Risk of Depression and Suicide in Diabetic Patients

Amomum tsao-ko crevost et lemaire ameliorates depression-like behaviors and hippocampal inflammation by inhibiting microglia activation and HMGB1/TLR4/NF-κB pathway in diabetic mice with depression

Diabetic depression may be closely related to hippocampal inflammation. We hypothesized that Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) might ameliorate depression-like behavior and glucose intolerance by modulating hippocampal inflammation. UPLC-Q-Exactive-MS/MS was used to identified the constituents in the ethanol extract of A. tsao-ko (EEAT). Then a diabetic depression (DD) model was established and treated for 4 weeks. Depression-like behaviors were assessed using the open field test, sucrose preference test and tail suspension test. The neuronal injury was observed by hematoxylin-eosin staining and Nissl staining. Oral glucose tolerance test, fasting blood glucose, hemoglobin Alc, fasting insulin and homeostasis model assessment of insulin resistance were used to evaluate the effects of EEAT on glucose metabolism. Serum lipids, neurotransmitters, neuroendocrine and inflammation levels were detected by biochemical kits and enzyme-linked immunosorbent assay. Activation of microglia markers was detected by immunofluorescence. Western blotting was used to detect the effect of EEAT on the HMGB1/TLR4/NF-κB protein expression. 48 chemical components were identified from EEAT. Animal experiments showed that EEAT improved the levels of glucose and lipid metabolism, alleviated depression-like behaviors, decreased the level of neurotransmitters and increased the secretion of neuroendocrine-related hormones. The activation of microglia and immunofluorescence intensity of neurogenesis also improved. At the same time, the expression of HMGB1, TLR4 and NF-κB proteins in the inflammatory pathway was also inhibited. EEAT had effects on improving DD symptoms, which may be achieved by down-regulating the HMGB1/TLR4/NF-κB protein expression.

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

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.

Suicide death, suicidal ideation and suicide attempt in patients with diabetes: A systematic review and meta-analysis

AIMS

Diabetes has been indicated to be a risk factor for suicide. We aim to estimate the prevalence of suicide in patients with diabetes.

DESIGN

A meta-analysis using PRISMA methodology was adopted to examine the incidence of suicide in diabetic patients.

DATA SOURCES

From inception to October 2022, three online databases (PubMed, China National Knowledge Infrastructure and Web of Science) were used to search studies.

REVIEW METHODS

We used random-effects model to analysis. And our primary outcome was the incidence of suicide death per 100 person-years, and other outcomes were prevalence of suicidal ideation and suicide attempt. To explore the sources of heterogeneity in our study, we performed subgroup and meta-regression analyses.

RESULTS

The suicide death rate in diabetic patients was 0.027 per 100 person-years, with a higher rate for Type 1 Diabetes Mellitus compared to Type 2 Diabetes Mellitus. The prevalence of suicidal ideation in diabetes patients was 0.175, with a higher prevalence in Type 1 Diabetes Mellitus compared to Type 2 Diabetes Mellitus. The prevalence of suicide attempts in diabetes patients was 0.033, indicating a higher rate for Type 2 Diabetes Mellitus compared to Type 1 Diabetes Mellitus.

CONCLUSIONS

The results indicate a high rate of suicide among people with diabetes, and this study identifies populations and regions at high risk for suicide. Our review emphasizes interventions in mental health and the improvement of suicide prevention programmes.

IMPACT

The study investigated suicide death, suicidal ideation and suicide attempt in diabetic individuals. Suicide rates are elevated among diabetic patients, and various patient groups face distinct suicide risks. It is important to prioritize the mental well-being of diabetic individuals and enhance interventions, including personalized approaches, to inform public health efforts aimed at preventing and addressing suicide among diabetic patients.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public involvement.

MONDEP: A unified SpatioTemporal MONitoring Framework for National DEPression Forecasting

Depression has become a prevalent mental disorder that significantly affects a person's emotions, behaviors, physical health, ability to perform daily tasks, and ability to maintain healthy relationships. Untreated depression can escalate the risk of suicide, making the situation even worse. Despite an abundance of models previously proposed for forecasting depression, the issue of foretelling the overall number of patients at each administrative level remains under-investigated. Therefore, in this paper, we propose a simple but effective SpatioTemporal Monitoring Framework for National Depression Forecasting (MONDEP). In particular, we analyze national depression statistics data in Thailand as a case study and create prediction models for a real-time depression forecasting system using machine learning and deep learning approaches. In order to forecast the prevalence of depression at various administrative levels, we use the hierarchical structure of depression aggregation. The proposed framework consists of three modules: Data Pre-processing to extract and pre-process the raw data, Exploratory Data Analysis (EDA) to visualize and analyze the data to get insight, and Model Training and Testing to predict future depression cases. The objective of our research is to construct a comprehensive MONDEP framework that utilizes machine learning and deep learning to predict depression profiles at the district and national levels using multivariate time series across various administrative levels. Our study illustrates the considerable association between a spatial-temporal component and demonstrates how depression profiles may be represented by employing lower administrative-level data to estimate the general level of mental health across the nation. Additionally, the best performance across all criteria is obtained when a deep learning model is used to exploit multivariate time series, showing a 13% improvement in MAE measure compared to the SARIMAX baseline. We believe the proposed framework could be used as a point of reference for decision-making regarding the management of depression and has the potential to be incredibly helpful for policymakers in successfully managing mental health services on time.

Neurobiology and medico-legal aspects of suicides among older adults: a narrative review

The task of preventing suicide in older adults is an important social burden as older adults aged above 65 are exposed to singular psychological aspects that increase suicide risks. Moreover, when an older adult corpse is found, the medico-legal inspection represents a fundamental tool to identify the exact cause of death, classifying or excluding it as suicide. In this scenario, this review aims to explore the neurobiological factors that could be related to suicidal behavior in older adults. A further goal of this review is the exploration of the medico-legal aspects surrounding older adult suicides, clarifying the importance of forensic investigation. Particularly, this review examines issues such as neurotransmitter imbalances, cognitive impairment, neuroinflammation, psychosocial factors related to geriatric suicide, and neurodegenerative diseases. Additionally, medico-legal aspects such as policy considerations, legal frameworks, mental health assessments, ethical implications and forensic investigation were explored. Considering the importance of this phenomenon, especially in western countries, a need has emerged for focused screening tools on suicidal behavior among older adults, in order to contain it. Therefore, this review makes an exhaustive appraisal of the literature giving insights into the delicate interplay between neurobiology as well as mental health in relation to older adult suicide within a medico-legal context. The comprehension of different aspects about this complex phenomenon is fundamental to propose new and more effective interventions, supporting tailored initiatives such as family support and improving healthcare, specifically towards vulnerable ageing societies to reduce older adult suicide risks.

Association of indoleamine 2,3 dioxygenase, brain derived neurotrophic factor and cellular senescence in type 2 diabetes mellitus with depression: a clinical approach

BACKGROUND AND AIM

Type 2 diabetes mellitus (T2DM) and depression are often linked. Several studies have reported the role of molecular markers either in diabetes or depression. The present study aimed at molecular level profiling of Indoleamine-2,3-dioxygenase (IDO), brain-derived neurotrophic factor (BDNF) and cellular senescence in patients with type 2 diabetes with and without depression compared to individuals with healthy controls.

METHODS

A total of 120 individuals diagnosed with T2DM were enlisted for the study, with a subset of participants with and without exhibiting depression. The gene expression analysis was done using quantitative real-time PCR.

RESULTS

Indoleamine 2,3 dioxygenase (p < 0.001) and senescence genes (p < 0.001) were significantly upregulated, while brain derived neurotrophic factor (p < 0.01) was significantly downregulated in T2DM patients comorbid with and without depression when compared to healthy controls.

CONCLUSION

Indoleamine 2,3 dioxygenase, Brain derived neurotrophic factor and cellular senescence may play a role in the progression of the disease. The aforementioned discoveries offer significant contributions to our understanding of the molecular mechanisms that underlie T2DM with depression, potentially aiding in the advancement of prediction and diagnostic methods for this particular ailment.

Prevalence, risk factors and clinical correlates of glucose disturbances in a large sample of Han Chinese patients with first-episode drug-naïve major depressive disorder

Glucose disturbances are a common comorbidity of major depressive disorder (MDD) patients and have been extensively studied in the past. However, few studies have explored glucose disturbances in first-episode drug-naïve (FEDN) MDD patients. The purpose of this study was to examine the prevalence and risk factors of glucose disturbances in FEDN MDD patients to understand the relationship between MDD and glucose disturbances in the acute early phase and provide important implications for therapeutic interventions. Using a cross-sectional design, we recruited a total of 1718 MDD patients. We collected their socio-demographic information, clinical data, and blood glucose indicators.17-item Hamilton Depression Rating Scale (HAMD), 14-item Hamilton Anxiety Rating Scale (HAMA), and the positive symptom subscale of the Positive and Negative Syndrome Scale (PANSS) were used to assess their depression, anxiety, psychotic symptoms, respectively. The prevalence of glucose disturbances in FEDN MDD patients was 13.6%. Depression, anxiety and psychotic symptoms, body mass index (BMI) levels and suicide attempts rates were higher in the group with glucose disorders than in the group without glucose disorders among patients with first-episode drug-naive MDD. Correlation analysis showed that glucose disturbances were associated with HAMD score, HAMA score, BMI, psychotic symptoms and suicide attempts. Furthermore, binary logistic regression showed that HAMD score and suicide attempts were independently associated with glucose disturbances in MDD patients. Our findings suggest that the prevalence of comorbid glucose disturbances is very high in FEDN MDD patients. Moreover, more severe depressive symptoms and higher suicide attempts are correlated with glucose disturbances in MDD FEDN patients in the early stage.

Impacts of inflammatory cytokines on depression: a cohort study

BACKGROUND

Inflammatory factors are associated with depression. We seek to investigate the correlation between inflammatory cytokines and prognosis of depression or suicidal ideation and behavior at 3 months in depression patients.

METHODS

Eighty-two depressed outpatients were recruited and treated as usual. Plasma cytokines were measured at baseline. Patients were followed up with Patient Health Questionnaire-9 and suicidal ideation and behavior according to the item 3 of Hamilton depression scale for 3 months.

RESULTS

Compared to the depression patients with low level of interleukin-1β, the high one had severe depressive symptoms at month 2 and 3 (B 0.92, P < 0.01; B 0.86, P = 0.02; respectively). The incidence of suicidal ideation or behavior was 18.3% at 3 months. Depression patients with high levels of tumor necrosis factor-α showed high risk of suicidal ideation and behavior than the low one (OR 2.16, 95% CI 1.00-4.65, P = 0.04).

CONCLUSIONS

High levels of interleukin-1β and tumor necrosis factor-α were predictive of middle-term severe depressive symptoms and suicidal ideation and behavior respectively.

Association of semaglutide with risk of suicidal ideation in a real-world cohort

Concerns over reports of suicidal ideation associated with semaglutide treatment, a glucagon-like peptide 1 receptor (GLP1R) agonist medication for type 2 diabetes (T2DM) and obesity, has led to investigations by European regulatory agencies. In this retrospective cohort study of electronic health records from the TriNetX Analytics Network, we aimed to assess the associations of semaglutide with suicidal ideation compared to non-GLP1R agonist anti-obesity or anti-diabetes medications. The hazard ratios (HRs) and 95% confidence intervals (CIs) of incident and recurrent suicidal ideation were calculated for the 6-month follow-up by comparing propensity score-matched patient groups. The study population included 240,618 patients with overweight or obesity who were prescribed semaglutide or non-GLP1R agonist anti-obesity medications, with the findings replicated in 1,589,855 patients with T2DM. In patients with overweight or obesity (mean age 50.1 years, 72.6% female), semaglutide compared with non-GLP1R agonist anti-obesity medications was associated with lower risk for incident (HR = 0.27, 95% CI = 0.200.32-0.600.36) and recurrent (HR = 0.44, 95% CI = 0.32-0.60) suicidal ideation, consistent across sex, age and ethnicity stratification. Similar findings were replicated in patients with T2DM (mean age 57.5 years, 49.2% female). Our findings do not support higher risks of suicidal ideation with semaglutide compared with non-GLP1R agonist anti-obesity or anti-diabetes medications.

Latent Autoimmune Diabetes in Adults and a Continuous Glucose Monitoring Device: An Unfortunate Outcome

Latent autoimmune diabetes in adults (LADA) is a slow-progressing form of autoimmune diabetes. A 44-year-old man with a four-year history of diabetes mellitus (DM), obsessive-compulsive disorder (OCD), and panic disorder was admitted to the hospital for diabetic ketoacidosis. LADA was confirmed with positive GAD-65 antibody. His occupation involved random working days with several weeks off in between projects. During workdays, his insulin dosage required frequent adjustments due to lower blood glucose (BG) readings. Owing to the variable work schedule and constantly changing insulin needs, he was recommended a continuous glucose monitoring (CGM) device. Few days after starting on the CGM device, he was seen in the emergency department because of elevated BG. His home BG readings ranged from 80 to 408 mg/dL. He was getting frustrated with the fluctuating BG readings. At home, he remained agitated and endlessly checked his CGM device. After discharge, he would repeatedly call the endocrinology office with his BG readings with the insulin dose being adjusted accordingly. Few weeks later, the office received a call from his wife informing us that the patient had shot himself in the head. According to his wife, lately he had trouble sleeping, was very anxious, and often had panic attacks. He seemed to struggle with ever-fluctuating BG readings and was obsessed with incessantly changing numbers on his CGM device. Patients with Type 1 DM are at increased risk of mental health disorders and suicide forms a sizeable proportion of deaths in these patients. This case highlights the importance of mental health, especially underlying OCD as a prognostic factor in the management of diabetes with CGM devices.

Diabetes mellitus type 2 as an underlying, comorbid or consequent state of mental disorders

Somatic disturbances that occur in parallel with psychiatric diseases are a major challenge in clinical practice. Various factors contribute to the development of mental and somatic disorders. Type 2 diabetes mellitus (T2DM) is a significant health burden worldwide, and the prevalence of diabetes in adults is increasing. The comorbidity of diabetes and mental disorders is very common. By sharing a bidirectional link, both T2DM and mental disorders influence each other in various manners, but the exact mechanisms underlying this link are not yet elucidated. The potential mechanisms of both mental disorders and T2DM are related to immune and inflammatory system dysfunction, oxidative stress, endothelial dysfunction, and metabolic disturbances. Moreover, diabetes is also a risk factor for cognitive dysfunction that can range from subtle diabetes-associated cognitive decline to pre-dementia and dementia. A complex re-lationship between the gut and the brain also represents a new therapeutic approach since gut-brain signalling pathways regulate food intake and hepatic glucose production. The aim of this minireview is to summarize and present the latest data on mutual pathogenic pathways in these disorders, emphasizing their complexity and interweaving. We also focused on the cognitive performances and changes in neurodegenerative disorders. The importance of implementing integrated approaches in treating both of these states is highlighted, along with the need for individual therapeutic strategies.

Social exclusion in people with diabetes: cross-sectional and longitudinal results from the German Ageing Survey (DEAS)

As social exclusion can be linked to worse health and overall reduced quality of life, we describe social exclusion in people with diabetes and assess whether diabetes can be considered as a risk factor for social exclusion. We analyzed two waves (2014, 2017, N = 6604) from a survey of community-dwelling people aged > 40 using linear regression, group comparison and generalized estimating equations to explore the association between diabetes, social exclusion, socioeconomic, physical and psychosocial variables. In the entire cohort, diabetes was cross-sectionally associated with social exclusion after adjusting for covariates (p = 0.001). In people with diabetes, social exclusion was further associated with self-esteem (p < 0.001), loneliness (p =  < 0.001), income (p = 0.017), depression (p = 0.001), physical diseases (p = 0.04), and network size (p = 0.043). Longitudinal data revealed that higher levels of social exclusion were already present before the diagnosis of diabetes, and future social exclusion was predicted by self-esteem, loneliness, depression, and income, but not by diabetes (p = .221). We conclude that diabetes is not a driver of social exclusion. Instead, both seem to co-occur as a consequence of health-related and psychosocial variables.

Depression and determinants among diabetes mellitus patients in Ethiopia, a systematic review and meta-analysis

INTRODUCTION

Primary studies have estimated the prevalence of depression and its determinants among diabetes patients. However, studies synthesizing this primary evidence are limited. Hence, this systematic review aimed to determine the prevalence of depression and identify determinants of depression among diabetes in Ethiopia.

METHODS

This systematic review and meta-analysis included a search of PubMed, Google Scholar, Scopus, Science Direct, PsycINFO, and Cochrane library. Data were extracted using Microsoft Excel and analyzed using STATA statistical software (v. 14). Data were pooled using a random-effects model. Forest plots, and Egger's regression test were all used to check for publication bias. Heterogeneity (I)² was computed. Subgroup analysis was done by region, publication year, and depression screening instrument. In addition, the pooled odds ratio for determinants was calculated.

RESULTS

Sixteen studies, including 5808 participants were analyzed. The prevalence of depression in diabetes was estimated to be 34.61% (95% CI: 27.31-41.91). According to subgroup analysis by study region, publication year, and screening instrument, the highest prevalence was observed in Addis Ababa (41.98%), studies published before 2020 (37.91%), and studies that used Hospital Anxiety and Depression Scale (HADS-D) (42.42%,) respectively. Older age > 50 years (AOR = 2.96; 95% CI: 1.71-5.11), being women (AOR = 2.31; 95% CI: 1.57, 3.4), longer duration with diabetes (above 5 years) (AOR = 1.98; 95% CI: 1.03-3.8), and limited social support (AOR = 2.37; 95% CI: 1.68-3.34), were the determinants of depression in diabetic patients.

CONCLUSION

The results of this study suggest that the prevalence of depression in diabetes is substantial. This result underscores the importance of paying particular attention to prevent depression among diabetes. Being older, not attending formal education, longer duration with diabetes, having comorbidity, and low adherence to diabetes management were all associated. These variables may help clinicians identify patients at high risk of depression. Future studies focusing on the causal association between depression and diabetes are highly recommended.

College students' screening early warning factors in identification of suicide risk

This study aimed to explore the main influencing factors of suicide risk among Chinese students and establish an early warning model to provide interventions for high-risk students. We conducted surveys of students in their first and third years from a cohort study at Jining Medical College. Logistic regression models were used to screen the early warning factors, and four machine learning models were used to establish early warning models. There were 8 factors related to suicide risk that were eventually obtained through screening, including age, having a rough father, and CES-D, OHQ, ASLEC-4, BFI-Neuroticism, BFI-Openness, and MMC-AF-C scores. A random forest model with SMOTE was adopted, and it verified that these 8 early warning signs, for suicide risk can effectively predict suicide risk within 2 years with an AUC score of 0.947. Among the factors, we constructed a model that indicated that different personality traits affected suicide risk by different paths. Moreover, the factors obtained by screening can be used to identify college students in the same year with a high risk of suicide, with an AUC score that reached 0.953. Based on this study, we suggested some interventions to prevent students going high suicide risk.