Differentiating Physical Attributes of Low-Carbohydrate and High-Carbohydrate Diets Through Food Nutrition Segmentation: A GH-Method Study
Mengmeng Pan* and Junyan Huang
Abstract
This study explores the physical characteristics of low-carbohydrate (LC) and high-carbohydrate (HC) meals using a data-driven analytical approach based on the GH-Method: Math-Physical Medicine (MPM). By employing food nutrition segmentation analysis, we assess key physical factors such as caloric density, macronutrient distribution, and postprandial metabolic impact. A comparative dataset of common LC and HC meals was evaluated, revealing distinct differences in fat, protein, and carbohydrate contents as well as total caloric load. The findings highlight how specific macronutrient configurations influence the metabolic burden and may offer insights for personalized dietary planning, especially in metabolic conditions such as diabetes and obesity. This research contributes to a deeper understanding of diet architecture using physical and mathematical modeling, supported by current literature up to 2024.