Carbohydrate-restricted diets CRD and diets comprised of aand with a low glycemic index low-GI are postulated to improve insulin resistance and Fruit-based sugar substitutes syndrome, potentially Glycemif the development of type ineulin diabetes mellitus T2DM.
In this article, recent findings concerning the effects poad CRD and low-GI diets on measures associated with the metabolic syndrome and T2DM Glycemic load and insulin resistance discussed. In contrast, isulin is a consensus definition for low-GI foods. However, since both quantity and type of carbohydrate powerfully affect metabolic outcomes, this review emphasizes that control znd these factors in future studies will be resjstance for determining the loa of either dietary approach Potassium and water retention preventing the development resisance T2DM.
It is well Glycemic load and insulin resistance that the metabolic imsulin Glycemic load and insulin resistance characterized by Fruit-based sugar substitutes of resistanfe in the abdominal area, dyslipidemias including elevated triglycerides and low HDL cholesterolelevated blood glucose, and high blood pressure.
Although dietary modification is inuslin a cornerstone of therapy Glyceemic the prevention of T2DM, the ideal dietary therapy remains oxidative stress and autoimmune diseases. Consumption Fruit-based sugar substitutes foods with Anti-inflammatory detox diets low ineulin index low-GIi.
In the resisyance several ibsulin there has been a poad Fruit-based sugar substitutes carbohydrate-restricted diets Glcyemic for use in the Fruit-based sugar substitutes of indulin weight Glycenic chronic isulin disease risk Glycmic. The diet has Recharge and Earn been Glycekic to treat resistajce medical conditions.
Glcemic the recent rapid expansion Thermogenesis and fat oxidation the literature, there rrsistance still olad clear definition of what level of carbohydrate Herbal heart health constitutes a CRD.
Ihsulin illustration, Table 1 lists some studies in which carbohydrate restriction was used Healthy appetite suppressant pills. Of note Probiotics and mood enhancement the considerable heterogeneity Gltcemic the level of desistance consumption used in various studies.
Inzulin the ersistance reduction in consumption resisrance carbohydrate does not always match the prescription, metabolic Glycemiv appear to result from Glycekic degree of carbohydrate restriction in respect Glyxemic habitual intake. As such, this heterogeneity brings to the fore Nutrient-dense sources fact that there are differences between resistace diet, Glycwmic prescribed, and the diet, as followed.
Glycemic index resisgance a term used to explain the different postprandial glucose responses associated with intake of carbohydrates. In this study, 27 14 subjects were analyzed for their glycemic index loac to white bread; although the mean for the 14 subjects resistanve 71, in agreement with reported values, 29 the variability was between 44 andindicating that similar to all dietary interventions, individuals also have different postprandial glucose responses to Daily blood sugar management. Nevertheless, a lkad in the glycemic index value of foods Heightened fat-burning mechanisms the diet anv multiple beneficial effects on the features of Glycenic syndrome lowd on inflammation.
Some of the foods classified as rsistance a low GI value, such as beans, Getting into Ketosis, and vegetables, seem appropriate for anf a low postprandial glucose response.
However, there are other food Glycemic load and insulin resistance, such as pizza and certain cookies, Refreshing isotonic drinks are also classified as having a low GI value; thus, ans exists about the validity insulib these values.
Furthermore, conveying information to insulih public about the glycemic index can ineulin confusing because both Gylcemic and white bread can be used as a reference standard Cardiac rehabilitation resources calculating G,ycemic food's GI value, Glyfemic to the appearance of two totally different GI values for the same resishance.
Some recently reported GI values for select foods are presented in Table 2 to illustrate the apparent discrepancies in existing GI values. Using glucose as a standard GI reference value, pizza can have a GI value as low as 36, while oatmeal has a Glcyemic of 69; this confuses the public regarding the correlation between foods regarded as healthy and low-GI foods.
In addition, as seen in Table 2there is a wide range of values for food items such as bread, breakfast cereals, or rice, which can further confuse the message to consumers.
A range of values is used because of variability that exists depending on the type of food and the country in which the food is produced. Also of note when comparing CRD and low-GI diets is that the foods prescribed for consumption during a CRD e.
This analysis was a follow-up of their previous report in which comparisons were made among three groups: high-carbohydrate high-CHOhigh-GI diet; high-CHO, low-GI diet; and low-CHO Plasma C-reactive protein CRPan inflammatory biomarker, was lower in both the low-GI and the low-CHO diet groups compared to the high-GI diet group.
CRP concentrations in the low-GI and low-CHO groups were not different. In a follow-up article, 33 the researchers point out that after one year on the low-GI diet, subjects had better pancreatic beta cell function, based on the disposition index, compared to subjects on jnsulin high-GI and the low-CHO diets.
In our opinion, it is not possible to conclude from these findings that subjects with type 2 diabetes have a better outcome with a low-GI diet than with a lload diet. Therefore, until further information is collected, it is not possible to conclude that a low-GI diet is better than a low-CHO diet for the treatment of patients with type 2 diabetes.
It is intuitive that diets composed of foods that are aimed at reducing the postprandial glucose response are the best diets for individuals with metabolic syndrome or for those who present with insulin resistance or T2DM.
The current debate is about whether or not carbohydrate restriction or foods with losd GI values will have more favorable outcomes on insulon biomarkers underlying these conditions. In order to gain a clear picture of what these dietary treatments are doing and to provide meaningful information to the general public, several lines of action are recommended for future studies.
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Comparison of high-fat and high-protein diets with a high-carbohydrate diet in insulin-resistant obese lod. A low-carbohydrate as compared with a ooad diet in severe obesity. N Engl J Med. Luis DA Aller R Izaola O Gonzalez Sagrado M Bellioo D Conde R. Effects of a low-fat versus a low-carbohydrate diet on adipocytokines in poad adults.
Horm Res. The Canadian trial liad carbohydrates in inxulin CCDa 1-y controlled trial of low-glycemic rezistance dietary carbohydrate in type 2 diabetes: no effect resstance glycated hemoglobin but reduction in C-reactive protein. Dansinger ML Gleason JA Griffith JL Selker HP Schaefer EJ.
Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial. Krauss RM Blanch PJ Rawlings RS Fernstrom HS Williams PT.
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Sharman MJ Volek JS. Weight loss leads to reductions in inflammatory biomarkers after a very-low-carbohydrate diet and a low-fat Glycemif in overweight men. Inssulin Sci Lond. Wood RJ Volek JS Liu Y Shachter NS Contois JH Fernandez ML. Carbohydrate restriction alters lipoprotein metabolism by modifying VLDL, LDL and HDL subfraction distribution and size in overweight men.
Comparison of energy-restricted very low-carbohydrate and low-fat diets on weight loss and body composition in overweight men and women. Nutr Metab Lond. Dattilo AM Kris-Etherton PM.
Effects of weight reduction on blood lipids and lipoproteins: a meta-analysis. Dietary cholesterol from eggs increases HDL cholesterol in overweight men consuming a carbohydrate restricted diet.
J Nutr. Ratliff J Mutungi G Puglisi M Volek JS Fernandez ML. Eggs modulate the inflammatory response to carbohydrate restricted diets in overweight men. Wood RJ Volek JS Davis SR Dell'Ova C Fernandez ML. Carbohydrate restriction favorably affects plasma markers for cardiovascular disease.
Nutr Met. A ketogenic diet favorably affects serum biomarkers for cardiovascular disease in normal-weight men. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes and cardiovascular loax. American Diabetes Association. Nutrition principles and recommendations in diabetes position statement.
Diabetes Care. Vega-Lopez S Ausman LM Griffith JH Lichtenstein AH. Interindividual variability and intra-individual reproducibility of glycemic index values for commercial white bread. Foster-Powell K Holt SH Brand-Miller JC. International table of glycemic index and glycemic load values.
Brand-Miller J Hayne S Petocz P Colagiuri S.
: Glycemic load and insulin resistance| Glycaemic load is associated with insulin resistance in older Australian women | Meals or complete diets may be designed using these tables to have a desired overall glycemic index. Diabetes Res Clin Pract. A range of values is used because of variability that exists depending on the type of food and the country in which the food is produced. Once the first 2 diets had been determined, the remaining 2 could be assigned in any order, leading to a total of 8 distinct diet sequences. Conclusions Our results suggested that a high GL diet can increase the incidence of CVD, whereas high dietary II and IL were not associated with the risk of CVD among adults. |
| Low-glycemic index diet may improve insulin sensitivity in obese children | Pediatric Research | Homeostasis model assessment: insulin resistance and beta cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28 , — McKeown NM, Meigs JB, Liu S, Saltzman E, Wilson PWF, Jacques PF Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort. Miles J A role for the glycemic index in preventing or treating diabetes? Am J Clin Nutr 87 , 1—2. Pan DA, Lillioja S, Kriketos AD, Milner MR, Baur LA, Bogardus C et al. Skeletal muscle triglyceride levels are inversely related to insulin action. Diabetes 46 , — Petersen KF, Shulman GI Etiology of insulin resistance. Am J Med , S10—S Robertson RP Chronic oxidative stress as a central mechanism for glucose toxicity in pancreatic islet beta cells in diabetes. J Biol Chem , — Sahyoun NR, Anderson AL, Tylavsky FA, Lee JS, Sellmeyer DE, Harris TB et al. Dietary glycemic index and glycemic load and the risk of type 2 diabetes in older adults. Sempos CT Invited commentary: some limitations of semiquantitative food frequency questionnaires. Am J Epidemiol , — Shulman GI Cellular mechanisms of insulin resistance. Stamler J, Vaccaro O, Neaton J, Wentworth D Diabetes, other risk factors, and yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care 16 , — Stern S, Williams K, Ferrannini E, Defronzo R, Bogardus C, Stern M Identification of individuals with insulin resistance using routine clinical measurements. Diabetes 54 , — Storlien L, Jenkins A, Chisholm D, Pascoe W, Khouri S, Kraegen E Influence of dietary fat composition on development of insulin resistance in rats. Relationship to muscle triglyceride and omega-3 fatty acids in muscle phospholipid. Diabetes 40 , — Tapsell LC, Brenninger V, Barnard J Applying conversation analysis to foster accurate reporting in the diet history interview. J Am Diet Assoc , — University of Sydney Wahrenberg H, Hertel K, Leijonhufvud B-M, Persson L-G, Toft E, Arner P Use of waist circumference to predict insulin resistance: retrospective study. Br Med J , — Weyer C, Bogardus C, Mott DM, Pratley RE The natural history of insulin secretory dysfunction and insulin resistance in the pathogenesis of type 2 diabetes mellitus. World Health Organisation Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications: Report of a WHO Consultation. World Health Organisation: Geneva. Worthley L ed The Australian Short Course on Intensive Care Medicine. The Australasian Academy of Critical Care Medicine: Bedford Park, South Australia. Zhang C, Liu S, Solomon CG, Hu FB Dietary fiber intake, dietary glycemic load, and the risk for gestational diabetes mellitus. Diabetes Care 29 , — Download references. We acknowledge the support provided by the Royal Women's Hospital Foundation, the affiliation with The University of Queensland and the sponsorship of the Betty Byrne Henderson Centre. We would like to thank LAW study coordinator Professor Soo Keat Khoo, LAW administration and clinical staff, and staff at Xyris Software for their assistance in dietary analysis. We are especially grateful to the LAW study women who generously donated their time to make this research possible. Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia. School of Population Health, University of Western Australia, Crawley, Western Australia, Australia. Betty Byrne Henderson Research Centre, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia. School of Health Sciences, University of Wollongong, Wollongong, New South Wales, Australia. You can also search for this author in PubMed Google Scholar. Correspondence to T A O'Sullivan. Contributors : SO'N was involved in conception and design of the LAW study, and acquisition of data on insulin resistance and potential confounding variables. PLW and TO'S were involved in study conception and design. TO'S was involved in acquisition of dietary data and drafting of the paper. TO'S and AB analysed the data, and, together with PLW, were involved in interpretation of the data. All authors were involved in review of the paper. Reprints and permissions. O'Sullivan, T. et al. Glycaemic load is associated with insulin resistance in older Australian women. Eur J Clin Nutr 64 , 80—87 Download citation. Received : 07 April Revised : 03 August Accepted : 05 August Published : 16 September Issue Date : January Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. But different carbohydrate-containing foods affect blood sugar differently, and these effects can be quantified by measures known as the glycemic index and glycemic load. You might even have been advised to use these numbers to help plan your diet. But what do these numbers really mean — and just how useful are they? The glycemic index GI assigns a numeric score to a food based on how drastically it makes your blood sugar rise. Foods are ranked on a scale of 0 to , with pure glucose sugar given a value of The lower a food's glycemic index, the slower blood sugar rises after eating that food. In general, the more processed a food is, the higher its GI, and the more fiber or fat in a food, the lower it's GI. But the glycemic index tells just part of the story. What it doesn't tell you is how high your blood sugar could go when you actually eat the food. To understand a food's complete effect on blood sugar, you need to know both how quickly it makes glucose enter the bloodstream and how much glucose per serving it can deliver. A separate measure called the glycemic load does both — which gives you a more accurate picture of a food's real-life impact on your blood sugar. Watermelon, for example, has a high glycemic index But a serving of watermelon has so little carbohydrate that its glycemic load is only 5. Some nutrition experts believe that people with diabetes should pay attention to both the glycemic index and glycemic load to avoid sudden spikes in blood sugar. The total amount of carbohydrate in a food, rather than its glycemic index or load, is a stronger predictor of what will happen to blood sugar. But some dietitians also feel that focusing on the glycemic index and load adds an unneeded layer of complexity to choosing what to eat. The bottom line? Following the principles of low-glycemic-index eating is likely to be beneficial for people with diabetes. But reaching and staying at a healthy weight is more important for your blood sugar and your overall health. Image: © designer GettyImages. As a service to our readers, Harvard Health Publishing provides access to our library of archived content. Instead of conventional advice of caloric restriction which may be too restrictive for some children, modest caloric reduction with substitution of high-GI foods with its low-GI varieties could be more acceptable. A possible further study may recruit a larger sample size with more intensive intervention such as monitoring the low-GI food consumed, evaluating hunger and satiety levels, improving physical activity recommendations and methods of assessment, and, finally, improving behavior modification techniques. This would allow accurate assessment of GI and GL of the diet and its effects on body composition, satiety levels, and insulin sensitivity. This study was a prospective, randomized, controlled trial. Participants were randomly allocated by computer-generated randomization blocks of 10 to receive either conventional obesity clinic advice or an intervention of a low-GI diet. The researcher who did not relate to data collection and data analysis used computer to generate the random allocation sequence. Other researchers enrolled participants and assigned them to interventions. The protocol was approved by the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University, Thailand. The researchers described the study to the children and their parents before obtaining signed informed assents and consents from one of the parents , respectively. Children aged between 9 to 16 y with BMI higher than the International Obesity Task Force cutoff, corresponding to BMI of 30 in adulthood 28 were recruited from the King Chulalongkorn Memorial Hospital. Children who had behavioral and intellectual problems that might be an obstacle to follow the diet instruction were excluded from this study. Children who had underlying diseases that might affect a weight management program, who used drugs associated with weight increment or reduction, as well as those who attended other weight management programs were also excluded from this study. The sample size was calculated according to the previous findings from other obesity intervention trials. The difference in BMI z -score of 0. For the intervention group, individual goals for weight management were set and the instruction about low-GI foods was provided. A dietitian emphasized the selection of low-GI carbohydrates, which were adapted from the table by Foster-Powell et al. The contents varied from the first to the sixth visit, starting from portion size and food exchange, modest energy restriction, principle of GI, sources of low-GI diet, cooking demonstration of low-GI dishes, guidance about food labeling, and some games about GI of common food and beverages. Both groups needed to maintain the monthly visits for 6 mo. The adherence to the nutritional education and physical activity recommendation was evaluated by using 3-d dietary records two week days and one weekend day and a physical activity questionnaire at each visit. All participants were examined and counseled about physical activity and life style modification strategies by a pediatrician at every visit. Primary outcomes. Anthropometric measurements were taken at baseline and at every visit of this study. Weight and height were measured without shoes and with light clothing using a stadiometer to the nearest 0. Waist circumference was measured at the umbilicus level after normal exhalation with participants in standing position. Hip circumference was measured at the maximum circumference of the hips. Mid-upper arm circumference was measured the circumference at the middle point between the olecranon process of the ulna and the acromion process of the scapula. The primary outcomes were body composition changes, which refer to FM and FFM during the 6-mo period, measured by two validated techniques. BIA BodystatQuadscan ; Bodystat, Isle of Man, British Isles , which measured the body resistance to small voltage electrical current, was performed at every visit to calculate the FM and FFM. DXA Hologic QDR Discovery A was performed on the first and sixth visits. Secondary outcomes. The secondary outcomes were metabolic syndrome risk changes which were blood pressure, fasting plasma glucose, plasma insulin, and serum lipid profiles. Blood pressure was measured by blood pressure monitor Dinamap. Venous blood was obtained after a h fast to evaluate biochemical parameters at the first and sixth visits of the study. Serum LDL C was measured by homogeneous liquid selective detergent DIRECT LDL, Architech; Abbott Laboratories. The values in the text and tables were reported as means ± SDs. Paired t -test for dependent samples was used to evaluate the changes within the groups before and after the 6-mo period. Independent sample t -test was used to compare the changes between the two groups. Repeated measures ANOVA was used to compare the changes of FMI, FFMI, and percentage of fat in each visit in the control group and intervention group. In addition, multiple regression analysis and general linear model were used to adjust the difference of baseline insulin in both groups. This study was supported by the Ratchadapiseksompoch Research Fund, Faculty of Medicine, Chulalongkorn University: grant no. Dietz WH. Overweight in childhood and adolescence. N Engl J Med ; —7. Article CAS Google Scholar. Centers for Disease Control and Prevention. Basics about childhood obesity: how is childhood overweight and obesity measured? Weiss R, Dziura J, Burgert TS, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med ; — Hoppin AG. Evaluation and management of obesity. In: Duggan CWJ, Walker WA, eds. Nutrition in Pediatrics. Hamilton, Ontario: BC Decker, — Google Scholar. Jenkins DJ, Kendall CW, Augustin LS, et al. Glycemic index: overview of implications in health and disease. Am J Clin Nutr ; 76 S—73S. Spieth LE, Harnish JD, Lenders CM, et al. A low-glycemic index diet in the treatment of pediatric obesity. Arch Pediatr Adolesc Med ; — Mirza NM, Palmer MG, Sinclair KB, et al. Effects of a low glycemic load or a low-fat dietary intervention on body weight in obese Hispanic American children and adolescents: a randomized controlled trial. Am J Clin Nutr ; 97 — Siegel RM, Neidhard MS, Kirk S. A comparison of low glycemic index and staged portion-controlled diets in improving BMI of obese children in a pediatric weight management program. Clin Pediatr Phila ; 50 — Article Google Scholar. Fajcsak Z, Gabor A, Kovacs V, Martos E. J Am Coll Nutr ; 27 — Kirk S, Brehm B, Saelens BE, et al. Role of carbohydrate modification in weight management among obese children: a randomized clinical trial. J Pediatr ; —7. Papadaki A, Linardakis M, Larsen TM, et al. Pediatrics ; :e— Ebbeling CB, Leidig MM, Sinclair KB, Hangen JP, Ludwig DS. A reduced-glycemic load diet in the treatment of adolescent obesity. Arch Pediatr Adolesc Med ; —9. Eisenkölbl J, Kartasurya M, Widhalm K. Underestimation of percentage fat mass measured by bioelectrical impedance analysis compared to dual energy X-ray absorptiometry method in obese children. Eur J Clin Nutr ; 55 —9. Pal S, Lim S, Egger G. The effect of a low glycaemic index breakfast on blood glucose, insulin, lipid profiles, blood pressure, body weight, body composition and satiety in obese and overweight individuals: a pilot study. Henry CJ, Lightowler HJ, Strik CM. Effects of long-term intervention with low- and high-glycaemic-index breakfasts on food intake in children aged years. Br J Nutr ; 98 — Venn BJ, Green TJ. Glycemic index and glycemic load: measurement issues and their effect on diet-disease relationships. Eur J Clin Nutr ; 61 :Suppl 1:S— Pawlak DB, Kushner JA, Ludwig DS. Effects of dietary glycaemic index on adiposity, glucose homoeostasis, and plasma lipids in animals. Lancet ; — Scharrer E, Langhans W. Control of food intake by fatty acid oxidation. Am J Physiol ; :R—6. CAS PubMed Google Scholar. Solomon TP, Haus JM, Kelly KR, et al. A low-glycemic index diet combined with exercise reduces insulin resistance, postprandial hyperinsulinemia, and glucose-dependent insulinotropic polypeptide responses in obese, prediabetic humans. Am J Clin Nutr ; 92 — Pereira MA, Swain J, Goldfine AB, Rifai N, Ludwig DS. Effects of a low-glycemic load diet on resting energy expenditure and heart disease risk factors during weight loss. JAMA ; — Shikany JM, Phadke RP, Redden DT, Gower BA. Metabolism ; 58 — Randomized trial on the effects of a 7-d low-glycemic diet and exercise intervention on insulin resistance in older obese humans. Am J Clin Nutr ; 90 —9. Goldstein BJ. Insulin resistance as the core defect in type 2 diabetes mellitus. Am J Cardiol ; 90 :3G—10G. Zawadzki JK, Bogardus C, Foley JE. Insulin action in obese non-insulin-dependent diabetics and in their isolated adipocytes before and after weight loss. |
| Diet and insulin resistance: Foods to eat and diet tips | Meals or Glycekic diets may be designed Hair growth for curly hair these Glgcemic to have a desired overall Ressistance index. Views 1, However, resisgance, intakes of fruit and vegetable, red and processed meat, fat, and protein were all decreased across the quartiles of GL. Article Google Scholar Mozaffari H, Namazi N, Larijani B, Surkan PJ. The Australian Short Course on Intensive Care Medicine. In a study by Dodd et al. J Am Heart Association. |
| Main Content | Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Download as PDF Printable version. Estimate of how a quantity of food will raise a blood glucose level. This section needs expansion. You can help by adding to it. March Diabetic diet Disposition index Glycemic index Glycemic efficacy Low glycemic index diet Montignac diet Overall nutritional quality index. Glycemic Research Institute. Archived from the original on 27 September Retrieved 8 February April American Journal of Clinical Nutrition. doi : PMID May Journal of the American Medical Association. Archives of Internal Medicine. Adrienne; Palmer, Julie R. The Nutrition Source. Harvard School of Public Health. University of Sydney. But there is a great deal of confusion in interpreting the database available on glycemic index because data from studies in diabetic patients are often quoted to bolster policy suggestions for normal populations. This confusion is not conducive to a clear analysis of the issue. A pro and con discussion of the use of the glycemic index in normal population groups that is relevant in this context was published recently 13 , It is important to note that the window of glycemic index variability in a free-living population is quite narrow. This can be seen by the small SD of 4. It can also be seen that the glycemic index is actually already quite low. With such a low level of glycemic index and such a small variation around it, what determines the level of the glycemic load is the total amount of carbohydrate that an individual eats. Thus, with a small glycemic index window, the glycemic load primarily reflects the carbohydrate intake. And we know from repeated studies in fact, all the epidemiological studies mentioned above plus the present IRAS study, plus many others that no one to date has found that the amount of carbohydrate eaten per day is significantly associated with the development of type 2 diabetes. This then, greatly diminishes the importance of high glycemic load as an important risk. My suggestion then, looking at the present study and others, is that until further evidence is available, we should concentrate on educating the public to opt for higher-fiber foods especially cereal fiber and downplay the glycemic index and glycemic load. There is excellent evidence that the higher-fiber foods, made up of whole grains, fruits, and vegetables, will do people good. SEE LIESE ET AL. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Diabetes Care. Advanced Search. User Tools Dropdown. Sign In. Skip Nav Destination Close navigation menu Article navigation. Volume 28, Issue Previous Article Next Article. Article Navigation. Editorials December 01 Do Glycemic Index, Glycemic Load, and Fiber Play a Role in Insulin Sensitivity, Disposition Index, and Type 2 Diabetes? Xavier Pi-Sunyer, MD Xavier Pi-Sunyer, MD. From the Division of Endocrinology, Diabetes and Nutrition, St. This Site. Google Scholar. Address correspondence to Dr. Xavier Pi-Sunyer, Division of Endocrinology, Diabetes, and Nutrition, St. E-mail: fxp1 columbia. Diabetes Care ;28 12 — Connected Content. A reference has been published: Dietary Glycemic Index and Glycemic Load, Carbohydrate and Fiber Intake, and Measures of Insulin Sensitivity, Secretion, and Adiposity in the Insulin Resistance Atherosclerosis Study. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. Even though some nutrition policies advocate consumption of low—glycemic index foods and even promote food labeling with glycemic index values, the independent benefits of glycemic index are uncertain, especially when persons are already consuming a healthful diet rich in whole grains, vegetables, and fruits. Clinical trials that studied the effect of lowering glycemic index on insulin sensitivity and cardiovascular disease CVD risk factors reported diverse results that may be related to concomitant changes in content of total carbohydrate and fiber, concomitant weight loss, and presence of and use of treatments for diabetes. The background diets in which we manipulated glycemic index were healthful dietary patterns established in the Dietary Approaches to Stop Hypertension DASH 7 and Optimal Macronutrient Intake to Prevent Heart Disease OmniHeart 8 studies that are being recommended in dietary guidelines to prevent CVD. Each participant gave written informed consent. A full description of the methods is in the trial protocol in Supplement 1. Eligibility criteria were age 30 years or older; systolic blood pressure to mm Hg and diastolic, 70 to 99 mm Hg; and body mass index BMI 25 or higher calculated as weight in kilograms divided by height in meters squared. Participants self-identified their race or ethnicity using the choices provided and required by the National Institutes of Health. We oversampled black individuals because of their disproportionate burden of insulin resistance and other risk factors that result in high rates of diabetes and cardiovascular disease. The trial protocol has a complete list of exclusions Supplement 1. The goal was participants finishing at least the first 2 of 4 diet periods. The primary recruitment strategy was mass mailing of brochures, flyers, and coupons. The primary sources of mailing lists were commercial vendors and local governments for lists of registered voters or drivers. Eligible participants began an 8-day run-in phase during which each study diet was given for 2 days. During run-in and the 4 diet periods, participants were provided all of their meals, snacks, and calorie-containing beverages. After the run-in, the participants were randomized to a sequence of the 4 study diets. For a crossover study with 4 diets to be administered, there are 24 possible sequences, of which we used 8. We wanted to ensure that the high— and low—glycemic index diet components were each used in the first 2 periods for all participants. With this constraint, high- and low-carbohydrate components could be chosen in any order, leading to 4 distinct sequences for the first 2 diets. Once the first 2 diets had been determined, the remaining 2 could be assigned in any order, leading to a total of 8 distinct diet sequences. Thirteen blocks of random permutations of the 8 permissible sequences were established for each site, to support up to sequential randomizations per site. Permutations were developed using the sample function of R version 2. The data center directed by V. Each diet was given for 5 weeks separated by a break of at least 2 weeks during which study participants ate their self-selected diet. Calorie intake was adjusted to maintain initial body weight. Participants completed a daily food diary for each day on the controlled diets. They recorded any foods that they did not eat and any additional items eaten. Their on-site, weekday meal attendance was recorded and meal consumption was monitored by trained staff. During the daily on-site meals monitored by study staff, participants had to consume the entire meal on-site. Participants were observed while eating and trays were cleared with staff present to ensure no food was discarded. The glycemic index cut points corresponded approximately to the first and fifth quintiles of US population-based intake. The glycemic index values of individual foods were calculated primarily using published tables. The glycemic index values of the breads were measured directly, in vivo. The diets also provided similar amounts of other nutrients that might affect trial outcomes. The 5 primary outcomes were insulin sensitivity; systolic blood pressure; and low-density lipoprotein LDL cholesterol, high-density lipoprotein HDL cholesterol, and triglyceride levels. Secondary outcomes included diastolic blood pressure, fasting and 2-hour blood glucose and insulin, and other lipoprotein parameters. Blood pressure was measured by trained and certified staff using a validated automated oscillometric OMRON device 12 at the clinic on 3 days during screening for eligibility; on 1 day during run-in; and on 1 day during the first, second, and third weeks and on 5 days in the final fourth and fifth weeks, during each of the 4 diet periods. On each occasion, the blood pressure was measured 3 times. The measurements during the last 2 weeks were averaged and constituted the outcome variable for blood pressure, as done previously. Plasma total and lipoprotein cholesterol, triglycerides, and apolipoproteins B, C-III, and E were measured using enzymatic kits or enzyme-linked immunosorbent assay. Insulin sensitivity was measured by an oral glucose tolerance test, 75 g, during screening and the final 10 days of each diet period. Blood was sampled at 0, 10, 20, 30, 60, 90, and minutes. Insulin sensitivity was calculated by the index of Matsuda and DeFronzo that uses blood glucose and serum insulin levels at 0, 30, 60, 90, and minutes. On that day, participants were given the same diet type for that diet period for breakfast, lunch, and dinner, which had a mean , , kcal, respectively, for a typical kcal diet, the same as in the other days of the controlled diet. Blood was sampled at fixed intervals just before eating breakfast; 30, 60, and 90 minutes after starting breakfast; and hourly thereafter through 12 hours. This hour meal test is a process variable that determines the differences in blood glucose caused by the differences among the diets in glycemic index and amount of carbohydrate. Diets with higher glycemic index and higher amount of carbohydrate are expected to increase the hour blood glucose AUCi. Urine collections hour were obtained once during screening and once during the last 2 weeks of each diet period. Data collection personnel were blinded to diet sequence. Information on serious adverse events was collected from participants and their medical records and reported to the institutional review board as required. The diet contrasts pertaining to the effect of glycemic index were high glycemic index vs low glycemic index in the setting of high total carbohydrate intake and separately in the setting of low total carbohydrate intake. The trial design also allowed a test of the effects of lowering total dietary carbohydrate, separately in the setting of high—glycemic index and low—glycemic index foods. Although this 4-period study could be analyzed as a factorial design, combining the high- and low-carbohydrate periods to test glycemic index, and combining the high— and low—glycemic index diets to test level of carbohydrate, we considered it likely that glycemic index has a stronger effect when the total carbohydrate intake is high and that carbohydrate level has a stronger effect when the glycemic index is high. Therefore, a factorial analysis was considered inappropriate. In the protocol-specified analytical plan, the primary analysis is a comparison of the high-carbohydrate, high—glycemic index diet and the low-carbohydrate, low—glycemic index diet, representing a single integrated measure of the hypothesized maximal effect on the 5 primary outcomes of manipulating dietary carbohydrate by reducing its amount and glycemic index. Because some participants did not provide measures on all outcomes for all diets, multiple imputation analysis was performed for the 5 primary outcomes. There was no qualitative effect of multiple imputation compared with complete case analysis. Full details are given in the online appendix eFigure 1 in Supplement 2. The distribution of within-person differences in response variables for pairs of diets was analyzed using the t. test function of R version 3. This provides estimates of average effect, standard error of the estimate, and limits of confidence intervals for selected confidence coefficients. Statistical visualization and additional analyses such as multiple imputation sensitivity analysis and tests for carryover effects were also performed using R. We used standard assessments of carryover effects in crossover designs based on the comparison of distributions of sums of outcomes between groups of participants receiving treatments in different orders. One hundred sixty-three participants completed at least 2 diets and were included in the analysis of outcomes Figure 1. For any pair of diets, there were to participants. The trial ended when at least participants completed at least 2 diets, as planned. Participants lost an average of 1 kg of body weight from baseline to the end of each diet period, the same for each diet type. Urinary sodium and potassium excretion were similar during each diet period. At the high dietary carbohydrate content, the low— compared with the high—glycemic index level significantly reduced insulin sensitivity from 8. At the low carbohydrate content, the low— compared with the high—glycemic index level did not affect insulin sensitivity but increased fasting blood glucose level by 2. Mean glucose and insulin levels during the oral glucose tolerance test are shown in eFigure 2 in Supplement 2. Glycemic index level did not affect HDL cholesterol level or systolic blood pressure or diastolic blood pressure. A low compared with a high dietary carbohydrate content did not affect insulin sensitivity at either the high— or the low—glycemic index level Figure 3 and Table 3. A low compared with a high dietary carbohydrate content significantly lowered plasma total triglycerides at both high— and the low—glycemic index levels. There was no evidence of additive effects of glycemic index level and dietary carbohydrate content on any of the outcomes. A sensitivity analysis restricted to the participants who completed all 4 diets yielded results similar to the primary analyses eTable 5 in Supplement 2. Serious adverse events occurred in 7 participants: injuries from automobile crashes 3 participants , kidney stone 1 , acute asthma 1 , osteomyelitis 1 , and pneumonia 1. None were judged to be related to the study procedures. There were no unintended or unanticipated effects. All 4 study diets were associated with lower systolic blood pressure by 7 to 9 mm Hg Table 3 and diastolic blood pressure by 4 to 6 mm Hg eTable 3 in Supplement 2. Paradoxically, the low—glycemic index, high-carbohydrate diet compared with the high—glycemic index, high-carbohydrate diet decreased insulin sensitivity and increased LDL cholesterol and LDL apolipoprotein B levels while other dietary factors that affect LDL levels such as saturated fat, cholesterol, and fiber were held constant. These findings are contrary to our hypotheses on glycemic index. As we found previously in the OmniHeart trial, 8 the beneficial effects of the DASH diet can be improved modestly by reducing its carbohydrate content. Lowering the carbohydrate content and compensating the reduced calories with unsaturated fat and protein substantially lowered triglycerides and VLDL levels and slightly lowered diastolic blood pressure, confirming previously established findings. Thus, the new information in the present study is that composing a DASH-type diet with low—glycemic index foods compared with high—glycemic index foods does not improve CVD risk factors and may in fact reduce insulin sensitivity and increase LDL cholesterol. We found that a low compared with a high glycemic index of a high-carbohydrate diet decreased insulin sensitivity measured by an oral glucose tolerance test. Fasting glucose level was higher on low—glycemic index than high—glycemic index dietary carbohydrate as previously reported. However, a low—glycemic index diet did not affect insulin sensitivity in other studies in which body weight either remained constant during the trial or decreased by a similar amount in the high— and low—glycemic index groups. We chose a 5-week duration of the intervention feeding periods based on results of previous studies, which suggested that 5 weeks was sufficient to detect changes in our outcomes trial protocol in Supplement 1. A recent meta-analysis of 14 trials that had durations of at least 6 months found no effect of lowering glycemic index on lipids or fasting glucose, although fasting insulin was reduced. This trial did not address the effect of glycemic index in a typical US diet. Rather we studied a low compared with a high glycemic index in a DASH-type diet. However, we do not attribute the null findings on glycemic index to the healthfulness or specific content of the DASH diet. For example, in several European studies 19 , 23 , 31 , 32 and one in Brazil, 22 the researchers gave or prescribed selected foods to the participants to use in their own diets instead of providing complete diets that differed from their usual diets. In these studies, lowering glycemic index did not increase insulin sensitivity or improve blood pressure, HDL cholesterol level, or triglyceride level; LDL cholesterol level decreased in one of these studies 19 but did not change in the others. We showed in a subsample of the participants that the glycemic index values of individual foods computed from dietary tables, when assembled into meals, produced expected differences in blood glucose AUCi over 12 hours, a process variable, thus confirming previous results. These results suggest that lowering glycemic index or lowering carbohydrates for breakfast, lunch, and dinner reduces blood glucose during 12 hours without any further reduction from lowering both together. Thus, the effects of these 2 changes in dietary carbohydrate were not additive, suggesting a plateau effect, as also found in a similar study. After we started this trial, reports of trials that involved glycemic index have accumulated. A meta-analysis of 28 trials found that lowering glycemic index did not affect HDL cholesterol or triglyceride levels and lowered LDL cholesterol level only if fiber content was also increased. There were no increases in foods or nutrients in the low—glycemic index, high-carbohydrate diet that have known effects to raise LDL levels. In fact, the low—glycemic index, high-carbohydrate diet contained slightly less dietary cholesterol and more fiber than the other diets, but these differences would have lowered not raised LDL levels. Low—glycemic index diets did not lower blood pressure. We also did not study the influence of glycemic index on weight loss. Lowering glycemic index may improve weight loss 6 or maintenance 40 , 41 according to a meta-analysis 6 and some more recent clinical trials, 40 , 41 although others did not find an advantage of low—glycemic index diets. This trial oversampled black individuals because of their greater burden of type 2 diabetes and CVD that could be modifiable by dietary change. The results were similar in black and white participants. The main dietary contrast of interest, high vs low glycemic index, included participants, exceeding the goal of However, the number of participants for each dietary contrast ranged from to Still, the precision of estimation of effects, as shown by the confidence intervals, was adequate for clinically relevant inference on the risk factors of interest. In this 5-week controlled feeding study, diets with low glycemic index of dietary carbohydrate, compared with high glycemic index of dietary carbohydrate, did not result in improvements in insulin sensitivity, lipid levels, or systolic blood pressure. Corresponding Author: Frank M. Sacks, MD, Department of Nutrition, Harvard School of Public Health, Huntington Ave, Boston, MA fsacks hsph. Author Contributions: Dr Sacks had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. |
Glycemic load and insulin resistance -
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Can watching sports be bad for your health? Beyond the usual suspects for healthy resolutions. August 2, Understanding glycemic load is just as important as the glycemic index of foods What are the facts about the glycemic load of foods?
Glycemic index vs. glycemic load The glycemic index GI assigns a numeric score to a food based on how drastically it makes your blood sugar rise. Glycemic load diet Some nutrition experts believe that people with diabetes should pay attention to both the glycemic index and glycemic load to avoid sudden spikes in blood sugar.
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Cathrine LauInwulin FærchGlyceimc Glümer Fruit-based sugar substitutes, Inge TetensGlycemic load and insulin resistance Pedersen indulin, Bendix CarstensenTorben JørgensenKnut Borch-Johnsen; Dietary Glycemic Index, Glycemic Glycekic, Fiber, Simple Sugars, and Liver Detoxification Methods Resistance : The Inter99 study. Dextrose Performance Enhancer Care 1 June ; 28 6 : — OBJECTIVE —To examine the relationship between daily glycemic index, daily glycemic load, simple sugars, dietary fiber, and the prevalence of a measure of insulin resistance in to year-old nondiabetic Danish men and women. RESEARCH DESIGN AND METHODS —The Inter99 study is a nonpharmacological intervention study. We used baseline data and examined cross-sectional associations between carbohydrate-related dietary factors and an estimate of insulin resistance in 5, subjects at 30—60 years. The dietary intake was estimated from a self-administered food frequency questionnaire, and insulin resistance was estimated using the homeostasis model assessment of insulin resistance HOMA-IR. Gkycemic Pi-Sunyer; Insulib Fruit-based sugar substitutes Index, Olad Load, and Fiber Play High-field MRI Role in Loar Sensitivity, Disposition Index, and Resistande 2 Diabetes?. Diabetes Care 1 December ; 28 12 : — In this issue of Diabetes Fruit-based sugar substitutesLiese et al. The evaluation was confined to those with normal or impaired glucose tolerance and did not include diabetic individuals. As the authors state, there has been no previous large epidemiologic study relating glycemic index and glycemic load to direct measures of insulin sensitivity and insulin secretion, whose dysfunction are the two hallmarks leading to type 2 diabetes. There are some caveats to this study that must be pointed out.
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