Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

(Qin et al., March 2020 pre-print)

Summary

Blood lymphocyte subsets were characterized in 452 severe and non-severe patients treated for COVID19 in Wuhan. A higher percentage of hypertension and cardiovascular disease was observed in the 286 patients with severe disease. Significantly elevated neutrophil-to-lymphocyte ratio and increased concentrations of IL-6, IL-8, IL-10 and TNFa was demonstrated as in previous smaller studies. Flow cytometry on cells from 44 of these patients showed no difference in CD8+ T cells or IFNg expression, but a decrease in memory CD4+ T cells regulatory T cells. These data corroborate the potential use of biologics (e.g. anti-IL-6) in severe COVID19, but do have limitations.

Research highlights

1. Severe patients were older but no significant gender effect

2. 44% of all 452 cases had chronic conditions. Severe patients were more likely to have hypertension and cardiovascular disease. However, no major predisposing condition identified

3. Severe (286/452) vs non-severe cases:

a. ↑ WBC and neutrophil lymphopenia ratio

b. ↑ infection-related markers: procalcitonin, serum ferritin and CRP

c. ↑ IL-2R, IL-6, IL-8, IL-10 and TNFa (assume serum but doesn’t say)

d. ↓ percentage of lymphocytes, monocytes, eosinophils, and basophils

e. ↑ percentage and total number of neutrophils

4. “Lymphocyte subsets” analysed in 44 patients

a. ↓ numbers of T cells and NK cells (greater ↓ in severe patients) – only significant for percentages not number of cells per ul

b. ↓ percentage of memory T cells and Tregs within the T cell compartment

c. No significant difference in

i. B cells (trend towards decrease)

ii. CD8+ T cells

iii. % IFNg-expressing CD4+, CD8+ T cells or NK cells

Research Impact

  • Most striking finding is increased numbers of neutrophils. It will be of interest to determine whether these cells show in increased level of activity, e.g. increased propensity to undergo netosis. Raises questions about contribution of neutrophils to pathogenesis of disease.
  • Elevated pro-inflammatory cytokines such as IL-6 could be targeted with biologics to counteract the cytokine storm

Methodology

· Retrospective study using blood collected from 452 laboratory confirmed COVID19 patients between January 10 to February 12, 2020 (286 had severe infection)

· Severe COVID19 cases counted as:

1) Respiratory distress with the respiratory rate over 30 per minute

2) Oxygen saturation ≤ 93% in the resting state

3) Arterial blood oxygen partial pressure (PaO2) / oxygen concentration (FiO2) ≤300mmHg

· Metadata: demographics, medical history, symptoms, signs and laboratory findings

· “Laboratory tests” for lymphocyte counts, infection markers and cytokine readouts, and flow cytometry for analysis of “T cell subsets”

Strengths and weaknesses of the paper

Strengths:

  • Relatively large number of patients compared to other studies
  • Done flow cytometry on T cell subsets as opposed to just looking at overall numbers which other studies have done

Limitations:

  • Just peripheral blood (no BAL or stool)
  • Single-centre study
  • A single cross-sectional sample
  • No healthy controls analysed alongside for comparison just shows the normal healthy range
  • No gating strategies, representative plots or graphs, all data shown in tables. Confidence intervals reported but difficult to visualise data spread
  • Don’t state what assays were used to measure infection-related biomarkers e.g. Luminex?
  • Limited selection of cytokines probed in sera (e.g. no Th17 or Th2 cytokines), or selective reporting of significant ones
  • Only show IFNg after PMA/ionomycin stimulation
  • Show % and number per ul of cells for some subsets, but then just one readout for some

Other points of note:

  • “T cell subsets” is slightly misleading because they look primarily at naïve vs memory, Tregs and activation state by unconventional markers, no transcription factors, prototypical cytokines or chemokine receptors for example to distinguish TH1/2/17 for example. They refer to CD8+ T cells as “suppressor” T cells, which is unclear.
  • Why looking at CD28 and HLA-DR for activation and markers of cytotoxicity? Normally you would look at CD25 or CD69 as activation markers
  • Conclusion that “COVID-19 might mainly act on lymphocytes, especially T lymphocytes” is not justified on evidence of data presented
  • Correlation of immune phenotype findings with clinical symptoms were not performed (e.g. fever)
  • Time-points of sample analysis compared with state of symptoms could add valuable information regarding surrogate and/or predictive markers for prognosis and disease progression