title: Prediction of postprandial glycemia and insulinemia in lean, young, healthy adults: glycemic load compared with carbohydrate content alone.
abstract: BACKGROUND
  Dietary glycemic load (GL; defined as the mathematical product of the glycemic index and carbohydrate content) is increasingly used in nutritional epidemiology. Its ability to predict postprandial glycemia and insulinemia for a wide range of foods or mixed meals is unclear.
  
  
  OBJECTIVE
  Our objective was to assess the degree of association between calculated GL and observed glucose and insulin responses in healthy subjects consuming isoenergetic portions of single foods and mixed meals.
  
  
  METHODS
  In study 1, groups of healthy subjects consumed 1000-kJ portions of 121 single foods in 10 food categories. In study 2, healthy subjects consumed 2000-kJ servings of 13 mixed meals. Foods and meals varied widely in macronutrient content, fiber, and GL. Glycemia and insulinemia were quantified as area under the curve relative to a reference food (= 100).
  
  
  RESULTS
  Among the single foods, GL was a more powerful predictor of postprandial glycemia and insulinemia than was the available carbohydrate content, explaining 85% and 59% of the observed variation, respectively (P < 0.001). Similarly, for mixed meals, GL was also the strongest predictor of postprandial glucose and insulin responses, explaining 58% (P = 0.003) and 46% (P = 0.01) of the variation, respectively. Carbohydrate content alone predicted the glucose and insulin responses to single foods (P < 0.001) but not to mixed meals.
  
  
  CONCLUSIONS
  These findings provide the first large-scale, systematic evidence of the physiologic validity and superiority of dietary GL over carbohydrate content alone to estimate postprandial glycemia and insulin demand in healthy individuals. This trial was registered at ANZCTR.org as ACTRN12610000484044.
authors.affiliation: Boden Institute of Obesity, Nutrition & Exercise and the School of Molecular Biosciences, University of Sydney, Sydney, Australia.
authors.forename: Jiansong
authors.initials: J
authors.lastname: Bao
authors.forename: Fiona
authors.initials: F
authors.lastname: Atkinson
authors.forename: Peter
authors.initials: P
authors.lastname: Petocz
authors.forename: Walter C
authors.initials: WC
authors.lastname: Willett
authors.forename: Jennie C
authors.initials: JC
authors.lastname: Brand-Miller
mesh_terms: D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D015992:Body Mass Index; D004040:Dietary Carbohydrates / Q000009:adverse effects*; D005502:Food / Q000009:adverse effects*; D005504:Food Analysis; D038321:Glycemic Index; D006801:Humans; D007328:Insulin / Q000097:blood*; D019518:Postprandial Period; D013851:Thinness / Q000097:blood*; D013997:Time Factors; D055815:Young Adult
chemical_list: D001786:Blood Glucose; D004040:Dietary Carbohydrates; D007328:Insulin