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      Dietary glycemic index, glycemic load, and cancer risk: results from the EPIC-Italy study

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          Abstract

          Factors linked to glucose metabolism are involved in the etiology of several cancers. High glycemic index (GI) or high glycemic load (GL) diets, which chronically raise postprandial blood glucose, may increase cancer risk by affecting insulin-like growth factor. We prospectively investigated cancer risk and dietary GI/GL in the EPIC-Italy cohort. After a median 14.9 years, 5112 incident cancers and 2460 deaths were identified among 45,148 recruited adults. High GI was associated with increased risk of colon and bladder cancer. High GL was associated with: increased risk of colon cancer; increased risk of diabetes-related cancers; and decreased risk of rectal cancer. High intake of carbohydrate from high GI foods was significantly associated with increased risk of colon and diabetes-related cancers, but decreased risk of stomach cancer; whereas high intake of carbohydrates from low GI foods was associated with reduced colon cancer risk. In a Mediterranean population with high and varied carbohydrate intake, carbohydrates that strongly raise postprandial blood glucose may increase colon and bladder cancer risk, while the quantity of carbohydrate consumed may be involved in diabetes-related cancers. Further studies are needed to confirm the opposing effects of high dietary GL on risks of colon and rectal cancers.

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          Most cited references47

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          Total energy intake: implications for epidemiologic analyses.

          Associations between intake of specific nutrients and disease cannot be considered primary effects of diet if they are simply the result of differences between cases and noncases in body size, physical activity, and metabolic efficiency. Epidemiologic studies of diet and disease should therefore be directed at the effect of nutrient intakes independent of total caloric intake in most instances. This is not accomplished with nutrient density measures of dietary intake but can be achieved by employing nutrient intakes adjusted for caloric intake by regression analysis. While pitfalls in the manipulation and interpretation of energy intake data in epidemiologic studies have been emphasized, these considerations also highlight the usefulness of obtaining a measurement of total caloric intake. For instance, if a questionnaire obtained information on only cholesterol intake in a study of coronary heart disease, it is possible that no association with disease would be found even if a real positive effect of a high cholesterol diet existed, since the caloric intake of cases is likely to be less than that of noncases. Such a finding could be appropriately interpreted if an estimate of total caloric intake were available. The relationships between dietary factors and disease are complex. Even with carefully collected measures of intake, consideration of the biologic implications of various analytic approaches is needed to avoid misleading conclusions.
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            Glycemic index of foods: a physiological basis for carbohydrate exchange.

            The determine the effect of different foods on the blood glucose, 62 commonly eaten foods and sugars were fed individually to groups of 5 to 10 healthy fasting volunteers. Blood glucose levels were measured over 2 h, and expressed as a percentage of the area under the glucose response curve when the same amount of carbohydrate was taken as glucose. The largest rises were seen with vegetables (70 +/- 5%), followed by breakfast cereals (65 +/- 5%), cereals and biscuits (60 +/- 3%), fruit (50 +/- 5%), dairy products (35 +/- 1%), and dried legumes (31 +/- 3%). A significant negative relationship was seen between fat (p less than 0.01) and protein (p less than 0.001) and postprandial glucose rise but not with fiber or sugar content.
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              International Tables of Glycemic Index and Glycemic Load Values: 2008

              OBJECTIVE—To systematically tabulate published and unpublished sources of reliable glycemic index (GI) values. RESEARCH DESIGN AND METHODS—A literature search identified 205 articles published between 1981 and 2007. Unpublished data were also included where the data quality could be verified. The data were separated into two lists: the first representing more precise data derived from testing healthy subjects and the second primarily from individuals with impaired glucose metabolism. RESULTS—The tables, which are available in the online-only appendix, list the GI of over 2,480 individual food items. Dairy products, legumes, and fruits were found to have a low GI. Breads, breakfast cereals, and rice, including whole grain, were available in both high and low GI versions. The correlation coefficient for 20 staple foods tested in both healthy and diabetic subjects was r = 0.94 (P < 0.001). CONCLUSIONS—These tables improve the quality and quantity of GI data available for research and clinical practice.
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                Author and article information

                Contributors
                vittorio.krogh@istitutotumori.mi.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 August 2017
                29 August 2017
                2017
                : 7
                : 9757
                Affiliations
                [1 ]ISNI 0000 0001 0807 2568, GRID grid.417893.0, , Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, ; Milan, Italy
                [2 ]ISNI 0000 0004 1758 0937, GRID grid.10383.39, , Department of Public Health, University of Parma, ; Parma, Italy
                [3 ]ISNI 0000 0004 1758 0566, GRID grid.417623.5, , Molecular and Nutritional Epidemiology Unit, ISPO-Cancer Research and Prevention Institute, ; Florence, Italy
                [4 ]ISNI 0000 0001 0790 385X, GRID grid.4691.a, , Department of Clinical and Experimental Medicine, Federico II University, ; Naples, Italy
                [5 ]ISNI 0000 0001 2336 6580, GRID grid.7605.4, , Department of Clinical and Biological Sciences, University of Turin, ; Turin, Italy
                [6 ]Unit of Epidemiology, Regional Health Service ASL TO3, Grugliasco, Turin Italy
                [7 ]ISNI 0000 0001 2336 6580, GRID grid.7605.4, , Unit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, ; Turin, Italy
                [8 ]Cancer Registry, Department of Medical Prevention, ASP Ragusa, Italy
                Author information
                http://orcid.org/0000-0001-8441-4611
                http://orcid.org/0000-0002-9178-5274
                Article
                9498
                10.1038/s41598-017-09498-2
                5575161
                28851931
                40278e81-8311-44fc-ac1e-45538573cbb1
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 April 2017
                : 27 July 2017
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