Assessing prevalence and predictors of depression in Type 2 Diabetes Mellitus (DM) patients - The DEPDIAB study

Microbial and metabolomic profiles of type 1 diabetes with depression: A case-control study

BACKGROUND

Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression.

METHODS

A case-control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD-), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman's rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD- were identified by a random forest (RF) classifying model.

RESULTS

In microbiota, 15 genera enriched in TD- and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD- were identified. Additionally, 5 genera (including Phascolarctobacterium, Butyricimonas, and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58-0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid (r = -0.28, p = 0.015) and malondialdehyde (r = -0.30, p = 0.012). Both Phascolarctobacterium (r = 0.27, p = 0.022) and Alistipes (r = 0.31, p = 0.009) were positively correlated with allopregnanolone.

CONCLUSIONS

T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium, Butyricimonas, and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota-gut-brain axis is involved in T1D with depression.

Construction of a depression risk prediction model for type 2 diabetes mellitus patients based on NHANES 2007-2014

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prevalent global health issue that has been linked to an increased risk of depression. The objective of this study was to construct a nomogram model for predicting depression in T2DM patients.

METHODS

A total of 4280 patients with T2DM were included in this study from the 2007-2014 NHANES. The entire dataset was split randomly into training set comprising 70 % of the data and a validation set comprising 30 % of the data. LASSO and multivariate logistic regression analyses identified predictors significantly associated with depression, and the nomogram was constructed with these predictors. The model was assessed by C-index, calibration curve, the hosmer-lemeshow test and decision curve analysis (DCA).

RESULTS

The nomogram model comprised of 9 predictors, namely age, gender, PIR, BMI, education attainment, smoking status, LDL-C, sleep duration and sleep disorder. The C-index of the training set was 0.780, while that of the validation set was 0.752, indicating favorable discrimination for the model. The model exhibited excellent clinical applicability and calibration in both the training and validation datasets. Moreover, the cut-off value of the nomogram is 223.

LIMITATIONS

This study has shortcomings in data collection, lack of external validation, and results non-extrapolation.

CONCLUSIONS

Our nomogram exhibits high clinical predictability, enabling clinicians to utilize this tool in identifying high-risk depressed patients with T2DM. It has the potential to decrease the incidence of depression and significantly improve the prognosis of patients with T2DM.

Analysis of depression incidence and influence factors among middle-aged and elderly diabetic patients in China: based on CHARLS data

BACKGROUND

To investigate the incidence of depression in middle-aged and elderly patients with diabetes in China and the influencing factors to provide a theoretical basis to improve the mental health of middle-aged and elderly patients with diabetes and formulate prevention, control, and intervention strategies.

METHODS

The sample of this study was obtained from the China Health and Aging Tracking Survey (CHARLS) 2018 survey data, and middle-aged and older patients with diabetes(responding "Yes" to the questionnaire: "Have you ever been told by a doctor that you have diabetes or elevated blood glucose [including abnormal glucose tolerance and elevated fasting glucose]?") aged ≥ 45 years were selected as study subjects (n = 2,613 ). Depressive symptoms of the study subjects were determined using the simplified version of the Depression Scale for Epidemiological Surveys scores(a score ≥ 10 was defined as depression), influence factors were analyzed using binary logistic regression, and proportion of depressive symptoms was standardized using the sex ratio of the seventh census.

RESULTS

Among the 2,613 middle-aged and elderly patients with diabetes, 1782 (68.2%) had depressive symptoms and 831 (31.8%) had no depressive symptoms. There were 481 (27.0%) patients aged 45-59 years, 978 (54.9%) aged 60-74 years, and 323 (18.1%) aged ≥ 75 years. The depression rate among middle-aged and elderly Chinese patients with diabetes after standardization correction was 67.5%. Binary logistic regression results showed that age, education level, life satisfaction, marital satisfaction, self-rated health grade, somatic pain, visual impairment, physical disability, and the presence of comorbid chronic diseases were factors that influenced the onset of depression in middle-aged and elderly Chinese patients with diabetes (P < 0.05).

CONCLUSION

According to a survey analysis of the CHARLS 2018 data, depression is influenced by a combination of factors among middle-aged and elderly patients with diabetes in China. Therefore, for this population, targeted prevention and control should be carried out for key populations, such as middle-aged and elderly people, poor physical health, and low life satisfaction and marital satisfaction, from various dimensions (e.g., demographic and sociological factors, physical health status, and life satisfaction and marital satisfaction).

Depressive Symptoms Associated with Peripheral Artery Disease and Predicting Mortality in Type 2 Diabetes

This retrospective cohort study aimed to assess the mortality risk in patients with type 2 diabetes mellitus (DM) by screening for depressive symptoms and peripheral artery disease (PAD). We enrolled patients aged ≥60 years who had undergone assessments of both the ankle-brachial index (ABI) and the five-item Geriatric Depression Scale (GDS-5). PAD and depression were defined as ABI ≤ 0.90 and GDS-5 ≥ 1, respectively. The primary endpoint was total mortality. In 1673 enrolled patients, the prevalence of PAD was higher in those with depression than in those without depression (8.9% vs. 5.7%, p = 0.021). After a median follow-up of 56.6 months (interquartile range: 47.0-62.3 months), a total of 168 (10.0%) deaths occurred. The patients in the depression and PAD subgroup had the highest hazard ratio of mortality, followed by the PAD without depression subgroup and the depression without PAD subgroup (2.209, 95%CI: 1.158-4.217; 1.958, 95%CI: 1.060-3.618; and 1.576, 95%CI: 1.131-2.196; respectively) in comparison to the patients without depression and PAD after adjustment for associated factors. In conclusion, a combination of depression and PAD predicted the highest mortality risk. Screening for depression and PAD is recommended in patients aged ≥60 years with type 2 DM.

Medication Adherence among Primary Care Patients with Common Mental Disorders and Chronic Medical Conditions in Rural India

Background

Only a few studies have explored the relationship between psychosocial factors and medication adherence in Indian patients with noncommunicable diseases (NCDs). We aimed to examine the association of psychosocial variables with medication adherence in people with NCDs and comorbid common mental disorders (CMDs) from primary care in rural southern India.

Methods

We performed a secondary analysis using baseline data from a randomized controlled trial in 49 primary care health centers in rural southern India (HOPE study). Participants were adults (≥30 years) with NCDs that included hypertension, diabetes, and/or ischemic heart disease, and comorbid depression or anxiety disorders. Medication adherence was assessed by asking participants if they had missed any prescribed NCD medication in the past month. Data were collected between May 2015 and November 2018. The association between psychosocial and demographic variables and medication nonadherence were assessed via logistic regression analyses.

Results

Of the 2486 participants enrolled, almost one-fifth (18.06%) reported missing medication. Male sex (OR = 1.74, 95% CI 1.37-2.22) and higher internalized mental illness stigma (OR = 1.46, 95% CI 1.07-2.00) were associated with higher odds of missing medication. Older age (OR = 0.40, 95% CI 0.26-0.60, for participants aged 64-75 years vs 30-44 years), reporting more social support (OR = 0.65, 95% CI 0.49-0.86), and higher satisfaction with health (OR = 0.74, 95% CI 0.61-0.89) were associated with lower odds of missing medication.

Conclusions

Greater internalized mental illness stigma and less social support are significantly associated with lower rates of medication adherence in patients with NCDs and comorbid CMDs in rural India.

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.

Biopsychosocial and Nutritional Factors of Depression among Type 2 Diabetes Mellitus Patients: A Systematic Review

The rising prevalence of depression among Type 2 Diabetes Mellitus (T2DM) patients has triggered an alarming situation, and further actions need to be taken by health care professionals and policymakers to curb the issue. There is a lack of evidence review in terms of the biopsychosocial and nutritional factors that are related to depression among T2DM. Hence, this review aimed to identify available evidence on the biopsychosocial and nutritional factors associated with depression among T2DM patients based on the existing literature. Articles were systematically searched from four databases, namely PubMed, Scopus, Web of Science, and EBSCOHost. The approach for the identification of the final articles followed PRISMA guidelines. The selected full-text articles were published between 2017 and 2021 in the English language, and included studies focused on depression among T2DM patients. Using AXIS tools, the eligible articles were evaluated to assess the quality of studies. A total of 19 studies were included in the review, and information related to research questions and associated factors was extracted. Biological, psychological, social, and nutritional factors were shown to be linked with depression among T2DM patients. Future studies need to considered using the biopsychosocial model and incorporating nutritional factors to manage the issues of depression among T2DM patients.