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Postprandial glycemia (PPG) is a relevant determinant of glucose control in people with type 2 diabetes (T2D). Epidemiological and pathophysiological studies indicate that PPG is a better risk predictor for cardiovascular disease and all-cause mortality than fasting plasma glucose. Therefore, both fasting and postprandial glycemia should be targeted to achieve optimal glycemic control and, thus, prevent or reduce the risk of diabetes complications.
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.
The participants will undergo a 7-day observational period and acute postprandial tests with a standardized meal. Before the study initiation, all participants will undergo a screening visit for the assessment of inclusion and exclusion criteria. Participants meeting the inclusion criteria will be instructed to collect a stool sample and will be scheduled for the experimental visit (day 0). At day 0, each participant will arrive at the diabetes clinic in the morning, after a 10-hour fasting, to undergo:
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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Men and post-menopausal women with type 2 diabetes on treatment with diet or diet plus metformin | Experimental | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Test meal with postprandial samples | Other | 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'. |
| Measure | Description | Time Frame |
|---|---|---|
| Post-prandial glucose response | Post-prandial glucose response will be calculated by the trapezoidal method as the area under the curve above the baseline value (iAUC). | 7 days (related to the 7-day continuous glucose monitoring) |
| Measure | Description | Time Frame |
|---|---|---|
| Energy intake | The energy intake expressed in kilocalories and derived by the analysis of the 7-day food record plus the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire will be evaluated in relation to the post-prandial glucose response (iAUC). | 7 days (related to the 7-day continuous glucose monitoring and food record) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Angela A Rivellese, Professor | Contact | 00390817463665 | rivelles@unina.it |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Clinical Medicine and Surgery Federico II University | Recruiting | Naples | 80131 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30040822 | Result | 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. | |
| 34711639 | Result | 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. |
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| Dietary components | The dietary components, namely available carbohydrates, total proteins, animal proteins, plant proteins, total fats, saturated fats, mono- and polyunsaturated fats, fibre and sugars (all expressed in grams), will be derived by the analysis of the 7-day food record and the (European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire, and evaluated in relation to the to the post-prandial glucose response (iAUC). | 7 days (related to the 7-day continuous glucose monitoring and food record) |
| Gut-microbiota composition | Gut microbiota composition will be assessed using relative bacterial taxonomic abundances and measures of community diversity and richness (derived from 16 S ribosomal ribonucleic acid (rRNA) high-throughput sequencing of baseline stool specimens), and will be evaluated in relation to the post-prandial glucose response (iAUC). | 7 days (related to the 7-day continuous glucose monitoring) |
| Physical activity levels | Physical activity levels as detected by the ActiGraph device (GT3X+ActiGraph LLC, Pensacola, Florida; sampling frequency: 30 Hz) worn for 7 days on the non-dominant wrist, and expressed as % of time spent in sedentary-light-moderate activity will be evaluated in relation to the post-prandial glucose response (iAUC). | 7 days (related to the 7-day continuous glucose and physical activity monitoring) |
| Sleep duration | Sleep metrics, namely total sleep time, sleep onset latency, wake after sleep onset (all measured in minutes), as detected by ActiGraph device worn for 7 days and nights and calculated by the software (ActiGraph LLC, Pensacola, Florida; version 6.13.4), will be evaluated in relation to the post-prandial glucose response (iAUC). | 7 days (related to the 7-day continuous glucose and sleep monitoring) |
| 36122866 | Result | 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. |
| 26590418 | Result | 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. |
| 32528151 | Result | 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. |
| D004700 | Endocrine System Diseases |