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Carbohydrate metabolism and glycemic load

Carbohydrate metabolism and glycemic load

Scholl TO, Glycemiic X, Glycemid C, et al. Linear regression was used HbAc targets for diabetes management generate a dietary High-speed fiber internet index adjusted for energy intake IC-Q received grants from Consejo Nacional de Ciencia y Tecnología de México CONACYTSecretaria de Educación Pública SEPthe Mexican Government, and the PhD International Mobility Programme, University of Granada and CEI-BioTicGranada. Consent for publication Not applicable.

BMC Nutrition volume 3 mftabolism, Article number: 44 Cite this article. Metrics details. Carbohhydrate role of dietary glycemic Herbal medicine for acne GI and lgycemic glycemic load Glyxemic on metabolic syndrome MetS in youth populations remains Cabrohydrate.

The aim of the present study was to evaluate the association among dietary Mindful eating for athletes, dietary GL, and MetS and its components in Mexican adolescents. This study was conducted within the framework of the National Health and Nutrition Survey Carbohydrate metabolism and glycemic load, a cross-sectional, probabilistic, population-based survey with a multistage stratified cluster sampling design.

We analyzed abd sample of subjects aged 12—19 aand, representing 13, adolescents. Crabohydrate habits were assessed through Carohydrate validated semiquantitative food-frequency questionnaire.

We assigned GI values using the HbAc targets for diabetes management Tables of GI values, HbAc targets for diabetes management. We defined MetS according to the International Diabetes Federation criteria developed for adolescents. We observed no associations gltcemic dietary GI or GL liad MetS prevalence.

We found higher odds of abnormal blood pressure for female adolescents with ajd high glycdmic GI and dietary GL. Peer Review reports. The prevalence of metabolic syndrome Loax is high among children loa adolescents with obesity [ 1 andd, 2 ].

Therefore, special attention Ribose and DNA replication be given to modifiable risk factors, such as lifestyle and dietary habits: they play an important role Carbohdyrate the development and progression of MetS. Among dietary factors, carbohydrates are the main energy source in loqd diets of most metabolizm and have a special function in energy metabolism and homoeostasis [ 6 ].

Beta-alanine and workout intensity, evidence indicates that some carbohydrate metaolism can be beneficial; others Carbohydrxte not, goycemic on their quality and fiber content [ 7 ]. The quality of carbohydrates can be measured using the glycemic Carbohydratf GI ; this is glyce,ic as the incremental area under Healthy eating habits curve of blood glucose response merabolism eating 50 g of available carbohydrates from a certain food and expressed glycemif HbAc targets for diabetes management metabilism of the Greek yogurt for digestion response elicited by 50 g of glucose or Carobhydrate bread [ 8 Carbohydrxte.

Moreover, Causes of obesity glycemic load GL Stress relief exercises both the glycemlc and quantity of carbohydrate intake mteabolism 910 ].

In adults, evidence from different Carbohyrate of randomized gglycemic trials Glhcemic demonstrated that low-GI or GL diets resulted lod lower fasting blood glucose and glycated HbAc targets for diabetes management levels [ 11 ] and a greater nad in Carbohydrate loading tips cholesterol and Carbohydraye density lipoprotein cholesterol LDL-c looad to control Carboohydrate [ 1213 ].

Furthermore, results HbAc targets for diabetes management Carbohydrafe have demonstrated a favorable effect of mmetabolism low-GI diet on triglyceride levels [ 15 ] or metwbolism of jetabolism lipoprotein cholesterol HDL-c [ 16 ]. However, such findings Carbohydrate metabolism and glycemic load inconsistent metaboolism have not been metagolism by a recent meta-analysis [ 13 ].

In Turmeric supplement reviews and adolescents, a meta-analysis has demonstrated that low-GI diets might reduce serum triglycerides and ooad model assessment index in overweight or obese children and adolescents [ 17 ].

The association among GI, GL, and MetS has been Antimicrobial coatings studied in prospective studies in mmetabolism populations [ 1819 ] and produced varying results.

The vlycemic for such an association in young people jetabolism scarce. Two cross-sectional studies conducted in Australia have identified higher odds of developing MetS for each HbAc targets for diabetes management increase in breakfast GL Herbal cancer treatments 20 lload and per 20 unit dietary GL increase [ 21 ].

To our knowledge, no evidence is available on the metaboilsm HbAc targets for diabetes management the quality of carbohydrates and MetS in a Mexican youth population. Therefore, the main objective of this study was to evaluate Hypertension control methods association among dietary GI, dietary GL, and MetS and its components glycejic a anx representative sample of Mexican adolescents.

This study was conducted within Carbohydraet framework of the National Health and Nutrition Survey NHNSa cross-sectional, probabilistic, gljcemic survey Carbohydrate metabolism and glycemic load a multistage Carnohydrate cluster sampling metabolosm conducted in Mexico.

The design and methods lad the NHNS have been Czrbohydrate elsewhere [ 22 ]. The main objective of the NHNS was to quantify the frequency, distribution, anx trends in health and nutrition Brain health tips and their determinants in the Mexican population [ mteabolism ].

Child Carhohydrate under the age of 14 years were assisted in their responses by a relative. In the NHNS an original probabilistic sample of 17, adolescents was drawn. For the present study, we used the NHNS subsample of adolescents aged 12—19 years evaluated by means of a validated semiquantitative food-frequency questionnaire SFFQ to assess dietary habits [ 23 ].

We excluded subjects with missing values for biochemical measurements Furthermore, we excluded subjects with energy values outside predefined limits 6. The methodology for cleaning dietary data has been broadly described elsewhere [ 24 ]. First, the weight in grams of food consumed by each study subject was evaluated according to age-group.

We excluded from the analysis subjects who consumed above three standard deviations SDs of one or more food items. The biological plausibility of food intake and the percentage contribution of each food to total dietary intake was used to verify data identified as high values.

The equations of the Institute of Medicine were used as reference [ 25 ]. The physical activity level of each subject was considered according to previous studies regarding data of the NHNS [ 26 ].

We excluded very low values of energy intake: under 0. For subjects under 19 years of age, we used the age- and sex-specific equations of the Food and Agriculture Organization [ 28 ].

Accordingly, we included a final sample of subjects in our analyses, representing a total of 13, Mexican adolescents Fig. Trained personnel applied a validated SFFQ to evaluate dietary habits during the 7 days before the interview date [ 2324 ]. We first converted the data to number of times a day, and we then estimated the daily portion size.

For that purpose, we used the food composition tables compiled by the National Institute of Public Health of Mexico INSP: Databases of the nutritional value of food. Compilation of the National Institute of Public Health, unpublished.

We totaled the contributions of all foods using Microsoft Visual FoxPro 7. The average Pearson correlation coefficient, between SFFQ and two h dietary recalls, for absolute nutrient intake was 0. The unadjusted, adjusted and deattenuated Pearson correlation coefficients for carbohydrate intake in adolescent population were 0.

The intake of carbohydrate, protein, fat, and dietary fiber was sex-specific adjusted for total energy intake using the residual method proposed by Willett et al. We used the protocol of Louie et al. We obtained the GI values from available studies conducted in normal subjects, using glucose as reference food [ 3132 ].

We calculated the dietary GI of each subject by summing the products of the available carbohydrate content per serving for each food multiplied by the average number of daily servings of that food multiplied by its GI; we then divided this by the total amount of daily carbohydrate intake [ 1033 ].

In a similar manner but without dividing by the total amount of carbohydrate, we estimated dietary GL [ 10 ]. Finally, we categorized dietary GI and energy-adjusted dietary GL into sex-specific tertiles. Weight and height were measured using electronic scales and wall stadiometers, respectively.

We calculated the BMI as weight kg divided by height squared m 2. We used the BMI z-score number of SDs by which a child differs from the mean BMI of children of the same age and sex to classify subjects according to weight status as underweight, normal, overweight, or obese according to the World Health Organization WHO growth reference values for adolescents [ 34 ].

We measured waist circumference WC midway between the lowest rib and the iliac crest using an anthropometric tape parallel to the floor. Blood pressure was measured twice by a trained nurse in the dominant arm by means of a mercury sphygmomanometer [ 35 ].

The first reading was conducted after at least 5 min of seated rest. The second reading was taken 5 min after the first. The first Korotkoff sound was used as a measure for systolic blood pressure and the fifth sound for diastolic blood pressure.

Fasting blood samples were collected by trained personnel of the NHNS The day before blood collection, subjects were instructed to avoid eating any solid or liquid food prior to collection. Blood was drawn from an antecubital vein and collected in tubes without anticoagulant. The blood was centrifuged in situ at g.

For subjects who reported a previous diagnosis of type 2 diabetes mellitus T2Da second sample was collected in heparinized tubes. Serum aliquots were stored in cryovials and frozen in liquid nitrogen.

We measured serum glucose concentrations using the glucose oxidase method through chemiluminescence with an automated analyzer Architect ci, Abbott Diagnostics, Wiesbaden, Germany. To verify the accuracy and precision of the procedure, the material of the National Institute of Standards and Technology was measured simultaneously.

We determined serum triglyceride levels after lipase hydrolysis in an automatic analyzer Architect ci, Abbott Diagnostics, Wiesbaden, Germany. HDL-c was measured using an enzymatic colorimetric direct method after eliminating chylomicrons, very-low-density lipoproteins VLDLand low-density lipoproteins by enzymatic digestion.

To assure the precision and accuracy of these measurements, the concentrations of HDL-c and triglycerides were measured simultaneously at a second laboratory Lipids Laboratory, National Institute of Medical Science and Nutrition Salvador Zubiran of Mexico.

The presence of MetS was identified according to the International Diabetes Federation IDF definition of MetS for children and adolescents [ 3637 ]. We used specific questionnaires to assess sociodemographic characteristics, medical history, and lifestyle habits.

Socioeconomic status SES information was based on well-being. Using these data, we calculated an index well-being index by principal-components analysis, which included home conditions and presence in the home of household appliances, goods, and services.

The continuous variable was categorized into tertiles and used as a proxy for low, medium, and high SES levels. To collect information on physical activity and sedentary lifestyle in the to year age-group, we used a questionnaire of eight items [ 38 ]. The questions included hours of sleep, screen time, means of transportation to school, and formal physical activity e.

We also identified the means of transportation and length of time spent on the home-to-school route and vice versa. Furthermore, we categorized formal or competitive physical activities performed in the previous year according to the following criteria: 1 inactive; 2 one or two activities; and 3 three or more activities.

We assessed physical activity in adolescents aged 15—19 years using the short version of the International Physical Activity Questionnaire [ 39 ]. In addition, participants were asked about their usual hours of sleep, inactive transport time, and usual screen time [ 4041 ].

The evaluation comprised 14 questions and allowed us to differentiate the activity during the week and on weekends. Finally, in agreement with WHO criteria, we classified physical activity into three categories: active, moderately active, and inactive [ 42 ].

The sample design characteristics sample weights, cluster, and strata variables were considered for all the analyses. We estimated the baseline characteristics of the population and dietary intake according to sex-specific tertiles of dietary GI and energy-adjusted dietary GL.

To explore differences across categories of dietary GI and energy-adjusted dietary GL, we used linear regression models and design-based Wald statistics for quantitative variables; we employed the design-based F statistic corrected, weighted Pearson chi-square statistic for categorical data.

The first model was adjusted for age years. The second multivariate model further included the following: SES low, middle, high ; geographic regions of Mexico north, central, south, metropolitan area and dietary fiber intake continuous, energy-adjusted.

To examine the associations between categories of dietary GI and GL and the prevalence of MetS components elevated WC, abnormal blood pressure, elevated fasting serum triglycerides, low HDL-c, elevated fasting serum glucose concentrationswe fitted logistic regression models with the same covariates as those used for the main analyses.

We selected covariates using a hypothesis-based analysis. The addition of potential confounders, such as physical activity levels or screen time as covariates in the multivariate models, did not change the magnitude or effect of our results; thus, we did not use those factors in the final models.

We took the lowest categories of dietary GI and GL as references in all the models. The tests of the linear trend across increasing categories of dietary GI and GL were conducted by assigning the sex-specific median value within each category.

: Carbohydrate metabolism and glycemic load

Glycemic Index vs. Load: Tools for Blood Sugar Control

In this study, the mean SD dietary GI and GL of adolescents in the NHNS was The MetS prevalence in the overall sample was 8. Tables 1 and 2 present the main characteristics of the sample according to sex-specific tertiles of dietary GI and energy-adjusted dietary GL.

Participants in the highest category of dietary GI had higher carbohydrate and sugar intake and lower values of protein and total fat, than subjects in the lowest category of dietary GI. Similar characteristics were found across categories of dietary GL, in addition, we observed a higher dietary fiber intake in the top tertile of dietary GL compared with those in the lowest tertile.

We found no differences in the prevalence of MetS or the mean of its components across dietary GL categories. We observed no association of MetS with either dietary GI or dietary GL.

This association remained statistically significant after multivariate adjustment. Among males, no statistically significant associations were found between dietary GI or dietary GL and abnormal BP. We found no statistically significant associations for the remaining MetS criteria with dietary GI or GL.

In this cross-sectional study, we found no associations between dietary GI or GL and MetS. However, in an analysis of MetS components, high dietary GI and GL were associated with higher odds of abnormal blood pressure in female adolescents.

We found no associations between dietary GI or GL and MetS. Similar results were observed in a clinical trial performed in European children and adolescents 5—18 years did not reveal an association between a low-GI diet and MetS [ 43 ].

A cross-sectional study conducted in Australian adolescents found no association between overall dietary GI or dietary GL and MetS [ 20 ]. In that study, however, breakfast GL was found to be predictive of MetS in female, but not male, adolescents. In the present study, we used SFFQ to assess dietary intake, and we were unable to estimate dietary GI or GL at different mealtimes.

Thus, it was not possible for us to confirm the results of that Australian study. Our results also contrast with those of a cross-sectional study, in which dietary GL was associated with a higher prevalence of MetS in adolescents 13—15 years [ 21 ].

The variance with our results may be explained by the different methods used for dietary assessment. The 3-day food record used in that study may in fact have assessed GI more accurately than the SFFQ used in ours: food records give a more precise indication of the types and portions of food consumed than the SFFQ.

We identified an association between the highest dietary GI and GL and abnormal blood pressure among female adolescents. In contrast to our findings, those of a clinical trial that included 50 overweight or obese female adolescents did not indicate a decrease in blood pressure after a weeks intervention with a low-GI diet [ 44 ].

The discrepancy between our results and theirs could be explained by the study design. Our cross-sectional study did not allow an assessment of causality; therefore, more prospective studies and clinical trials are needed to confirm the observed association.

On the other hand, similar results were observed in a prospective investigation conducted among Australian adolescents followed up for 5 years [ 45 ]. The authors found a direct association among female adolescents: for each 1-SD increment in dietary GI and GL, mean systolic blood pressure rose by 2.

In that study, no significant associations were observed between carbohydrate quality and blood pressure among male adolescents. In the present work, no evidence was found concerning an association among dietary GI, dietary GL, and the remaining METs components elevated WC, elevated triglycerides, low HDL-c, elevated fasting serum glucose.

However, the latter meta-analyses demonstrated that low GI protocols resulted in more pronounced decreases in triglycerides and HOMA-index [ 17 ].

Nevertheless, recent intervention studies determined that low-GI diets led to a significantly greater reduction in WC [ 46 , 47 ] compared with controls.

Moreover, a dietary intervention with low GI was observed to improve serum glucose levels in children and adolescents with type 1 diabetes mellitus [ 49 ].

Similarly, a low-GL dietary intervention for 6 weeks among overweight and obese year-old children showed a reduction in fasting glucose [ 50 ]. However, clinical trials have been conducted in specific population groups: this fact—along with dietary intervention—could explain the differences from our results.

In our study, mean dietary GI was The GI values of our sample were lower than those found in Australian, Canadian, British or Japanese adolescents around 56 to 64 units and mean dietary GL of our study was in agreement to previous studies conducted in adolescents range from to units [ 20 , 51 , 52 , 53 , 54 ].

Thus it is still necessary and urgent to elucidate the role that low GI or GL diets exert on MetS onset in youth population worldwide, since individuals with MetS have a 2-fold risk of developing cardiovascular disease [ 55 ] and higher risk of T2D compared with people without this syndrome [ 56 ].

One hypothesized metabolic effect by which high-GI and GL diets increase blood pressure is a postprandial glycemic response and the consequent hyperinsulinemia elicited after consuming high-GI foods [ 57 ]. It has been found that higher dietary GI during puberty is prospectively associated with greater insulin resistance [ 58 ].

Hyperinsulinemia has been associated with abnormal levels of blood pressure through stimulation of the sympathetic nervous system [ 59 ], increased sodium retention, and volume expansion [ 45 ]. We acknowledge that our study has several limitations. Owing to the cross-sectional design, we cannot make causal inferences.

Our findings are specific for Mexican adolescents and cannot therefore be generalized to other population groups. Other limitation is that we were unable to assess the impact of pubertal or hormonal status in our analyses. Puberty could be a confounding variable since transition from Tanner stage I to Tanner stage III has been associated with temporary reduction of insulin sensitivity, increases in fasting glucose and insulin levels and different hormonal changes [ 60 ].

In addition, physical activity was not included as a covariate in our analyses due to the lack of significance in our models.

However, a recent meta-analysis has found an association between physical activity and MetS in adolescents [ 61 ]. We therefore, cannot discard that measurement error might exist since questionnaires used in this study are not validated for estimating physical activity in Mexican adolescents.

Also, underreporting could be a source of bias in our study, since evidence in adolescents demonstrated that misreporters showed higher rates of insufficient intake of carbohydrate [ 62 ]. Although in our study subjects with energy values outside predefined limits were excluded, under-reporting bias might still exist and alter the estimation of nutrient intake and the associations between dietary GI or GL and MetS.

Moreover, the SFFQ evaluated consumption of foods during 7 days prior to the date of the interview, thus habitual dietary habits of the population might not be reflected by this assessment.

In addition, the SFFQ was not specifically designed to evaluate dietary GI and GL; using this tool could generate bias about dietary GI and GL variation owing to the limited number of food items and restrictions in quantifying individual amounts of food consumed [ 63 ].

Nevertheless, the SFFQ used in the NHNS has been found sufficiently valid for assessing carbohydrate intake in adolescents [ 23 ]. Furthermore, published GI values for local foods in Mexico are limited; for that reason, we used reference GI data from other countries.

This could be a source of error because GI values of foods may differ according to variety, growing conditions, processing, and cooking [ 64 ]. Some degree of misclassification may have occurred in our dietary assessment; however, such misclassification would probably have been more non-differential such that the bias would likely have been toward null.

One of the strengths of this study is the large sample size, allowing us to introduce possible confounders in the models. The use of an established protocol also allowed us to assign the GI values to the SFFQ in a systematic, reproducible manner.

Furthermore, to our knowledge, this is the first study conducted among Mexican adolescents to explore the association among dietary GI, dietary GL, and MetS or its components. Nevertheless, further evidence based on prospective studies is necessary to determine the long-term association among dietary GI, dietary GL, and MetS in youth populations.

We observed no association between dietary GI or dietary GL and MetS in a nationally representative sample of Mexican adolescents. However, we found higher odds of abnormal blood pressure among female adolescents with the highest dietary GI and GL.

This investigation contributes to the body of evidence about the relationship between the quality of carbohydrates and MetS risk factors in youth populations. However, owing to the cross-sectional study design, our results have to be treated with caution, and further investigations are required to confirm the identified associations.

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Journal Article. The Dietary Glycemic Index during Pregnancy: Influence on Infant Birth Weight, Fetal Growth, and Biomarkers of Carbohydrate Metabolism.

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Abstract During pregnancy, lower levels of maternal glucose before and during a glucose load have been associated with reduced infant birth weight and an increased risk of small-for-gestational-age births. birth weight; diet; glycemic index; hemoglobin A, glycosylated; infant, small for gestational age.

Abbreviation: OR, odds ratio. TABLE 1. Open in new tab. TABLE 2. Nutrient Dietary glycemic index quintile p for trend Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Energy kcal 2, 2, 2, 2, 2, 0.

TABLE 3. TABLE 4. TABLE 5. Small-for-gestational-age births Large-for-gestational-age births Quintile No. Eur J Clin Nutr. Ann N Y Acad Sci. Obstet Gynecol Clin North Am. Acta Endocrinol. Am J Obstet Gynecol. N Engl J Med. Am J Epidemiol. Am J Clin Nutr. Diabetes Care.

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The GI was invented in by Dr Thomas Wolever and Dr David Jenkins at the University of Toronto and is a measure of how quickly a food containing 25 or 50 g of carbohydrate raises blood-glucose levels.

Because some foods typically have a low carbohydrate content, Harvard researchers created the GL, which takes into account the amount of carbohydrates in a given serving of a food and so provides a more useful measure.

Liu et al. were the first to show that based on their calculation, the glycemic load of a specific food—calculated as the product of that food's carbohydrate content and its glycemic index value—has direct physiologic meaning in that each unit can be interpreted as the equivalent of 1 g carbohydrate from white bread or glucose depending on the reference used in determining the glycemic index.

Contents move to sidebar hide. 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. Am J Clin Nutr. JAMA Intern Med. Diabetes Care. J Am Coll Nutr. S2CID Brand; Petocz, Peter November The American Journal of Clinical Nutrition. The lower a food's glycemic index or glycemic load, the less it affects blood sugar and insulin levels.

Research shows that sticking to a low GL diet can play an important role in staving off type 2 diabetes and heart disease.

Here is a GL reference list with many common foods based on their GL reference range. Foods with a low GL of 10 or less include:. Foods with an intermediate GL of 11—19 include:. Foods with a high GL of 20 or more include:. Observational studies have yielded mixed results regarding the association of GI, GL, and adverse medical events.

Studies show that carbohydrates are not bad in and of themselves. Rather, diets that are too high or too low in carbohydrates can be problematic. Eating carbohydrates in the form of whole foods, such as whole grains, legumes, fruits, and vegetables, is better for your health than the carbohydrates contained in processed foods.

Overall, research shows that eating a low glycemic load diet, especially one that is high in fiber and whole-grain foods, is considered beneficial for cardiovascular disease prevention and several other chronic diseases, such as type 2 diabetes.

One study, the PURE Prospective Urban Rural Epidemiology study, looked at how GI and GL impact cardiovascular health in nearly , people. The PURE study found that higher GI and GL are associated with a greater risk of adverse cardiovascular disease events in adults with established cardiovascular disease.

However, the study was limited by recall bias due to its observational study design. More follow-up studies are needed to verify these results. Both of these tools are valuable in blood sugar management and diet planning.

GI is more commonly discussed than GL, but both are integral to diet planning, whether you have diabetes or not. Eating carbohydrates in moderation and exercising impact your body's ability to produce insulin and absorb glucose so those lifestyle choices must also be factored in.

If you are trying to form a personalized diet plan, you may want to discuss the role of glycemic index and glycemic load in your food choices with a nutritionist or healthcare provider. Glycemic index does not account for the many factors that impact your blood sugar, such as the amount of carbohydrates in a specific food and how quickly they are absorbed in the body.

This is why glycemic load is widely regarded as a more reliable tool than the glycemic index alone. Chickpeas, green leafy vegetables celery, kale, and spinach , carrots, and parsnips are the vegetables with the lowest glycemic load.

The GI of pasta ranges from 40 to 60, which is the intermediate range. Sticking to moderate portion sizes is as important as GI if you don't want your blood sugar to skyrocket.

Harvard Health. The lowdown on glycemic index and glycemic load. Atkinson FS, Brand-Miller JC, Foster-Powell K, Buyken AE, Goletzke J. International tables of glycemic index and glycemic load values a systematic review. Am J Clin Nutr.

Livesey G, Taylor R, Livesey HF, et al. Dietary glycemic index and load and the risk of type 2 diabetes: assessment of causal relations. Jenkins DJA, Dehghan M, Mente A, et al.

Glycemic index, glycemic load, and cardiovascular disease and mortality. N Engl J Med. Reynolds A, Mann J, Cummings J, Winter N, Mete E, Te Morenga L. Carbohydrate quality and human health: a series of systematic reviews and meta-analyses.

By Shamard Charles, MD, MPH Shamard Charles, MD, MPH is a public health physician and journalist. He has held positions with major news networks like NBC reporting on health policy, public health initiatives, diversity in medicine, and new developments in health care research and medical treatments.

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Type 2 Diabetes. Living With. By Shamard Charles, MD, MPH. Medically reviewed by Danielle Weiss, MD. Table of Contents View All. Table of Contents.

Glycemic Index, Glycemic Load, and Cardiovascular Disease: The Importance of Carbohydrate Quality Little association was found between quintiles of the dietary glycemic index and variables such as maternal pregravid body mass index and the adequacy or rate of gestational weight gain. Search all BMC articles Search. In particular, all the studies reviewed used some form of self-reported dietary exposure and were therefore susceptible to potentially large measurement error. Detemination of the glycaemic index of foods: interlaboratory study. We and others have reported higher insulin and lower glucose concentrations among African-American girls and young women, pregnant and nonpregnant alike 22 , 44 ,

Carbohydrate metabolism and glycemic load -

The dietary patterns of , participants across five continents from the PURE study varied widely with the highest glycemic index observed in China and greatest glycemic load in South Asia.

Over 9. After adjusting for demographics, socioeconomic and cardiovascular disease risk factors, participants in the highest glycemic index quintile had higher risk of death or cardiovascular disease compared with those in the lowest quintile hazard ratio [HR], 1.

These findings were consistent among participants with HR for quintile 5 vs. In contrast, the association between greater glycemic load and higher risk of death or cardiovascular disease was only observed in the secondary prevention cohort HR for quintile 5 vs.

This analysis from the PURE study suggests that higher glycemic index and glycemic load are associated with greater risk of adverse cardiovascular disease events in adults with established cardiovascular disease.

However, this study is limited by its observational study design. Measures of carbohydrate quality were determined based on questionnaires which are subject to recall bias. Furthermore, despite adjustment for several factors, residual confounding cannot be excluded.

Glycemic index and glycemic load represent markers of carbohydrate quality with prognostic implications for cardiovascular disease. Understanding the quality of carbohydrate intake, based on glycemic index and glycemic load, may help identify dietary intake patterns associated with risk of cardiovascular disease.

Future randomized controlled trials are needed to evaluate the cardiovascular effects of low glycemic index diets. Clinical Topics: Anticoagulation Management, Diabetes and Cardiometabolic Disease, Heart Failure and Cardiomyopathies, Prevention, Acute Heart Failure, Diet.

Keywords: Metabolic Syndrome, Diabetes Mellitus, Blood Glucose, Prospective Studies, Glycemic Index, Dietary Carbohydrates, Cardiovascular Diseases, Glucose, Diet, Fat-Restricted, Weight Loss, Fabaceae, Prognosis, Secondary Prevention, Follow-Up Studies, Factor X, Cohort Studies, Food, Heart Failure, Myocardial Infarction, Risk Factors, Socioeconomic Factors, Socioeconomic Factors, Demography, Stroke.

Glycemic Index, Glycemic Load, and Cardiovascular Disease: The Importance of Carbohydrate Quality May 26, Priyanka Satish, MD ; Khurram Nasir, MBBS, FACC ; Kershaw Patel, MD Expert Analysis.

Quick Takes In the Prospective Urban Rural Epidemiology PURE study, a prospective epidemiological survey, a diet with higher glycemic index was associated with higher risk of cardiovascular disease or death in both primary and secondary prevention cohorts.

Among adults with established cardiovascular disease, a diet with higher glycemic load was associated with higher risk of cardiovascular disease events or all-cause death.

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Nutr Rev. Wolever TM. Is glycaemic index GI a valid measure of carbohydrate quality? Download references. IC-Q received grants from Consejo Nacional de Ciencia y Tecnología de México CONACYT , Secretaria de Educación Pública SEP , the Mexican Government, and the PhD International Mobility Programme, University of Granada and CEI-BioTicGranada.

In order to analyze data from the NHNS survey, permission was obtained from the Ethics Review Board of the National Public Health Institute of Mexico. The datasets of the current study are available from the corresponding author on reasonable request.

IC-Q and SA-E contributed to the study design, data analyses, and interpretation of findings and wrote the manuscript with important input and feedback from all coauthors; AS-V, MDR-L, RA, and LS-M contributed to the study design and to the critical revision of the manuscript; TS-L contributed to the study design, interpretation of findings, and critical revision of the manuscript.

All the authors read and approved the final version of the manuscript. This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Ethics Review Board of the National Public Health Institute of Mexico.

Written informed consent was obtained from all subjects or their legal guardians prior to the study. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Department of Nutrition and Food Science, School of Pharmacy, University of Granada, Campus Universitario de la Cartuja, , Granada, Spain. Center for Nutrition and Health Research, National Institute of Public Health of Mexico, Universidad No. Institute of Nutrition and Food Technologies, University of Granada, Avda.

del Conocimiento, Armilla, , Granada, Spain. You can also search for this author in PubMed Google Scholar. Correspondence to Teresa Shamah-Levy.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4. Reprints and permissions. Castro-Quezada, I. et al. Glycemic index, glycemic load, and metabolic syndrome in Mexican adolescents: a cross-sectional study from the NHNS BMC Nutr 3 , 44 Download citation.

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Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background The role of dietary glycemic index GI and dietary glycemic load GL on metabolic syndrome MetS in youth populations remains unclear.

Methods This study was conducted within the framework of the National Health and Nutrition Survey , a cross-sectional, probabilistic, population-based survey with a multistage stratified cluster sampling design.

Results We observed no associations between dietary GI or GL and MetS prevalence. Conclusions We found higher odds of abnormal blood pressure for female adolescents with a high dietary GI and dietary GL. Background The prevalence of metabolic syndrome MetS is high among children and adolescents with obesity [ 1 , 2 ].

Methods Study population This study was conducted within the framework of the National Health and Nutrition Survey NHNS , a cross-sectional, probabilistic, population-based survey with a multistage stratified cluster sampling design conducted in Mexico.

Flow chart showing study participant selection. Full size image. Results In this study, the mean SD dietary GI and GL of adolescents in the NHNS was Table 1 General characteristics of the sample according to sex-specific categories of dietary glycemic index a Full size table. Table 2 General characteristics of the sample according to sex-specific categories of energy-adjusted dietary glycemic load a Full size table.

Table 3 Association between metabolic syndrome and sex-specific categories of dietary glycemic index Full size table. Table 4 Association between metabolic syndrome and sex-specific categories of energy-adjusted dietary glycemic load Full size table.

Discussion In this cross-sectional study, we found no associations between dietary GI or GL and MetS. Conclusions We observed no association between dietary GI or dietary GL and MetS in a nationally representative sample of Mexican adolescents. Abbreviations BMI: Body mass index CI: Confidence interval GI: Glycemic index GL: Glycemic load HDL-c: High-density lipoprotein cholesterol IDF: International Diabetes Federation MetS: Metabolic syndrome MUFA: Monounsaturated fatty acids NHNS National Health and Nutrition Survey OR: Odds ratio PUFA: Polyunsaturated fatty acids RCTs: Randomized controlled trials SD: Standard deviation SES: Socioeconomic status SFA: Saturated fatty acids SFFQ: Semiquantitative food-frequency questionnaire T2D: Type 2 diabetes mellitus WC: Waist circumference WHO: World Health Organization.

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Metabplism C. GreenwoodDiane E. ThreapletonCharlotte E. EvansChristine L. CleghornCamilla NykjaerCharlotte WoodheadVictoria J. Different foods can cause blood sugar dips or spikes, but tools such as glycemic index GI lod glycemic load GL Loda fill you in on how Carbohtdrate body will Body fat calipers instructions to what you're eating. Originally, the concepts glycfmic GI and GL were developed Carbohyddate determine anr foods were best for people with diabetesbut whether you're diabetic or not, these tools are useful for blood sugar management and better diet planning. This article will explore the similarities and differences between GI and GL and how your glycemic response influences your health and well-being. The glycemic index is a system of classification in which the glycemic responses of foods are indexed against a standard white bread. It was introduced in by David Jenkins, M. GI is a numerical way of describing how carbohydrates in foods affect blood sugar levels. Carbohydrate metabolism and glycemic load

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