Brain injury, endothelial injury and inflammatory markers are elevated and express sex-specific alterations after COVID-19

Abstract

Objective: Although COVID-19 is a respiratory disease, all organs can be affected including the brain. To date, specific investigations of brain injury markers (BIM) and endothelial injury markers (EIM) have been limited. Additionally, a male bias in disease severity and mortality after COVID-19 is evident globally. Sex differences in the immune response to COVID-19 may mediate this disparity. We investigated BIM, EIM and inflammatory cytokine/chemokine (CC) levels after COVID-19 and in across sexes.

Methods: Plasma samples from 57 subjects at < 48 h of COVID-19 hospitalization, and 20 matched controls were interrogated for the levels of six BIMs-including GFAP, S100B, Syndecan-1, UCHLI, MAP2 and NSE, two EIMs-including sICAM1 and sVCAM1. Additionally, several cytokines/chemokines were analyzed by multiplex. Statistical and bioinformatics methods were used to measure differences in the marker profiles across (a) COVID-19 vs. controls and (b) men vs. women.

Results: Three BIMs: MAP2, NSE and S100B, two EIMs: sICAM1 and sVCAM1 and seven CCs: GRO IL10, sCD40L, IP10, IL1Ra, MCP1 and TNFα were significantly (p < 0.05) elevated in the COVID-19 cohort compared to controls. Bioinformatics analysis reveal a stronger positive association between BIM/CC/EIMs in the COVID-19 cohort. Analysis across sex revealed that several BIMs and CCs including NSE, IL10, IL15 and IL8 were significantly (p < 0.05) higher in men compared to women. Men also expressed a more robust BIM/ EIM/CC association profile compared to women.

Conclusion: The acute elevation of BIMs, CCs, and EIMs and the robust associations among them at COVID-19 hospitalization are suggestive of brain and endothelial injury. Higher BIM and inflammatory markers in men additionally suggest that men are more susceptible to the risk compared to women.

Keywords: Brain injury; COVID-19; Endothelial injury; Inflammation; SARS-CoV-2; Sex differences.

Fig. 1

Difference in BIM, EIM and CC profile across COVID-19 subjects and matched controls. A BIMs including MAP2, NSE and S100B were significantly (p < 0.05) higher immediately after COVID-19 hospitalization. B sICAM1 and SVCAM1 levels were significantly higher after COVID-19 hospitalization. C Several inflammatory markers including GRO, IL10, sCD40L, IL1RA, IP10, MCP1 and TNFa were significantly higher after COVID-19. MDC and MIP1a were significantly lower after COVID-19. D Chord diagram reveals significant (p < 0.05, Fishers’ test) differences in interactions between the BIM, EM, and CCs across COVID-19 and control groups

Fig. 2

Differences across severity and clinical outcomes. Differences across severity and clinical outcomes. A Difference in SICAM1, sCD40L and IL1RA across controls, mild, moderate and severe COVID groups. B Difference in CC levels across functional outcomes at discharge. EOTAXIN, MDC and MIP1b were significantly different across the good and the poor functional outcome groups. However, when adjusted for age and sex, only MIP1b was independently associated with outcomes

Fig. 3

Sex difference in BIM and CC profile across COVID-19 subjects. A NSE was significantly (p < 0.05) higher after COVID-19. S100b, tau and MAP2 were higher but differences were not statistically significant B IL10, IL15, IL8 and MIP1a were significantly (p < 0.05) in males compared to females. MIP1b, FGF2, GM-CSF and IL6 indicated a non-significant trend of being elevated in males. C The BIM–EM–CC interactions reveal that males have a much robust interactions compared to females. The results indicate that there was a robust association between the inflammatory-brain injury markers in ‘males’ compared to ‘females’. Males had 75 significant (p < 0.05) associations between the markers compared to females who only had 39 significant associations indicating a higher overall inflammatory-mediated brain injury marker mechanisms in male compared to females. The similarity index (SI) between two matrices is 0.71, a p-value < 0.0001 using the random skewers method