Document Type

Article

Publication Date

8-6-2026

Comments

This article is the author’s final published version in Nutrients, Volume 18, Issue 15, 2026, Article number 2578.

The published version is available at https://doi.org/10.3390/nu18152578. Copyright © 2026 by the author.

 

Abstract

Background: Type 2 diabetes is characterized by substantial interindividual variability in postprandial glucose responses, yet continuous glucose monitoring (CGM)-derived glucose patterns are commonly described using quantitative metrics without sufficient consideration of the physiological mechanisms that generate them. Objective: To develop a physiology-based framework for interpreting CGM-derived glycemic phenotypes in relation to dietary exposures and individual metabolic characteristics, thereby supporting precision nutrition in adults with type 2 diabetes. Methods: A structured narrative review of the literature was conducted using PubMed to identify studies examining CGM, postprandial glucose regulation, dietary exposures, metabolic physiology, and precision nutrition. Evidence was synthesized within a predefined conceptual framework linking dietary exposures, underlying physiology, individual metabolic characteristics, CGM-derived glycemic phenotypes, and their clinical interpretation. Results: Postprandial glucose responses reflect coordinated interactions among nutrient digestion and absorption, hormonal regulation, hepatic glucose production, peripheral glucose disposal, and individual metabolic characteristics rather than dietary carbohydrate exposure alone. The proposed framework interprets recurring CGM-derived glucose patterns as physiologically meaningful glycemic phenotypes, including characteristic patterns of postprandial excursion, recovery, baseline glycemia, and glycemic variability. This framework provides a structured approach for physiology-informed interpretation of CGM observations and individualized nutritional assessment. Although direct evidence supporting phenotype-guided nutritional interventions and long-term clinical benefit remains limited, emerging computational approaches may further strengthen this interpretive process by integrating CGM with complementary biological and behavioral data. Conclusions: Rather than viewing CGM solely as a technology for measuring glucose, the proposed framework positions it as a physiological lens through which dietary exposures, metabolic regulation, and individual metabolic characteristics can be integrated to support precision nutrition in type 2 diabetes.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

PubMed ID

42588201

Language

English

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