Individual Determinants of Postprandial Glucose Response in Type 2 Diabetes
NCT ID: NCT06057246
Last Updated: 2023-10-11
Study Results
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Basic Information
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UNKNOWN
NA
144 participants
INTERVENTIONAL
2023-05-15
2025-05-31
Brief Summary
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Post-prandial glucose response (PGR) cannot be predicted based solely on the meals' carbohydrate content. Recent research using continuous glucose monitoring (CGM) systems has identified different patterns of PGR to a standard meal among both healthy people and individuals with type 1 diabetes. Different contributors to the PGR have emerged, including genotype, hormonal and metabolic factors, phenotype, gut microbiota composition, background diet, sleep habits, physical activity levels.
The present project aims at exploring the PGR in a real-life setting in a cohort of people with T2D, and identifying person-specific factors associated with different postprandial glucose patterns.
To this purpose, 144 individuals with T2D on treatment with diet alone or diet plus metformin will be characterized for their anthropometric, metabolic, and gut-microbiome features and will undergo a one-week observational period through CGM system, while properly recording their food intake, physical activity, and sleep habits. A mixed-nutrient standardized meal will be consumed at home in two occasions by each participant to investigate the intra-individual variability of the PGR. Moreover, in a subgroup of participants (n=60), divided according to anthropometric and metabolic features, hormonal and metabolic response to the standardized meal will be evaluated at the hospital, to explore the contribution of different T2D phenotypes to the PGR.
A further step will be developing a prediction algorithm of PGR based on the intra- and inter-individual factors shown to influence postprandial glucose, able to further optimize the management of T2D with precision therapeutic strategies.
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Detailed Description
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* venous blood sampling for biochemical determinations
* anthropometrics and blood pressure measurements
* body composition evaluation (bioimpedance analysis)
* basal metabolic rate evaluation (indirect calorimetry)
* administrations of questionnaires for the evaluation of lifestyle habits
* positioning of a subcutaneous sensor for continuous glucose monitoring (CGM).
Information regarding characteristics of bowel movements in the day of stool sampling will be collected according to the Bristol stool scale.
The participants will be instructed to consume at home a mixed-nutrient meal at breakfast on the second day and on the last day of the observational period. PGR will be assessed by CGM.
Over the following 7 days, participants will undergo CGM while keeping stable their own dietary and lifestyle habits and will be asked to record the following information by using a dedicated app:
* a 7-day food diary reporting all foods and drinks consumed, including dressing, portions by household measures (cup, spoons, etc.) or weight, and providing as much details as possible (i.e., cooking methods, brands names). Seven-day food records will be discussed with a skilled dietitian to check potential mistakes and missing information. Energy intake and dietary composition (nutrients and non-nutrients), and food groups consumption will be calculated using the Food Composition Database for epidemiological Studies in Italy of the European Institute of Oncology;
* any stressful event or drug assumption.
In parallel, during the 7-day observational period, data on physical activity levels and sleep habits will be collected by using an accelerometer (Actigraph medical device).
At the 8th day, a subgroup of 60 participants (20 with prevalent impairment of insulin sensitivity, 20 with prevalent impairment of insulin secretion, and 20 with prevalent visceral obesity - as determined by anthropometrics, fasting C-peptide, and plasma triglycerides) will return to the hospital and consume the same standardized meal consumed at home. Before and over 4 hours after the meal, venous blood samples will be collected at fixed time points for biochemical evaluations.
CGM data from 30 min before meal to 6 h after meal will be analyzed. Postprandial blood glucose changes will be calculated with the trapezoidal method as the incremental area under the curve above the baseline value (iAUC). Predictors of early blood glucose response (iAUC0-3h), late blood glucose response (iAUC3-6h), total blood glucose response (iAUC0-6h), and, as indicator of time-course of the blood glucose changes, the difference between late and early response (iAUC3-6h minus 0-3h) will be evaluated using mixed-effect linear regression considering the patient's identification number (ID) as a random effect. To evaluate the significance of the random-effect factor, ANOVA will be used to test for the difference between the model with and without the random effect.
Conditions
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Study Design
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NA
SINGLE_GROUP
TREATMENT
NONE
Study Groups
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Men and post-menopausal women with type 2 diabetes on treatment with diet or diet plus metformin
Each participant will undergo anthropometrics and blood pressure measurements, bioimpedance analysis, indirect calorimetry, administrations of questionnaires for the evaluation of lifestyle habits, 7 days continuous glucose monitoring in parallel with diet, physical activity and sleep monitoring, and standard mixed-nutrient meals at home; a subgroup of 60 participants will undergo venous blood sampling for biochemical determinations before and after a standard mixed-nutrient meal.
Test meal with postprandial samples
The mixed-nutrient test meal consists of a sandwich made with bread, spread cheese, air-cured beef, and one apple. Nutritional composition of the meal will be: total energy (TE) content 532 kcal, carbohydrates 68,4 g (49.3% TE), total fat 17.1 g (29.5% TE), protein 27.8 g (21.4% TE), and fibre 6 g. Before and over 4 hours after the meal, venous blood samples will be taken for biochemical evaluations at the time points -15'; 0'; 15'; 30'; 60'; 120'; 180'; 240'.
Interventions
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Test meal with postprandial samples
The mixed-nutrient test meal consists of a sandwich made with bread, spread cheese, air-cured beef, and one apple. Nutritional composition of the meal will be: total energy (TE) content 532 kcal, carbohydrates 68,4 g (49.3% TE), total fat 17.1 g (29.5% TE), protein 27.8 g (21.4% TE), and fibre 6 g. Before and over 4 hours after the meal, venous blood samples will be taken for biochemical evaluations at the time points -15'; 0'; 15'; 30'; 60'; 120'; 180'; 240'.
Eligibility Criteria
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Inclusion Criteria
* HbA1c ≤ 7.5%
* Body Mass Index (BMI) 20-39.9 kg/m2
* Ability to comply with the study protocol activities.
Exclusion Criteria
* Pregnancy or breast-feeding
* Use of antibiotics, probiotics, or prebiotics during the last three months.
30 Years
70 Years
ALL
No
Sponsors
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Federico II University
OTHER
Responsible Party
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rivellese angela
Full Professor
Locations
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Department of Clinical Medicine and Surgery Federico II University
Naples, , Italy
Countries
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Central Contacts
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Facility Contacts
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Angela A Rivellese, Professor
Role: primary
References
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Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, Snyder M. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018 Jul 24;16(7):e2005143. doi: 10.1371/journal.pbio.2005143. eCollection 2018 Jul.
Shilo S, Godneva A, Rachmiel M, Korem T, Kolobkov D, Karady T, Bar N, Wolf BC, Glantz-Gashai Y, Cohen M, Zuckerman Levin N, Shehadeh N, Gruber N, Levran N, Koren S, Weinberger A, Pinhas-Hamiel O, Segal E. Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data. Diabetes Care. 2022 Mar 1;45(3):502-511. doi: 10.2337/dc21-1048.
Bozzetto L, Pacella D, Cavagnuolo L, Capuano M, Corrado A, Scida G, Costabile G, Rivellese AA, Annuzzi G. Postprandial glucose variability in type 1 diabetes: The individual matters beyond the meal. Diabetes Res Clin Pract. 2022 Oct;192:110089. doi: 10.1016/j.diabres.2022.110089. Epub 2022 Sep 17.
Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.
Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, Capdevila J, Hadjigeorgiou G, Davies R, Al Khatib H, Bonnett C, Ganesh S, Bakker E, Hart D, Mangino M, Merino J, Linenberg I, Wyatt P, Ordovas JM, Gardner CD, Delahanty LM, Chan AT, Segata N, Franks PW, Spector TD. Human postprandial responses to food and potential for precision nutrition. Nat Med. 2020 Jun;26(6):964-973. doi: 10.1038/s41591-020-0934-0. Epub 2020 Jun 11.
Other Identifiers
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31/2023
Identifier Type: -
Identifier Source: org_study_id
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