Coronavirus disease 2019 (COVID-19) is a newly recognized infectious disease that has spread rapidly throughout Wuhan, Hubei, China, to other provinces in China and several countries around the world. The number of fatalities owing to COVID-19 is escalating. Previous studies have described the general clinical characteristics and epidemiological findings of patients with COVID-19, and some of the clinical observations have shown that the condition of some patients with COVID-19 deteriorates rapidly.^1-4

Trained immunity refers to the long-term memory of the innate immune cells. However, little is known about how environmental nutrient availability influences trained immunity. This study finds that physiologic carbon sources impact glucose contribution to the tricarboxylic acid (TCA) cycle and enhance cytokine production of trained monocytes. Our experiments demonstrate that trained monocytes preferentially employe lactate over glucose as a TCA cycle substrate, and lactate metabolism is required for trained immune cell responses to bacterial and fungal infection. Except for the contribution to the TCA cycle, endogenous lactate or exogenous lactate also supports trained immunity by regulating histone lactylation. Further transcriptome analysis, ATAC-seq, and CUT&Tag-seq demonstrate that lactate enhance chromatin accessibility in a manner dependent histone lactylation. Inhibiting lactate-dependent metabolism by silencing lactate dehydrogenase A (LDHA) impairs both lactate fueled the TCA cycle and histone lactylation. These findings suggest that lactate is the hub of immunometabolic and epigenetic programs in trained immunity.

Cardiac arrhythmias and the resulting sudden cardiac death are significant cardiovascular complications that continue to impose a heavy burden on patients and society. An emerging body of evidence indicates that nonalcoholic fatty liver disease (NAFLD) is closely associated with the risk of cardiac arrhythmias, independent of other conventional cardiometabolic comorbidities. Although most studies focus on the relationship between NAFLD and atrial fibrillation, associations with ventricular arrhythmias and cardiac conduction defects have also been reported. Mechanistic investigations suggest that a number of NAFLD-related pathophysiological alterations may potentially elicit structural, electrical, and autonomic remodeling in the heart, contributing to arrhythmogenic substrates in the heart. NAFLD is now the most common liver and metabolic disease in the world. However, the upsurge in the prevalence of NAFLD as an emerging risk factor for cardiac arrhythmias has received little attention. In this review, we summarize the clinical evidence and putative pathophysiological mechanisms for the emerging roles of NAFLD in cardiac arrhythmias, with the purpose of highlighting the notion that NAFLD may serve as an independent risk factor and a potential driving force in the development and progression of cardiac arrhythmias.

(1) Background: In spite of the undeniable clinical value of the index of microvascular resistance (IMR) in assessing the status of coronary microcirculation, its use globally remains very low. The aim of this study was to validate the novel single-view, pressure-wire- and adenosine-free angiographic microvascular resistance (AMR) index, having the invasive wire-based IMR as a reference standard. (2) Methods: one hundred and sixty-three patients (257 vessels) were investigated with pressure wire-based IMR. Microvascular dysfunction (CMD) was defined by IMR ≥ 25. AMR was independently computed from the diagnostic coronary angiography in a blinded fashion. (3) Results: AMR demonstrated a good correlation (r = 0.83, p < 0.001) and diagnostic performance (AUC 0.94; 95% CI: 0.91 to 0.97) compared with wire-based IMR. The best cutoff value for AMR in determining IMR ≥ 25 was 2.5 mmHg*s/cm. The overall diagnostic accuracy of AMR was 87.2% (95% CI: 83.0% to 91.3%), with a sensitivity of 93.5% (95% CI: 87.0% to 97.3%), a specificity of 82.7% (95% CI: 75.6% to 88.4%), a positive predictive value of 79.4% (95% CI: 71.2% to 86.1%) and a negative predictive value of 94.7% (95% CI: 89.3% to 97.8%). No difference in terms of CMD rate was described among different clinical presentations. (4) Conclusions: AMR derived solely from a single angiographic view is a feasible computational alternative to pressure wire-based IMR, with good diagnostic accuracy in assessing CMD.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89–0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.