Depression and diabetes distress in patients with diabetes

Development and Validation of a Community-Based Prediction Model for Depression in Elderly Patients with Diabetes: A Cross-Sectional Study

Background

In elderly diabetic patients, depression is often overlooked because professional evaluation requires psychiatrists, but such specialists are lacking in the community. Therefore, we aimed to create a simple depression screening model that allows earlier detection of depressive disorders in elderly diabetic patients by community health workers.

Methods

The prediction model was developed in a primary cohort that consisted of 210 patients with diabetes, and data were gathered from December 2022 to February 2023. The independent validation cohort included 99 consecutive patients from February 2023 to March 2023. Multivariable logistic regression analysis was used to develop the predictive model. We incorporated common demographic characteristics, diabetes-specific factors, family structure characteristics, the self-perceived burden scale (SPBS) score, and the family APGAR (adaptation, partnership, growth, affection, resolution) score. The performance of the nomogram was assessed with respect to its calibration (calibration curve, the Hosmer-Lemeshow test), discrimination (the area under the curve (AUC)), and clinical usefulness (Decision curve analysis (DCA)).

Results

The prediction nomogram incorporated 5 crucial factors such as glucose monitoring status, exercise status, monthly income, sleep disorder status, and the SPBS score. The model demonstrated strong discrimination in the primary cohort, with an AUC of 0.839 (95% CI, 0.781-0.897). This discriminative ability was further validated in the validation cohort, with an AUC of 0.857 (95% CI, 0.779-0.935). Moreover, the nomogram exhibited satisfactory calibration. DCA suggested that the prediction of depression in elderly patients with diabetes mellitus was of great clinical value.

Conclusion

The prediction model provides precise and user-friendly guidance for community health workers in preliminary screenings for depression among elderly patients with diabetes.

Efficacy of acupuncture for depression: a systematic review and meta-analysis

Introduction

Depression is a pervasive mental health challenge with substantial global ramifications. Contemporary therapeutic strategies predominantly consist of psychological interventions and pharmacological treatments. Acupuncture, deeply rooted in ancient traditions and bolstered by a plethora of clinical trials, demonstrates considerable efficacy in depression. This study aims to elucidate the efficacy and safety of acupuncture as a standalone therapy for depression patients by reviewing randomized controlled trials that compare acupuncture treatment with conventional drug therapy.

Methods

Comprehensive searches were conducted across six databases in both Chinese and English: CNKI, Wanfang, VIP, Embase, Medline, and CENTRAL. The literature search spanned from 1 July 2013, to 1 July 2023. Two researchers independently carried out literature screening and data extraction. Review Manager 5.4 was utilized for data analysis and bias risk assessment. A total of 20 randomized controlled trials were included in the qualitative synthesis, involving 1,376 participants and 43 relevant acupoints.

Results

The Meta-analysis results, based on the HAMD scale scores, revealed that acupuncture regimens (RR: -1.63; 95% CI: -2.49 to -0.76; P = 0.0002; I2 = 86%; n = 1,668) were significantly more effective compared to standalone medication. Moreover, efficacy metrics from the HAMD highlighted a substantial advantage of acupuncture (RR: 2.6; 95% CI: 1.6 to 4.23; P = 0.0001; I2 = 0; n = 614). Further assessments utilizing SERS and TESS demonstrated a lower incidence of side effects and adverse outcomes in the acupuncture group.

Conclusions

Acupuncture, when compared to conventional pharmacotherapy, exhibits significant efficacy as a standalone treatment after four weeks of intervention, with fewer side effects and adverse reactions. However, further investigation is needed to determine the most effective acupoints and appropriate types of acupuncture techniques for treating depression.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023443711.

Psychological resilience mediates the relationship between diabetes distress and depression among persons with diabetes in a multi-group analysis

The aim to examine the link between diabetes distress and depression in individuals with diabetes, assess the mediating role of psychological resilience in this relationship, and analyses if these relationships differ between Type 1 and Type 2 diabetes. The study utilized a cross-sectional design. A total of 181 (age 33-72 years, mean = 54.76 years, and SD = 9.05 years) individuals diagnosed with diabetes who were receiving treatment from State Specialist Hospitals in Okitipupa were selected for the study using the convenient sampling technique. The data were analysed using Pearson Multiple correlation and multi-group mediation analysis. The analyses were carried out with Smartpls and IBM/SPSS Version 28.0. The results revealed a significant positive correlation between diabetes distress and depression (r = .80, p < .05), suggesting that higher levels of diabetes distress were associated with increased depression scores. Additionally, psychological resilience partially mediated the relationship between diabetes distress and depression (b = - 0.10, p < .05), signifying that resilience played a crucial role in mitigating the impact of diabetes distress on depression. Furthermore, a multi-group analysis was conducted to explore potential differences between Type 1 and Type 2 diabetes subgroups. The relationship between diabetes distress and depression was found to be more pronounced in the Type 1 subgroup (difference = 0.345, p < .05), while the relationship between psychological resilience and depression was negatively stronger in the Type 2 subgroup (difference = - 0.404, p < .05) compared to the Type 1 subgroup. There is an intricate linkage between diabetes distress, resilience, and depression, emphasizing the differential roles of resilience in Type 1 and Type 2 diabetes. The insights gleaned from this study underscore the importance of considering the type of diabetes when designing interventions and support mechanisms for individuals with diabetes who are also suffering from depression. By advancing our understanding of these dynamics, we can strive for more effective and personalized approaches to improve the overall well-being of those living with diabetes.

Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study

OBJECTIVE

Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients.

MATERIALS AND METHODS

The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University for unsupervised contrastive learning on heterogeneous electronic health records (EHRs). Secondly, the fine-tuned model of DAP used case-control cohorts (17 491 patients) selected from 149 596 T2DM patients' EHRs in the Nanjing Health Information Platform (NHIP). The DAP model was validated in 1028 patients from PHS in NHIP. Evaluation included receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC), and decision curve analysis (DCA).

RESULTS

The pre-training step allowed the DAP model to converge at a faster rate. The fine-tuned DAP model significantly outperformed the baseline models (logistic regression, extreme gradient boosting, and random forest) with ROC-AUC of 0.91±0.028 and PR-AUC of 0.80±0.067 in 10-fold internal validation, and with ROC-AUC of 0.75 ± 0.045 and PR-AUC of 0.47 ± 0.081 in external validation. The DCA indicate the clinical potential of the DAP model.

CONCLUSION

The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model's potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients.