential indicators of metabolic dysfunction.

Metabolic health disorders, such as insulin resistance and impaired glucose metabolism, are known to be associated with an increased risk of developing conditions like type 2 diabetes, cardiovascular diseases, and metabolic syndrome. Traditionally, glucose levels have been used to assess metabolic health; however, fasting insulin and HOMA-IR provide valuable insights into the underlying insulin dysregulation that precedes the onset of these conditions.

Disparities in insulin levels have been observed across different racial and ethnic groups. These variations may arise from genetic predispositions, differences in lifestyle, or a combination of both, thus highlighting the need to explore these factors comprehensively. BMI, a measure of body composition, has been strongly associated with elevated insulin levels and insulin resistance. Individuals with obesity often exhibit dysregulated insulin metabolism, leading to higher fasting insulin and HOMA-IR values. Furthermore, PCOS, a common endocrine disorder affecting reproductive-age women, is frequently associated with insulin resistance. Studying the insulin profiles among women with PCOS will shed light on the potential metabolic implications and help tailor interventions for this at-risk population.

The study will employ a cross-sectional design, enrolling a large sample of participants from diverse backgrounds. Fasting insulin levels will be measured using standardized laboratory methods, and HOMA-IR scores will be calculated based on fasting insulin and glucose values. Statistical analyses, including regression models and subgroup comparisons, will be conducted to assess the associations between fasting insulin, HOMA-IR, and the demographic and clinical factors of interest.

This research aims to emphasize the importance of incorporating fasting insulin and HOMA-IR measurements alongside glucose assessments to enhance the identification and understanding of metabolic health disorders. The findings are expected to contribute to a more comprehensive approach in diagnosing, managing, and preventing metabolic diseases, ultimately leading to improved patient outcomes and public health interventions.