Julian Chan

Background/Objectives: Diabetes mellitus is a global public health crisis, with cases projected to rise to 1.3 billion by 2050. Lifestyle interventions are crucial in preventing and managing Type 2 diabetes. This study used a machine learning approach to explore the relationship between dietary components and fasting blood glucose in a young adult population, with a focus on potential sex-specific differences. Methods: This cross-sectional study analyzed data from 288 young adults (195 females, 93 males; mean age 23 years). Participants provided two-day diet records, and their fasting capillary blood glucose was measured. Machine learning was used to predict blood glucose based on a variety of dietary variables, including fiber, macronutrient proportions, and fat types. Energy expenditure was used as a proxy for energy intake. Models were created for the overall population, males, and females. Results: In the overall population, the most important predictor of fasting blood glucose was fiber intake. For females, the most important predictor was energy expenditure, followed by fat quality (linoleic to alpha-linolenic acid ratio and saturated fat intake). For males, the most predictive factor was the percentage of calories from fat, followed by alpha-linolenic acid intake. Conclusions: The findings suggest that predictors of blood glucose differ between males and females, highlighting the need for sex-specific strategies in blood glucose management. The models emphasize the importance of increasing fiber intake, maintaining a healthy energy intake, and improving fat quality by prioritizing essential fatty acids. This approach can be used to inform personalized dietary recommendations for the prevention and management of diabetes.

Multiple studies have demonstrated strong links between diet and anemia, but few have explored the impact of food groups on hemoglobin (Hb). We analyzed the correlation between Ghanaian diet and Hb levels to explore reduction of anemia prevalence through dietary interventions. Demographics, food frequency questionnaires (FFQ), and blood samples were obtained from 140 volunteers (ages 18–65) in five locations across Ghana. Hb was measured; estimated iron consumption was calculated. FFQ items were grouped by food type, and a regression analysis was performed to determine the most important dietary predictors of Hb. Moreover, 47% of total participants were anemic; 64% of females and 28% of males. Hb levels were highest in Mole (13.9 g/dL, SD = ±1.9), independent of sex distribution. The regression model revealed a 62.7% adjusted correlation between food groups and Hb levels. Animal foods (β = 0.016, t = 5.08, p < 0.01) and plant protein (β = 0.013, t = 2.86, p < 0.01) were the most influential groups to Hb levels. It is of vital importance to emphasize the benefits of consuming animal foods and plant proteins within the Ghanaian population. The ease of access to plant proteins makes it likely that this food group will be most influential and have the greatest impact in reduction of anemia in the Ghanaian population.

MyPlate is a guidance system for healthier eating choices. In this cross-sectional study, we investigated the influence of MyPlate food group consumption and exercise on metabolic syndrome (MetS) parameters in college students. Participant (n = 462) blood was analyzed using Cholestech for triglycerides (TG), glucose, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). In addition, weight, waist circumference (WC), and blood pressure (BP) were measured. Diet and Wellness Plus was used to compute participant diet records. Regression analysis and a recursive decision tree were made to predict MetS using RStudio (V.1.1.463). BP decision tree predicted high risk of elevated blood pressure with a recall rate of 93.7%. For males; exercise, empty calories, dairy, and protein were main predictors. For females, vegetable and empty calorie consumption were primary determinants. HDL-C decision tree had a recall rate of 91.8% and showed that the main low HDL-C risk determinants for males were; exercise and grain consumption. Conversely, for females; empty calories, grain, and vegetable consumption were the key factors determining low HDL-C risk. This study shows that MyPlate recommendations are valuable to achieve adequate HDL-C and blood pressure and provides insight into the importance of tailoring food intake guidance based on gender.

This article introduces a new condition based on odds ratios for sensitivity analysis. The analysis involves the average effect of a treatment or exposure on a response or outcome with estimates adjusted for and conditional on a single, unmeasured, dichotomous covariate. Results of statistical simulations are displayed to show that the odds ratio condition is as reliable as other commonly used conditions for sensitivity analysis. Other conditions utilize quantities reflective of a mediating covariate. The odds ratio condition can be applied when the covariate is a confounding variable. As an example application we use the odds ratio condition to analyze and interpret a positive association observed between Zika virus infection and birth defects.