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An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods.

The Insulin Index

The Insulin Index

Lots of people are wondering whatever happened to the insulin index. I wondered too, so I asked Susanna Holt. Dr. Holt developed the insulin index about a decade ago when she was working on her Ph.D. at Australia’s University of Sydney. Her work was exciting but preliminary. She tested just 38 foods and found that their glycemic index and insulin index values were highly correlated. But there was a big exception. Their most interesting finding was that "protein-rich foods and bakery products (rich in fat and refined carbohydrate) elicited insulin responses that were disproportionately higher than their glycemic responses." The American Journal of Clinical Nutrition published her findings in a 1997 article, "An Insulin Index of Foods: The Insulin Demand Generated by 1000-kJ Portions of Common Foods". My 2003 article about the insulin index summarized and popularized that research. Some people think that the insulin index is even more useful than the glycemic index. It makes sense to compare these two indexes, because Dr. Holt developed the insulin index while working with Dr. Jennie Brand-Miller, who has done the most work on the glycemic index. We still don’t know why there are differences between glycemic and insulin index values and what significance they have. "Some foods (such as meat, fish and eggs) that contain no carbohydrate, just protein and fat (and essentially have a GI value of zero), still stimulate significant rises in blood insulin," Dr. Brand-Miller wrote in her best-selling book, The New Glucose Revolution (New York: Marlowe and Company, 2003, pages 57-58). "We don’t know how to interpret this type of response (low glycemia, high insulinemia) for long-term health. It may be a good outcome because the rise in insulin has contributed to the low leve Continue reading >>

The Latest Food Insulin Index Data

The Latest Food Insulin Index Data

Understanding the factors that our requirement for insulin is critical to good managing and avoiding diabetes and maintaining good metabolic health. Living with someone who has Type 1 Diabetes for fifteen years I’ve gained an intimate understanding of how different foods will take your blood glucose levels on a wild ride. In this article, I will share my insights from the latest food insulin index data and how we can apply it to optimise our insulin and blood glucose response to the food we eat. Since she was ten, my wife Monica’s had to manually manage her blood sugars as they swing up with food and then drop again when she injects insulin. High blood glucose levels make her feel “yucky”. Plummeting blood glucose levels due to the mega doses of insulin don’t feel good either. Low blood glucose levels drive you to eat until you feel good again. This wild blood glucose roller coaster ride leaves you exhausted. If you have diabetes, you are likely familiar with this feeling. The dietary advice she received over the past three decades living with Type 1 has been sketchy at best. When she was first diagnosed, Monica tells the story of being made to eat so much high carb food that she hid it in the pot plants in her hospital room. When we decided we wanted to have kids, we found a great doctor who helped us to understand how to match insulin with carbs, but moderating the input of carbohydrate that necessitates insulin was never mentioned by physicians, endocrinologists or diabetes educators. Then in early 2014, I came across Jason Fung’s Aetiology of Obesity series on YouTube where he discussed the food insulin index research that had been carried out at the University of Sydney which seemed to provide more insight into our insulin response to food. I hoped that Continue reading >>

Insulin Index

Insulin Index

Insulin Index J S Coleman Bionomic Nutrition Forum, 2000 This article has been written to clear up some of the dietary inaccuracies and myths surrounding the role of different types of food, and how they affect insulin secretion. Some dietary authorities and fad diet groups are now proposing dietary changes based on the glycemic (i.e. glucose in blood) index (GI) method. At the moment the GI method has yet to be shown to be anything other than a crude yardstick, and so it had not gained broad scientific acceptance. The GI method was developed to rank foods according to the extent to which they increase blood glucose concentrations, this being a useful guide to help those people with diabetes choose foods with lower glycemic responses. Insulin promotes the uptake of glucose from general circulation, into the cells for use in energy production. Some of the over-simplistic concepts being circulated run along these lines: foods high in carbohydrate have higher glycemic indexes than protein rich foods... and therefore high protein foods (meat/fish) are safer fruits are high in sugar... and therefore have higher glycemic indexes a higher glycemic index means the body must produce more insulin... and therefore low glycemic index foods are safer A better attempt at understanding how diet affects insulin levels has been proposed by Susanne HA Holt, Janette C Brand Miller and Peter Petocz in their paper entitled "An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods" (Am. J. Clin. Nutr., Nov 1997, Vol.66, Iss.5, p.1264-76). The authors point out that their results are "preliminary", and it must also be noted that only a few foods (38) have been studied. Even so, their food choice method is more realistic, and their method more thorough than t Continue reading >>

An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods

An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods

The aim of this study was to systematically compare postprandial insulin responses to isoenergetic 1000-kJ (240-kcal) portions of several common foods. Correlations with nutrient content were determined. Thirty-eight foods separated into six food categories (fruit, bakery products, snacks, carbohydrate-rich foods, protein-rich foods, and breakfast cereals) were fed to groups of 11-13 healthy subjects. Finger-prick blood samples were obtained every 15 min over 120 min. An insulin score was calculated from the area under the insulin response curve for each food with use of white bread as the reference food (score = 100%). Significant differences in insulin score were found both within and among the food categories and also among foods containing a similar amount of carbohydrate. Overall, glucose and insulin scores were highly correlated (r = 0.70, P < 0.001, n = 38). However, protein-rich foods and bakery products (rich in fat and refined carbohydrate) elicited insulin responses that were disproportionately higher than their glycemic responses. Total carbohydrate (r = 0.39, P < 0.05, n = 36) and sugar (r = 0.36, P < 0.05, n = 36) contents were positively related to the mean insulin scores, whereas fat (r = -0.27, NS, n = 36) and protein (r = -0.24, NS, n = 38) contents were negatively related. Consideration of insulin scores may be relevant to the dietary management and pathogenesis of non-insulin- dependent diabetes mellitus and hyperlipidemia and may help increase the accuracy of estimating preprandial insulin requirements. Continue reading >>

Dietary Insulin Load And Insulin Index Are Associated With The Risk Of Insulin Resistance: A Prospective Approach In Tehran Lipid And Glucose Study

Dietary Insulin Load And Insulin Index Are Associated With The Risk Of Insulin Resistance: A Prospective Approach In Tehran Lipid And Glucose Study

Abstract The aim of this study was to investigate the relationship between dietary insulin load (DIL) and insulin index (DII) and the risk of insulin resistance in Tehranian adults. In this study, 927 men and women, aged 22–80 years, participated in Tehran Lipid and Glucose Study were included. Fasting serum insulin and glucose were measured at baseline and again after a 3-year of follow-up. Usual dietary intakes were measured using a validated 168 item semi-quantitative food frequency questionnaire and DIL and DII were calculated. Logistic regression models were used to estimate the occurrence of the IR across tertile categories of DIL and DII with adjustment for potential confounding variables. Mean age of participants was 40.71 ± 12.14 y, and mean body mass index (BMI) was 27.23 ± 4.9 kg/m2, at baseline. Mean of DIL and DII was 937 ± 254 and 84.0 ± 6.3. Participants with higher DIL had higher weight and waist circumference at baseline (P < 0.05). A borderline positive association was observed between DII and the risk of insulin resistance in fully adjusted model (odds ratio = 1.66, 95 % confidence interval = 0.96–2.86, P for trend = 0.06). After adjustment of potential confounders, highest compared to the lowest tertile of DIL was also significantly associated with increased risk of insulin resistance (odds ratio = 1.69, 95 % confidence interval = 1.01–2.89, P for trend = 0.06). Dietary insulin load and DII could be considered as independent dietary risk factors for development of insulin resistance. Dietary insulin index Dietary insulin load Tertile1 Tertile2 Tertile3 Tertile1 Tertile2 Tertile3 <81.6 81.6–86.3 ≥86.3 <794 794–1097 ≥1097 Age at baseline (yr) 40.7 ± 12.0 38.8 ± 12.1 41.0 ± 12.2 39.53 ± 11.54 40.60 ± 12.17 40.46 ± 12.66 Men (%) 3 Continue reading >>

Insulin Index

Insulin Index

Rating: 0.0/5 (0 votes cast) login to rate The Insulin Index is a measure used to quantify the typical insulin response to various foods. The index is similar to the Glycemic Index and Glycemic Load, but rather than relying on blood glucose levels, the Insulin Index is based upon blood insulin levels. This measure can be more useful than either the Glycemic Index or the Glycemic Load because certain foods (e.g., lean meats and proteins) cause an insulin response despite there being no carbohydrates present, and some foods cause a disproportionate insulin response relative to their carbohydrate load. Holt et al. have noted that the glucose and insulin scores of most foods are highly correlated, but high-protein foods and bakery products that are rich in fat and refined carbohydrates "elicit insulin responses that were disproportionately higher than their glycemic responses." They also conclude that insulin indices may be useful for dietary management and avoidance of non-insulin-dependent diabetes mellitus and hyperlipidemia. Explanation of Index The insulin index shows how much insulin is present in people's blood as a result of a particular food, the glucose index shows how much glucose is present in the blood as a result of a particular food, and the satiety index shows how much a particular food decreases one's propensity to eat more. Glucose (glycemic) and insulin scores were determined by feeding 1000 kilojoules (239 kilocalories) of the food to the participants and recording the area under the glucose/insulin curve for 120 minutes then dividing by the area under the glucose/insulin curve for white bread. The result being that all scores are relative to white bread. The satiety score was determined by comparing how much food was eaten by participants at a buffet af Continue reading >>

Insulin Index

Insulin Index

One problem with David Mendosa’s article about the insulin index, which I referenced yesterday, is that the insulin index table he gives doesn’t tell you very much about the foods contained in it. For example, as a correspondent pointed out to me, is the “beef” listed there ground beef with 25 percent fat? or is it a nice, lean sirloin tip steak with only 3 percent fat? Luckily, I was able to find the original study that Mendosa was writing about: “An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods” by SH Holt, JC Miller and P Petocz, American Journal of Clinical Nutrition, Vol 66, 1264-1276 (1997). The full article is much more specific than Mendosa’s “popularization” of it on the foods used & exactly how they were prepared. For example, what Mendosa listed simply as beef is revealed in the full study to be: Food: Beef steak Variety, manufacturer, or place of purchase: Lean topside beef fillets bought in bulk from supermarket, trimmed and stored frozen Preparation: Grilled the day before serving, cut into standard bite-sized pieces, and stored at 4° C overnight; reheated in microwave oven for 2 mm immediately before serving [Table 1, page 1265] The nutritional composition of the beef, according to Table 2 (page 1267), was 7.7 grams of fat & 42.0 grams of protein (0.0 carb), in a 158 gram serving. (All the foods were tested in 1000-kilojoule servings.) The white fish used in the study (ling fish fillets) was even leaner: 1.0 grams of fat & 56.3 grams of protein (0.0 carb). The other protein-rich foods chosen (cheese, eggs, lentils, baked beans) all had more fat & at least some carb (esp. the lentils & beans). That kind of detail is given for all the foods tested in the study, & the article gives a very full met Continue reading >>

An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods.

An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods.

Abstract The aim of this study was to systematically compare postprandial insulin responses to isoenergetic 1000-kJ (240-kcal) portions of several common foods. Correlations with nutrient content were determined. Thirty-eight foods separated into six food categories (fruit, bakery products, snacks, carbohydrate-rich foods, protein-rich foods, and breakfast cereals) were fed to groups of 11-13 healthy subjects. Finger-prick blood samples were obtained every 15 min over 120 min. An insulin score was calculated from the area under the insulin response curve for each food with use of white bread as the reference food (score = 100%). Significant differences in insulin score were found both within and among the food categories and also among foods containing a similar amount of carbohydrate. Overall, glucose and insulin scores were highly correlated (r = 0.70, P < 0.001, n = 38). However, protein-rich foods and bakery products (rich in fat and refined carbohydrate) elicited insulin responses that were disproportionately higher than their glycemic responses. Total carbohydrate (r = 0.39, P < 0.05, n = 36) and sugar (r = 0.36, P < 0.05, n = 36) contents were positively related to the mean insulin scores, whereas fat (r = -0.27, NS, n = 36) and protein (r = -0.24, NS, n = 38) contents were negatively related. Consideration of insulin scores may be relevant to the dietary management and pathogenesis of non-insulin-dependent diabetes mellitus and hyperlipidemia and may help increase the accuracy of estimating preprandial insulin requirements. Continue reading >>

Review The Β-cell Burden Index Of Food: A Proposal

Review The Β-cell Burden Index Of Food: A Proposal

Abstract The quantity and quality of dietary fat and/or carbohydrate may alter one or more of the basic components of the insulin-glucose system, which in turn affect the pathways leading to alterations in glucose homeostasis and, possibly, to cardiovascular disease. This viewpoint article, reviewing some of the currently available tools aiming at quantifying the impact of dietary carbohydrates on the glucose-insulin homeostatic loop, highlights the unmet need of a more thorough assessment of the complex interaction between dietary factors and the glucose-insulin system. A novel index, the “β-cell burden index”, may turn out to be a valuable tool to quantify the role played by the diet in shaping the risk of type 2 diabetes, cardiovascular disease and other metabolic and degenerative disorders, ideally orienting their prevention with strategies based on dietary modifications. Continue reading >>

An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods

An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods

SH Holt, JC Miller and P Petocz Department of Biochemistry, University of Sydney, Australia. The aim of this study was to systematically compare postprandial insulin responses to isoenergetic 1000-kJ (240-kcal) portions of several common foods. Correlations with nutrient content were determined. Thirty-eight foods separated into six food categories (fruit, bakery products, snacks, carbohydrate-rich foods, protein-rich foods, and breakfast cereals) were fed to groups of 11-13 healthy subjects. Finger-prick blood samples were obtained every 15 min over 120 min. An insulin score was calculated from the area under the insulin response curve for each food with use of white bread as the reference food (score = 100%). Significant differences in insulin score were found both within and among the food categories and also among foods containing a similar amount of carbohydrate. Overall, glucose and insulin scores were highly correlated (r = 0.70, P < 0.001, n = 38). However, protein-rich foods and bakery products (rich in fat and refined carbohydrate) elicited insulin responses that were disproportionately higher than their glycemic responses. Total carbohydrate (r = 0.39, P < 0.05, n = 36) and sugar (r = 0.36, P < 0.05, n = 36) contents were positively related to the mean insulin scores, whereas fat (r = -0.27, NS, n = 36) and protein (r = -0.24, NS, n = 38) contents were negatively related. Consideration of insulin scores may be relevant to the dietary management and pathogenesis of non-insulin-dependent diabetes mellitus and hyperlipidemia and may help increase the accuracy of estimating preprandial insulin requirements. Continue reading >>

Tag: Holt, S.h., J.c. Miller, And P. Petocz. An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods. Am. J. Clin. Nutr. 66:1264-1276, 1997.

Tag: Holt, S.h., J.c. Miller, And P. Petocz. An Insulin Index Of Foods: The Insulin Demand Generated By 1000-kj Portions Of Common Foods. Am. J. Clin. Nutr. 66:1264-1276, 1997.

by Jonette E. Keri, MD, PhD, and Adena E. Rosenblatt Department of Dermatology, University of Miami Miller School of Medicine, Miami, Florida Disclosure: The authors identified no conflicts of interest. Continue reading >>

Glycemic Index Fails To Help You

Glycemic Index Fails To Help You

Tweet A Word about the Glycemic Index and Why It Does Not Work! The glycemic index is a popular way to numerically grade carbohydrate foods based upon the criteria of how much and how quickly the food results in elevated glucose in the bloodstream. This laboratory-derived list of foods is found in every reference work on diet and nutrition. The glycemic index is inappropriately but commonly used as evidence that a particular food is “good” or “bad.” The logic of the glycemic index follows that good carbs are slow to result in glucose in the bloodstream, while bad carbs are faster to result in measurable blood glucose. Of course, foods that have no carbohydrate content are not on this scale, so fats and proteins have no inherent glycemic response. There are problems with the overly simplistic glycemic index. Here are three reasons why you will not find the ubiquitous “glycemic index” in The Blood Code: Reason #1: Food combination, like in a “balanced meal,” is not accounted for in the glycemic index. Fat and soluble fiber with the meal effectively lowers the glycemic index of any carbohydrate food at that meal. The same effect is also seen if fermented foods and vinegar are added to a carbohydrate meal. Reason #2: Excess glucose from carbs can clearly aggravate insulin resistance, but too much pure protein and large-volume meals trigger a problematic release of insulin, despite their lack of effect on blood sugar.[i] Reason #3: I have saved the biggest reason for last: Fructose! Fructose is very low on the glycemic index, so it actually ends up in the “good” category, but fructose delays its harmful effect. Your body cannot immediately access the glucose molecule bound up in the fructose molecule. Many hours after you eat, fructose—whether from frui Continue reading >>

Insulin Elevation From Low-carb Foods...what's The Deal?

Insulin Elevation From Low-carb Foods...what's The Deal?

I was also worried about this when I first started LCing after encountering GCBC. Taubes himself addresses this briefly in an interview and Mark Sisson has also briefly addressed it. I think the most important point practically, is that it doesn't much matter that meat is insulinemic, since you'll only be getting around 25% of your calories from protein anyway. The important factor is that the rest of your calories will be coming from fat, which is in itself non-insulinemic (although any actual fat-foods you eat will produce a slight response, in the sense that even thinking about food produces a slight insulin response. Taubes makes the connected point that the meat looks a lot more insulinemic on the chart, because they only use very lean meat. In addition, having fat with the protein will blunt the glucagon/insulin response, both by slowing digestion and because fatty acids in the bloodstream inhibits glucagon (obviously no need to release sugar into the blood if it's awash with fatty acid/ketoacids). The glucagon is significant because it means that the insulinemic food won't have the same impact as carbohydrate. Whereas carbohydrate forces a rise and drop in blood sugar, glucagon ensures a steady release of glucose into the bloodstream, so it won't have the same hunger-producing properties. [See image below]. This study also stresses how hyperglycemia rather than hyperinsulinemia is a problem to be avoided; although Taubes might stress that it is insulin that is at the centre of weight gain/loss, it's clear that high blood sugar produces its own separate problems (glycation etc). It also counts quite heavily against lactose/whey containing dairy, that it's so insulinemic. Continue reading >>

Improving The Estimation Of Mealtime Insulin Dose In Adults With Type 1 Diabetes

Improving The Estimation Of Mealtime Insulin Dose In Adults With Type 1 Diabetes

OBJECTIVE Although carbohydrate counting is routine practice in type 1 diabetes, hyperglycemic episodes are common. A food insulin index (FII) has been developed and validated for predicting the normal insulin demand generated by mixed meals in healthy adults. We sought to compare a novel algorithm on the basis of the FII for estimating mealtime insulin dose with carbohydrate counting in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS A total of 28 patients using insulin pump therapy consumed two different breakfast meals of equal energy, glycemic index, fiber, and calculated insulin demand (both FII = 60) but approximately twofold difference in carbohydrate content, in random order on three consecutive mornings. On one occasion, a carbohydrate-counting algorithm was applied to meal A (75 g carbohydrate) for determining bolus insulin dose. On the other two occasions, carbohydrate counting (about half the insulin dose as meal A) and the FII algorithm (same dose as meal A) were applied to meal B (41 g carbohydrate). A real-time continuous glucose monitor was used to assess 3-h postprandial glycemia. RESULTS Compared with carbohydrate counting, the FII algorithm significantly decreased glucose incremental area under the curve over 3 h (–52%, P = 0.013) and peak glucose excursion (–41%, P = 0.01) and improved the percentage of time within the normal blood glucose range (4–10 mmol/L) (31%, P = 0.001). There was no significant difference in the occurrence of hypoglycemia. CONCLUSIONS An insulin algorithm based on physiological insulin demand evoked by foods in healthy subjects may be a useful tool for estimating mealtime insulin dose in patients with type 1 diabetes. In type 1 diabetes management, premeal insulin dosage and physiological insulin requirement mus Continue reading >>

Insulin Index

Insulin Index

The research on the insulin index of foods is intriguing but limited. Only 16 peer-reviewed articles in MEDLINE even mention the term "insulin index," and only one of them actually reports the results of food comparisons. By comparison, 244 peer-reviewed articles mention the glycemic index. They…found that glycemic and insulin scores were highly correlated. That study is "An Insulin Index of Foods: The Insulin Demand Generated by 1000-kJ Portions of Common Foods" in the American Journal of Clinical Nutrition 1997, Vol. 66: pages 1264-1276 by Susanne HA Holt, Janette C. Brand Miller, and Peter Petocz. The three co-authors were then associated with the University of Sydney in Australia. Susanne Holt was then a graduate student working under the supervision of Janette Brand Miller, and Peter Petocz provided statistical support. Subsequently, Ms. Holt—now Dr. Susanna Holt—obtained her Ph.D. degree and is directs the Glycemic Index Research Service (SUGiRS) in the University of Sydney's department of biochemistry. Ms. Brand Miller—now Professor Jennie Brand-Miller—directs glycemic index research at the University of Sydney's department of biochemistry. They tested only 38 foods and found that glycemic and insulin scores were highly correlated. Their most interesting finding was that "protein-rich foods and bakery products (rich in fat and refined carbohydrate) elicited insulin responses that were disproportionately higher than their glycemic responses." One J.S. Coleman finds the insulin index to be superior to the glycemic index. Comparing the insulin index study cited above with glycemic index studies, that article (which is no longer online) states that "their food choice method is more realistic, and their method more thorough than the GI method." What that aut Continue reading >>

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