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. 2025 Mar 5:16:1555142.
doi: 10.3389/fendo.2025.1555142. eCollection 2025.

Predictive factors and risk model for depression in patients with type 2 diabetes mellitus: a comprehensive analysis of comorbidities and clinical indicators

Chengzheng Duan #  1 , Cheng Luo #  1 , Weifeng Jiang  1 , Hui Xu  1 , Yexing Chen  2 , Shiyu Xu  1 , Xiaofang Zhang  1 , Xiaoli Chen  1 , Dongjuan He  1
Affiliations

Predictive factors and risk model for depression in patients with type 2 diabetes mellitus: a comprehensive analysis of comorbidities and clinical indicators

Chengzheng Duan et al. Front Endocrinol (Lausanne). .

Abstract

Objective: Depression is highly prevalent among individuals with type 2 diabetes mellitus (T2DM), often compounded by multiple chronic conditions. This study aimed to identify the key factors influencing depression in this population, with a particular focus on the relationship between the Cumulative Illness Rating Scale (CIRS) score and depression, and to evaluate the predictive value of a model incorporating sex, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), and CIRS score.

Methods: A total of 308 hospitalized patients with type 2 diabetes from Quzhou Hospital, Wenzhou Medical University were enrolled. Their clinical and biochemical data were collected, alongside assessments of comorbidities and depressive symptoms using the CIRS and Self-Rating Depression Scale (SDS), respectively. LASSO regression with 10-fold cross-validation was used to identify the optimal variables for the predictive model. Multivariate analysis was performed to assess the independent associations between sex, BMI, LDL-C, and CIRS score with depression. The relationship between CIRS scores and depression was further explored across various subgroups. The predictive model's value was assessed through ROC curve analysis.

Results: Female sex (OR: 2.48, 95% CI: 1.50-4.10, p < 0.001), lower BMI (OR: 0.92, 95% CI: 0.86-0.98, p = 0.015), lower LDL-C (OR: 0.77, 95% CI: 0.61-0.98, p = 0.031), and higher CIRS scores (OR: 1.11, 95% CI: 1.05-1.18, p < 0.001) were independently linked to depression after adjusting for clinical variables. A strong association between CIRS score and depression was observed, particularly in males, patients under 60 years old, those with a disease duration of less than 5 years, and individuals with no history of smoking or alcohol consumption. Additionally, a predictive model incorporating sex, BMI, LDL-C, and CIRS score demonstrated high accuracy in identifying patients at risk for depression.

Conclusions: Female, lower BMI, lower LDL-C and higher CIRS score were independently associated with depression in patients with type 2 diabetes. The CIRS score appeared to be more effective in predicting depression risk in people who were male, younger, shorter DM duration, no smoking or no drinking. A more comprehensive prediction model could help clinicians identify patients with type 2 diabetes who are at risk for depression.

Keywords: comorbidity; cumulative illness rating scale; depression; predictive model; type 2 diabetes.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart for screening research subjects.
Figure 2
Figure 2
Predictor selection using LASSO regression. (A) The coefficient profile generated for non-zero coefficients based on the logarithmic (lambda) sequence. (B) The optimal lambda value selected via tenfold cross-validation. The partial likelihood deviation (binomial deviation) curve relative to log (lambda) was plotted. A virtual vertical line at the optimal value was drawn using one SE of minimum criterion (the 1-SE criterion).
Figure 3
Figure 3
Curves of receiver operating characteristic.
See this image and copyright information in PMC

References

    1. Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. . Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. (2023) 402:203–34. doi: 10.1016/S0140-6736(23)01301-6 - DOI - PMC - PubMed
    1. The L . Diabetes: a defining disease of the 21st century. Lancet. (2023) 401(10394):2087. doi: 10.1016/S0140-6736(23)01296-5 - DOI - PubMed
    1. Aguado A, Moratalla-Navarro F, López-Simarro F, Moreno V. MorbiNet: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity. Sci Rep. (2020) 10(1):2416. doi: 10.1038/s41598-020-59336-1 - DOI - PMC - PubMed
    1. Teck J. Diabetes-associated comorbidities. Primary Care: Clinics Office Practice. (2022) 49:275–86. doi: 10.1016/j.pop.2021.11.004 - DOI - PubMed
    1. Zghebi SS, Steinke DT, Rutter MK, Ashcroft DM. Eleven-year multimorbidity burden among 637 255 people with and without type 2 diabetes: a population-based study using primary care and linked hospitalisation data. BMJ Open. (2020) 10(7):e033866. doi: 10.1136/bmjopen-2019-033866 - DOI - PMC - PubMed

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