ArticleNutrients2025
Sex-Specific Dietary Predictors of Blood Glucose Identified Through Decision Tree Modeling in Adults.
Article in Nutrients, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesDiabetes 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.
methodsThis 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.
resultsIn 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.
conclusionsThe 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.