Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs.

Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs.

Changes in Food Insecurity Among Individuals Using a Telehealth and Nutrition Platform: Longitudinal Study.

Background: Food insecurity is a complex public health problem affecting many individuals in the United States. Digital health interventions that promote behavior change and provide access to affordable and healthy food may help to alleviate food insecurity.

Objective: The aim of this study was to characterize food-insecure users of Foodsmart, a telehealth and nutrition platform with meal planning, food ordering, nutrition education, budgeting, and grocery discount features, and to evaluate changes in diet and food insecurity.

Blood Pressure Control in Individuals With Hypertension Who Used a Digital, Personalized Nutrition Platform: Longitudinal Study.

Background: While there is a strong association between adhering to a healthy dietary pattern and reductions in blood pressure, adherence remains low. New technologies aimed to help facilitate behavior change may have an effect on reducing blood pressure among individuals with hypertension.

Objective: This study aims to evaluate characteristics of participants with stage 2 hypertension who used Foodsmart and to assess changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP).

Changes in Glycemic Control Among Individuals With Diabetes Who Used a Personalized Digital Nutrition Platform: Longitudinal Study.

Background: Diabetes-related costs are the highest across all chronic conditions in the United States, with type 2 diabetes accounting for up to 95% of all cases of diabetes. A healthy diet is strongly associated with lowering glycated hemoglobin A (HbA) levels among individuals with diabetes, which can help curtail other health complications. Digital health platforms can offer critical support for improving diet and glycemic control among individuals with diabetes.