<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03024983</url>
  </required_header>
  <id_info>
    <org_study_id>DWP1</org_study_id>
    <nct_id>NCT03024983</nct_id>
  </id_info>
  <brief_title>Pasta and Other Durum Wheat-based Products: Effects on Post-prandial Glucose Metabolism</brief_title>
  <official_title>Pasta and Other Durum Wheat-based Products: Effects on Post-prandial Glucose Metabolism</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Parma</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of Parma</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      Carbohydrate-based products can influence the post-prandial glycemic response differently&#xD;
      based on their ability to be digested, absorbed and to affect rises in plasma glucose. Pasta&#xD;
      is one of the major carbohydrate-rich foods consumed in Italy. Studies from the literature&#xD;
      describe a lower glycemic response after the consumption of pasta compared with other&#xD;
      wheat-based products, such as bread. Among the factors affecting post-prandial glycemia after&#xD;
      consumption of carbohydrate-based products, the technological process represents a central&#xD;
      one.In fact, the different technological processes alter the food matrix which can affect the&#xD;
      post-prandial metabolism of glucose differently. Thus, the present study aims at&#xD;
      investigating the effect induced by the principal steps of the process of pasta production on&#xD;
      the reduction of post-prandial glycemic response (post-prandial glucose, insulin, GLP-1, GIP&#xD;
      plasma concentrations).&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The different glycemic responses after the consumption of carbohydrate-based products are&#xD;
      associated with different rates of digestion and absorption of the carbohydrates in the human&#xD;
      body. Therefore, food products rich in carbohydrates can be classified based on their ability&#xD;
      to be digested, absorbed and to affect post-prandial glycemia. Epidemiological studies&#xD;
      suggest that following a diet including carbohydrate-based foods inducing a low and slow&#xD;
      glycemic response is associated with reduced risk to develop some non-communicable diseases&#xD;
      (such as type 2 diabetes (Livesey et al, 2013; Dong et al, 2011) and cardiovascular disease&#xD;
      (Ludwig, 2002; Blaak et al, 2012)), to control inflammatory status (Ma et al, 2012; Sieri et&#xD;
      al, 2010), which is the trigger of several pathologies, and to reduce fasting insulin&#xD;
      (Schwingshackl &amp; Hoffmann, 2013). Depending on the food composition, a low glycemic response&#xD;
      is not always associated with a low plasma insulin concentration. For instance, high protein&#xD;
      or lipid concentrations in the food matrix have been demonstrated to induce low post-prandial&#xD;
      glycemic responses, but not a reduction in insulin secretion (Gannon et al, 1988; Gannon et&#xD;
      al, 1993; Collier et al. 1988). Avoiding a high insulin post-prandial response after&#xD;
      consumption of foods represents a preventive factor against the risk of overweight and&#xD;
      hyperlipidemia (Ostlund et al, 1990), type 2 diabetes (Weyer et al, 2001), and cancer&#xD;
      (Onitilo et al, 2014). Therefore, the evaluation of both glycemic and insulinemic&#xD;
      post-prandial response curves is necessary in order to demonstrate the true beneficial effect&#xD;
      of the consumption of low glycemic index foods. Among several factors which can influence the&#xD;
      post-prandial glycemic and insulinemic responses (such as macronutrient composition and the&#xD;
      cooking process), the technological aspects through which the foods are produced represent an&#xD;
      important one. Several studies reported a low glycemic response after the consumption of&#xD;
      pasta compared with bread (Jenkins et al, 1988; Jenkins et al, 1981; Wolever et al, 1986),&#xD;
      and this is due to the technological structures characterizing the two matrices (Petitot et&#xD;
      al, 2009). Pasta is one of the major sources of carbohydrates consumed in Italy. Therefore,&#xD;
      the aim of the present study is to investigate the effect of pasta and other durum wheat&#xD;
      based products on the plasma response of glucose, insulin, and other hormones related to the&#xD;
      glucose metabolism (c-peptide, GLP-1 and GIP) in order to clearly discriminate the different&#xD;
      biological effect induced by the technological process in the production of pasta, compared&#xD;
      to foods beginning with the same ingredients. Moreover, the study aims to create a solid&#xD;
      basis for future studies for evaluating the effect of pasta consumption, as the main source&#xD;
      of carbohydrates, in a context of a balanced diet, for maintaining health.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date>September 2015</start_date>
  <completion_date type="Actual">July 2016</completion_date>
  <primary_completion_date type="Actual">June 2016</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Crossover Assignment</intervention_model>
    <primary_purpose>Basic Science</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>incremental area under the curve for plasma glucose</measure>
    <time_frame>2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>post-prandial insulin plasma concentration</measure>
    <time_frame>2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>post-prandial c-peptide plasma concentration</measure>
    <time_frame>2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>post-prandial GLP-1 plasma concentration</measure>
    <time_frame>2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>post-prandial GIP plasma concentration</measure>
    <time_frame>2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>post-prandial glucagon plasma concentration</measure>
    <time_frame>2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)</time_frame>
  </secondary_outcome>
  <number_of_arms>6</number_of_arms>
  <enrollment type="Actual">18</enrollment>
  <condition>Dietary Modification</condition>
  <arm_group>
    <arm_group_label>Control</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Glucose monohydrate (isoglucidic portion -50 g of available carbohydrates-)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Semolina</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Semolina soup (isoglucidic portion -50 g of available carbohydrates-)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Bread</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Bread (isoglucidic portion -50 g of available carbohydrates-)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Short pasta (fresh)</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Fresh penne (isoglucidic portion -50 g of available carbohydrates-)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Short pasta (dry)</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Short pasta (dry) (isoglucidic portion -50 g of available carbohydrates-)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Long pasta (dry)</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Long pasta (dry) (isoglucidic portion -50 g of available carbohydrates-)</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Glucose</intervention_name>
    <description>Glucose monohydrate (55 g) dissolved with 500 mL of water</description>
    <arm_group_label>Control</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Semolina soup</intervention_name>
    <description>Semolina soup (322 g) eaten with 500 mL of water</description>
    <arm_group_label>Semolina</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Bread</intervention_name>
    <description>Bread (122 g) eaten with 500 mL of water</description>
    <arm_group_label>Bread</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Penne (fresh)</intervention_name>
    <description>Cooked penne (132 g) eaten with 500 mL of water</description>
    <arm_group_label>Short pasta (fresh)</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Penne (dry)</intervention_name>
    <description>Cooked penne (142 g) eaten with 500 mL of water</description>
    <arm_group_label>Short pasta (dry)</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Spaghetti (dry)</intervention_name>
    <description>Cooked spaghetti (142 g) eaten with 500 mL of water</description>
    <arm_group_label>Long pasta (dry)</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion criteria:&#xD;
&#xD;
          -  healthy male and female (age ≥ 18 years)&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  celiac disease&#xD;
&#xD;
          -  metabolic disorders (diabetes, hypertension, dislipidemia, glucidic intolerance)&#xD;
&#xD;
          -  chronic drug therapies for any pathologies (including psychiatric diseases)&#xD;
&#xD;
          -  intense physical activity&#xD;
&#xD;
          -  dietary supplements affecting the metabolism&#xD;
&#xD;
          -  anemia&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>60 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Francesca Scazzina, Professor</last_name>
    <role>Principal Investigator</role>
    <affiliation>Department of Food Science, University of Parma</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Furio Brighenti, Professor</last_name>
    <role>Study Director</role>
    <affiliation>Department of Food Science, University of Parma</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Department of Food Science, University of Parma</name>
      <address>
        <city>Parma</city>
        <zip>43125</zip>
        <country>Italy</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Italy</country>
  </location_countries>
  <reference>
    <citation>Blaak EE, Antoine JM, Benton D, Bjorck I, Bozzetto L, Brouns F, Diamant M, Dye L, Hulshof T, Holst JJ, Lamport DJ, Laville M, Lawton CL, Meheust A, Nilson A, Normand S, Rivellese AA, Theis S, Torekov SS, Vinoy S. Impact of postprandial glycaemia on health and prevention of disease. Obes Rev. 2012 Oct;13(10):923-84. doi: 10.1111/j.1467-789X.2012.01011.x. Epub 2012 Jul 11.</citation>
    <PMID>22780564</PMID>
  </reference>
  <reference>
    <citation>Collier GR, Greenberg GR, Wolever TM, Jenkins DJ. The acute effect of fat on insulin secretion. J Clin Endocrinol Metab. 1988 Feb;66(2):323-6. doi: 10.1210/jcem-66-2-323.</citation>
    <PMID>3276722</PMID>
  </reference>
  <reference>
    <citation>Dong JY, Zhang L, Zhang YH, Qin LQ. Dietary glycaemic index and glycaemic load in relation to the risk of type 2 diabetes: a meta-analysis of prospective cohort studies. Br J Nutr. 2011 Dec;106(11):1649-54. doi: 10.1017/S000711451100540X. Epub 2011 Sep 29.</citation>
    <PMID>22017823</PMID>
  </reference>
  <reference>
    <citation>Gannon MC, Nuttall FQ, Neil BJ, Westphal SA. The insulin and glucose responses to meals of glucose plus various proteins in type II diabetic subjects. Metabolism. 1988 Nov;37(11):1081-8. doi: 10.1016/0026-0495(88)90072-8.</citation>
    <PMID>3054432</PMID>
  </reference>
  <reference>
    <citation>Jenkins DJ, Wolever TM, Jenkins AL. Starchy foods and glycemic index. Diabetes Care. 1988 Feb;11(2):149-59. doi: 10.2337/diacare.11.2.149.</citation>
    <PMID>3383733</PMID>
  </reference>
  <reference>
    <citation>Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, Bowling AC, Newman HC, Jenkins AL, Goff DV. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. 1981 Mar;34(3):362-6. doi: 10.1093/ajcn/34.3.362.</citation>
    <PMID>6259925</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>23364021</PMID>
  </reference>
  <reference>
    <citation>Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA. 2002 May 8;287(18):2414-23. doi: 10.1001/jama.287.18.2414.</citation>
    <PMID>11988062</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>22727193</PMID>
  </reference>
  <reference>
    <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.</citation>
    <PMID>23962874</PMID>
  </reference>
  <reference>
    <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. doi: 10.1056/NEJM199001253220404.</citation>
    <PMID>2403660</PMID>
  </reference>
  <reference>
    <citation>Petitot, M., Abecassis, J. &amp; Micard, V. Structuring of pasta components during processing: impact on starch and protein digestibility and allergenicity. Trends Food Sci Tech. 2009;20,521-532</citation>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>23786819</PMID>
  </reference>
  <reference>
    <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.</citation>
    <PMID>20386010</PMID>
  </reference>
  <reference>
    <citation>Weyer C, Funahashi T, Tanaka S, Hotta K, Matsuzawa Y, Pratley RE, Tataranni PA. Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia. J Clin Endocrinol Metab. 2001 May;86(5):1930-5. doi: 10.1210/jcem.86.5.7463.</citation>
    <PMID>11344187</PMID>
  </reference>
  <reference>
    <citation>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. doi: 10.2337/diacare.9.4.401.</citation>
    <PMID>3743316</PMID>
  </reference>
  <verification_date>January 2017</verification_date>
  <study_first_submitted>January 16, 2017</study_first_submitted>
  <study_first_submitted_qc>January 18, 2017</study_first_submitted_qc>
  <study_first_posted type="Estimate">January 19, 2017</study_first_posted>
  <last_update_submitted>January 18, 2017</last_update_submitted>
  <last_update_submitted_qc>January 18, 2017</last_update_submitted_qc>
  <last_update_posted type="Estimate">January 19, 2017</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University of Parma</investigator_affiliation>
    <investigator_full_name>Francesca Scazzina Ph.D.</investigator_full_name>
    <investigator_title>Assistant Professor</investigator_title>
  </responsible_party>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03024983
org study id: DWP1
nct id: NCT03024983
lead sponsor: 
has dmc: No
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).
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.
allocation: Randomized
intervention model: Crossover Assignment
primary purpose: Basic Science
masking: None (Open Label)
measure: incremental area under the curve for plasma glucose
time frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
measure: post-prandial insulin plasma concentration
time frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
measure: post-prandial c-peptide plasma concentration
time frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
measure: post-prandial GLP-1 plasma concentration
time frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
measure: post-prandial GIP plasma concentration
time frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
measure: post-prandial glucagon plasma concentration
time frame: 2 hours (-10 and 0 -fasting-, 15, 30, 45, 60, 90, 120 minutes)
arm group label: Control
arm group type: Active Comparator
description: Glucose monohydrate (isoglucidic portion -50 g of available carbohydrates-)
arm group label: Semolina
arm group type: Experimental
description: Semolina soup (isoglucidic portion -50 g of available carbohydrates-)
arm group label: Bread
arm group type: Experimental
description: Bread (isoglucidic portion -50 g of available carbohydrates-)
arm group label: Short pasta (fresh)
arm group type: Experimental
description: Fresh penne (isoglucidic portion -50 g of available carbohydrates-)
arm group label: Short pasta (dry)
arm group type: Experimental
description: Short pasta (dry) (isoglucidic portion -50 g of available carbohydrates-)
arm group label: Long pasta (dry)
arm group type: Experimental
description: Long pasta (dry) (isoglucidic portion -50 g of available carbohydrates-)
intervention type: Other
intervention name: Glucose
description: Glucose monohydrate (55 g) dissolved with 500 mL of water
arm group label: Control
intervention type: Other
intervention name: Semolina soup
description: Semolina soup (322 g) eaten with 500 mL of water
arm group label: Semolina
intervention type: Other
intervention name: Bread
description: Bread (122 g) eaten with 500 mL of water
arm group label: Bread
intervention type: Other
intervention name: Penne (fresh)
description: Cooked penne (132 g) eaten with 500 mL of water
arm group label: Short pasta (fresh)
intervention type: Other
intervention name: Penne (dry)
description: Cooked penne (142 g) eaten with 500 mL of water
arm group label: Short pasta (dry)
intervention type: Other
intervention name: Spaghetti (dry)
description: Cooked spaghetti (142 g) eaten with 500 mL of water
arm group label: Long pasta (dry)
criteria: 
gender: All
minimum age: 18 Years
maximum age: 60 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Francesca Scazzina, Professor
role: Principal Investigator
affiliation: Department of Food Science, University of Parma
last name: Furio Brighenti, Professor
role: Study Director
affiliation: Department of Food Science, University of Parma
facility: 
country: Italy
citation: Blaak EE, Antoine JM, Benton D, Bjorck I, Bozzetto L, Brouns F, Diamant M, Dye L, Hulshof T, Holst JJ, Lamport DJ, Laville M, Lawton CL, Meheust A, Nilson A, Normand S, Rivellese AA, Theis S, Torekov SS, Vinoy S. Impact of postprandial glycaemia on health and prevention of disease. Obes Rev. 2012 Oct;13(10):923-84. doi: 10.1111/j.1467-789X.2012.01011.x. Epub 2012 Jul 11.
PMID: 22780564
citation: Collier GR, Greenberg GR, Wolever TM, Jenkins DJ. The acute effect of fat on insulin secretion. J Clin Endocrinol Metab. 1988 Feb;66(2):323-6. doi: 10.1210/jcem-66-2-323.
PMID: 3276722
citation: Dong JY, Zhang L, Zhang YH, Qin LQ. Dietary glycaemic index and glycaemic load in relation to the risk of type 2 diabetes: a meta-analysis of prospective cohort studies. Br J Nutr. 2011 Dec;106(11):1649-54. doi: 10.1017/S000711451100540X. Epub 2011 Sep 29.
PMID: 22017823
citation: Gannon MC, Nuttall FQ, Neil BJ, Westphal SA. The insulin and glucose responses to meals of glucose plus various proteins in type II diabetic subjects. Metabolism. 1988 Nov;37(11):1081-8. doi: 10.1016/0026-0495(88)90072-8.
PMID: 3054432
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responsible party type: Principal Investigator
investigator affiliation: University of Parma
investigator full name: Francesca Scazzina Ph.D.
investigator title: Assistant Professor
sharing ipd: No