The interface of COVID-19, diabetes, and depression

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

BACKGROUND

Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions.

METHODS

The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling.

RESULTS

From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68).

CONCLUSION

Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

Prevalence and factors associated with depression, anxiety, and stress symptoms among home isolated COVID-19 patients in Western Nepal

Introduction

Globally, COVID-19 pandemic has a significant impact on mental health. In Nepal, COVID-19 positive cases have to self-isolate at home in multi-generational and multi-family households. This could be strongly associated with depression, anxiety, and stress-related health outcomes. Additionally, COVID-19 related stigma and fear of transmission may intensify depression, anxiety, and stress symptoms. This study determined the prevalence of depression, anxiety, and stress symptoms and their association with presence of COVID-19 symptoms and comorbid conditions among home isolated COVID-19 positives in the Karnali province, Nepal.

Methods

We conducted a cross-sectional study to assess depression, anxiety, and stress symptoms among 402 home isolated COVID-19 patients of Karnali province from January to May 2021 using "Depression, Anxiety and Stress Scale-21 (DASS-21)". We interviewed patients to collect socio-demographic, DASS-21, COVID-19 symptoms, comorbid conditions, and self-treatment. We conducted a telephonic interview using a standardized questionnaire using Kobotoolbox. We calculated the prevalence of depression, anxiety, and stress symptoms. We utilized univariate and multivariate logistic regression to determine their association with the presence of COVID-19 symptoms and comorbid conditions. In multivariate logistic regression, we adjusted sociodemographic factors (age, gender, ethnicity, marital status, monthly family income, education level), smoking status and history of self-treatment. We reported adjusted odds ratios (aOR) with 95% confidence intervals. All analyses were conducted in R (version: 4.0.3).

Results

The prevalence of depression, anxiety and stress symptoms among home isolated COVID-19 patients were 8.0% (95% CI: 5.5 to 11.1), 11.2% (95% CI: 8.3 to 14.7), and 4.0% (95% CI: 2.3 to 6.4) respectively. Higher odds of depression symptoms (aOR: 2.86; 95% CI: 1.10-7.44, p = 0.03), anxiety symptoms (aOR: 3.81; 95% CI: 1.62 to 8.93; p = <0.01) and stress symptoms (aOR: 7.78; 95% CI: 1.43 to 42.28; p = 0.02) were associated significantly with presence of COVID-19 symptoms in past week. Higher odds of anxiety symptoms were associated with the presence of comorbid conditions (aOR = 2.92; 95% CI: 1.09 to 7.80; p = 0.03).

Conclusion

Depression, anxiety, and stress symptoms were present in a significant proportion of home isolated COVID-19 patients in western Nepal and positively associated with the presence of COVID-19 symptoms. In this global COVID-19 pandemic, it is important to provide timely counseling to high-risk groups like those with comorbidities and COVID-19 symptoms to maintain a high level of mental health among home isolated COVID-19 patients.

Machine learning-based identification and related features of depression in patients with diabetes mellitus based on the Korea National Health and Nutrition Examination Survey: A cross-sectional study

Patients with diabetes mellitus (DM) are twice as likely as nondiabetic individuals to develop depression, which is a prevalent but often undiagnosed psychiatric comorbidity. Patients with DM who are depressed have poor glycemic control, worse quality of life, increased risk of diabetic complications, and higher mortality rate. The present study aimed to develop machine learning (ML) models that identify depression in patients with DM, determine the best performing model by evaluating multiple ML algorithms, and investigate features related to depression. We developed six ML models, including random forest, K-nearest neighbor, support vector machine (SVM), Adaptive Boosting, light gradient-boosting machine, and Extreme Gradient Boosting, based on the Korea National Health and Nutrition Examination Survey. The results showed that the SVM model performed well, with a cross-validated area under the receiver operating characteristic curve of 0.835 (95% confidence interval [CI] = 0.730-0.901). Thirteen features were related to depression in patients with DM. Permutation feature importance showed that the most important feature was subjective health status, followed by level of general stress awareness; stress recognition rate; average monthly income; triglyceride (mg/dL) level; activity restriction status; European quality of life (EuroQoL): usual activity and lying in a sickbed in the past 1 month; EuroQoL: pain / discomfort, self-care, and physical discomfort in the last 2 weeks; and EuroQoL: mobility and chewing problems. The current findings may offer clinicians a better understanding of the relationship between DM and depression using ML approaches and may be an initial step toward developing a more predictive model for the early detection of depressive symptoms in patients with DM.

The Psychological Impact of Rhino-Orbital Mucormycosis During the Second Wave of COVID-19 Pandemic From South East Asian Country

AIM

The present study addressed overcoming the lacunae in the literature of psychiatric manifestations associated with rhino-orbital mucormycosis. The current study aimed to assess the symptoms of depression, anxiety, stress, coping measures, suicidal intent, and visual disability in patients of rhino-orbital mucormycosis (ROM) during the epidemic of the disease at the nodal tertiary care center in North India.

METHODS

Fifty-four inpatients of laboratory-proven rhino-orbital mucor-mycosis (ROM) were included for an observational, cross-sectional study at nodal, designated COVID-19, and mucormycosis treating tertiary care hospital. Patients with Hindi Mini-Mental State Examination score <24, prior psychiatric illness, and severely ill requiring ventilator support were excluded. The psychological variables were assessed using Depression, Anxiety, and Stress Scale 21 (DASS 21), Beck's Suicide intent Scale, Coping Scale Questionnaire, and Visual disability scale (IND-VFQ33). Their socioeconomic status was assessed using the Modified Kuppuswamy Scale.

RESULTS

Ninety percent of patients with ROM had diabetes mellitus. The majority (44%) of patients belonged to lower socioeconomic strata. Higher frequencies of severe depression (28%), extremely severe anxiety (26%), and mild stress (17%) were noted in the study participants. On the Tukey test, depression score was higher in patients of ROM compared to COVID (with ROM) (p-value= 0.016). On Tukey analysis, anxiety score was significantly higher in ROM patients compared to COVID (with ROM) patients (p-value = 0.018). Coping scores were significantly higher in COVID (with ROM) patients compared to ROM patients (p value = 0.035). Mild to moderate visual disability was noted in the study participants.  Conclusion: The current study reflects the association of higher depression and anxiety scores in cases with ROM that indicated higher mental health needs. Early assessment, early detection, and early intervention for psychological help, along with the multidisciplinary team, helped to improve the overall psychological outcome of the affected patients.

Evaluation of oral health-related quality of life and its association with mental health status of patients with type 2 diabetes mellitus in the post-COVID-19 pandemic era: A study from Central Saudi Arabia

Background and objectives

The association between oral and mental health is reciprocal, in which poor oral health may lead to several mental health issues, especially among patients with diabetes. The present study evaluated oral health-related quality of life (OHRQOL) and its association with mental health conditions among patients with type 2 diabetes mellitus (T2DM) in central Saudi Arabia.

Methods

The Arabic version of the Oral Health Impact Profile-14 (OHIP-14) questionnaire and the Depression, Anxiety, and Stress Scale-21 Items (DASS-21) were used to assess the OHRQOL and mental health status of patients with diabetes. We utilized logistic regression analysis to identify the predictors of poor OHRQOL, and Spearman's correlation test to identify any correlations between OHIP-14 and overall DASS-21 scores, as well as each subscale.

Results

Of the 677 patients included in the present study, 52.7% had a poor OHRQOL, which was significantly higher (positive association) among patients with a longer duration of diabetes (adjusted odds ratio [AOR] = 3.31; 95% confidence interval [CI] = 1.96-4.17) and those who did not periodically monitor their oral health (AOR = 2.85; 95% CI = 1.76-3.89). Some forms (mild, moderate, severe, or extremely severe) of depression, anxiety, and stress were observed in 59.7, 71.1, and 67.1% of the participants, respectively. Furthermore, we found that the total OHRQOL scores had a significant positive association with depression (AOR = 2.32, 95% CI = 1.34-3.71, p = 0.001), anxiety (AOR = 1.81, 95% CI = 1.22-2.79, p = 0.003), and stress (AOR = 1.43, 95% CI = 1.14-2.19, p = 0.026).

Conclusion

The results of the present study suggest the importance of appropriate and targeted health education programs for T2DM patients to ensure periodic dental examinations and oral health. Additionally, we recommend counseling sessions for all T2DM patients with trained healthcare providers to improve their mental health status during follow-up visits at outpatient diabetes care centers.

Post COVID and Apheresis - Where are we Standing?

A continual increase in cases of Long/Post COVID constitutes a medical and socioeconomic challenge to health systems around the globe. While the true extent of this problem cannot yet be fully evaluated, recent data suggest that up to 20% of people with confirmed SARS-CoV-2 suffer from clinically relevant symptoms of Long/Post COVID several weeks to months after the acute phase. The clinical presentation is highly variable with the main symptoms being chronic fatigue, dyspnea, and cognitive symptoms. Extracorporeal apheresis has been suggested to alleviate symptoms of Post/COVID. Thus, numerous patients are currently treated with apheresis. However, at present there is no data from randomized controlled trials available to confirm the efficacy. Therefore, physicians rely on the experience of practitioners and centers performing this treatment. Here, we summarize clinical experience on extracorporeal apheresis in patients with Post/COVID from centers across Germany.

Depression and Related Factors in Korean Adults During the Coronavirus Disease 2019 Outbreak

OBJECTIVE

We aimed to determine the status of depression and its related factors among adult Koreans during the coronavirus disease 2019 (COVID-19) outbreak.

METHODS

We used data from the 2020 Korea Community Health Survey (KCHS). We assessed depressive feelings and symptoms using the Patient Health Questionnaire-9 (PHQ-9 ≥10). In addition, we assessed general and COVID-19-related characteristics, including isolation due to and concerns about COVID-19. We analyzed the data using chi-square tests and multiple logistic regression analyses.

RESULTS

The rates of depressive feelings and symptoms were 5.9% and 2.9%, respectively. Of the adult respondents, 68.5% were concerned about COVID-19, while 75.9% were concerned about economic harm due to COVID-19. The adjusted odds ratios for depressive symptoms assessed using the PHQ-9 were significantly high among women responders, adults aged 19-44 years, low-income households, those who experienced COVID-19-related symptoms, and those concerned about death due to COVID-19 and economic harm due to COVID-19. Similar results were obtained for depressive feeling.

CONCLUSION

Concerns related to COVID-19 infection are related to depression. This suggests that COVID-19 significantly affects mental health. Therefore, during public health crises, such as new communicable diseases, mental health and the incidence of the infectious disease require assessment and monitoring.

Diabetes and COVID-19: Short- and Long-Term Consequences

When the corona pandemic commenced more than two years ago, it was quickly recognized that people with metabolic diseases show an augmented risk of severe COVID-19 and an increased mortality compared to people without these comorbidities. Furthermore, an infection with SARS-CoV-2 has been shown to lead to an aggravation of metabolic diseases and in single cases to new-onset metabolic disorders. In addition to the increased risk for people with diabetes in the acute phase of COVID-19, this patient group also seems to be more often affected by long-COVID and to experience more long-term consequences than people without diabetes. The mechanisms behind these discrepancies between people with and without diabetes in relation to COVID-19 are not completely understood yet and will require further research and follow-up studies during the following years. In the current review, we discuss why patients with diabetes have this higher risk of developing severe COVID-19 symptoms not only in the acute phase of the disease but also in relation to long-COVID, vaccine breakthrough infections and re-infections. Furthermore, we discuss the effects of lockdown on glycemic control.

Long-COVID, Metabolic and Endocrine Disease

In the aftermath of the corona pandemic, long-COVID or post-acute COVID-19 syndrome still represents a great challenge, and this topic will continue to represent a significant health problem in the coming years. At present, the impact of long-COVID on our health system cannot be fully assessed but according to current studies, up to 40% of people who have been infected with SARS-CoV-2 suffer from clinically relevant symptoms of long-COVID syndrome several weeks to months after the acute phase. The main symptoms are chronic fatigue, dyspnea, and various cognitive symptoms. Initial studies have shown that people with overweight and diabetes mellitus have a higher risk of developing long-COVID associated symptoms. Furthermore, repeated treatment of acute COVID-19 and long-COVID with steroids can contribute to long-term metabolic and endocrine disorders. Therefore, a structured program with rehabilitation and physical activity as well as optimal dietary management is of utmost importance, especially for patients with metabolic diseases and/or long-COVID. Furthermore, the removal of autoantibodies and specific therapeutic apheresis procedures could lead to a significant improvement in the symptoms of long-COVID in individual patients.

Fluoxetine Treatment Decreases Cardiac Vagal Input and Alters the Serotonergic Modulation of the Parasympathetic Outflow in Diabetic Rats

Comorbid diabetes and depression constitutes a major health problem, worsening associated cardiovascular diseases. Fluoxetine's (antidepressant) role on cardiac diabetic complications remains unknown. We determined whether fluoxetine modifies cardiac vagal input and its serotonergic modulation in male Wistar diabetic rats. Diabetes was induced by alloxan and maintained for 28 days. Fluoxetine was administered the last 14 days (10 mg/kg/day; p.o). Bradycardia was obtained by vagal stimulation (3, 6 and 9 Hz) or i.v. acetylcholine administrations (1, 5 and 10 μg/kg). Fluoxetine treatment diminished vagally-induced bradycardia. Administration of 5-HT originated a dual action on the bradycardia, augmenting it at low doses and diminishing it at high doses, reproduced by 5-CT (5-HT_1/7 agonist). 5-CT did not alter the bradycardia induced by exogenous acetylcholine. Decrease of the vagally-induced bradycardia evoked by high doses of 5-HT and 5-CT was reproduced by L-694,247 (5-HT_1D agonist) and blocked by prior administration of LY310762 (5-HT_1D antagonist). Enhancement of the electrical-induced bradycardia by 5-CT (10 μg/kg) was abolished by pretreatment with SB269970 (5-HT₇ receptor antagonist). Thus, oral fluoxetine treatment originates a decrease in cardiac cholinergic activity and changes 5-HT modulation of bradycardic responses in diabetes: prejunctional 5-HT₇ receptors augment cholinergic-evoked bradycardic responses, whereas prejunctional 5-HT_1D receptors inhibit vagally-induced bradycardia.