Sleep Quality in Depressive Disorder and Comorbid Diabetes: A PSQI-Based Study

The Pittsburgh Sleep Quality Index (PSQI) is a self-reported questionnaire assessing sleep based on seven components: subjective sleep quality (C1), sleep latency (C2), sleep duration (C3), habitual sleep efficiency (C4), sleep disturbances (C5), use of sleep medications (C6), and daytime disturbances (C7). Type 2 diabetes mellitus is frequently associated with sleep disorders and depression. This study aimed to evaluate the impact of poor sleep quality, as measured by PSQI components, on patients with depressive disorder and comorbid diabetes, while also exploring their predictive capacity for disease severity. Among the 60 patients with depressive disorder examined, 45 had comorbid diabetes treated with oral antidiabetic medication. Depressive symptoms and diabetes severity were indicated by the Beck Depression Inventory (BDI-II) and glycated hemoglobin (HbA1c), respectively. Poor sleep quality was reported by 88.4% of patients with depressive disorder and comorbid diabetes, similar to 93.3% of those without the comorbidity. Patients with moderate depressive symptoms had significantly higher overall PSQI scores, alongside elevated scores in various PSQI components, compared to those with milder symptoms (PSQI: 11.94±3.92 vs. 7.53±3.22, p=0.0003; C1: 1.76±0.83 vs. 1.03±0.42, p=0.007; C2: 2.23±0.90 vs. 1.46±0.96, p=0.014; C6: 1.35±1.22 vs. 0.42±0.95, p=0.006). A positive correlation was observed between the severity of depressive symptoms and the severity of diabetes (rs=0.32, p=0.032). Regression analyses revealed that depressive disorder was a significant predictor of overall sleep quality, as evidenced by the PSQI (F=7.38, p=0.0018, R2=0.26; β=0.50, p=0.0005) and specific component scores (C1: F=6.15, p=0.0015, R2=0.31; β=0.47, p=0.011; C3: F=6.43, p=0.0011, R2=0.32; β=0.63, p=0.021). In contrast, diabetes was not a predictor for any PSQI factor. Given that each PSQI component reflects a distinct dimension of sleep quality, these findings indicate that deconstructing sleep into its specific components can facilitate our understanding of the potential mechanisms linking sleep disturbances with the comorbidity between diabetes and depressive disorder.