Pasta and Couscous Prepared With Durum Wheat Semolina: Effect on Post-prandial Glucose and Insulin Metabolism

Dietary Modification Clinical Trial

Official title:

Pasta and Couscous Prepared With Durum Wheat Semolina: Effect on Post-prandial Glucose and Insulin Metabolism

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03098017
• Other study ID #: Couscous2
• Status: Completed
• Phase: N/A
• Start date: March 13, 2017
• Est. completion date: July 13, 2018

Study information

• Verified date: September 2018
• Source: University of Parma
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Carbohydrate-based products can influence the post-prandial glycemic response differently based on their ability to be digested, absorbed and to affect rises in plasma glucose. Pasta is one of the major carbohydrate-rich foods consumed in Italy. Studies from the literature describe a lower glycemic response after the consumption of pasta compared with other wheat-based products, such as couscous. Among the factors affecting post-prandial glycemia after consumption of carbohydrate-based products, the technological process represents a central one. In fact, the different technological processes alter the food matrix which can affect the post-prandial metabolism of glucose and insulin differently. Thus, the present study aims at investigating the effect induced by the principal steps of the process of pasta production on the reduction of post-prandial glycemic and insulinemic responses compared to a similar durum wheat based product, couscous.

Description:

The different glycemic responses after the consumption of carbohydrate-based products are associated with different rates of digestion and absorption of the carbohydrates in the human body. Therefore, food products rich in carbohydrates can be classified based on their ability to be digested, absorbed and to affect post-prandial glycemia. Epidemiological studies suggest that following a diet including carbohydrate-based foods inducing a low and slow glycemic response is associated with reduced risk to develop some non-communicable diseases (such as type 2 diabetes and cardiovascular disease), to control inflammatory status, which is the trigger of several pathologies, and to reduce fasting insulin. Depending on the food composition, a low glycemic response is not always associated with a low plasma insulin concentration. For instance, high protein or lipid concentrations in the food matrix have been demonstrated to induce low post-prandial glycemic responses, but not a reduction in insulin secretion. Avoiding a high insulin post-prandial response after consumption of foods represents a preventive factor against the risk of overweight and hyperlipidemia, type 2 diabetes, and cancer. Therefore, the evaluation of both glycemic and insulinemic post-prandial response curves is necessary in order to demonstrate the true beneficial effect of the consumption of low glycemic index foods. Among several factors which can influence the post-prandial glycemic and insulinemic responses (such as macronutrient composition and the cooking process), the technological aspects through which the foods are produced represent an important one. Several studies reported a low glycemic response after the consumption of pasta compared with bread, and this is due to the technological structures characterizing the two matrices. Pasta is one of the major sources of carbohydrates consumed in Italy. Therefore, the aim of the present study is to investigate the effect of pasta and couscous on the plasma response of glucose and insulin, as well as c-peptide in order to clearly discriminate the different biological effect induced by the technological process in the production of pasta, compared to foods beginning with the same ingredients. Moreover, the study aims to create a solid basis for future studies for evaluating the effect of pasta consumption, as the main source of carbohydrates, in a context of a balanced diet, for maintaining health.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 30
• Est. completion date: July 13, 2018
• Est. primary completion date: July 13, 2018
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• healthy male and female

Exclusion Criteria:

• BMI>30kg/m2

• celiac disease

• metabolic disorders (diabetes, hypertension, dislipidemia, glucidic intolerance)

• chronic drug therapies for any pathologies (including psychiatric diseases)

• intense physical activity

• dietary supplements affecting the metabolism

• anemia

Related Conditions & MeSH terms

• Dietary Modification

Intervention

Other:
• Short pasta (dry)
50g available carbohydrate portion of penne (~71g uncooked) will be cooked according to package instructions and served with 500mL water
• Long pasta (dry)
50g available carbohydrate portion of spaghetti (~71g uncooked) will be cooked according to package instructions and served with 500mL water
• Couscous
50g available carbohydrate portion of couscous (~70g uncooked) will be cooked according to package instructions and served with 500mL water
• Glucose
50g available carbohydrate portion of glucose monohydrate (~55g) will be dissolved in 500mL water

Locations

Country	Name	City	State
Italy	Department of Food Science, University of Parma	Parma

Sponsors (1)

Lead Sponsor	Collaborator
University of Parma

Country where clinical trial is conducted

Italy, 

References & Publications (16)

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Jenkins DJ, Wolever TM, Jenkins AL. Starchy foods and glycemic index. Diabetes Care. 1988 Feb;11(2):149-59. — View Citation

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Livesey G, Taylor R, Livesey H, Liu S. Is there a dose-response relation of dietary glycemic load to risk of type 2 diabetes? Meta-analysis of prospective cohort studies. Am J Clin Nutr. 2013 Mar;97(3):584-96. doi: 10.3945/ajcn.112.041467. Epub 2013 Jan 30. Review. — View Citation

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Ma XY, Liu JP, Song ZY. Glycemic load, glycemic index and risk of cardiovascular diseases: meta-analyses of prospective studies. Atherosclerosis. 2012 Aug;223(2):491-6. doi: 10.1016/j.atherosclerosis.2012.05.028. Epub 2012 Jun 6. Review. — View Citation

Onitilo AA, Stankowski RV, Berg RL, Engel JM, Glurich I, Williams GM, Doi SA. Type 2 diabetes mellitus, glycemic control, and cancer risk. Eur J Cancer Prev. 2014 Mar;23(2):134-40. doi: 10.1097/CEJ.0b013e3283656394. — View Citation

Ostlund RE Jr, Staten M, Kohrt WM, Schultz J, Malley M. The ratio of waist-to-hip circumference, plasma insulin level, and glucose intolerance as independent predictors of the HDL2 cholesterol level in older adults. N Engl J Med. 1990 Jan 25;322(4):229-34. — View Citation

Petitot, M., Abecassis, J. & Micard, V. Structuring of pasta components during processing: impact on starch and protein digestibility and allergenicity. Trends Food Sci Tech. 2009;20,521-532

Schwingshackl L, Hoffmann G. Long-term effects of low glycemic index/load vs. high glycemic index/load diets on parameters of obesity and obesity-associated risks: a systematic review and meta-analysis. Nutr Metab Cardiovasc Dis. 2013 Aug;23(8):699-706. doi: 10.1016/j.numecd.2013.04.008. Epub 2013 Jun 17. Review. — View Citation

Sieri S, Krogh V, Berrino F, Evangelista A, Agnoli C, Brighenti F, Pellegrini N, Palli D, Masala G, Sacerdote C, Veglia F, Tumino R, Frasca G, Grioni S, Pala V, Mattiello A, Chiodini P, Panico S. Dietary glycemic load and index and risk of coronary heart disease in a large italian cohort: the EPICOR study. Arch Intern Med. 2010 Apr 12;170(7):640-7. doi: 10.1001/archinternmed.2010.15. — View Citation

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Wolever TM, Jenkins DJ, Kalmusky J, Giordano C, Giudici S, Jenkins AL, Thompson LU, Wong GS, Josse RG. Glycemic response to pasta: effect of surface area, degree of cooking, and protein enrichment. Diabetes Care. 1986 Jul-Aug;9(4):401-4. — View Citation

* Note: There are 16 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	incremental area under the curve for blood glucose	postprandial IAUC of blood glucose	Time Frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
Primary	incremental area under the curve for plasma insulin	postprandial IAUC for plasma insulin	Time Frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
Secondary	post-prandial c-peptide plasma concentration	postprandial IAUC for plasma c-peptide	Time Frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)