DEPRESSION AND THE RISK OF DIABETES MELLITUS AMONG ADULTS: A CROSS-SECTIONALPERSPECTIVE

Abstract

Background: The bidirectional relationship between depression and type 2 diabetes mellitus (T2DM) has been widely reported, with depression increasing the risk of diabetes through biological and behavioral mechanisms. However, evidence remains heterogeneous across study designs and populations. Narrative reviews provide flexibility in synthesizing recent evidence but carry risks of selection bias. Complementary simulated data may help illustrate patterns where empirical datasets are unavailable. Objective: This study aimed to explore the association between depression and T2DM by synthesizing evidence from recent cross-sectional studies and analyzing a simulated dataset to demonstrate potential patterns of prevalence and metabolic outcomes. Methods: A narrative review was conducted using studies published between 2019 and 2024. Five key studies were identified that examined depression as a risk factor for T2DM. Adjusted odds ratios (AORs) ranged from 1.79 to 2.44, indicating a consistent association across diverse populations. In addition, a simulated cross-sectional dataset of 1,000 adults was generated, including variables for depression, diabetes status, fasting glucose, and HbA1c levels. Descriptive statistics and subgroup analyses were applied. Results: Evidence from the five reviewed studies confirmed that depression significantly increases the risk of T2DM, with AORs consistently above 1.7. In the simulated dataset, diabetes prevalence was 44.1% among depressed individuals compared to 30.5% among non-depressed participants. Average HbA1c and fasting glucose levels were higher in the depressed group, indicating poorer blood sugar control. This supports previous research showing that depression can worsen metabolic problems. Conclusion: depression is an important factor influencing the risk of type 2 diabetes and blood sugar management outcomes. Mechanisms may involve hypothalamic pituitary adrenal (HPA) axis dysregulation, chronic inflammation, and unhealthy lifestyle behaviors. While simulated findings illustrate plausible patterns, they cannot be generalized. Future research should use larger datasets, control for confounders, and apply longitudinal designs to clarify causal links.