Rising global rates of overweight and obesity are linked to an increasing prevalence of hyperglycaemia. Although nutritional interventions based on various healthy dietary patterns serve as first-line therapies to improve glycaemic control and have demonstrated efficacy at the population level in certain studies, a significant proportion of individuals still fail to achieve an adequate outcome. This limited effectiveness may be attributed to several factors. First, conventional dietary recommendations often adopt a “one-size-fits-all” approach, neglecting the considerable inter-individual heterogeneity in postprandial glucose responses to the same foods. Second, the absence of immediate feedback on how dietary changes affect glycaemic levels may hinder long-term behavioural adherence. Recent advances in continuous glucose monitoring (CGM) have uncovered pronounced variability in postprandial glycaemic responses among both diabetic and healthy individuals. Nevertheless, gaps persist in the comprehensive characterisation of this heterogeneity across diverse populations, as well as in the elucidation of its underlying mechanisms and biomarkers. Leveraging CGM-driven insights into individual glycaemic variability and providing real-time feedback present an opportunity to tailor dietary patterns into personalized nutrition strategies. Such an approach could enhance both glycaemic outcomes and long-term adherence, ultimately allowing each individual to adopt and maintain a dietary regimen that delivers optimal metabolic benefits.

This Research Topic aims to address the critical gap between generalised dietary guidelines and the significant inter-individual variability in glycaemic responses and long-term adherence observed across different dietary patterns. We seek to investigate how personalised, continuous glucose monitoring (CGM)-informed nutritional recommendations can improve both glycaemic control and behavioural adherence. Furthermore, this topic will explore predictive biomarkers, CGM-derived phenotypes, and psychosocial factors that contribute to heterogeneous postprandial glucose excursions. By integrating multi-omics, wearable sensors, and machine learning, we aim to develop scalable, evidence-based personalised nutrition algorithms. These tools will assist individuals and clinicians in selecting, tailoring, and sustaining optimal dietary strategies to improve long-term glyceamic management and reduce the global burden of diabetes-related chronic diseases.

This Research Topic welcomes original research, reviews, and clinical trials addressing personalised nutrition across the dysglycaemia spectrum—including insulin resistance, overweight, obesity, prediabetes, type 1 diabetes (T1D), type 2 diabetes (T2D), gestational diabetes mellitus (GDM) and Polycystic Ovary Syndrome (PCOS）—as well as studies in healthy populations for comparative insight. We invite submissions focusing on, but not limited to, the following specific themes:

-Glyceamic phenotyping via CGM under various dietary patterns, such as Mediterranean diet, low-carbohydrate diet, Dietary Approaches to Stop Hypertension (DASH), plant-based diet, and time-restricted eating with emphasis on meal composition, sequence, and timing;

-Multi-omics and sensor-based biomarkers, such as genomic, metabolomic, microbiomic, CGM elucidating postprandial glucose variability;

-Digital monitoring (e.g., ecological momentary assessment) and psychosocial predictors of adherence to dietary interventions;

-AI and machine learning approaches for generating scalable, personalized dietary recommendations.

We encourage submission of articles in the following types: Original Research, Systematic Review, Meta-analysis, Case Report, Methods/Protocol

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Glycaemic Response and Behavioural Adherence Across Dietary Patterns: Precision Assessment and Personalized Strategies