The purpose of this longitudinal study is to construct a prediction model for Covid-19 level of concern using established Covid-19 socio-demographic, lifestyle and health risk characteristics and to examine specific contributions of obesity-related cardiometabolic health characteristics as predictors of Covid-19 level of concern among a representative sample of U.S. older adults. We performed secondary analyses of existing data on 2872 2006–2020 Health and Retirement Study participants and examined 19 characteristics in relation to the outcome of interest using logistic regression and machine learning algorithms. In mixed-effects ordinal logistic regression models, a history of diabetes, stroke as well as 1–2 cardiometabolic risk factors and/or chronic conditions were associated with greater Covid-19 level of concern, after controlling for confounders. Female sex, birth cohort, minority race, Hispanic ethnicity and total wealth as well as depressive symptoms were associated with higher level of Covid-19 concern, and education was associated with lower level of Covid-19 concern in fully adjusted mixed-effects ordinal logistic regression models. The selected socio-demographic, lifestyle and health characteristics accounted for < 70% of the variability in Covid-19 level of concern based on machine learning algorithms. Independent risk factors for Covid-19 level of concern among U.S. older adults include socio-demographic characteristics and depressive symptoms. Advanced research is needed to identify relevant predictors and elucidate underlying mechanisms of observed relationships.

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection likely ranks among the deadliest diseases in human history. As with other coronaviruses, SARS-CoV-2 infection damages not only the lungs but also the heart and many other organs that express angiotensin-converting enzyme 2 (ACE2), a receptor for SARS-CoV-2. COVID-19 has upended lives worldwide. Dietary behaviors have been altered such that they favor metabolic and cardiovascular complications, while patients have avoided hospital visits because of limited resources and the fear of infection, thereby increasing out-hospital mortality due to delayed diagnosis and treatment. Clinical observations show that sex, age, and race all influence the risk for SARS-CoV-2 infection, as do hypertension, obesity, and pre-existing cardiovascular conditions. Many hospitalized COVID-19 patients suffer cardiac injury, acute coronary syndromes, or cardiac arrhythmia. SARS-CoV-2 infection may lead to cardiomyocyte apoptosis and necrosis, endothelial cell damage and dysfunction, oxidative stress and reactive oxygen species production, vasoconstriction, fibrotic and thrombotic protein expression, vascular permeability and microvascular dysfunction, heart inflammatory cell accumulation and activation, and a cytokine storm. Current data indicate that COVID-19 patients with cardiovascular diseases should not discontinue many existing cardiovascular therapies such as ACE inhibitors, angiotensin receptor blockers, steroids, aspirin, statins, and PCSK9 inhibitors. This review aims to furnish a framework relating to COVID-19 and cardiovascular pathophysiology.

• Angiotensin-converting enzyme 2
• COVID-19
• Cardiovascular disease
• Hypertension
• Risk factor
• Severe acute respiratory syndrome coronavirus 2

• COVID-19
• SARS-CoV-2
• acute respiratory distress syndrome
• angiotensin-converting enzyme 2
• cardiovascular disease

• Angiotensin-Converting Enzyme 2/metabolism
• COVID-19/metabolism
• COVID-19/physiopathology
• Coronavirus Infections/metabolism
• Coronavirus Infections/physiopathology
• Endothelium, Vascular/metabolism
• Humans
• Renin-Angiotensin System
• SARS-CoV-2/metabolism

• MR/M010422/1 Medical Research Council

• COVID-19
• Computed tomography
• Coronavirus
• Diagnostic imaging
• Future trends
• Outcomes
• Pandemic
• Radiography

• COVID-19
• Heart Failure
• In-Hospital Mortality
• NT-proBNP