Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. However, the role of the gut microbiota in regulating these responses has not been thoroughly investigated. In order to identify mechanisms underpinning microbiota interactions with host immune and metabolic systems that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalized COVID-19 patients and compared those with the most severe outcome (i.e. death, n = 41) to those with severe non-fatal disease (n = 89), or mild/moderate disease (n = 42), that recovered. A distinct subset of 8 cytokines (e.g. TSLP) and 140 metabolites (e.g. quinolinate) in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the fecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts, such as Enterococcus . In contrast, less severe clinical outcomes are associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus , short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.

Previous experimental studies showed that increasing high-density lipoprotein cholesterol (HDL) cholesterol shortens cardiac ventricular repolarization and the QT interval corrected for heart rate (QTc). However, little is known about the epidemiological relationship between HDL and QTc. The potential antiarrhythmic effect of HDL cholesterol remains a speculative hypothesis. In this cross-sectional population based study in adults living in the Italian-speaking part of Switzerland, we aimed to explore the association between HDL cholesterol and the QTc interval in the general population. A total of 1202 subjects were screened. electrocardiogram (ECG) recordings, measurements of lipid parameters and other laboratory tests were performed. QTc was corrected using Bazett’s (QTcBaz) and Framingham (QTcFram) formulas. HDL was categorized according to percentile distributions: <25th (HDL-1; ≤1.39 mmol/L); 25th–<50th (HDL-2; 1.40–1.69 mmol/L); 50th–<75th (HDL-3; 1.69–1.99 mmol/L); and ≥75th (HDL-4; ≥2.0 mmol/L). After exclusion procedures, data of 1085 subjects were analyzed. Compared with the HDL reference group (HDL-1), HDL-2 and HDL-3 were associated with a reduction of QTcBaz and QTcFram duration in crude (HDL-2, QTcBaz/QTcFram: β-11.306/–10.186, SE 4.625/4.016; p = 0.016/0.012; HDL-3, β-12.347/–12.048, SE 4.875/4.233, p = 0.012/<0.001) and adjusted (HDL-2: β-11.697/–10.908, SE 4.333/4.151, p < 0.001/0.010; HDL-3 β-11.786/–11.002, SE 4.719/4.521, p = 0.014/0.016) linear regression models in women. In adjusted logistic regression models higher HDL, were also associated with lower risk of prolonged QTcBaz/QTcFram (HDL-2: OR 0.16/0.17, CI 0.03–0.83/0.47–0.65; HDL-3: OR 0.10/0.14, CI 0.10–0.64/0.03–0.63) in women. Restricted cubic spline analysis confirmed a non linear association (p < 0.001). The present findings indicate an epidemiological association between HDL cholesterol and QTc duration. To draw firm conclusions, further investigations in other populations and with a prospective cohort design are needed.

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. In order to understand potential mechanisms and interactions that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalised COVID-19 patients and compared those with the most severe outcome (i.e. death) to those with severe non-fatal disease, or mild/moderate disease, that recovered. A distinct subset of 8 cytokines and 140 metabolites in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the faecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts. In contrast, less severe clinical outcomes associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus, short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.