Association of the glucose patterns after a single nonstandardized meal with the habitual diet composition and features of the daily glucose profile in individuals without diabetes.

Annalisa Giosuè, Wayne W Campbell, Marilena Vitale, Giuseppina Costabile, Gabriele Riccardi, Viktor Skantze, Therese Hjorth, Mikael Wallman, Rosalba Giacco, Mats Jirstrand, Rikard Landberg, Carl Brunius, Anna Hjort, Robert Bergia

Journal: The American journal of clinical nutrition 2025;121(2):246-255

PMID: 39615596

Free Full Text on Elsevier Science View on PubMed

Plain Language Summary

Blood sugar levels naturally rise and fall throughout the day, especially after eating. Even in people without diabetes, large fluctuations in blood sugar may be linked to a higher risk of future metabolic and cardiovascular diseases. However, it is not fully understood how a person’s usual diet affects their blood sugar response to a single meal. This observational study was conducted to explore using a continual glucose monitor (CGM) whether blood sugar responses after one meal reflect an individual’s typical eating habits and daily glucose patterns.

The results showed that individuals who had larger increases in blood sugar after a single meal also tended to have greater fluctuations in blood sugar levels throughout the day. These responses were also linked to aspects of their usual diet, suggesting that everyday eating habits influence how the body handles blood sugar after meals.

In conclusion, blood sugar responses to a single meal may reflect broader patterns of blood sugar regulation and dietary habits, even in people without diabetes. Healthcare professionals may use this information to better understand individual differences in blood sugar responses and to guide personalised dietary advice aimed at improving metabolic health and reducing future disease risk.

Abstract

BACKGROUND

The postprandial glucose response (PPGR), contributing to the glycemic variability (GV), is positively associated with cardiovascular disease risk in people without diabetes, and can thus represent a target for cardiometabolic prevention strategies.

OBJECTIVES

The study aimed to distinguish patterns of PPGR after a single nonstandardized meal and to evaluate their relationship with the habitual diet and the daily glucose profile (DGP) in individuals at high-cardiometabolic risk.

METHODS

Baseline 4-d continuous glucose monitoring was performed in 159 adults recruited in the MEDGI-Carb trial. After a nonstandardized breakfast, parameters of the PPGR were estimated by a mechanistic model: baseline glucose; amplitude-the magnitude of postmeal glucose concentrations; frequency-the velocity of postmeal glucose oscillations; damping-the rate of postmeal glucose decay. PPGR patterns were identified by cluster analysis. Differences between clusters and the relationship between PPGR parameters and individual features were explored by one-way analysis of variance and correlation analysis, respectively.

RESULTS

Two patterns of PPGR emerged. Pattern A had a higher baseline, amplitude, frequency, and damping than B. Individuals in cluster A compared with B had higher energy (2002 ± 526 compared with 1766 ± 455 kcal, P = 0.025), protein (82 ± 22 compared with 72 ± 21 g, P = 0.028), and fat (87 ± 30 compared with 75 ± 22 g, P = 0.041), but not carbohydrate habitual intake. Pattern A compared to B associated with a higher average daily glucose (6.12 ± 0.50 compared with 5.88 ± 0.62 mmol/L, P = 0.019) and lower GV (11.67 ± 3.52 compared with 13.43 ± 3.78%, P = 0.010). Mean daily glucose correlated directly with baseline (r = 0.419, P < 0.001) and amplitude (r = 0.189, P = 0.022) of the PPGR, whereas DGP variability correlated directly with amplitude (r = 0.218, P = 0.008), and inversely with frequency (r = -0.179, P = 0.031) and damping (r = -0.309, P < 0.001).

CONCLUSIONS

Two PPGR patterns after a single nonstandardized breakfast were identified in high-cardiometabolic risk individuals. The habitual diet was associated with the patterns and their dynamic parameters, which, in turn, could predict the individuals' DGP. Our findings could support the implementation of dietary strategies targeting the PPGR to ameliorate the cardiometabolic risk profile.

TRIAL REGISTRATION NUMBER

This study was registered at clinicaltrials.gov as NCT03410719.

Address: Nutrition, Diabetes and Metabolism Unit, Department of Clinical Medicine and Surgery, "Federico II" University, Naples, Italy; Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden. Electronic address: [email protected].; Department of Systems and Data Analysis, Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden.; Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.; Nutrition, Diabetes and Metabolism Unit, Department of Clinical Medicine and Surgery, "Federico II" University, Naples, Italy; Institute of Food Sciences, National Research Council, Avellino 83100, Italy.; Nutrition, Diabetes and Metabolism Unit, Department of Clinical Medicine and Surgery, "Federico II" University, Naples, Italy.; Department of Nutrition Science, Purdue University, West Lafayette, IN, United States.; Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden; Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

DOI: 10.1016/j.ajcnut.2024.11.028