Reduced Cholesterol Levels during Acute Human Babesiosis.

BACKGROUND: Babesiosis, an intra-erythrocytic protozoan disease, is an emerging zoonotic parasitic disease worldwide. Cholesterol levels are correlated with severe infections, such as sepsis and COVID-19, and anecdotal reports suggest that high-density lipoprotein (HDL) cholesterol declines during acute babesiosis. Our aim was to describe the cholesterol levels in patients with acute babesiosis diagnosed in an endemic area in New York, hypothesizing that HDL levels correlate with the severity of infection. METHODS: We reviewed the medical records of adult patients with babesiosis diagnosed by identification of Babesia parasites on a thin blood smear and confirmed by polymerase chain reaction from 2013 to 2018, who also had available a lipid profile drawn at the time of clinical presentation. Additional lipid profile levels were considered as "baseline" if they were drawn within 2 months before or after the infection as part of routine care. RESULTS: A total of 39 patients with babesiosis had a lipid profile drawn on presentation. The patients were divided into two groups for comparison based on the treating physician's clinical decision: 33 patients who were admitted to the hospital and 8 patients who were evaluated as outpatients. A history of hypertension was more common in admitted patients (37% vs. 17%, p = 0.02). The median levels of low-density lipoprotein (LDL) and HDL were significantly reduced in admitted patients compared to non-admitted patients (46 vs. 76 mg/dL, p = 0.04; and 9 vs. 28.5 mg/dL, p = 0.03, respectively). In addition, LDL and HDL levels returned to baseline values following resolution of acute babesiosis. CONCLUSION: LDL and HDL levels are significantly reduced during acute babesiosis, suggesting that cholesterol depletion may predict disease severity. Pathogen and host factors may contribute to a reduction in serum cholesterol levels during acute babesiosis.

Metabolic and Immune Markers for Precise Monitoring of COVID-19 Severity and Treatment.

Deep understanding of the SARS-CoV-2 effects on host molecular pathways is paramount for the discovery of early biomarkers of outcome of coronavirus disease 2019 (COVID-19) and the identification of novel therapeutic targets. In that light, we generated metabolomic data from COVID-19 patient blood using high-throughput targeted nuclear magnetic resonance (NMR) spectroscopy and high-dimensional flow cytometry. We find considerable changes in serum metabolome composition of COVID-19 patients associated with disease severity, and response to tocilizumab treatment. We built a clinically annotated, biologically-interpretable space for precise time-resolved disease monitoring and characterize the temporal dynamics of metabolomic change along the clinical course of COVID-19 patients and in response to therapy. Finally, we leverage joint immuno-metabolic measurements to provide a novel approach for patient stratification and early prediction of severe disease. Our results show that high-dimensional metabolomic and joint immune-metabolic readouts provide rich information content for elucidation of the host's response to infection and empower discovery of novel metabolic-driven therapies, as well as precise and efficient clinical action.

Profiling of immune dysfunction in COVID-19 patients allows early prediction of disease progression.

With a rising incidence of COVID-19-associated morbidity and mortality worldwide, it is critical to elucidate the innate and adaptive immune responses that drive disease severity. We performed longitudinal immune profiling of peripheral blood mononuclear cells from 45 patients and healthy donors. We observed a dynamic immune landscape of innate and adaptive immune cells in disease progression and absolute changes of lymphocyte and myeloid cells in severe versus mild cases or healthy controls. Intubation and death were coupled with selected natural killer cell KIR receptor usage and IgM+ B cells and associated with profound CD4 and CD8 T-cell exhaustion. Pseudo-temporal reconstruction of the hierarchy of disease progression revealed dynamic time changes in the global population recapitulating individual patients and the development of an eight-marker classifier of disease severity. Estimating the effect of clinical progression on the immune response and early assessment of disease progression risks may allow implementation of tailored therapies.