Logic Algebra (Second Edition)

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Please select author name in each record! Close Find out more. Concurrency limit. Sign out. Search: Keyword Advanced Browse all content Thesaurus. Please use quotation marks for searching phrases e. Your products All Products. Browse by : Author Author Serial Subject. Enter author surname:. Display : 25 50 Previous record Next record. Actions Tools Choose a colour. This book aims to provide a credible, complete and accurate source of information about all the aspects of glycaemic index.

It contains chapters on glycaemic index definition and measurement and how glycaemic index information can be applied to meals and diets. Discussions on the reasons why foods The Glycaemic Index: a physiological classification of dietary carbohydrate. Buy this book Author s : Wolever, T.

Glycemic Index – Gastrointestinal Society

Discussions on the reasons why foods have different glycaemic index values and the impact of altering the glycaemic index of diets on health and disease are presented as well. Back to top. Edit annotation. Cancel Edit annotation. Add annotation. Cancel Add annotation. Print citation. Cancel Print.

ISBN 13: 9781845937225

The 16 different test meals consisted of 50 g anhydrous glucose Sigma Chemical ; mL tap water; 0, 5, 10, or 30 g fat 0, 5, 10, or 30 g corn oil; Mazola, ACH Food ; and 0, 5, 10, or 30 g protein 0, 5. Subjects took mL water with each test meal. Test meals were administered according to a randomized block design; each block consisted of 1 level of fat g of fat, 0, 5, 10 or 30, respectively, added to 50 g glucose [F0, F5, F10, or F30] with each of the 4 levels of protein g of protein, 0, 5, 10 or 30, respectively, added to 50 g glucose [P0, P5, P10, and P30].

The order of the blocks and the order of tests within blocks were randomized. Glucose alone F0P0 was added to 2 other blocks so that each subject tested F0P0 3 times.


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Nutrient intakes were estimated using 3-d diet records 2 weekdays and 1 weekend day , filled out during the experimental period, and analyzed using Food Processor SQL edition, version 9. Insulin sensitivity was estimated using the homeostasis assessment model HOMA; Body fat percentage was estimated from age, sex, BMI, and ethnicity using regression equations developed in different ethnic populations Peak rise PR was the maximum glucose concentration achieved minus fasting glucose.

Incremental areas under the curve AUC , ignoring area below fasting, were calculated as previously described RGR was the primary outcome. The shape of the dose-response relations was assessed by testing if there was significant reduction of residual variation when a term for dose 2 of fat or protein was added to the linear regression model Prism 4 for Windows, GraphPad Software.

A significant effect would indicate that a nonlinear quadratic model fit the data significantly better than a linear model. The ability of fat or protein to reduce postprandial glucose in each subject was defined as the slope of the regression line of RGR on dose of fat or protein. Correlations between these slopes and other variables were determined by simple and multiple linear regression analysis Lotus 97 Edition, Lotus Development. The significance of differences between individual means was assessed using Tukey's test to control for multiple comparisons.

Hyper[I] subjects were similar to control with respect to sex, ethnicity, age, height, weight, BMI, and percent body fat Table 1. One hyper[I] subject had metabolic syndrome according to the International Diabetes Federation consensus definition Details of subjects studied 1. Mean blood glucose after the different test meals did not differ significantly between control and hyper[I] subjects Fig.

Error bars not shown if they are smaller than the symbol or overlap other bars or symbols. Main effects of fat black circles and bars and protein white circles and bars on glucose responses expressed as AUC A , RGR B , and PR C after nondiabetic humans consumed 50 g glucose plus different levels of fat and protein. AUC, RGR, and peak rises in whole blood glucose in nondiabetic humans after they consumed 50 g glucose plus different levels of fat and protein 1.

The ability of fat or protein to reduce glycemic responses is termed fat slope or protein slope, respectively. Fat and protein slopes were not related to percent body fat. Relations between fat top and protein slopes bottom in individual subjects and their FPI left and WC right at screening. Fat or protein slope is the extent to which fat or protein, added to 50 g glucose, reduces RGR expressed as a percentage of the response elicited by glucose alone. Lines are regression lines. Fat slope was not related to dietary fat intake.

Relation between dietary fiber intake and protein slope in individual subjects. Protein slope is the extent to which protein, added to 50 g glucose, reduces RGR expressed as a percentage of the response elicited by glucose alone. The regression line is shown.

The results showed that both protein and fat reduced the glycemic response elicited by oral glucose in normal humans. The effects of protein and fat were independent of each other, but gram-for-gram, protein had a 2 to 3 times larger effect than fat. Fat reduced glycemic responses to a greater extent in subjects with low FPI, whereas protein had more effect in subjects with a high WC and a high intake of dietary fiber. Previous studies suggest that adding fat and protein to carbohydrate reduces glycemic responses nonlinearly, with the glycemic impact reaching a plateau as more and more protein and fat are added 6 , 8.

However, we found no evidence for a nonlinear dose response over the range of doses used 0—30 g. This may have been due in part to a lack of statistical power or to differences in study design. We previously found that adding margarine to bread reduced glucose PR in a significantly nonlinear fashion 8 ; by contrast, in this study, liquid test meals were used. Because of differences in how solids and liquids empty from the stomach 27 , the shape of the dose-response curve for added fat may differ for solid and liquid meals.

A number of our conclusions are based on using linear regressions as quantitative estimates of the effects of fat and protein on glycemic responses in individual subjects. The linear model was considered valid because there was no evidence for nonlinear relations. The validity of pooling the results for the different levels of 1 nutrient i. The effect of fat on glycemic responses did not differ in hyper[I] vs.

This suggests that our definition of hyper[I], i. This is consistent with studies showing that fat has no effect on glycemic responses in subjects with diabetes 9. Taken together, the results suggest that insulin resistance rather than diabetes per se may be responsible for the lack of fat effect. Our finding that fiber intake and WC modulated the glucose-lowering effect of protein may help explain the inconsistent effects of protein reported in the literature 6 , 7 , although other factors, such as protein digestibility 28 and differences in the ability of specific amino acids to stimulate insulin 29 , may also be important.

However, the lack of correlation between fat slopes and protein slopes in the different subjects and the 2—3 times greater effect of protein than fat, suggest that the mechanisms by which fat reduces glycemic responses differ from those for protein. We expected the glucose-lowering effects of both fat and protein to be smaller in hyper[I] subjects than controls, because these effects are thought to be mediated by gut hormones that are reduced in insulin resistance and obesity 38 — Also, we anticipated that any effects related to FPI would also be related to WC, a marker of abdominal obesity, because insulin resistance and hyperinsulinemia are closely associated with abdominal obesity Indeed, this suggests that abdominal obesity and hyperinsulinemia have distinct metabolic implications, an idea that has been proposed from the positive association between FPI and increased risk of cardiovascular disease independent of variation in WC Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index.

BIAS AGAINST GI

Search ADS. The effects of fat and protein on glycemic responses in non-diabetic humans vary with waist-circumference, fasting plasma insulin and dietary fiber intake. Plasma insulin responses after ingestion of different amino acid or protein mixtures with carbohydrate. Postprandial lipemia in subjects with the T54 variant of fatty acid-binding protein 2 FABP2 gene is dependent on the type of fat ingested. Differential effect of protein and fat ingestion on blood glucose responses to high- and low-glycemic-index carbohydrates in noninsulin-dependent diabetic subjects.

The use of glycaemic index tables to predict glycaemic index of composite breakfast meals. The glycaemic index: a physiological classification of dietary carbohydrate. Nutrition discussion forum: the use of glycaemic index tables to predict glycaemic index of breakfast meals. Issue Section:. Download all figures. View Metrics. Email alerts New issue alert. Advance article alerts. Article activity alert. Receive exclusive offers and updates from Oxford Academic.

The Glycaemic Index: A Physiological Classification of Dietary Carbohydrate

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