Impact of cardiometabolic multimorbidity and ethnicity on cardiovascular/renal complications in patients with COVID-19

Cardiometabolic risk management: insights from a European Society of Cardiology Cardiovascular Round Table

Metabolic comorbidities are common in patients with cardiorenal disease; they can cause atherosclerotic cardiovascular disease (ASCVD), speed progression, and adversely affect prognosis. Common comorbidities are Type 2 diabetes mellitus (T2DM), obesity/overweight, chronic kidney disease (CKD), and chronic liver disease. The cardiovascular system, kidneys, and liver are linked to many of the same risk factors (e.g. dyslipidaemia, hypertension, tobacco use, diabetes, and central/truncal obesity), and shared metabolic and functional abnormalities lead to damage throughout these organs via overlapping pathophysiological pathways. The COVID-19 pandemic has further complicated the management of cardiometabolic diseases. Obesity, T2DM, CKD, and liver disease are associated with increased risk of poor outcomes of COVID-19 infection, and conversely, COVID-19 can lead to worsening of pre-existing ASCVD. The high rates of these comorbidities highlight the need to improve recognition and treatment of ASCVD in patients with obesity, insulin resistance or T2DM, chronic liver diseases, and CKD and equally, to improve recognition and treatment of these diseases in patients with ASCVD. Strategies to prevent and manage cardiometabolic diseases include lifestyle modification, pharmacotherapy, and surgery. There is a need for more programmes at the societal level to encourage a healthy diet and physical activity. Many pharmacotherapies offer mechanism-based approaches that can target multiple pathophysiological pathways across diseases. These include sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, selective mineralocorticoid receptor antagonists, and combined glucose-dependent insulinotropic peptide/glucagon-like peptide-1 receptor agonist. Non-surgical and surgical weight loss strategies can improve cardiometabolic disorders in individuals living with obesity. New biomarkers under investigation may help in the early identification of individuals at risk and reveal new treatment targets.

Overview of the cardio-metabolic impact of the COVID-19 pandemic

Evidence has shown that cardiometabolic disorders (CMDs) are amongst the top contributors to COVID-19 infection morbidity and mortality. The reciprocal impact of COVID-19 infection and the most common CMDs, the risk factors for poor composite outcome among patients with one or several underlying diseases, the effect of common medical management on CMDs and their safety in the context of acute COVID-19 infection are reviewed. Later on, the changes brought by the COVID-19 pandemic quarantine on the general population's lifestyle (diet, exercise patterns) and metabolic health, acute cardiac complications of different COVID-19 vaccines and the effect of CMDs on the vaccine efficacy are discussed. Our review identified that the incidence of COVID-19 infection is higher among patients with underlying CMDs such as hypertension, diabetes, obesity and cardiovascular disease. Also, CMDs increase the risk of COVID-19 infection progression to severe disease phenotypes (e.g. hospital and/or ICU admission, use of mechanical ventilation). Lifestyle modification during COVID-19 era had a great impact on inducing and worsening of CMDs. Finally, the lower efficacy of COVID-19 vaccines was found in patients with metabolic disease.

SARS-CoV-2 antibody responses associate with sex, age and disease severity in previously uninfected people admitted to hospital with COVID-19: An ISARIC4C prospective study

The SARS-CoV-2 pandemic enables the analysis of immune responses induced against a novel coronavirus infecting immunologically naïve individuals. This provides an opportunity for analysis of immune responses and associations with age, sex and disease severity. Here we measured an array of solid-phase binding antibody and viral neutralising Ab (nAb) responses in participants (n=337) of the ISARIC4C cohort and characterised their correlation with peak disease severity during acute infection and early convalescence. Overall, the responses in a Double Antigen Binding Assay (DABA) for antibody to the receptor binding domain (anti-RBD) correlated well with IgM as well as IgG responses against viral spike, S1 and nucleocapsid protein (NP) antigens. DABA reactivity also correlated with nAb. As we and others reported previously, there is greater risk of severe disease and death in older men, whilst the sex ratio was found to be equal within each severity grouping in younger people. In older males with severe disease (mean age 68 years), peak antibody levels were found to be delayed by one to two weeks compared with women, and nAb responses were delayed further. Additionally, we demonstrated that solid-phase binding antibody responses reached higher levels in males as measured via DABA and IgM binding against Spike, NP and S1 antigens. In contrast, this was not observed for nAb responses. When measuring SARS-CoV-2 RNA transcripts (as a surrogate for viral shedding) in nasal swabs at recruitment, we saw no significant differences by sex or disease severity status. However, we have shown higher antibody levels associated with low nasal viral RNA indicating a role of antibody responses in controlling viral replication and shedding in the upper airway. In this study, we have shown discernible differences in the humoral immune responses between males and females and these differences associate with age as well as with resultant disease severity.

Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals

BACKGROUND

Although chronic kidney disease (CKD) is associated with high multimorbidity, polypharmacy, morbidity and mortality, existing classification systems (mild to severe, usually based on estimated glomerular filtration rate, proteinuria or urine albumin-creatinine ratio) and risk prediction models largely ignore the complexity of CKD, its risk factors and its outcomes. Improved subtype definition could improve prediction of outcomes and inform effective interventions.

METHODS

We analysed individuals ≥18 years with incident and prevalent CKD (n = 350,067 and 195,422 respectively) from a population-based electronic health record resource (2006-2020; Clinical Practice Research Datalink, CPRD). We included factors (n = 264 with 2670 derived variables), e.g. demography, history, examination, blood laboratory values and medications. Using a published framework, we identified subtypes through seven unsupervised machine learning (ML) methods (K-means, Diana, HC, Fanny, PAM, Clara, Model-based) with 66 (of 2670) variables in each dataset. We evaluated subtypes for: (i) internal validity (within dataset, across methods); (ii) prognostic validity (predictive accuracy for 5-year all-cause mortality and admissions); and (iii) medications (new and existing by British National Formulary chapter).

FINDINGS

After identifying five clusters across seven approaches, we labelled CKD subtypes: 1. Early-onset, 2. Late-onset, 3. Cancer, 4. Metabolic, and 5. Cardiometabolic. Internal validity: We trained a high performing model (using XGBoost) that could predict disease subtypes with 95% accuracy for incident and prevalent CKD (Sensitivity: 0.81-0.98, F1 score:0.84-0.97). Prognostic validity: 5-year all-cause mortality, hospital admissions, and incidence of new chronic diseases differed across CKD subtypes. The 5-year risk of mortality and admissions in the overall incident CKD population were highest in cardiometabolic subtype: 43.3% (42.3-42.8%) and 29.5% (29.1-30.0%), respectively, and lowest in the early-onset subtype: 5.7% (5.5-5.9%) and 18.7% (18.4-19.1%).

MEDICATIONS

Across CKD subtypes, the distribution of prescription medication classes at baseline varied, with highest medication burden in cardiometabolic and metabolic subtypes, and higher burden in prevalent than incident CKD.

INTERPRETATION

In the largest CKD study using ML, to-date, we identified five distinct subtypes in individuals with incident and prevalent CKD. These subtypes have relevance to study of aetiology, therapeutics and risk prediction.

FUNDING

AstraZeneca UK Ltd, Health Data Research UK.

Comorbidities, multimorbidity and COVID-19

The influence of comorbidities on COVID-19 outcomes has been recognized since the earliest days of the pandemic. But establishing causality and determining underlying mechanisms and clinical implications has been challenging-owing to the multitude of confounding factors and patient variability. Several distinct pathological mechanisms, not active in every patient, determine health outcomes in the three different phases of COVID-19-from the initial viral replication phase to inflammatory lung injury and post-acute sequelae. Specific comorbidities (and overall multimorbidity) can either exacerbate these pathological mechanisms or reduce the patient's tolerance to organ injury. In this Review, we consider the impact of specific comorbidities, and overall multimorbidity, on the three mechanistically distinct phases of COVID-19, and we discuss the utility of host genetics as a route to causal inference by eliminating many sources of confounding. Continued research into the mechanisms of disease-state interactions will be crucial to inform stratification of therapeutic approaches and improve outcomes for patients.

Cardiometabolic Risk after SARS-CoV-2 Virus Infection: A Retrospective Exploratory Analysis

OBJECTIVE

The aim of this study is to characterize the cardiometabolic risk of individuals who were infected with the SARS-CoV-2 virus and subsequently admitted to a hospital in a major city in mainland Portugal.

METHODS

This is a retrospective exploratory study using a sample of 102 patients, with data analysis including descriptive statistics, nonparametric measures of association between variables based on Spearman's rank-order correlation, a logistic regression model for predicting the likelihood that an individual might eventually pass away, and a multiple linear regression model to predict a likely increase in the number of days an infected patient remained in the hospital.

RESULTS

About 62.7% of the individuals required intensive care on the second day of hospitalization, remaining 14.2 days in the intensive care unit (ICU) on average. The likelihood that an individual might eventually pass away due to SARS-CoV-2 virus infection increases for the older than younger ones and increases even more if he/she suffers from cardiometabolic disorders such as obesity, especially cardiovascular disease. Older individuals and those with obesity and hypertension remained more days in the ICU.

CONCLUSIONS

A later age and the prevalence of cardiometabolic disorders severely affect the care pathway of individuals infected with the SARS-CoV-2 virus.

Cardiovascular considerations during the COVID-19 pandemic: A focused review for practice in Japan

The COVID-19 pandemic is continuing to have drastic consequences for patients, healthcare workers, and the health system. Its cardiovascular implications have been well described in previous studies, but original reports from Japan are sparse. Validating overseas findings in the Japanese clinical settings is crucial to improve local COVID-19 care and to clarify the pandemic's impacts in the country. This review of available literature demonstrates that in Japanese patients and clinical settings too, there is a close relationship between COVID-19 and the cardiovascular system including cardiovascular complications. On the contrary, secondary effects on cardiovascular practice including service disruptions, telemedicine, and epidemiological changes in Japan have been relatively small.