Effect of Moderate to Severe Hepatic Steatosis on Vaccine Immunogenicity against Wild-Type and Mutant Virus and COVID-19 Infection among BNT162b2 Recipients.

BACKGROUND: We aimed to investigate the effect of non-alcoholic fatty liver disease (NAFLD) on BNT162b2 immunogenicity against wild-type SARS-CoV-2 and variants and infection outcome, as data are lacking. METHODS: Recipients of two doses of BNT162b2 were prospectively recruited. Outcomes of interest were seroconversion of neutralizing antibody by live virus microneutralization (vMN) to SARS-CoV-2 strains (wild-type, delta and omicron variants) at day 21, 56 and 180 after first dose. Exposure of interest was moderate-to-severe NAFLD (controlled attenuation parameter ≥ 268 dB/M on transient elastography). We calculated adjusted odds ratio (aOR) of infection with NAFLD by adjusting for age, sex, overweight/obesity, diabetes and antibiotic use. RESULTS: Of 259 BNT162b2 recipients (90 (34.7%) male; median age: 50.8 years (IQR: 43.6-57.8)), 68 (26.3%) had NAFLD. For wild type, there was no difference in seroconversion rate between NAFLD and control groups at day 21 (72.1% vs. 77.0%; p = 0.42), day 56 (100% vs. 100%) and day 180 (100% and 97.2%; p = 0.22), respectively. For the delta variant, there was no difference also at day 21 (25.0% vs. 29.5%; p = 0.70), day 56 (100% vs. 98.4%; p = 0.57) and day 180 (89.5% vs. 93.3%; p = 0.58), respectively. For the omicron variant, none achieved seroconversion at day 21 and 180. At day 56, there was no difference in seroconversion rate (15.0% vs. 18.0%; p = 0.76). NAFLD was not an independent risk factor of infection (aOR: 1.50; 95% CI: 0.68-3.24). CONCLUSIONS: NAFLD patients receiving two doses of BNT162b2 had good immunogenicity to wild-type SARS-CoV-2 and the delta variant but not the omicron variant, and they were not at higher risk of infection compared with controls.

COVID-19 vaccine immunogenicity among chronic liver disease patients and liver transplant recipients: A meta-analysis.

BACKGROUND/AIMS: Data of coronavirus disease 2019 (COVID-19) vaccine immunogenicity among chronic liver disease (CLD) and liver transplant (LT) patients are conflicting. We performed meta-analysis to examine vaccine immunogenicity regarding etiology, cirrhosis status, vaccine platform and type of antibody. METHODS: We collected data via three databases from inception to February 16, 2022, and reported pooled seroconversion rate, T cell response and safety data after two vaccine doses. RESULTS: Twenty-eight (CLD only: 5; LT only: 18; both: 2; LT with third dose: 3) observational studies of 3,945 patients were included. For CLD patients, seroconversion rate ranged between 84% (95% confidence interval [CI], 76-90%) and 91% (95% CI, 83-95%), based predominantly on neutralizing antibody and anti-spike antibody, respectively. Seroconversion rate was 81% (95% CI, 76-86%) in chronic hepatitis B, 96% (95% CI, 93-97%) in non-alcoholic fatty liver disease, 85% (95% CI, 75-91%) in cirrhosis and 85% (95% CI, 78-90%) in non-cirrhosis, 86% (95% CI, 78-92%) for inactivated vaccine and 89% (95% CI, 71-96%) for mRNA vaccine. The pooled seroconversion rate of anti-spike antibody was 66% (95% CI, 55-75%) after two doses of mRNA vaccines and 88% (95% CI, 58-98%) after third dose among LT recipients. T cell response rate was 65% (95% CI, 30-89%). Prevalence of adverse events was 27% (95% CI, 18-38%) and 63% (95% CI, 39-82%) among CLD and LT groups, respectively. CONCLUSION: CLD patients had good humoral response to COVID-19 vaccine, while LT recipients had lower response.

Effect of moderate-to-severe hepatic steatosis on neutralising antibody response among BNT162b2 and CoronaVac recipients.

BACKGROUND/AIMS: Studies of hepatic steatosis (HS) effect on COVID-19 vaccine immunogenicity are lacking. We aimed to compare immunogenicity of BNT162b2 and CoronaVac among moderate/severe HS and control subjects. METHODS: Two hundred ninety-five subjects who received BNT162b2 or CoronaVac vaccines from five vaccination centers were categorized into moderate/severe HS (controlled attenuation parameter ≥268 dB/m on transient elastography) (n=74) or control (n=221) groups. Primary outcomes were seroconversion rates of neutralising antibody by live virus Microneutralization (vMN) assay (titer ≥10) at day21 (BNT162b2) or day28 (CoronaVac) and day56 (both). Secondary outcome was highest-tier titer response (top 25% of vMN titer; cutoff: 160 [BNT162b2] and 20 [CoronaVac]) at day 56. RESULTS: For BNT162b2 (n=228, 77.3%), there was no statistical differences in seroconversion rates (day21: 71.7% vs. 76.6%; day56: 100% vs. 100%) or vMN geometric mean titer (GMT) (day21: 13.2 vs. 13.3; day56: 91.9 vs. 101.4) among moderate/severe HS and control groups respectively. However, lower proportion of moderate/severe HS patients had highest-tier response (day56: 5.0% vs. 15.5%; P=0.037). For CoronaVac (n=67, 22.7%), there was no statistical differences in seroconversion rates (day21: 7.1% vs. 15.1%; day56: 64.3% vs. 83.0%) or vMN GMT (5.3 vs. 5.8,) at day28. However, moderate/severe HS patients had lower vMN GMT (9.1 vs. 14.8, P=0.021) at day 56 with lower proportion having highest-tier response (21.4% vs. 52.8%, P=0.036). CONCLUSION: While there was no difference in seroconversion rate between moderate/severe HS and control groups after two doses of vaccine, a lower proportion of moderate/severe HS patients achieved highest-tier response for either BNT162b2 or CoronaVac.

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. METHODS: Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. RESULTS: The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. CONCLUSIONS: Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.