Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 infection induces an exacerbated inflammation driven by innate immunity components. Dendritic cells (DCs) play a key role in the defense against viral infections, for instance plasmacytoid DCs (pDCs), have the capacity to produce vast amounts of interferon-alpha (IFN-α). In COVID-19 there is a deficit in DC numbers and IFNα production, which has been associated with disease severity. In this work, we described that in addition to the DC deficiency, several DC activation and homing markers were altered in acute COVID-19 patients, which were associated with multiple inflammatory markers. Remarkably, previously hospitalized and nonhospitalized patients remained with decreased numbers of CD1c+ myeloid DCs and pDCs seven months after SARS-CoV-2 infection. Moreover, the expression of DC markers such as CD86 and CD4 were only restored in previously nonhospitalized patients, while no restoration of integrin β7 and indoleamine 2,3-dyoxigenase (IDO) levels were observed. These findings contribute to a better understanding of the immunological sequelae of COVID-19.

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The aim was to assess the ability of nasopharyngeal SARS-CoV-2 viral load at first patient’s hospital evaluation to predict unfavorable outcomes. We conducted a prospective cohort study including 321 adult patients with confirmed COVID-19 through RT-PCR in nasopharyngeal swabs. Quantitative Synthetic SARS-CoV-2 RNA cycle threshold values were used to calculate the viral load in log10 copies/mL. Disease severity at the end of follow up was categorized into mild, moderate, and severe. Primary endpoint was a composite of intensive care unit (ICU) admission and/or death (n = 85, 26.4%). Univariable and multivariable logistic regression analyses were performed. Nasopharyngeal SARS-CoV-2 viral load over the second quartile (≥ 7.35 log10 copies/mL, p = 0.003) and second tertile (≥ 8.27 log10 copies/mL, p = 0.01) were associated to unfavorable outcome in the unadjusted logistic regression analysis. However, in the final multivariable analysis, viral load was not independently associated with an unfavorable outcome. Five predictors were independently associated with increased odds of ICU admission and/or death: age ≥ 70 years, SpO2, neutrophils > 7.5 × 103/µL, lactate dehydrogenase ≥ 300 U/L, and C-reactive protein ≥ 100 mg/L. In summary, nasopharyngeal SARS-CoV-2 viral load on admission is generally high in patients with COVID-19, regardless of illness severity, but it cannot be used as an independent predictor of unfavorable clinical outcome.

BACKGROUND: Maximal doses of potent statins are the basement of treatment of familial hypercholesterolemia (FH). Little is known about the use of different statin regimens in FH. OBJECTIVES: The objectives of the study were to describe the treatment changes and low-density lipoprotein cholesterol (LDL-C) goal achievement with atorvastatin (ATV) and rosuvastatin (RV) in the SAFEHEART cohort, as well as to analyze the incidence of atherosclerotic cardiovascular events (ACVEs) and changes in the cardiovascular risk. METHODS: SAFEHEART is a prospective follow-up nationwide cohort study in a molecularly defined FH population. The patients were contacted on a yearly basis to obtain relevant changes in life habits, medication, and ACVEs. RESULTS: A total of 1939 patients were analyzed. Median follow-up was 6.6 years (5-10). The estimated 10-year risk according the SAFEHEART risk equation was 1.61 (0.67-3.39) and 1.22 (0.54-2.93) at enrollment for ATV and RV, respectively (P , .001). There were no significant differences at the follow-up: 1.29 (0.54-2.82) and 1.22 (0.54-2.76) in the ATV and RV groups, respectively (P 5 .51). Sixteen percent of patients in primary prevention with ATV and 18% with RV achieved an LDL-C ,100 mg/dL and 4% in secondary prevention with ATV and 5% with RV achieved an LDL-C ,70 mg/dL. The use of ezetimibe was marginally greater in the RV group. One hundred sixty ACVEs occurred during follow-up, being its incidence rate 1.1 events/100 patient-years in the ATV group and 1.2 in the RV group (P 5 .58). CONCLUSION: ATV and RV are 2 high-potency statins widely used in FH. Although the reduction in LDL-C levels was greater with RV than with ATV, the superiority of RV for reducing ACVEs was not demonstrated.

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Atherosclerotic cardiovascular diseases (ASCVD) are the leading cause of morbidity and mortality in Western societies. Statins are the first-choice therapy for dislipidemias and are considered the cornerstone of ASCVD. Statin-associated muscle symptoms are the main reason for dropout of this treatment. There is an urgent need to identify new biomarkers with discriminative precision for diagnosing intolerance to statins (SI) in patients. MicroRNAs (miRNAs) have emerged as evolutionarily conserved molecules that serve as reliable biomarkers and regulators of multiple cellular events in cardiovascular diseases. In the current study, we evaluated plasma miRNAs as potential biomarkers to discriminate between the SI vs. non-statin intolerant (NSI) population. It is a multicenter, prospective, case-control study. A total of 179 differentially expressed circulating miRNAs were screened in two cardiovascular risk patient cohorts (high and very high risk): (i) NSI (n = 10); (ii) SI (n = 10). Ten miRNAs were identified as being overexpressed in plasma and validated in the plasma of NSI (n = 45) and SI (n = 39). Let-7c-5p, let-7d-5p, let-7f-5p, miR-376a-3p and miR-376c-3p were overexpressed in the plasma of SI patients. The receiver operating characteristic curve analysis supported the discriminative potential of the diagnosis. We propose a three-miRNA predictive fingerprint (let-7f, miR-376a-3p and miR-376c-3p) and several clinical variables (non-HDLc and years of dyslipidemia) for SI discrimination; this model achieves sensitivity, specificity and area under the receiver operating characteristic curve (AUC) of 83.67%, 88.57 and 89.10, respectively. In clinical practice, this set of miRNAs combined with clinical variables may discriminate between SI vs. NSI subjects. This multiparametric model may arise as a potential diagnostic biomarker with clinical value.