Search

AbstractBackground We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses.Methods For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient’s hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies.Results A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy.Conclusion COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

DNA methylation (DNAm) is vulnerable to dysregulation by environmental exposures during epigenetic reprogramming that occurs in embryogenesis. Sexual dimorphism in environmentally induced DNAm dysregulation has been identified and therefore it is important to understand sex-specific DNAm patterns. DNAm at several autosomal sites has been consistently associated with sex in cord blood and placental foetal tissues. However, there is limited research comparing sex-specific DNAm across tissues, particularly differentially methylated regions (DMRs). This study leverages DNAm data measured using the Illumina HumanMethylation450 BeadChip in cord blood (N = 179), placenta (N = 229), and umbilical artery samples (N = 229) in the PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort to identify autosomal DMRs and differentially methylated positions (DMPs). A replication analyses was conducted in an independent cohort (GEO Accession GSE129841). We identified 183, 257, and 419 DMRs and 2119, 2281, and 3405 DMPs (pBonferroni < 0.05) in cord blood, placenta, and artery samples, respectively. Thirty-nine DMRs overlapped in all three tissues, overlapping with genes involved in spermatogenesis (NKAPL, PIWIL2 and AURKC) and X–inactivation (LRIF1). In replication analysis, 85% of DMRs overlapped with those identified in PRISM. Overall, DMRs and DMPs had higher methylation levels among females in cord blood and artery samples, but higher methylation levels among males in placenta samples. Further research is necessary to understand biological mechanisms that contribute to differences in sex-specific DNAm signatures across tissues, as well as to determine if sexual dimorphism in the epigenome impacts response to environmental stressors.

Maternal stress is associated with adverse child health. Breast milk microRNAs encapsulated in extracellular vesicles (EVs) are involved in mother-infant biochemical communication during early-life programming. We leverage the PRogramming of Intergenerational Stress Mechanisms (PRISM) pregnancy cohort to investigate associations between maternal stress and breast milk EV-microRNAs. Lifetime stress and negative life events (NLEs) during pregnancy were assessed using the Life Stressor Checklist-Revised (LSCR) and the Crisis in Family Systems-Revised surveys, respectively. RNA was extracted from breast milk EVs (N = 80; collected 6.1 ± 5.9 weeks postnatally), and microRNAs were profiled using the TaqMan OpenArray Human miRNA panel. Associations between stress scores and detection (yes/no) of 173 microRNAs identified in 20–80% of samples were assessed using logistic regression; associations with expression levels of 205 EV-microRNAs identified in >50% of samples were assessed using linear regression. In adjusted models, detection of 60 and 44 EV-microRNAs was associated with higher LSCR and NLE scores, respectively (p 0.2. Enriched KEGG pathways for microRNAs associated with stress scores included fatty acid metabolism and the Hippo signaling pathway. Maternal lifetime stress and NLEs during pregnancy were both associated with detection and expression level of breast milk EV-microRNAs, although associations with microRNA profiles differed between stress measures. Further research is needed to identify biological pathways impacted by associated microRNAs and investigate relationships with child health outcomes. Abbreviations: EV: extracellular vesicle; PRISM: PRogramming of Intergenerational Stress Mechanisms pregnancy cohort; LSCR: Life Stressor Checklist-Revised survey; NLE: negative life event; CRISYS-R: Crisis in Family Systems-Revised survey; KEGG: Kyoto Encyclopaedia of Genes and Genomes; NYC: New York City; SD: standard deviation; IQR: interquartile range; Cq: relative cycle threshold values; PCA: principal component analysis