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Glycemic Index vs Glycemic Load (In Simple Terms) – Dr. Berg Download PDF. CONFLICTS OF Blood sugar diet No Glycemic load and glycemic variability conflict of interest relevant glyceimc this article was reported. T1DM, type Glycemic load and glycemic variability diabetes mellitus; HbA1c, glycosylated hemoglobin; Vafiability, type variabulity diabetes mellitus. Skip Navigation Skip variabiilty Glycemic load and glycemic variability Search Home Current Current issue Hydration for recreational sports print Browse All issues Article ajd category Article loar topic Article by Category Best paper variabiligy the year Most Glycmeic Glycemic load and glycemic variability cited Funded articles Diabetes Metab J Search Author index Collections Guidelines in DMJ Fact sheets in DMJ COVID in DMJ For contributors For Authors Instructions to authors Article processing charge e-submission For Reviewers Instructions for reviewers How to become a reviewer Best reviewers For Readers Readership Subscription Permission guidelines About Aims and scope About the journal Editorial board Management team Best practice Metrics Contact us Editorial policy Research and publication ethics Peer review policy Copyright and open access policy Article sharing author self-archiving policy Archiving policy Data sharing policy Preprint policy Advertising policy E-Submission. mobile menu button. Author information Article notes Copyright and License information 1 Division of Endocrinology and Metabolism, Department of Internal Medicine, Dong-A Medical Center, Dong-A University College of Medicine, Busan, Korea. Corresponding author: Jae Hyeon Kim.Glycemic load and glycemic variability -
While the group with obesity had a higher mean blood glucose MBG , mean amplitude of glycemic excursions MAGE , and continuous overall glycemic action-1 h CONGA-1 than the group without obesity, these differences were not found to be significant.
The standard deviation of blood glucose SDBG and mean of daily differences MODD were found to be higher, although not significantly, in the group without obesity than in the group with obesity. Table 2. Results extracted from continuous glucose monitoring and dietary logs in each group.
For the diet assessment, only 20 of the 28 participants maintained detailed food logs with corresponding photo submissions. Based on the limited data, two Registered Dietitians focused the analysis on carbohydrate quality and quantity—estimating the proportion of refined carbohydrate versus fiber, as well as the total carbohydrate content of the meal.
Of the 20 diets that could be analyzed, 12 were low glycemic load and 8 were high glycemic load. Chi-square analysis showed that of the 20 adults who kept dietary logs, there was a difference in glycemic load between the two cohorts, as seen in Table 2.
Tables 3 and 4 show the covariates analyzed through linear regression analysis. When assessing glycemic variability and insulin resistance using MAGE and HOMA-IR as proxies respectively , only HOMA-IR demonstrated significant results.
BMI, WC, adults with obesity, high glycemic diet, HbA1c, and fasting insulin levels maintained an independent association with HOMA-IR.
Table 3. Univariate linear regression analysis of factors associated with mean amplitude of glycemic excursions MAGE. Table 4. Univariate and multivariable linear regression analysis of factors associated with HOMA-IR.
This pilot study, which recruited normoglycemic men and women of diverse backgrounds, showed that glucometric data measuring glucose variability was similar in groups with obesity and without obesity. In contrast, even in this small sample, fasting insulin and HOMA-IR were significantly higher in participants with obesity.
Surprisingly, while not statistically significant, the standard deviation of blood glucose was higher in participants without obesity. On linear regression analysis, we found associations with HOMA-IR—and not MAGE—for BMI, WC, HbA1c, and fasting insulin levels.
On multivariable analysis, WC and fasting insulin levels remained significantly associated with HOMA-IR. Taken together, these preliminary results suggest that the rise in insulin may be secondary to the development of insulin resistance and a compensatory mechanism in the glucose regulation process.
The results of this study lend support to the Energy Balance model for obesity. If a high glycemic load were the impetus for development of obesity, then greater glycemic variability would be seen in our cohort with obesity, which was not the case.
To obtain further insight, we had patients keep dietary logs that were reviewed by Registered Dietitians, which showed that although our cohort with obesity on average ate meals with a higher glycemic load, their glycemic variability was not different from that of the cohort without obesity.
However, we acknowledge that our small sample size and the difficulty in obtaining accurate dietary logs limits interpretation of these results. Consistent with this are studies that demonstrate that excess nutrients within the skeletal muscle cells signal the cell membrane to block insulin-dependent glucose uptake by muscle cells.
Petersen KF et al. demonstrated that insulin-resistant individuals have marked defects in muscle glycogen synthesis and divert their ingested energy into hepatic de novo lipogenesis. When insulin-resistant individuals are challenged with a high glycemic meal challenge, their post-prandial plasma glucose concentrations were similar to insulin-sensitive individuals For CGM to be effective, it must be assumed that glucose values are reflective of insulin secretion.
However, this assumption has not been proven to be true in normoglycemic populations. Furthermore, excess fructose has been implicated in the development of obesity, diabetes, cardiovascular disease, non-alcoholic fatty liver disease, and cancer 24 — Fructokinase C, present in the liver, converts fructose into metabolites such as citrate and uric acid that result in the net breakdown of ATP and endothelial dysfunction Interestingly, fructose ingestion does not markedly raise glucose values as both dextrose or sucrose do and thus its intake would not be sharply detected by CGM 30 , The data for the use of CGM devices in normoglycemic individuals with obesity are not robust Salkind et al.
While there was no difference in glycemic variability between the two groups, Salkind et al. state that both groups had greater glycemic variability as compared with historical controls.
However, one limitation of this study, and many other CGM studies in the literature, is that there is no contemporaneously studied normal weight control group, which our study indeed has In another study, Ma et al.
In contrast to our study, there was a difference in baseline mean blood glucose within their two cohorts, a smaller difference in BMI, and they only analyzed males with central obesity.
Within Asian populations, the prevalence of diabetes with lower body mass index levels is well-documented 36 , and we speculate that many of the patients in this cohort may have been pre-diabetic as baseline HbA1c was not documented.
More consistent with our findings is a recent study using CGM in adolescents with obesity Investigators used CGM to compare whether glucose variability is altered during time-restricted eating TRE.
They found no difference in variability when TRE was compared to a diet that was not time limited. Theoretically, time restriction lowers insulin levels. The absence of any difference could be that TRE as used in this study does not result in the anticipated reduction in insulin levels, or that glucose value is not a sensitive method of measuring insulin.
While it is known that dietary interventions for normalizing glycemic levels are associated with changes in health markers, including fasting blood glucose level and HbA1c, the role of CGM in weight management is not established 38 , Although the idea of CGM translating to improved health outcomes is compelling, the commercialization of this technology has begun without clinical, peer-reviewed evidence of efficacy for weight loss.
Advocates of CGM have suggested that nutrition can be personalized by identifying specific foods that cause spikes in glucose levels. However, the standard American diet involves consumption of a variety of food groups at the same time and furthermore, failure to raise blood sugar levels is not synonymous with healthy eating.
When a sugary dessert is eaten after a heavy meal, it causes less of a rise in blood sugar than when eaten on an empty stomach The temporal sequence of carbohydrate ingestion during a meal has a significant impact on post-prandial glucose excursions.
Thus, there are many factors that contribute to glucose excursions or the lack thereof; and while CGM can identify glucose excursions, its role in weight loss for the modern consumer is not established. However, our results point toward metrics for insulin resistance, such as HOMA-IR, as potentially stronger clinical markers for dysglycemia and metabolic syndrome.
Furthermore, there may be harm related to the use of CGM in its capacity to raise false alarms and lead to unnecessary healthcare use, including excess clinical visits and inappropriate medication administration.
Users may detect glycemic drops or spikes—prompting health changes such as increased snacking—when in reality, these values are biologically insignificant. One major limitation of our pilot study is that it occurred during the COVID pandemic, which restricted recruitment.
Our preliminary findings need to be further explored in a larger cohort, and we cannot eliminate the possibility that with a greater sample there would not be a subtle difference in glycemic variability and other measured parameters. However, despite the small sample size, there remained a difference in fasting insulin and HOMA-IR between the two groups, suggesting rising insulin levels being a compensatory process rather than merely the result of a high glycemic load.
This was further demonstrated through linear regression analysis, which showed HOMA-IR to be associated with BMI, WC, HbA1c, and fasting insulin levels, while MAGE was not found to be associated with these factors. Additionally, the best measures of glycemic variability and insulin response remain unknown.
MAGE, as extracted from CGM, is viewed as the most comprehensive index for assessment of intraday glycemic variability, but it does not account for insulin responsiveness and other processes of post-prandial cellular metabolism. Future studies should recruit a larger cohort of normoglycemic adults to assess the utility of CGM in predicting dysglycemia and aiding weight loss efforts.
While there is much excitement surrounding the use of commercial CGM products in management of obesity, our preliminary results suggest that fasting insulin and HOMA-IR values may be more clinically useful than CGM data alone.
The absence of increased glycemic variability in normoglycemic individuals is suggestive that the Energy Balance model may represent a more accurate conceptual framework for obesity. Finally, the application of CGM in weight loss should await further trials.
The original contributions presented in this study are included in this article, further inquiries can be directed to the corresponding author. The studies involving human participants were reviewed and approved by Northwell Health Institutional Review Board no.
DC and MR contributed in conception, design, statistical analysis, and supervised the study. DC, SZ, BF, and AB contributed to data collection, data analysis, and manuscript drafting. All authors had final approval of the submitted and published versions of the manuscript.
The research, including Continuous Glucose Monitoring equipment, was supported by a medical education grant from Medtronic. We acknowledge all study participants who volunteered their time during this study.
We thank Doreen Olvet for her help with statistical analysis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.
Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Wang Y, Beydoun MA, Liang L, Caballero B, Kumanyika SK. Will all Americans become overweight or obese?
Estimating the progression and cost of the US obesity epidemic. Obesity Silver Spring. doi: PubMed Abstract CrossRef Full Text Google Scholar. Rodríguez-Rodríguez R, Miralpeix C. Hypothalamic regulation of obesity. Int J Mol Sci. Quiñones M, Martínez-Grobas E, Fernø J, Pérez-Lois R, Seoane LM, Al Massadi O.
Hypothalamic actions of SIRT1 and SIRT6 on energy balance. Dimitri P. Treatment of acquired hypothalamic obesity: now and the future. Front Endocrinol. Breton C.
The hypothalamus-adipose axis is a key target of developmental programming by maternal nutritional manipulation. J Endocrinol. Wang Q, Zhang B, Stutz B, Liu ZW, Horvath TL, Yang X. However, there was no significant difference in postprandial glycemic responses between high-GI and low-GI breakfasts of similar fiber content In this study, meal GI values derived from published data failed to correctly predict postprandial glucose response, which appeared to be essentially influenced by the fiber content of meals.
Since the amounts and types of carbohydrate, fat, protein , and other dietary factors in a mixed meal modify the glycemic impact of carbohydrate GI values, the GI of a mixed meal calculated using the above-mentioned formula is unlikely to accurately predict the postprandial glucose response to this meal 3.
Using direct measures of meal GIs in future trials — rather than estimates derived from GI tables — would increase the accuracy and predictive value of the GI method 2 , 6.
In addition, in a recent meta-analysis of 28 studies examining the effect of low- versus high-GI diets on serum lipids , Goff et al. indicated that the mean GI of low-GI diets varied from 21 to 57 across studies, while the mean GI of high-GI diets ranged from 51 to 75 Therefore, a stricter use of GI cutoff values may also be warranted to provide more reliable information about carbohydrate-containing foods.
The glycemic index GI compares the potential of foods containing the same amount of carbohydrate to raise blood glucose. However, the amount of carbohydrate contained in a food serving also affects blood glucose concentrations and insulin responses.
For example, the mean GI of watermelon is 76, which is as high as the GI of a doughnut see Table 1. Yet, one serving of watermelon provides 11 g of available carbohydrate, while a medium doughnut provides 23 g of available carbohydrate.
The concept of glycemic load GL was developed by scientists to simultaneously describe the quality GI and quantity of carbohydrate in a food serving, meal, or diet.
The GL of a single food is calculated by multiplying the GI by the amount of carbohydrate in grams g provided by a food serving and then dividing the total by 4 :. Using the above-mentioned example, despite similar GIs, one serving of watermelon has a GL of 8, while a medium-sized doughnut has a GL of Dietary GL is the sum of the GLs for all foods consumed in the diet.
It should be noted that while healthy food choices generally include low-GI foods, this is not always the case. For example, intermediate-to-high-GI foods like parsnip, watermelon, banana, and pineapple, have low-to-intermediate GLs see Table 1.
The consumption of high-GI and -GL diets for several years might result in higher postprandial blood glucose concentration and excessive insulin secretion. This might contribute to the loss of the insulin-secreting function of pancreatic β-cells and lead to irreversible type 2 diabetes mellitus A US ecologic study of national data from to found that the increased consumption of refined carbohydrates in the form of corn syrup, coupled with the declining intake of dietary fiber , has paralleled the increased prevalence of type 2 diabetes In addition, high-GI and -GL diets have been associated with an increased risk of type 2 diabetes in several large prospective cohort studies.
Moreover, obese participants who consumed foods with high-GI or -GL values had a risk of developing type 2 diabetes that was more than fold greater than lean subjects consuming low-GI or -GL diets However, a number of prospective cohort studies have reported a lack of association between GI or GL and type 2 diabetes The use of GI food classification tables based predominantly on Australian and American food products might be a source of GI value misassignment and partly explain null associations reported in many prospective studies of European and Asian cohorts.
Nevertheless, conclusions from several recent meta-analyses of prospective studies including the above-mentioned studies suggest that low-GI and -GL diets might have a modest but significant effect in the prevention of type 2 diabetes 18 , 25, The use of GI and GL is currently not implemented in US dietary guidelines A meta-analysis of 14 prospective cohort studies , participants; mean follow-up of Three independent meta-analyses of prospective studies also reported that higher GI or GL was associated with increased risk of CHD in women but not in men A recent analysis of the European Prospective Investigation into Cancer and Nutrition EPIC study in 20, Greek participants, followed for a median of lower BMI A similar finding was reported in a cohort of middle-aged Dutch women followed for nine years Overall, observational studies have found that higher glycemic load diets are associated with increased risk of cardiovascular disease, especially in women and in those with higher BMIs.
A meta-analysis of 27 randomized controlled trials published between and examining the effect of low-GI diets on serum lipid profile reported a significant reduction in total and LDL - cholesterol independent of weight loss Yet, further analysis suggested significant reductions in serum lipids only with the consumption of low-GI diets with high fiber content.
In a three-month, randomized controlled study, an increase in the values of flow-mediated dilation FMD of the brachial artery, a surrogate marker of vascular health, was observed following the consumption of a low- versus high-GI hypocaloric diet in obese subjects High dietary GLs have been associated with increased concentrations of markers of systemic inflammation , such as C-reactive protein CRP , interleukin-6, and tumor necrosis factor-α TNF-α 40, In a small week dietary intervention study, the consumption of a Mediterranean-style, low-GL diet without caloric restriction significantly reduced waist circumference, insulin resistance , systolic blood pressure , as well as plasma fasting insulin , triglycerides , LDL-cholesterol, and TNF-α in women with metabolic syndrome.
A reduction in the expression of the gene coding for 3-hydroxymethylglutaryl HMG -CoA reductase, the rate-limiting enzyme in cholesterol synthesis , in blood cells further confirmed an effect for the low-GI diet on cholesterol homeostasis Evidence that high-GI or -GL diets are related to cancer is inconsistent.
A recent meta-analysis of 32 case-control studies and 20 prospective cohort studies found modest and nonsignificant increased risks of hormone -related cancers breast, prostate , ovarian, and endometrial cancers and digestive tract cancers esophageal , gastric , pancreas , and liver cancers with high versus low dietary GI and GL A significant positive association was found only between a high dietary GI and colorectal cancer Yet, earlier meta-analyses of prospective cohort studies failed to find a link between high-GI or -GL diets and colorectal cancer Another recent meta-analysis of prospective studies suggested a borderline increase in breast cancer risk with high dietary GI and GL.
Adjustment for confounding factors across studies found no modification of menopausal status or BMI on the association Further investigations are needed to verify whether GI and GL are associated with various cancers. Whether low-GI foods could improve overall blood glucose control in people with type 1 or type 2 diabetes mellitus has been investigated in a number of intervention studies.
A meta-analysis of 19 randomized controlled trials that included diabetic patients with type 1 diabetes and with type 2 diabetes found that consumption of low-GI foods improved short-term and long-term control of blood glucose concentrations, reflected by significant decreases in fructosamine and glycated hemoglobin HbA1c levels However, these results need to be cautiously interpreted because of significant heterogeneity among the included studies.
The American Diabetes Association has rated poorly the current evidence supporting the substitution of low-GL foods for high-GL foods to improve glycemic control in adults with type 1 or type 2 diabetes 51, A randomized controlled study in 92 pregnant women weeks diagnosed with gestational diabetes found no significant effects of a low-GI diet on maternal metabolic profile e.
The low-GI diet consumed during the pregnancy also failed to improve maternal glucose tolerance , insulin sensitivity , and other cardiovascular risk factors, or maternal and infant anthropometric data in a three-month postpartum follow-up study of 55 of the mother-infant pairs At present, there is no evidence that a low-GI diet provides benefits beyond those of a healthy, moderate-GI diet in women at high risk or affected by gestational diabetes.
Obesity is often associated with metabolic disorders, such as hyperglycemia , insulin resistance , dyslipidemia , and hypertension , which place individuals at increased risk for type 2 diabetes mellitus , cardiovascular disease , and early death 56, Lowering the GI of conventional energy-restricted, low-fat diets was proven to be more effective to reduce postpartum body weight and waist and hip circumferences and prevent type 2 diabetes mellitus in women with prior gestational diabetes mellitus Yet, the consumption of a low-GL diet increased HDL - cholesterol and decreased triglyceride concentrations significantly more than the low-fat diet, but LDL -cholesterol concentration was significantly more reduced with the low-fat than low-GI diet Weight loss with each diet was equivalent ~4 kg.
Both interventions similarly reduced triglycerides, C-reactive protein CRP , and fasting insulin , and increased HDL-cholesterol. Yet, the reduction in waist and hip circumferences was greater with the low-fat diet, while blood pressure was significantly more reduced with the low-GL diet Additionally, the low-GI diet improved fasting insulin concentration, β-cell function, and insulin resistance better than the low-fat diet.
None of the diets modulated hunger or satiety or affected biomarkers of endothelial function or inflammation. Finally, no significant differences were observed in low- compared to high-GL diets regarding weight loss and insulin metabolism It has been suggested that the consumption of low-GI foods delayed the return of hunger, decreased subsequent food intake, and increased satiety when compared to high-GI foods The effect of isocaloric low- and high-GI test meals on the activity of brain regions controlling appetite and eating behavior was evaluated in a small randomized , blinded, cross-over study in 12 overweight or obese men During the postprandial period, blood glucose and insulin rose higher after the high-GI meal than after the low-GI meal.
In addition, in response to the excess insulin secretion, blood glucose dropped below fasting concentrations three to five hours after high-GI meal consumption. Cerebral blood flow was significantly higher four hours after ingestion of the high-GI meal compared to a low-GI meal in a specific region of the striatum right nucleus accumbens associated with food intake reward and craving.
If the data suggested that consuming low- rather than high-GI foods may help restrain overeating and protect against weight gain, this has not yet been confirmed in long-term randomized controlled trials.
However, the dietary interventions only achieved a modest difference in GI ~5 units between high- and low-GI diets such that the effect of GI in weight maintenance remained unknown.
Table 1 includes GI and GL values of selected foods relative to pure glucose Originally written in by: Jane Higdon, Ph. Linus Pauling Institute Oregon State University. Updated in December by: Jane Higdon, Ph. Updated in February by: Victoria J.
Drake, Ph. Updated in March by: Barbara Delage, Ph. Reviewed in March by: Simin Liu, M. Professor of Epidemiology, Professor of Medicine Brown University. Liu S, Willett WC. Dietary glycemic load and atherothrombotic risk. Curr Atheroscler Rep. Brouns F, Bjorck I, Frayn KN, et al.
Glycaemic index methodology. Nutr Res Rev. Augustin LS, Kendall CW, Jenkins DJ, et al. Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium ICQC. Nutr Metab Cardiovasc Dis. Monro JA, Shaw M.
Glycemic impact, glycemic glucose equivalents, glycemic index, and glycemic load: definitions, distinctions, and implications. Am J Clin Nutr. The University of Sydney. About Glycemic Index. The International Organization for Standardization.
Food products - Determination of the glycaemic index GI and recommendation for food classification. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. Willett WC. Eat, Drink, and be Healthy: The Harvard Medical School Guide to Healthy Eating.
Dodd H, Williams S, Brown R, Venn B. The epidemiological evidence, which is more or less confined to postprandial hyperglycemia and postglucose load glycemia, is also rather compelling in favor of the hypothesis, although certainly not fully conclusive as there are also a number of conflicting results.
The strongest cons are seen in the missing evidence as derived from randomized prospective intervention studies targeting postprandial hyperglycemia longer term, i.
In fact, several such intervention studies in men have recently failed to produce the intended beneficial outcome results. As this evidence by intervention is, however, key for the ultimate approval of a treatment concept in patients with diabetes, the current net balance of attained evidence is not in favor of the hypothesis here under debate, i.
The absence of a uniformly accepted standard of how to estimate these parameters adds a further challenge to this whole debate. Although undoubtedly diabetes, i. Fasting plasma glucose FPG , postprandial hyperglycemia, and glucose variability all contribute to the net balance of the long-term glycemic parameter HbA 1c not to forget that hypoglycemia has recently re-emerged as an independent risk predictor of major cardiovascular CV and other negative events in its own right, but that is not the focus of this article.
Does it not suffice to concentrate on HbA 1c values, because they have been shown by several meta-analyses in based on all available data from randomized intervention trials on blood glucose BG -lowering therapies to be clearly independent determinants of major CV events, especially myocardial infarction 2 , 3?
This article, therefore, aims to evaluate the pros and cons of a specific impact of postprandial hyperglycemia and glycemic variability on the vascular complications in diabetes, and whether they matter.
Three areas of evidence mainly are to be considered: the epidemiology, the pathophysiology, and randomized prospective intervention trials. As a basis, methods of assessing postprandial hyperglycemia and glycemic variability are briefly discussed.
Table 1 gives an overview of the glucose-related measures used in studying the relationship with CV parameters, both short- and longer-term. So far, no uniformly accepted standard of measurement has emerged, which poses a challenge in its own when comparing or planning studies. The postprandial parameters are self-explanatory.
Numerous measures of glycemic variability have been proposed in the literature 4. Some of these tools are easy to use; others are very complex or difficult to apply in clinical practice, even when using new methods such as continuous glucose self-monitoring.
Table 1 focuses on only a few of the most important methods. The calculation of the glycemic average was thought to provide better insight into glycemic variability because several study groups could demonstrate that people with diabetes—and therefore a higher mean glycemic value—produced larger amounts of compounds related to oxidative stress i.
But, the mere average turned out to be inadequate in evaluating glycemic oscillations. Therefore, the SD is considered to be the simplest tool for describing glycemic variability. In fact, the software incorporated in most of modern measuring devices provides information on the number of measurements per day, average glucose value, and SD.
Unfortunately, this SD is calculated over the total number of measurements taken by the meter and includes all oscillations without a weighting of the minor or major variations.
The calculation of the hyperglycemic index is based on self-monitored BG measurements and is defined as the area under the glucose curve above the normal range divided by the total time of the observation period.
The cutoff for the normal glucose range is set at 6. Mean amplitude of glycemic excursions MAGE 8 was designed to take into account the glycemic peaks and nadirs encountered during a day, beyond average glucose values, according to the formula:.
where λ is the difference from peak to nadir, x is the number of valid observations, and y is 1 SD of mean glucose in a h period. The objective of this parameter is to more heavily consider the major variations of glucose levels and to give less weight to the minor ones. Only the variations exceeding 1 SD of the average glycemic value during the observation period are considered.
MAGE is a popular measure especially in studies based on continuous glucose monitoring systems. A study by Monnier et al. However, MAGE has some inherent limitations.
Firstly, it does not discern the total number of oscillations of BG levels because the selection of 1 SD or multiple or fraction of 1 SD as the cut-off point is completely arbitrary. Secondly, it is a relative measure because it is relative to the mean. Thirdly, the MAGE value can be biased: if only one major decline or increase occurs during the observation period, this nevertheless yields a high result.
Other problems with MAGE may occur, such as potential dependence on sampling frequency and the ambiguity as to where a peak or nadir begins and ends. The concept of the continuous overlapping net glycemic action CONGA was first described by McDonnell et al.
Contrary to methods that illustrate the interday variation of glucose levels, CONGA is designed to analyze intraday glycemic variability. For each observation after the first n hours of observations, the difference between the current observation and the observation n hours prior is calculated.
CONGAn is defined as the SD of the differences. Mathematically, CONGAn can be described by the formula:. The most recently proposed measure of glycemic variability is the approach of Kovatchev et al. The basic underlying idea of this concept is the asymmetry of the BG scale, i.
This leads to a skewed distribution of glucose readings. Consequently, classical statistical measures like the mean of glucose values and the SD will describe the underlying data only poorly because these measures require a normal distribution.
Thus a logarithmic transformation of the glucose scale has been proposed that is symmetrical about 0 and defines 6. This results in the transformed BG readings exhibiting a normal distribution.
Since , over 15 observational studies have been published showing that elevated postprandial glucose values, even in the high nondiabetic impaired glucose tolerance IGT range, contribute to an approximately threefold increase in the risk of developing coronary heart disease or a CV event. Table 2 contains an overview of these studies in greater detail.
This trend is confirmed in the meta-analysis by Coutinho et al. Controversy, however, exists whether elevated FPG and postload glucose contribute differently to all-cause mortality or CV outcomes, respectively, as the meta-analysis by Coutinho et al.
suggests that both parameters contribute more or less equally, in contrast to publications, e. The still ongoing prospective Australian Diabetes, Obesity and Lifestyle AusDiab Study, which follows a representative cohort 14 of more than 10, people across Australia after an initial glucose tolerance test, has indicated a dose-effect relationship between glucose exposure and CV mortality after some 5 years of follow-up in the rank order from low to high risk of normal glucose tolerance, prediabetes, newly diagnosed diabetes by screening, and known diabetes, with no difference, however, between the two prediabetic states of impaired fasting glucose IFG and IGT.
In a more recent follow-up, the AusDiab Study reports that after 6 years there is a strikingly similar continuous relationship between all three glycemic parameters—FPG, PPG, and HbA 1c —and all-cause and CV mortality, with the exception that very low FPG values were also associated with a higher mortality risk CHD, coronary heart disease; CVD, CV disease; HR, hazard ratio; NHANES II, Second National Health and Nutrition Examination Survey; OR, odds ratio; PG, plasma glucose; RR, relative risk.
The relationship between glucose peaks and increased risk for stroke is analyzed less explicitly, albeit most of the studies described in Table 2 included stroke as a form of CV disease in the outcome parameters.
It was determined that the relative risk increased by 1. Only a few prospective studies have analyzed the relationship between PPG and CV risk in overt diabetes.
One of the first studies of this kind, the Diabetes Intervention Study 32 , investigated the effect of PPG values 1 h after a meal in more than 1, subjects with newly diagnosed type 2 diabetes who were followed for 11 years.
More recently, Cavalot et al. Some prospective studies have also analyzed the effect of glycemic variability on patient-relevant outcomes. Recently, Krinsley 33 reported a strong and independent relationship between glycemic variability and mortality in a large cohort of patients with a variety of medical, surgical, and trauma diagnoses in an intensive care unit.
The mortality rate in patients with the lowest quartile of glycemic variability, as assessed by the SD of the MBG values, was Also, the length of stay was shorter among patients in the first quartile compared with those in the other three quartiles.
The strong association between glycemic variability and intensive care unit mortality was also described by Egi et al. Japanese studies have shown a relationship between PPG and nephropathy But, the impact of short-term glucose toxicity seems less clear than it is in macrovascular complications because contradictory results have also been published However, as mentioned previously, contradictory results are available So, in all, although the accumulated data looks impressive that PPG seems to be important, especially for glucose variability, the evidence is still inconclusive in terms of a unique role for long-term prediction of CV and even microvascular sequelae of diabetes and its prestates, above and beyond other glycemic parameters like FPG and HbA 1c.
Acute increases of plasma glucose levels have significant hemodynamic effects, even in nondiabetic subjects. These hemodynamic effects were abolished by infusion of glutathione, suggesting that they were mediated by an oxidative pathway. If this is so, one would expect glucose levels to affect endothelial function as well.
Indeed, a study of flow-mediated endothelium-dependent vasodilation of the brachial artery among 52 subjects during an oral glucose tolerance test found significant decreases at 1 and 2 h among those with IGT or diabetes, but not among the control subjects.
In fact, plasma glucose levels were negatively correlated with endothelium-dependent vasodilation. Endothelial function also normalized after 2 h in the control subjects but not in the group with IGT or diabetes This evidence is also in line with the finding that modulating postprandial hyperglycemia, e.
Postprandial hyperglycemia also has been found to cause myocardial perfusion defects. In a recent prospective study 42 , 20 patients with well-controlled diabetes and 20 healthy control subjects were given a standard mixed meal, and a myocardial contrast echocardiography was used to assess myocardial perfusion.
Before the meal, the two groups had similar myocardial flow velocity, blood volume, and blood flow. In the postchallenge state, all these parameters increased significantly in the healthy control subjects, but flow velocity and flow decreased significantly among the patients with diabetes.
There was a significant correlation between changes in blood volume and the degree of postprandial hyperglycemia in the diabetic patients. These data suggest that postprandial myocardial perfusion defects are related to impaired coronary microvascular circulation and represent an early marker of diabetic CV damage.
A follow-up study showed that treatment with a short-acting insulin analog significantly decreased postprandial hyperglycemia and partly restored the postprandial myocardial perfusion defects to normal So, there seems to be a consistent proof of principle that endothelial dysfunction can be normalized by intervening postprandial hyperglycemia.
Several laboratory studies have also approached the issue of glucose variability. A deleterious effect of glucose fluctuations on renal mesangial, renal tubulointerstitial, umbilical endothelial, and pancreatic β-cells has been reported. Specifically, mesangial and tubulointerstitial cells cultured in periodic high glucose concentration increase matrix production more than cells cultured in high stable glucose.
Increased apoptotic cell death was observed in both β- and endothelial cells in response to fluctuating as compared with continuous high glucose. Oxidative stress, in particular the increased superoxide production at the mitochondrial level, has been suggested as the key link between hyperglycemia and diabetes complications.
Evidence suggests that the same phenomenon underlines the deleterious effect of oscillating glucose, leading to a more enhanced deleterious effect of fluctuating glucose compared with constant high glucose 44 —
John Service; Glucose Variability. Diabetes 1 May ; 62 5 blycemic — The proposed Muscle growth hormones of variabillty Glycemic load and glycemic variability to the Thermogenesis and muscle growth of the variabiliity of diabetes vsriability that of glycemic exposure is supported by reports that oxidative stress, the putative mediator of such complications, is greater for intermittent as opposed to sustained hyperglycemia. Variability of glycemia in ambulatory conditions defined as the deviation from steady state is a phenomenon of normal physiology. Comprehensive recording of glycemia is required for the generation of any measurement of glucose variability.
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