HbA1c% is the most commonly used metric for assessing glycemic status, but only represents an averaged, indirect measure. Continuous Glucose Monitors (CGM) provides additional, elaborated assessment metrics. A recent study, [Zeevi et al., 2015, Cell ] showed that glycemic responses to foods vary across individuals and can be predicted using a machine learning framework. Moreover, it showed that personally tailored diets based on this framework improves Postprandial Glycemic Response (PPGR) in one-week interventions. Here, we tested the ability of such framework to improve longer term measures of glycemic control. A cohort of 28 diabetic individuals, not using short term insulin, were tracked. Each participant was tested for initial HbA1c, and connected to a CGM for a period of 14 days. Then, participant's anthropometrics, lifestyle, clinical parameters, and gut microbiome composition, were fed into a machine learning algorithm built into a personalized mobile application. Using the application, participants could define meals by combining foods, and obtain instant scores indicating the predicted PPGR for each meal. Participants were instructed to limit consumption to highly scoring meals. After 4-20 months of using the application, participants were re-connected to CGM, and HbA1c% levels were measured again. Results: Significant improvements in multiple endpoints: Average HbA1c% dropped from 7.2% to 6.5% (p-value: 1.2e-8). Average %time-in-range [70,140] mg/dl increased from 69.1% to 79.6% (p-value: 0.005). Average %time-in-range [70,180] mg/dl increased from 89.6% to 94.2% (p-value: 0.002). Mean glucose levels decreased from 125.6 mg/dl to 114.6 mg/dl (p-value: 0.0002). These observations, coupled with the short-term benefits shown in recent work, imply that drugless, personalized, nutrition-based interventions, may be key to achieving significant improvements in glycemic control of type 2 diabetic patients. Disclosure Y. Ben Shlomo: Employee; Self; DayTwo. S. Azulay: Employee; Self; DayTwo. T. Raveh-Sadka: Employee; Self; DayTwo. Y. Cohen: Employee; Self; DayTwo. A. Hanemann: Employee; Self; DayTwo.

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Introduction: Throughout the SARS-CoV-2 pandemic, resources for various aspects of patient care have been limited, necessitating risk-stratification. The need for good risk-stratification tools has been enhanced by the availability of new Covid-19 therapeutics that are effective at preventing severe disease among high-risk patients if given promptly following SARS-CoV-2 infection. We describe the development of two points-based models for predicting the risk of deterioration to severe disease from an Omicron-variant SARS-CoV-2 infection. Methods: We developed two logistic regression-based models for predicting the risk of severe Covid-19 within a 21-days follow-up period among Clalit Health Services members aged 18 and older, with confirmed SARS-CoV-2 infection from December 25, 2021 to March 16, 2022. In the first model, aimed for the use of healthcare providers, the model coefficients were linearly transformed into integer risk points. In the second model, a simplified version designed for self-assessment by the general public, the risk points were further scaled down to smaller numbers with less variability across risk factors. Results: 613,513 individuals met the inclusion criteria, of which 1,763 (0.287%) developed the outcome. The AUROC estimates for both models were 0.95, although the 'full' model demonstrated more granular risk-stratification capabilities (77 vs. 27 potential thresholds on the test set). Both models proved effective in identifying small subsets of the population enriched with individuals who ended up deteriorating. For example, prioritizing the top 1%, 5% or 10% individuals in the population for interventions with the full model results in coverage of 36%, 68% or 83% (respectively) of the individuals that actually end up deteriorating. Risk point count increased with age, number of chronic conditions and previous hospitalizations, and decreased with recent vaccination and infection. Discussion: The models presented, one more expressive and one more accessible, are transparent and explainable models applicable to the general population that can be used in the prioritization of Covid-19-related resources, including therapeutics.