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Pasta and Other Durum Wheat-based Products: Effects on Post-prandial Glucose Metabolism

Pasta and Other Durum Wheat-based Products: Effects on Post-prandial Glucose Metabolism

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT03024983
Enrollment
18
Registered
2017-01-19
Start date
2015-09-30
Completion date
2016-07-31
Last updated
2017-01-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Dietary Modification

Brief 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 bread. 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 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 response (post-prandial glucose, insulin, GLP-1, GIP plasma concentrations).

Detailed 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 (Livesey et al, 2013; Dong et al, 2011) and cardiovascular disease (Ludwig, 2002; Blaak et al, 2012)), to control inflammatory status (Ma et al, 2012; Sieri et al, 2010), which is the trigger of several pathologies, and to reduce fasting insulin (Schwingshackl & Hoffmann, 2013). 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 (Gannon et al, 1988; Gannon et al, 1993; Collier et al. 1988). Avoiding a high insulin post-prandial response after consumption of foods represents a preventive factor against the risk of overweight and hyperlipidemia (Ostlund et al, 1990), type 2 diabetes (Weyer et al, 2001), and cancer (Onitilo et al, 2014). 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 (Jenkins et al, 1988; Jenkins et al, 1981; Wolever et al, 1986), and this is due to the technological structures characterizing the two matrices (Petitot et al, 2009). 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 other durum wheat based products on the plasma response of glucose, insulin, and other hormones related to the glucose metabolism (c-peptide, GLP-1 and GIP) 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.

Interventions

OTHERGlucose

Glucose monohydrate (55 g) dissolved with 500 mL of water

OTHERSemolina soup

Semolina soup (322 g) eaten with 500 mL of water

OTHERBread

Bread (122 g) eaten with 500 mL of water

OTHERPenne (fresh)

Cooked penne (132 g) eaten with 500 mL of water

OTHERPenne (dry)

Cooked penne (142 g) eaten with 500 mL of water

OTHERSpaghetti (dry)

Cooked spaghetti (142 g) eaten with 500 mL of water

Sponsors

University of Parma
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
BASIC_SCIENCE
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to 60 Years
Healthy volunteers
Yes

Inclusion criteria

* healthy male and female (age ≥ 18 years)

Exclusion criteria

* 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

Design outcomes

Primary

MeasureTime frame
incremental area under the curve for plasma glucose2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)

Secondary

MeasureTime frame
post-prandial GIP plasma concentration2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
post-prandial glucagon plasma concentration2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
post-prandial GLP-1 plasma concentration2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
post-prandial insulin plasma concentration2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
post-prandial c-peptide plasma concentration2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)

Countries

Italy

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 18, 2026