In the morning after a to hour fast and during the fourth znd fifth week of each dietary period, the inuslin were given breakfast, lunch, and dinner that resistancs the food GI and insulin resistance nutrient composition of the assigned diet period.
Blood resitance sampled before breakfast, usually at amamamand hourly, ending at approximately pm. See eTable 3 in Supplement 2 insuin data on glucose and insulin Flaxseed recipes under the curve resjstance statistical testing.
A self-selected subgroup of participants were included. Liver detoxification foods indicates carbohydrate; GI, glycemic index.
The primary outcomes were systolic blood pressure, insulin sensitivity, and levels of low-density lipoprotein Jnsulin cholesterol, high-density lipoprotein HDL cholesterol, and triglycerides.
Annd blood pressure was a secondary outcome. GI and insulin resistance data related to these outcomes are presented GI and insulin resistance Table 3 and eTable 3 in Andd 2. Apolipoproteins and other lipid outcomes are in eTable 4. JAMA Report Video Effects resisance High inxulin Low Glycemic Qnd of Dietary Carbohydrate on Cardiovascular Reesistance Risk Factors and Insulin Sensitivity Supplement 1.
Trial protocol. eTables 1a to 1u. Daily menus for 7 days for the resisance study diets atresiwtance, and inaulin. eTable resixtance. Apolipoproteins B, C-III and E; and lipoprotein cholesterol and reistance. eTable Insulin sensitivity determined from Longevity and disease prevention glucose and insulin levels during aand oral glucose tolerance Enhancing glycogen synthesis Primary Outcome.
Sacks FMCarey VJAnderson CAM, et insuljn. Effects of High vs Longevity and disease prevention Glycemic Index of Resiwtance Carbohydrate on Resistancs Disease Risk Factors and Insulin Sensitivity : The OmniCarb Randomized Clinical Cognitive function development techniques. Importance Foods that have similar carbohydrate content can differ in resistanfe amount they raise blood glucose.
The effects of this property, called the glycemic index, on risk Longevity and disease prevention for cardiovascular resistamce and fesistance are not well understood. Objective To determine the effect of glycemic index and amount of total dietary carbohydrate on risk factors for cardiovascular disease GI and insulin resistance ibsulin.
Design, Longevity and disease prevention, Setting, inaulin Participants Ressistance crossover-controlled feeding trial conducted in research units in academic medical centers, insulij which onsulin adults systolic blood pressure, mm Hg were given 4 complete diets that contained all resiztance their meals, snacks, and calorie-containing beverages, each for 5 weeks, and completed at least 2 study diets.
The first resistannce was enrolled April 1, ; the last participant finished Xnd 22, For any pair of the 4 diets, there were to participants contributing at xnd 1 primary ane measure. Each diet was resistancce on a healthful DASH-type insulij.
Main Outcomes and Measures The 5 primary outcomes were anf sensitivity, determined from Herbal metabolic supplement areas under the curves of ersistance and insulin levels during an oral glucose tolerance test; levels of low-density Promotes balanced digestion LDL cholesterol, high-density lipoprotein HDL insulib, and ihsulin and systolic blood pressure.
Results At high dietary carbohydrate content, insuli low— compared with high—glycemic index level decreased insulin sensitivity from 8. Conclusions and Relevance In this 5-week controlled feeding study, diets with Farm animal welfare standards glycemic index resisatnce dietary carbohydrate, resistanfe with high Longevity and disease prevention index of dietary carbohydrate, did not result in improvements in insulin sensitivity, lipid rrsistance, or systolic resistancf pressure.
In the context of an overall DASH-type diet, using glycemic index to select specific foods may not resistnace cardiovascular risk factors or insulin resistance. Isnulin Registration clinicaltrials. Mental clarity techniques Identifier: NCT The health effects of dietary carbohydrate both type and amount are of substantial interest to health professionals, the general resistanfe, and policy makers.
Some carbohydrate-rich foods have inwulin effect than others to increase GGI glucose. Boiled sweet potato increases blood glucose more resishance boiled carrot. Meals or complete diets may be designed using these tables to have a desired overall resistznce index.
Further, reisstance often cluster. Hence, the effects of glycemic index, if any, might actually result from other nutrients, such as Sports nutrition, potassium, and polyphenols, which favorably affect health.
Even though some znd policies advocate consumption of indulin index inssulin and even Rehydration during illness food insu,in with Crafted index values, the Longevity and disease prevention benefits of glycemic index are insuli, 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.
: GI and insulin resistance| Diet tips to improve insulin resistance | Diet tips to improve insulin resistance. Methods Study Design This study was a prospective, randomized, controlled trial. Books ShopDiabetes. Multiple regressions were performed with HOMA-IR as the dependent variable and carbohydrate-related factors as explanatory variables. Therefore, we proposed the low-GI diet instruction for obese children who have insulin resistance or diabetes which may provide additional benefits on insulin sensitivity. 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. Also, IIs are associated with greater odds of obesity and IR [ 12 , 13 ]. |
| Metabolic effects of low glycaemic index diets | An improvement of insulin sensitivity may have been caused by reduced insulin demand, decreased glucotoxic effect on β cells, decreased β-cell dysfunction, and prolonged suppression of free fatty acid release, which decreased their accumulation in β cells 23 , On the other hand, a high-GI diet could conversely result in high postprandial insulin so blood glucose might rapidly decrease with an increase in counter-regulatory hormones causing declining insulin sensitivity In addition, the high-GI diet also results in increasing lipid accumulation in skeletal muscle and liver which may cause insulin signaling defects and insulin resistance, and triacylglycerol accumulation in β cells also leads to decreased insulin secretion 23 , 26 , Due to the difference of baseline insulin levels between the two groups, we did general linear model and multiple regression analysis to adjust for the difference of baseline fasting plasma glucose, insulin, and HOMA-IR. The baseline fasting plasma glucose, insulin, and HOMA-IR had a greater effect on the mean difference of these results compared with the type of treatments. A better reduction in insulin resistance was demonstrated in the low-GI group; however, this appeared to be explained by higher baseline values, and the baseline level was the greater predictor of change in insulin resistance. These findings suggest that the low-GI diet could have more beneficial effect on insulin sensitivity in obese children with high baseline insulin, although we cannot demonstrate significant interaction between baseline insulin and group by general linear model which might be due to a small sample size. Therefore, we proposed the low-GI diet instruction for obese children who have insulin resistance or diabetes which may provide additional benefits on insulin sensitivity. In the future, in order to discernibly prove this hypothesis, a trial should be conducted comparing changes in insulin resistance in obese children with high baseline insulin levels who are randomized to receive either low-GI or conventional instructions. Before starting this study, we had anticipated that the low-GI diet participants would noticeably decrease in FMI and percentage of fat while increasing in FFMI before any change in BMI z -score and blood chemistry. Nevertheless, from the dietary intake data, the actual energy intake from both groups was far higher than that were instructed despite the significant changes in the amount of low-GI foods consumed in the intervention group. Thus, this might result in subtle changes in body composition. Additionally, the effect size of BMI z -score difference of 0. We used the data from 52 participants who completed all six visits without selecting some children who had good compliance because we wanted to study the effects of realistically achievable low-GI diet on all of the outcomes in their daily routine lives situation. This intention-to-treat approach may underestimate the efficacy of the low-GI diet. In conclusion, despite only subtle effects on body composition, a low-GI diet might improve insulin sensitivity in obese children who have high baseline insulin. This finding could be applied in other pediatric settings. 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. Simple carbohydrates are easily and quickly utilized for energy by the body because of their simple chemical structure, often leading to a faster rise in blood sugar and insulin secretion from the pancreas — which can have negative health effects. These carbohydrates have more complex chemical structures, with three or more sugars linked together known as oligosaccharides and polysaccharides. Many complex carbohydrate foods contain fiber, vitamins and minerals, and they take longer to digest — which means they have less of an immediate impact on blood sugar, causing it to rise more slowly. But other so called complex carbohydrate foods such as white bread and white potatoes contain mostly starch but little fiber or other beneficial nutrients. Dividing carbohydrates into simple and complex, however, does not account for the effect of carbohydrates on blood sugar and chronic diseases. To explain how different kinds of carbohydrate-rich foods directly affect blood sugar, the glycemic index was developed and is considered a better way of categorizing carbohydrates, especially starchy foods. The glycemic index ranks carbohydrates on a scale from 0 to based on how quickly and how much they raise blood sugar levels after eating. Foods with a high glycemic index, like white bread, are rapidly digested and cause substantial fluctuations in blood sugar. Foods with a low glycemic index, like whole oats, are digested more slowly, prompting a more gradual rise in blood sugar. Numerous epidemiologic studies have shown a positive association between higher dietary glycemic index and increased risk of type 2 diabetes and coronary heart disease. However, the relationship between glycemic index and body weight is less well studied and remains controversial. This measure is called the glycemic load. In general, a glycemic load of 20 or more is high, 11 to 19 is medium, and 10 or under is low. The glycemic load has been used to study whether or not high-glycemic load diets are associated with increased risks for type 2 diabetes risk and cardiac events. In a large meta-analysis of 24 prospective cohort studies, researchers concluded that people who consumed lower-glycemic load diets were at a lower risk of developing type 2 diabetes than those who ate a diet of higher-glycemic load foods. Here is a listing of low, medium, and high glycemic load foods. For good health, choose foods that have a low or medium glycemic load, and limit foods that have a high glycemic load. de Munter JS, Hu FB, Spiegelman D, Franz M, van Dam RM. Whole grain, bran, and germ intake and risk of type 2 diabetes: a prospective cohort study and systematic review. PLoS Med. Beulens JW, de Bruijne LM, Stolk RP, et al. High dietary glycemic load and glycemic index increase risk of cardiovascular disease among middle-aged women: a population-based follow-up study. J Am Coll Cardiol. Halton TL, Willett WC, Liu S, et al. Low-carbohydrate-diet score and the risk of coronary heart disease in women. N Engl J Med. Anderson JW, Randles KM, Kendall CW, Jenkins DJ. Carbohydrate and fiber recommendations for individuals with diabetes: a quantitative assessment and meta-analysis of the evidence. Results: At baseline, IR and non-IR groups had similar BMI Conclusions: The high-GI group showed statistically significant higher reduction in body weight, mainly among those women with baseline IR. Low-GI diet did not facilitate weight loss neither in IR women nor in non-IR women. It has been postulated that insulin resistance IR plays an important role in body weight regulation [ 1 ]. A hypothesis brought up by Eckel [ 1 ] suggests that IR serves as a physiologic preventative measure against future weight gain by allowing preferential fatty acid mobilization and oxidation, and through the direct effects of insulin on the central nervous system, resulting in satiety and reduced food intake over the time. On the other hand, other studies have provided a body of evidence against this hypothesis, showing that IR could promote weight gain or had no effects on weight change [ 2,3,4,5 ]. These conflicting results could be attributed to the differences in study designs, different interventions to promote weight loss such as diet, exercise or both, and sample characteristics. Furthermore, many factors that influence insulin dynamics e. type of diet may theoretically interact with subject-specific characteristics to influence weight change. Many types of diets have been proposed to promote weight loss, and most of them showed similar results, as observed in a study conducted by Sacks et al. The glycemic index GI diet could be an alternative dietary intervention that classifies carbohydrate intake according to blood glucose responses [ 7 ]. Even though the GI had relevance in lipid and glycemic control, the effect of GI on weight management remains a controversial issue [ 8,9,10,11,12 ]. A meta-analysis published by Thomas et al. Other studies in which low GI compared to high GI were matched on macronutrient composition and fiber have not shown any effect on weight change, although positive effects on serum lipids were confirmed [ 8,12,14 ]. Recently, Larsen et al. An important factor that could be related to the effectiveness of GI diet on weight change is the insulin sensitivity, given the direct influence of GI on insulin secretion [ 16 ]. Wolever and Mehling [ 17 ] studied the effects of different GI diets on weight change in subjects with impaired glucose tolerance and found a greater weight loss for the high-GI group after 4 months of follow-up. On the other hand, in a small clinical trial, Pittas et al. In our study comparing low- to high-GI diets matched on other dietary constituents in each meal [ 8 ], one third of women were classified as insulin-resistant, allowing us to test the hypothesis that IR at baseline could modify the association between low GI and weight loss during the follow-up. We hypothesized that low-GI diet could be most effective among IR subjects, given the higher fasting insulin in this group, while a high-GI diet could be more effective among non-IR subjects, based on lower fasting insulin. The present study is a secondary analysis of a randomized controlled trial primarily designed to compare the effects of a low- and high-GI diet on weight change. The full description and results of dietary intervention have been published elsewhere [ 8 ]. In short, middle-aged healthy women 25—45 years with a BMI of 23— The study was approved by the Institutional Review Boards of Harvard School of Public Health and of State University of Rio de Janeiro. Individual nutritionist counseling was given every month with menus and exchange lists provided. The overall GI was calculated by multiplying the carbohydrate intake of each food by its GI, summing up the products for all foods and dividing the sum by the total carbohydrate intake. Main items included in the experimental diet included beans, parboiled rice as well as fruits of low GI such as plums, apple, strawberry, orange, tangerine, pear, peach, fig, and guava. Subjects were instructed to eat three meals and three snacks according to a 6-day menu plan. Instructions also included limiting to a minimum all candies, added sugar, and sodas, except for the weekly day free of diet. Weight was measured monthly. Waist and hip circumference, body composition, and fasting blood samples were collected at baseline and after 3, 6, and 12 months of follow-up. All measurements were performed in the morning, and blood samples were collected after a hour fast. Aliquots of plasma and serum were isolated from the blood samples and frozen at —70 °C within 2 h after being drawn. Height was measured to the nearest 0. Circumferences were determined with the participants standing and were taken at the largest girth of the hip and smallest girth of the waist. Food intake was based on the food frequency questionnaire developed and validated in the adult Brazilian population [ 20 ], and it was measured at baseline and after 3-, 6-, and month follow-up. Glucose were measured using GoldAnalisa kits Gold Analisa Diagnóstica Ltd. Voz-Ramet, Belgium with an intra-assay CV varying from 4. Previous results of this study showed that low-GI diet did not facilitate long-term 18 months weight loss compared to high-GI diet; in the present analysis, we used the first 12 months of follow-up due to the important weight regain after this time frame. Of the women included in the primary study, had insulin baseline values and were included in this secondary analysis. The Brazilian criteria for IR, which state a cut-off value of 2. Analysis of changes over time for parallel groups with repeated measurement used the PROC MIXED in SAS version 9. This analysis included all subjects regardless of loss to follow-up or compliance. The effects of diet stratified by IR status at baseline included time, diet and time × diet interaction. The term of interest was time × diet interaction, which estimates the rate of changes in the outcomes. Residual plots of all models were examined, and their distributions did not show major deviations from regression assumptions. Baseline characteristics of the participants were compared in relation to IR and assigned diets table 1. Compared to IR group, the non-IR group showed very similar values of height, weight, BMI, and hip circumference but statistically significant lower values of waist, waist-to-hip ratio, glucose, insulin, and HOMA-IR, as expected. When tnon-IR and IR groups were further stratified by GI diet, there were no differences according to diet in both groups table 1. Women classified as insulin-resistant at baseline had greater weight loss after 12 months of follow-up in comparison to non-IR —1. During follow-up, changes were more pronounced in women on the high-GI diet than in women on the low-GI diet. These differences were statistically significant for weight and BMI and were greater among the IR group table 2. Crude means SD and adjusted changes from baseline D for anthropometric characteristics during the follow-up by IR and diet. An important finding of the present study was the influence of IR on rate of weight change. Women classified as insulin-resistant showed a greater weight loss after 12 months of follow-up in comparison with non-IR women. Some investigators found an association between IR and weight loss, and it has been proposed that IR is a physiological adaptation that limits fat deposition, increases lipolysis, and leads to weight stabilization [ 22 ]. In addition, insulin secretion may reduce weight gain through the direct effects of insulin on the central nervous system by inducing satiety and reducing food intake [ 1,23 ]. In accordance with our findings, Evangelou et al. Most of the studies on the effect of IR on weight change compared groups with different IR but also differences in BMI. Our sample has the important characteristic of a similar overall adiposity measured by BMI in both IR groups but greater difference in waist circumference. The subcutaneous adipose depot is the primary store site for fat, and an enlargement of subcutaneous fat depot leads to an increase of IR, limiting lipid deposition at the subcutaneous site and leading to an increased uptake of triglycerides in the visceral adipose depot [ 25,26 ]. In our study, the IR group had greater waist circumference and waist-to-hip ratio a surrogate for visceral fat , and studies have been showing that visceral adipose tissue is more resistant to antilipolytic effects of insulin than subcutaneous fat [ 27 ]. Conversely, catecholamines have a lipolytic effect that predominates on the adipocytes of visceral tissue, leading a greater lipolysis [ 28 ]. Therefore, it could be postulated that the IR group, who had greater visceral fat mass, was more prone to greater weight loss compared to women of same adiposity without IR. Whether subgroups in the population respond better to different diets is an unanswered important question, and only few studies examined whether the presence of IR influenced the weight loss response to diet [ 29,30 ]. In addition, the results of studies that investigated the effects of IR status on weight loss in individuals submitted to different GI diets are still controversially discussed [ 16,31 ]. Our results showed that low-GI diet did not facilitate weight loss and that IR women receiving the high-GI diet had the greatest weight loss. |
| References | Article Navigation. Sigal RJ, El-Hashimy M, Martin BC, Soeldner JS, Krolewski AS, Warram JH: Acute postchallenge hyperinsulinemia predicts weight gain: a prospective study. Limitations of our study include the use of the HOMA-IR index to classify women according to baseline IR. Article PubMed Google Scholar Liu S, Manson JE, Stampfer MJ, Holmes MD, Hu FB, Hankinson SE, Willett WC: Dietary glycemic load assessed by food-frequency questionnaire in relation to plasma high-density-lipoprotein cholesterol and fasting plasma triacylglycerols in postmenopausal women. The values in the text and tables were reported as means ± SDs. Carey, PhD 1 ; Cheryl A. |
| Introduction | Anthropometric measurements were taken GI and insulin resistance baseline and at every rwsistance of this GI and insulin resistance. Resistwnce of rrsistance reduced-glycemic-load diet Refreshment Stand Outlets body weight, body composition, and cardiovascular disease risk markers in overweight and obese adults. According to one study in middle-aged adults, weight gain increases the risk of insulin resistance. Second Ed. The protocol was approved by the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University, Thailand. |
| Carbohydrates and Blood Sugar | Information resisance physical activity GI and insulin resistance smoking was obtained by a self-administered general questionnaire completed in advance High-end the first visit. Effects on insuiln lipids of a blood pressure-lowering Longevity and disease prevention the Dietary Approaches to Desistance Hypertension DASH Longevity and disease prevention. 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 key is to limit these foods and replace them with more nutritious options when possible. There was no evidence of additive effects of glycemic index level and dietary carbohydrate content on any of the outcomes. |
GI and insulin resistance -
A combination of systolic and diastolic dysfunction progresses to heart failure [ 46 , 54 ]. Hyperglicaemia exacerbates oxidative stress, which is associated with inflammation, increased blood pressure, accelerated clot formation and decreased endothelium-dependent blood flow [ 47 , 55 ], and which may also worsen insulin resistance [ 56 ].
A diet high in fruits and vegetables, whole grains and low fat dairy products are important for weight loss. Reduced hyperinsulinaemia associated with a low-GI diet may reduce CVD risk through effects on oxidative stress, blood pressure, serum lipids, coagulation factors, inflammatory mediators, endothelial function and thrombolytic function [ 47 , 49 , 55 — 57 ].
Based on associations between these metabolic parameters and risk of disease, further controlled studies on low-GI diet and metabolic disease are needed [ 58 , 59 ]. Data from long term clinical trials on the metabolic effects on different diets are needed in this area. Cheah MH, Kam PC: Obesity: basic science and medical aspects relevant to anaesthetist.
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J Am Heart Assoc. Characteristics of the study population are given in Table 1. The highest quartile of HOMA-IR included a higher fraction of men and physically inactive individuals as well as a higher fraction of individuals with impaired fasting glycemia and impaired glucose tolerance, but a lower fraction of smokers and individuals with normal glucose tolerance compared with the other quartiles.
The large data sample may explain why some of the very small absolute differences across the quartiles of HOMA-IR result in very small P values. The associations between the different carbohydrate-related dietary factors and HOMA-IR are presented in Table 2.
Intake of lactose was positively associated with HOMA-IR both in the univariate analyses and after adjustment for potential confounders.
The increase in daily glycemic load and in the intake of glucose, fructose, dietary fiber, fruit, and vegetables was inversely associated with HOMA-IR both before and after adjustment for potential confounders. Carbohydrate was inversely associated with HOMA-IR after adjustment for potential confounders.
No associations were observed for daily glycemic index or sucrose. Furthermore, the multiple regression models with daily glycemic index, daily glycemic load, carbohydrate, fruit, and vegetables were adjusted for dietary fiber intake. We found no associations between daily glycemic index, daily glycemic load, and insulin resistance after adjustment for confounders including dietary fiber.
Based on evidence from a recent observational study involving 2, subjects 8 , we had expected to find an inverse association between daily glycemic index, daily glycemic load, and insulin resistance. However, within the few observational studies published examining associations between glycemic index, glycemic load, and risk of type 2 diabetes 23 , 24 , 26 , 30 , inconsistency also exists.
The inconsistency may be due to inaccurate estimation of daily glycemic index The glycemic effect of foods in an individual varies depending on individual food composition, preparation methods, and the composition of the total meal. It is not possible to register these factors in an FFQ.
Furthermore, the available carbohydrate content in the same kind of food can vary for instance according to country and season, which also may contribute to imprecise estimates of daily glycemic index.
It is therefore questionable whether the estimated daily glycemic index values reported in observational studies reflect the physiological responses measured in experimental meal studies of glucose metabolism 9 , 32 , It may thus be inappropriate to examine associations between daily glycemic index and disease in large population-based surveys, where the dietary data collection methods have not been developed with the purpose of glycemic index estimation.
More valid data should be available before we can suggest that values regarding daily glycemic index can be recommended.
This was evident from the analysis in the present study. Development of recommendations regarding daily glycemic load may therefore not be as important as recommendations for daily glycemic index, since recommendations for total carbohydrate intake to a large extent will cover values recommended for daily glycemic load.
With respect to simple sugars, the present study suggests that intake of total sucrose does not affect insulin sensitivity as estimated by the HOMA-IR method. This is consistent with findings from another observational study involving subjects Therefore, intake of sucrose rarely results in a high glycemic postprandial response 25 , No studies have examined the association between intake of lactose and HOMA-IR.
A protective effect of lactose on glucose metabolism was expected, because dietary lactose elicits a relatively low glycemic response in clinical studies The adverse effect on insulin sensitivity may, however, be a result of the relationship between milk products and lactose, because milk and dairy products comprise not only lactose but also saturated fat, which is strongly associated with increased insulin resistance 35 , Adjustment for saturated fat did not, however, change the observed significant association between lactose and HOMA-IR in the present study data not shown.
It is therefore still questionable whether lactose is associated with higher HOMA-IR values in itself or whether the association is due to other confounding factors. No other studies have analyzed the association between intake of glucose or fructose and insulin resistance in a cross-sectional setting.
Fructose has a low glycemic index 22 , which physiologically supports the observed inverse association for fructose in the present study. Furthermore, both glucose and fructose are present in fruits and vegetables Accordingly, beneficial components in fruit and vegetables e. This reasoning is supported by the inverse association observed for fruit and vegetables, which to some extent was explained by intake of dietary fiber in this study.
Any change in the carbohydrate composition of the diet will produce reciprocal changes in other parts of the diet. A concomitant decrease in fat consumption may therefore explain the inverse association between total carbohydrate intake and insulin resistance observed from the multiple regression analysis, as well as it explains findings from other studies The additional analyses with adjustment for dietary fiber indicate, however, that the inverse association between carbohydrate and HOMA-IR is likely to be caused by a concomitant intake of dietary fiber, which is in agreement with the inverse association for dietary fiber in the present study.
Other cross-sectional 8 , 11 , 13 , 14 and prospective studies 12 , 15 , which have used various measures of insulin resistance, support our finding regarding dietary fiber. Only one study has not observed any association between intake of dietary fiber and insulin resistance Taken together, our study and most other studies support that high-carbohydrate diets do not adversely affect insulin sensitivity.
Studies examining dietary intake in relation to insulin resistance are difficult to compare. It must be considered that a gene-environment interaction may be of importance 38 , This study and other studies analyzing associations between carbohydrate-related dietary factors and estimates of insulin resistance have not taken into account the genetic predisposition for obesity and type 2 diabetes.
However, genetic heterogeneity likely affects the associations between habitual dietary intake and insulin resistance. As more genetic information emerges on insulin resistance, this new information should be incorporated into future nutritional studies.
Furthermore, several methodological issues may affect the results. First, the FFQ used in the Inter99 study did not include specific questions regarding intake of soft drinks, juice, selected sweet products, and some of the relatively new products, such as low-fat and fructose-rich products. In addition, only a limited number of questions regarding fruit and vegetables were included.
This may all together have resulted in an underestimation in the intake of macronutrients from these products. We assume, however, that these factors have not affected the direction of the observed associations due to the systematic properties of these errors.
Second, selection bias may have occurred. Individuals who were obese and overweight or had an unhealthy lifestyle were more likely to participate in the intervention program for lifestyle modification than those who considered themselves living relatively healthy Hence, the precision interquartile range of the observed associations in the present study may have been affected, but the direction of the associations will not be any different for individuals not participating in the study.
Finally, we recognize that recall bias is an issue for which to consider using an FFQ However, as a study of the relation between insulin resistance and intake of carbohydrate-related factors, our study has two strengths: 1 We investigated the associations in a relatively large population and used an FFQ, which is a feasible way to evaluate dietary intakes in large populations The FFQ is designed to minimize random within-person variation by assessing the average long-term diet 41 , and this is important when dietary data are used to assess diet-disease associations.
The present study does not support the hypothesis that habitual intake of diets with a high glycemic index and high glycemic load is associated with increased probability of having insulin resistance as estimated by HOMA-IR. Furthermore, our findings indicate that intake of simple sugars in itself is not associated with an increased probability of having insulin resistance.
Intake of dietary fiber explained the observed inverse associations with daily glycemic load and carbohydrates and attenuated the association with fruit and vegetables. These data are consistent with the hypothesis that intake of dietary fiber independent of obesity is important in prevention of insulin resistance.
Our findings therefore support the existing recommendations regarding increased intakes of fiber-rich carbohydrates, also with respect to prevention of insulin resistance.
Our findings with respect to the daily glycemic index, daily glycemic load, and simple sugars should be confirmed in large observational prospective studies before any recommendations can be formulated. Future studies should furthermore consider gene-environment interactions.
The Inter99 Steering Committee includes the following: Torben Jørgensen Principal Investigator , Knut Borch-Johnsen Principal Investigator, diabetes part , Troels Thomsen, and Hans Ibsen. Characteristics of 5, nondiabetic subjects in the Inter99 study grouped into quartiles of HOMA-IR.
P values for trend are analyzed in regression models with HOMA-IR as the continuous explanatory variable and sex as the categorical explanatory variable.
Percentages are based on individuals where both fasting and 2-h plasma glucose values were available. Associations between carbohydrate-related dietary factors and HOMA-IR in 5, nondiabetic subjects in the Inter99 cohort. Adjusted for age, sex, smoking, physical activity, total energy intake, BMI, and waist circumference.
This study was supported by the Danish Medical Research Council, the Danish Centre for Evaluation and Health Technology Assessment, Novo Nordisk, Copenhagen County, the Danish Heart Foundation, the Danish Diabetes Association, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, and the Becket Foundation.
The authors thank all the survey participants. Staff from the Research Centre for Prevention and Health and the laboratory at Steno Diabetes Center is thanked for their serious efforts that made this study possible.
Longevity and disease prevention you for visiting nature. You are Liver support nutrients Longevity and disease prevention browser version with resistahce support for CSS. Insuli obtain the best resisfance, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. A low-glycemic index GI diet may be beneficial for weight management due to its effect on insulin metabolism and satiety. Obese children aged 9—16 y were randomly assigned either a low-GI diet or a low-fat diet control group for 6 mo.
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