Diabetes, Heart Failure, and COVID-19: An Update

Cross-sectional study to assess the impact of the COVID-19 pandemic on healthcare services and clinical admissions using statistical analysis and discovering hotspots in three regions of the Greater Toronto Area

OBJECTIVES

The COVID-19 pandemic disrupted healthcare services, leading to the cancellation of non-urgent tests, screenings and procedures, a shift towards remote consultations, stalled childhood immunisations and clinic closures which had detrimental effects across the healthcare system. This study investigates the impact of the COVID-19 pandemic on clinical admissions and healthcare quality in the Peel, York and Toronto regions within the Greater Toronto Area (GTA).

DESIGN

In a cross-sectional study, the negative impact of the pandemic on various healthcare sectors, including preventive and primary care (PPC), the emergency department (ED), alternative level of care (ALC) and imaging, procedures and surgeries is investigated. Study questions include assessing impairments caused by the COVID-19 pandemic and discovering hotspots and critical subregions that require special attention to recover. The measuring technique involves comparing the number of cases during the COVID-19 pandemic with before that, and determining the difference in percentage. Statistical analyses (Mann-Whitney U test, analysis of variance, Dunn's test) is used to evaluate sector-specific changes and inter-relationships.

SETTING

This work uses primary data which were collected by the Black Creek Community Health Centre. The study population was from three regions of GTA, namely, the city of Toronto, York and Peel. For all health sectors, the sample size was large enough to have a statistical power of 0.95 to capture 1% variation in the number of cases during the COVID-19 pandemic compared with before that.

RESULTS

All sectors experienced a significant decline in patient volume during the pandemic. ALC admissions surged in some areas, while IPS patients faced delays. Surgery waitlists increased by an average of 9.75%, and completed IPS procedures decreased in several subregions.

CONCLUSIONS

The COVID-19 pandemic had a universally negative impact on healthcare sectors across various subregions. Identification of the hardest-hit subregions in each sector can assist health officials in crafting recovery policies.

Bayesian networks and imaging-derived phenotypes highlight the role of fat deposition in COVID-19 hospitalisation risk

Objective: Obesity is a significant risk factor for adverse outcomes following coronavirus infection (COVID-19). However, BMI fails to capture differences in the body fat distribution, the critical driver of metabolic health. Conventional statistical methodologies lack functionality to investigate the causality between fat distribution and disease outcomes. Methods: We applied Bayesian network (BN) modelling to explore the mechanistic link between body fat deposition and hospitalisation risk in 459 participants with COVID-19 (395 non-hospitalised and 64 hospitalised). MRI-derived measures of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat were included. Conditional probability queries were performed to estimate the probability of hospitalisation after fixing the value of specific network variables. Results: The probability of hospitalisation was 18% higher in people living with obesity than those with normal weight, with elevated VAT being the primary determinant of obesity-related risk. Across all BMI categories, elevated VAT and liver fat (>10%) were associated with a 39% mean increase in the probability of hospitalisation. Among those with normal weight, reducing liver fat content from >10% to <5% reduced hospitalisation risk by 29%. Conclusion: Body fat distribution is a critical determinant of COVID-19 hospitalisation risk. BN modelling and probabilistic inferences assist our understanding of the mechanistic associations between imaging-derived phenotypes and COVID-19 hospitalisation risk.

Cardiovascular complications of diabetes: role of non-coding RNAs in the crosstalk between immune and cardiovascular systems

Diabetes mellitus, a group of metabolic disorders characterized by high levels of blood glucose caused by insulin defect or impairment, is a major risk factor for cardiovascular diseases and related mortality. Patients with diabetes experience a state of chronic or intermittent hyperglycemia resulting in damage to the vasculature, leading to micro- and macro-vascular diseases. These conditions are associated with low-grade chronic inflammation and accelerated atherosclerosis. Several classes of leukocytes have been implicated in diabetic cardiovascular impairment. Although the molecular pathways through which diabetes elicits an inflammatory response have attracted significant attention, how they contribute to altering cardiovascular homeostasis is still incompletely understood. In this respect, non-coding RNAs (ncRNAs) are a still largely under-investigated class of transcripts that may play a fundamental role. This review article gathers the current knowledge on the function of ncRNAs in the crosstalk between immune and cardiovascular cells in the context of diabetic complications, highlighting the influence of biological sex in such mechanisms and exploring the potential role of ncRNAs as biomarkers and targets for treatments. The discussion closes by offering an overview of the ncRNAs involved in the increased cardiovascular risk suffered by patients with diabetes facing Sars-CoV-2 infection.

Diabetes, COVID-19, and questions unsolved

Recent evidence suggests a role for Diabetes Mellitus in adverse outcomes from COVID-19 infection; yet the underlying mechanisms are not clear. Moreover, attention has turned to prophylactic vaccination to protect the population from COVID-19-related illness and mortality. We performed a comprehensive peer-reviewed literature search on an array of key terms concerning diabetes and COVID-19 seeking to address the following questions: 1. What role does diabetes play as an accelerator for adverse outcomes in COVID-19?; 2. What mechanisms underlie the differences in outcomes seen in people with diabetes?; 3. Are vaccines against COVID-19 efficacious in people with diabetes? The current literature demonstrates that diabetes is associated with an increased risk of adverse outcomes from COVID-19 infection, and post-COVID sequelae. Potential mechanisms include dysregulation of Angiotensin Converting Enzyme 2, Furin, CD147, and impaired immune cell responses. Hyperglycaemia is a key exacerbator of these mechanisms. Limited studies are available on COVID-19 vaccination in people with diabetes; however, the current literature suggests that vaccination is protective against adverse outcomes for this population. In summary, people with diabetes are a high-risk group that should be prioritised in vaccination efforts. Glycaemic optimisation is paramount to protecting this group from COVID-19-associated risk. Unsolved questions remain as to the molecular mechanisms underlying the adverse outcomes seen in people with diabetes; the functional impact of post-COVID symptoms on people with diabetes, their persistence, and management; how long-term vaccine efficacy is affected by diabetes, and the antibody levels that confer protection from adverse outcomes in COVID-19.

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil

The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.

Comorbidities and clinical complications associated with SARS-CoV-2 infection: an overview

The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes major challenges to the healthcare system. SARS-CoV-2 infection leads to millions of deaths worldwide and the mortality rate is found to be greatly associated with pre-existing clinical conditions. The existing dataset strongly suggests that cardiometabolic diseases including hypertension, coronary artery disease, diabetes and obesity serve as strong comorbidities in coronavirus disease (COVID-19). Studies have also shown the poor outcome of COVID-19 in patients associated with angiotensin-converting enzyme-2 polymorphism, cancer chemotherapy, chronic kidney disease, thyroid disorder, or coagulation dysfunction. A severe complication of COVID-19 is mostly seen in people with compromised medical history. SARS-CoV-2 appears to attack the respiratory system causing pneumonia, acute respiratory distress syndrome, which lead to induction of severe systemic inflammation, multi-organ dysfunction, and death mostly in the patients who are associated with pre-existing comorbidity factors. In this article, we highlighted the key comorbidities and a variety of clinical complications associated with COVID-19 for a better understanding of the etiopathogenesis of COVID-19.