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Among more than 1.7 million persons, BNT162b2 vaccination was associated with increased risks of myocarditis (risk ratio, 3.24), lymphadenopathy, appendicitis, and herpes zoster infection; in comparison, Covid-19 increased the risks of myocarditis (risk ratio, 18.28), pericarditis, arrhythmia, deep-vein thrombosis, pulmonary embolism, myocardial infarction, intracranial hemorrhage, and thrombocytopenia.

REGEN-COV, a combination of the monoclonal antibodies casirivimab and imdevimab, has been approved as a treatment for high-risk patients infected with SARS-CoV-2 within five days of their diagnosis. We performed a retrospective cohort study, and used data repositories of Israel’s largest healthcare organization to determine the real-world effectiveness of REGEN-COV treatment against COVID-19-related hospitalization, severe disease, and death. We compared patients infected with Delta variant and treated with REGEN-COV (n = 289) to those infected but not-treated with REGEN-COV (n = 1,296). Demographic and clinical characteristics were used to match patients and for further adjustment as part of the C0x model. Estimated treatment effectiveness was defined as one minus the hazard ratio. Treatment effectiveness of REGEN-COV was 56.4% (95% CI: 23.7–75.1%) in preventing COVID-19 hospitalization, 59.2% (95% CI: 19.9–79.2%) in preventing severe COVID-19, and 93.5% (95% CI: 52.1–99.1%) in preventing COVID-19 death in the 28 days after treatment. In conclusion, REGEN-COV was effective in reducing the risk of severe sequelae in high-risk COVID-19 patients. REGEN-COV is a SARS-CoV-2 combined monoclonal antibody treatment which has been shown to be effective in randomised controlled trials. Here, the authors assess its real-world effectiveness using data from Israel during the Delta wave and find that it reduced the risk of hospitalisation, severe disease and death.

During the 2022 clinical rollout of nirmatrelvir in Israel, most patients (78%) had previous SARS-CoV-2 immunity. Benefit was seen in patients at highest risk for Covid-19 progression, such as those 65 years of age or older.

Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels. To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed. This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied. Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed. Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels. Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

REGEN-COV, a combination of the monoclonal antibodies casirivimab and imdevimab, has been approved as a treatment for high-risk patients infected with SARS-CoV-2 within 5 days of their diagnosis. We used the data repositories of Israel’s largest healthcare organization to determine the real-world effectiveness of REGEN-COV treatment against COVID-19 related hospitalization, severe disease, and death. We compared patients infected with the delta variant of SARS-CoV-2 and treated with REGEN-COV (n=289) to those infected but untreated with REGEN-COV (n=1,294). Patients were matched and further adjusted on demographic and clinical characteristics, with estimated treatment effectiveness defined as one minus the hazard ratio. Treatment effectiveness of REGEN-COV was 55.2% (95% CI: 21.5-74.5%) in preventing COVID-19 hospitalization, 59.4% (95% CI: 20.2-79.4%) in preventing severe COVID-19, and 93.8% (95% CI: 54.4-99.2%) in preventing COVID-19 death in the 28 days after treatment. In conclusion, REGEN-COV was effective in reducing the risk of severe sequelae in high-risk COVID-19 patients.