The Impact of Vaccination Time on the Antibody Response to an Inactivated Vaccine against SARS-CoV-2 (IMPROVE-2): A Randomized Controlled Trial.

There is still controversy about whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination at different times of day will induce a stronger immune response. Therefore, a randomized controlled trial (ChiCTR2100045109) is conducted to investigate the impact of vaccination time on the antibody response to the inactivated vaccine against SARS-CoV-2 from April 15 to 28, 2021. Participants are randomly assigned in a 1:1 ratio to receive inactivated SARS-CoV-2 vaccine in the morning or afternoon. The primary endpoint is the change of neutralizing antibody between baseline and 28 days after the second dose. In total, 503 participants are randomized, and 469 participants (238 in the morning group and 231 in the afternoon group) complete the follow-up. There is no significant difference in the change of neutralizing antibody between baseline and 28 days after the second dose between the morning and afternoon groups (22.2 [13.2, 45.0] AU mL-1  vs 22.0 [14.4, 40.7] AU mL-1 , P = 0.873). In prespecified age and sex subgroup analyses, there is also no significant difference in the morning and afternoon group (all P > 0.05). This study demonstrates that the vaccination time does not affect the antibody response of two doses of inactivated SARS-CoV-2 vaccine.

A fourth dose of the inactivated SARS-CoV-2 vaccine redistributes humoral immunity to the N-terminal domain.

The effectiveness of a 3rd dose of SARS-CoV-2 vaccines waned quickly in the Omicron-predominant period. In response to fast-waning immunity and the threat of Omicron variant of concern (VOC) to healthcare workers (HCWs), we conduct a non-randomized trial (ChiCTR2200055564) in which 38 HCWs volunteer to receive a homologous booster of inactivated vaccines (BBIBP-CorV) 6 months after the 3rd dose. The primary and secondary outcomes are neutralizing antibodies (NAbs) and the receptor-binding domain (RBD)-directed antibodies, respectively. The 4th dose recalls waned immunity while having distinct effects on humoral responses to different antigens. The peak antibody response to the RBD induced by the 4th dose is inferior to that after the 3rd dose, whereas responses to the N-terminal domain (NTD) of spike protein are further strengthened significantly. Accordingly, the 4th dose further elevates the peak level of NAbs against ancestral SARS-CoV-2 and Omicron BA.2, but not BA.1 which has more NTD mutations. No severe adverse events related to vaccination are recorded during the trial. Here, we show that redistribution of immune focus after repeated vaccinations may modulate cross-protective immune responses against different VOCs.

The impact of circadian rhythms on the immune response to influenza vaccination in middle-aged and older adults (IMPROVE): a randomised controlled trial.

BACKGROUND: Vaccination is important in influenza prevention but the immune response wanes with age. The circadian nature of the immune system suggests that adjusting the time of vaccination may provide an opportunity to improve immunogenicity. Our previous cluster trial in Birmingham suggested differences between morning and afternoon vaccination for some strains in the influenza vaccine in older adults. Whether this effect is also seen in a younger age group with less likelihood of compromised immunity is unknown. We therefore conducted an individual-based randomized controlled trial in Guangzhou to test the hypothesis that influenza vaccination in the morning induces a stronger immune response in older adults than afternoon vaccination. We included adults in middle age to determine if the effect was also seen in younger age groups. RESULTS: Of the 418 participants randomised, 389 (93.1%, 191 middle-aged adults aged 50-60 years and 198 older adults aged 65-75 years) were followed up. Overall, there was no significant difference between the antibody titers (geometric mean /95% CI) after morning vs afternoon vaccination (A/H1N1: 39.9 (32.4, 49.1) vs. 33.0 (26.7, 40.7), p = 0.178; A/H3N2: 92.2 (82.8, 102.7) vs. 82.0 (73.8, 91.2), p = 0.091; B: 15.8 (13.9, 17.9) vs. 14.4 (12.8, 16.3), p = 0.092), respectively. However, in pre-specified subgroup analyses, post-vaccination titers for morning versus afternoon vaccination in the 65-75 years subgroup were (A/H1N1): 49.5 (36.7, 66.6) vs. 32.9 (24.7, 43.9), p = 0.050; (A/H3N2): 93.5 (80.6, 108.5) vs. 73.1 (62.9, 84.9), p = 0.021; (B): 16.6 (13.8, 20.1) vs. 14.4 (12.3, 17.0), p = 0.095, respectively. Among females, antibody titers for morning versus afternoon vaccination were (A/H1N1): 46.9 (35.6, 61.8) vs. 31.1 (23.8, 40.7), p = 0.030; (A/H3N2): 96.0 (83.5, 110.3) vs. 84.7 (74.4, 96.5), p = 0.176; (B): 14.8 (12.7, 17.3) vs. 13.0 (11.3, 14.9), p = 0.061, respectively. In the 50-60 years old subgroup and males, there were no significant differences between morning and afternoon vaccination. CONCLUSIONS: Morning vaccination may enhance the immunogenicity to influenza vaccine in adults aged over 65 and women. An intervention to modify vaccination programs to vaccinate older individuals in the morning is simple, cost free and feasible in most health systems.

Effect of Inactivated SARS-CoV-2 Vaccine on Thyroid Function and Autoimmunity Within 28 Days After the Second Dose.

Background: The safety of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines is widely appreciated. However, there is limited knowledge regarding the potential impact of SARS-CoV-2 vaccines on the thyroid. Methods: We performed two prospective clinical trials between April and June, 2021, enrolling recipients of the inactivated SARS-CoV-2 vaccine (BBIBP-CorV and CoronaVac). Thyroid function, antithyroid antibody levels, and SARS-CoV-2 neutralizing antibody levels were detected for each participant before receiving the first vaccine dose and 28 days after receiving the second vaccine dose. Results: A total of 657 recipients participated in the study. The overall median thyroid function and levels of antithyroid antibodies before and after SARS-CoV-2 vaccination were within the normal range. Among the 564 participants with normal thyroid function at baseline, 36 (6.38% [confidence interval; CI 4.51-8.73]) developed thyroid dysfunction. Of the 545 recipients with negative antithyroid antibodies at baseline, none developed abnormal antibodies after vaccination. Notably, 75.27% (70/93 [CI 65.24-83.63]) of the 93 recipients with thyroid dysfunction returned to normal function after vaccination. The levels of antithyroid peroxidase antibody (96.20% [CI 89.30-99.21]) and antithyroglobulin antibody (TgAb; 88.31% [CI 78.97-94.51]) remained positive after vaccination in most patients with abnormal values at baseline. However, the TgAb levels in more than half of the patients (48/77) decreased. All of 11 abnormal thyrotropin receptor antibody levels at baseline decreased postvaccination. Conclusions: Vaccination with an inactivated SARS-CoV-2 vaccine had no significant adverse impact on thyroid function or antithyroid antibodies within the first 28 days after the second dose. Clinical Trial Registration: ChiCTR2100045109 and ChiCTR2100042222.

Robust induction of B cell and T cell responses by a third dose of inactivated SARS-CoV-2 vaccine.

SARS-CoV-2 inactivated vaccines have shown remarkable efficacy in clinical trials, especially in reducing severe illness and casualty. However, the waning of humoral immunity over time has raised concern over the durability of immune memory following vaccination. Thus, we conducted a nonrandomized trial among the healthcare workers (HCWs) to investigate the long-term sustainability of SARS-CoV-2-specific B cells and T cells stimulated by inactivated vaccines and the potential need for a third booster dose. Although neutralizing antibodies elicited by the standard two-dose vaccination schedule dropped from a peak of 29.3 arbitrary units (AU)/mL to 8.8 AU/mL 5 months after the second vaccination, spike-specific memory B and T cells were still detectable, forming the basis for a quick recall response. As expected, the faded humoral immune response was vigorously elevated to 63.6 AU/mL by 7.2 folds 1 week after the third dose along with abundant spike-specific circulating follicular helper T cells in parallel. Meanwhile, spike-specific CD4+ and CD8+ T cells were also robustly elevated by 5.9 and 2.7 folds respectively. Robust expansion of memory pools by the third dose potentiated greater durability of protective immune responses. Another key finding in this trial was that HCWs with low serological response to two doses were not truly "non-responders" but fully equipped with immune memory that could be quickly recalled by a third dose even 5 months after the second vaccination. Collectively, these data provide insights into the generation of long-term immunological memory by the inactivated vaccine, which could be rapidly recalled and further boosted by a third dose.

Nomogram for the prediction of diabetic nephropathy risk among patients with type 2 diabetes mellitus based on a questionnaire and biochemical indicators: a retrospective study.

PURPOSE: Develop a diabetic nephropathy incidence risk nomogram in a Chinese population with type 2 diabetes mellitus. RESULTS: Predictors included systolic blood pressure, diastolic blood pressure, fasting blood glucose, glycosylated hemoglobin A1c, total triglycerides, serum creatinine, blood urea nitrogen and body mass index. The model displayed medium predictive power with a C-index of 0.744 and an area under curve of 0.744. Internal verification of C-index reached 0.737. The decision curve analysis showed the risk threshold was 20%. The value of net reclassification improvement and integrated discrimination improvement were 0.131, 0.05, and that the nomogram could be applied in clinical practice. CONCLUSION: Diabetic nephropathy incidence risk nomogram incorporating 8 features is useful to predict diabetic nephropathy incidence risk in type 2 diabetes mellitus patients. METHODS: Questionnaires, physical examinations and biochemical tests were performed on 3489 T2DM patients in six communities in Shanghai. LASSO regression was used to optimize feature selection by running cyclic coordinate descent. Logistic regression analysis was applied to build a prediction model incorporating the selected features. The C-index, calibration plot, curve analysis, forest plot, net reclassification improvement, integrated discrimination improvement and internal validation were used to validate the discrimination, calibration and clinical usefulness of the model.

Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study.

OBJECTIVES: This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). METHODS: A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. RESULTS: Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. CONCLUSION: Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.

Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study.

PURPOSE: This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample. METHODS: We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model. RESULTS: The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice. CONCLUSION: This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.