Glycemic load and glycemic variability -
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 — Experiments in animals also support the hypothesis of a deleterious effect of fluctuating glucose.
Recently, Azuma et al. Using this method, the investigators have demonstrated that repetitive fluctuation of hyperglycemia resulted in significantly induced monocyte-endothelial adhesion as compared with sustained hyperglycemia Furthermore, to assess the role of glucose fluctuations on atherogenesis, they used atherogenic-prone mice fed maltose twice daily to model repetitive glucose spikes The results show that fluctuations in BG concentrations accelerated macrophage adhesion to endothelial cells and the formation of fibrotic arteriosclerotic lesions.
All the above laboratory data are consistent with clinical data. Specifically, repeated fluctuations of glucose produce increased circulating levels of inflammatory cytokines as compared with stable high glucose in healthy subjects, as well as endothelial dysfunction in both healthy and type 2 diabetic patients The role of oxidative stress also seems to be a key causative factor clinically because the use of an antioxidant reduced the phenomenon in both the studies Consistent with the hypothesis of an involvement of oxidative stress is the evidence that daily glucose fluctuations in type 2 diabetes are strongly predictive of increased generation of oxidative stress 5.
However, the same results have not been confirmed in type 1 diabetes Even if oxidative stress generation appears to be the key player of all the phenomena reported above, the precise mechanism through which oscillating glucose may be worse than constant high glucose still remains to be fully elucidated.
Although further studies are certainly warranted, these would be quite difficult to accomplish in humans. A possible explanation is that the cells are not able to sufficiently increase their own intracellular antioxidant defenses in oscillating glucose conditions 53 , a condition that has been suggested to favor the development of diabetes complications In this regard, a recent study showed that during acute hyperglycemia in healthy subjects, several genes involved in free radical detoxification were downregulated Table 3 summarizes potential mechanisms involved in linking especially postprandial hyperglycemia and CV risk.
Overall, the pathophysiological evidence looks highly suggestive for PPG, IGT, and glucose variability being important key determinants of vascular damage. The ultimate proof for pathophysiological concepts has to come from interventional trials attempting to target and abolish a given risk constellation and, by doing so, improving clinically relevant outcomes.
Several controlled, prospective, and randomized clinical studies, e. It is important to emphasize that although surrogate markers for CV damage are of interest, such as intima-media thickening at the carotid artery level or biomarkers such as high-sensitivity C-reactive protein, they are not good enough to substantiate final proof for the effectiveness of an intervention as has been seen in the context with the BG-lowering thiazolidinedione rosiglitazone.
In this case, a wealth of potentially beneficial effects had been established on intima-media thickening, in-stent stenosis, and a number of biomarkers, but the randomized clinical outcome studies with that drug were rather disappointing and—at best—showed no CV harm with the exception of heart failure , but certainly no CV benefit, e.
By targeting PPG with use of the α-glucosidase inhibitor acarbose in subjects with IGT, the Stop-NIDDM Trial 56 provided evidence that this approach not only was highly effective to prevent the manifestation of overt type 2 diabetes, but also to prevent the occurrence of myocardial infarction and overall CV events.
CV outcomes, however, had been prespecified as secondary outcomes only, so these results are seen as hypothesis generating, but no final proof.
So it is of great importance that the ongoing ACE Trial is seeking to confirm the results of the Stop-NIDDM Trial 56 in IGT patients with a prior myocardial infarction where CV outcomes are predefined as primary outcomes and independently adjudicated.
Earlier in , the NAVIGATOR Trial 58 produced negative results in this regard. Postprandial hyperglycemia was targeted by randomized administration of the short-acting sulfonylurea analog nateglinide in IGT patients, but this type of blinded intervention neither reduced the manifestation of overt type 2 diabetes nor did it reduce hard CV composite outcomes such as myocardial infarction, stroke, and others over a 6-year follow-up.
Postload glucose values, however, were not lower in the nateglinide arm, where the drug was withheld on the day of the oral glucose tolerance test, as compared with the control arm. Finally, the HEART2D Trial 57 was also a negative trial in terms of the effectiveness of targeting postprandial hyperglycemia by a specific insulin regimen in diabetic patients after myocardial infarction.
On the other hand, the study also failed to achieve the intended difference for postprandial hyperglycemia by far, so the negative result over a 4-year follow-up may not be a total surprise.
If the four intervention studies are taken together, there certainly is no definite proof that targeting postprandial hyperglycemia results in a more beneficial outcome of CV complications in IGT patients or overt type 2 diabetic subjects.
No intervention trials are available in studying the benefits of minimizing glucose variability. The concept of postprandial hyperglycemia as well as high glucose variability as important independent risk determinants of vascular and especially CV complications in subjects with IGT or type 2 diabetes is highly intriguing.
It is best supported by impressive pathophysiological studies, also in the human situation. The epidemiological evidence that is more or less confined to postprandial hyperglycemia and postload glycemia is likewise rather compelling, although certainly not fully conclusive.
The biggest gap still is the missing evidence as derived from randomized prospective intervention studies targeting postprandial hyperglycemia and seeking to reduce hard CV end points. In fact, there has been some stark disappointment recently in this context.
As this evidence by intervention is, however, key for the ultimate approval of a treatment concept that it is mandatory to care for postprandial hyperglycemia and glucose variability beyond achieving appropriate glycemic control as assessed by HbA 1c , the current net balance of attained evidence is not favorable that we should care.
The absence of a uniformly accepted standard of how to estimate postprandial hyperglycemia and glucose variability adds a further challenge to this whole debate. This publication is based on the presentations at the 3rd World Congress on Controversies to Consensus in Diabetes, Obesity and Hypertension CODHy.
The Congress and the publication of this supplement were made possible in part by unrestricted educational grants from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Daiichi Sankyo, Eli Lilly, Ethicon Endo-Surgery, Generex Biotechnology, F. Sign In or Create an Account.
Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Diabetes Care. Advanced Search. User Tools Dropdown.
Sign In. Skip Nav Destination Close navigation menu Article navigation. Previous Article Next Article. Article Navigation. Diabetes and Cardiovascular Disease April 22 Postprandial Hyperglycemia and Glycemic Variability : Should we care?
Eberhard Standl, MD ; Eberhard Standl, MD. Corresponding author: Eberhard Standl, eberhard. standl lrz. This Site. Google Scholar. Oliver Schnell, MD ; Oliver Schnell, MD. Antonio Ceriello, MD Antonio Ceriello, MD. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest.
Table 1 Measures of postprandial glucose and glycemic variability. CGMS, continuous glucose monitoring system. View Large. where k is the number of glucose values GVs in a given individual. The ADRR is than calculated using the formula:. Table 2 Epidemiological studies on the effect of postprandial hyperglycemia on CV risk.
Year of publication. Duration of follow-up. Risk measure. Cardiovascular Health Study Smith et al. normoglycemic patients Chicago Heart Association Detection Project in Industry Study Lowe et al.
normoglycemic subjects Mauritius-Fiji-Nauru Study Shaw et al. normoglycemic control subjects San Luigi Gonzaga Study Cavalot et al. normoglycemic subjects Whitehall Study Brunner et al.
normoglycemic control subjects. Table 3 Mechanisms involving postprandial hyperglycmeia and CV risk. Excessive postprandial hyperglycemia: some pathogenetic links with CV disease.
No potential conflicts of interest relevant to this article were reported. Search ADS. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials.
Control Group. The assessment of glycemic variability and its impact on diabetes-related complications: an overview. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. Should minimal blood glucose variability become the gold standard of glycemic control?
A novel approach to continuous glucose analysis utilizing glycemic variation. The relationship between glucose and incident cardiovascular events: a metaregression analysis of published data from 20 studies of 95, individuals followed for The DECODE study group; European Diabetes Epidemiology Group.
Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria. Diabetes epidemiology: collaborative analysis of diagnostic criteria in Europe. Impaired glucose tolerance is a risk factor for cardiovascular disease, but not impaired fasting glucose: the Funagata Diabetes Study.
Risk of cardiovascular and all-cause mortality in individuals with diabetes mellitus, impaired fasting glucose, and impaired glucose tolerance: the Australian Diabetes, Obesity, and Lifestyle Study AusDiab.
Continuous relationships between non-diabetic hyperglycaemia and both cardiovascular disease and all-cause mortality: the Australian Diabetes, Obesity, and Lifestyle AusDiab study.
Fasting and 2-hour postchallenge serum glucose measures and risk of incident cardiovascular events in the elderly: the Cardiovascular Health Study. T1DM, type 1 diabetes mellitus; HbA1c, glycosylated hemoglobin; T2DM, type 2 diabetes mellitus.
Skip Navigation Skip to contents Search Home Current Current issue Ahead-of print Browse All issues Article by category Article by topic Article by Category Best paper of the year Most view Most 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. Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul , Korea.
jaehyeon skku. ABSTRACT Chronic hyperglycemia is the primary risk factor for the development of complications in diabetes mellitus DM ; however, it is believed that frequent or large glucose fluctuations may independently contribute to diabetes-related complications.
Postprandial spikes in blood glucose, as well as hypoglycemic events, are blamed for increased cardiovascular events in DM. Glycemic variability GV includes both of these events; hence, minimizing GV can prevent future cardiovascular events. Correcting GV emerges as a target to be pursued in clinical practice to safely reduce the mean blood glucose and to determine its direct effects on vascular complications in diabetes.
Modern diabetes management modalities, including glucagon-related peptidebased therapy, newer insulins, modern insulin pumps and bariatric surgery, significantly reduce GV.
However, defining GV remains a challenge primarily due to the difficulty of measuring it and the lack of consensus regarding the optimal approach for its management. The purpose of this manuscript was not only to review the most recent evidence on GV but also to help readers better understand the available measurement options and how the various definitions relate differently to the development of diabetic complications.
Keywords : Diabetes complications ; Diabetes mellitus ; Glycemic variability. Patient B has relatively small variations during the day and on different days; this patient should have little difficulty in lowering daily mean blood glucose concentrations without inducing hypoglycemia.
In comparison, patient A has marked blood glucose variations on the same day and patient C has marked blood glucose variations on different days. If the difference from minimum to maximum is greater than the SD, this variation from mean measure is retained.
These troughs are retained and summed to achieve the MAGE. Table 1 Glycemic variability indices. Table 2 Indications for continuous glucose monitoring. Citations Citations to this article as recorded by.
PubReader Cite CITE. export Copy Format NLM AMA APA MLA. Download Citation Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.
John Emotional stress relief Glucose Variability. Diabetes 1 May ; 62 Cholesterol lowering diet plan : — The proposed contribution of variabilit variability to Glycemic load and glycemic variability development of the complications of diabetes beyond Gycemic of glycemic exposure is supported by reports that oxidative stress, the vriability mediator of such complications, is Cholesterol lowering diet plan for variabipity 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. To avoid distortion of variability to that of glycemic exposure, its calculation should be devoid of a time component. The salutary effect reported in the Diabetes Control and Complications Trial DCCT 1 and the UK Prospective Diabetes Study 2 on the development and progression of microvascular complications of diabetes has been ascribed to reduced glycemic exposure.Glycemic load and glycemic variability -
Ceriello A, De Cosmo S, Rossi MC, Lucisano G, Genovese S, Pontremoli R, Fioretto P, Giorda C, Pacilli A, Viazzi F, et al. Variability in HbA1c, blood pressure, lipid parameters and serum uric acid, and risk of development of chronic kidney disease in type 2 diabetes.
Diabetes Obes Metab. Lee CL, Chen CH, Wu MJ, Tsai SF. The variability of glycated hemoglobin is associated with renal function decline in patients with type 2 diabetes.
Ther Adv Chronic Dis. Viazzi F, Russo GT, Ceriello A, Fioretto P, Giorda C, De Cosmo S, Pontremoli R. Natural history and risk factors for diabetic kidney disease in patients with T2D: lessons from the AMD-annals.
J Nephrol. Lee MY, Huang JC, Chen SC, Chiou HC, Wu PY. Association of HbA1C variability and renal progression in patients with type 2 diabetes with chronic kidney disease stages 3 - 4. Int J Mol Sci. Rama Chandran S, Tay WL, Lye WK, Lim LL, Ratnasingam J, Tan ATB, Gardner DSL.
Beyond HbA1c: comparing glycemic variability and glycemic indices in predicting hypoglycemia in type 1 and type 2 diabetes. Diab Technol Ther. Gomez AM, Munoz OM, Marin A, Fonseca MC, Rondon M, Robledo Gomez MA, Sanko A, Lujan D, Garcia-Jaramillo M, Leon Vargas FM.
Different indexes of glycemic variability as identifiers of patients with risk of hypoglycemia in type 2 diabetes mellitus. Gomez AM, Henao DC, Imitola Madero A, Taboada LB, Cruz V, Robledo Gomez MA, Rondon M, Munoz-Velandia O, Garcia-Jaramillo M, Leon Vargas FM.
Defining high glycemic variability in type 1 diabetes: comparison of multiple indexes to identify patients at risk of hypoglycemia.
DeVries JH, Bailey TS, Bhargava A, Gerety G, Gumprecht J, Heller S, Lane W, Wysham CH, Zinman B, Bak BA, et al. Day-to-day fasting self-monitored blood glucose variability is associated with risk of hypoglycaemia in insulin-treated patients with type 1 and type 2 diabetes: a post hoc analysis of the SWITCH Trials.
Klimontov VV, Myakina NE. Glucose variability indices predict the episodes of nocturnal hypoglycemia in elderly type 2 diabetic patients treated with insulin. Diabetes Metab Syndr. Uemura F, Okada Y, Torimoto K, Tanaka Y. Relation between hypoglycemia and glycemic variability in type 2 diabetes patients with insulin therapy: a study based on continuous glucose monitoring.
Zhong VW, Juhaeri J, Cole SR, Shay CM, Gordon-Larsen P, Kontopantelis E, Mayer-Davis EJ. HbA1C variability and hypoglycemia hospitalization in adults with type 1 and type 2 diabetes: a nested case-control study. J Diabetes Complications. Lanspa MJ, Dickerson J, Morris AH, Orme JF, Holmen J, Hirshberg EL.
Coefficient of glucose variation is independently associated with mortality in critically ill patients receiving intravenous insulin. Crit Care London, England. Timmons JG, Cunningham SG, Sainsbury CA, Jones GC.
Inpatient glycemic variability and long-term mortality in hospitalized patients with type 2 diabetes. Lee CL, Sheu WH, Lee IT, Lin SY, Liang WM, Wang JS, Li YF.
Trajectories of fasting plasma glucose variability and mortality in type 2 diabetes. Orsi E, Solini A, Bonora E, Fondelli C, Trevisan R, Vedovato M, Cavalot F, Gruden G, Morano S, Nicolucci A, et al.
Haemoglobin A1c variability is a strong, independent predictor of all-cause mortality in patients with type 2 diabetes.
Xu D, Fang H, Xu W, Yan Y, Liu Y, Yao B. Fasting plasma glucose variability and all-cause mortality among type 2 diabetes patients: a dynamic cohort study in Shanghai, China.
Sci Rep. Echouffo-Tcheugui JB, Zhao S, Brock G, Matsouaka RA, Kline D, Joseph JJ. Visit-to-visit glycemic variability and risks of cardiovascular events and all-cause mortality: the ALLHAT Study.
Akirov A, Diker-Cohen T, Masri-Iraqi H, Shimon I. High glucose variability increases mortality risk in hospitalized patients. J Clin Endocrinol Metab. Walker GS, Cunningham SG, Sainsbury CAR, Jones GC. HbA1c variability is associated with increased mortality and earlier hospital admission in people with Type 1 diabetes.
Diab Med. Critchley JA, Carey IM, Harris T, DeWilde S, Cook DG. Variability in glycated hemoglobin and risk of poor outcomes among people with type 2 diabetes in a large primary care cohort study.
Sheng CS, Tian J, Miao Y, Cheng Y, Yang Y, Reaven PD, Bloomgarden ZT, Ning G. Prognostic significance of long-term HbA 1c variability for all-cause mortality in the ACCORD Trial.
Forbes A, Murrells T, Mulnier H, Sinclair AJ. Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study. Lancet Diabetes Endocrinol. Ravona-Springer R, Heymann A, Schmeidler J, Moshier E, Guerrero-Berroa E, Soleimani L, Sano M, Leroith D, Preiss R, Tzukran R, et al.
Hemoglobin A1c variability predicts symptoms of depression in elderly individuals with type 2 diabetes. Li TC, Yang CP, Tseng ST, Li CI, Liu CS, Lin WY, Hwang KL, Yang SY, Chiang JH, Lin CC. Visit-to-visit variations in fasting plasma glucose and HbA1c Associated With an Increased Risk of Alzheimer Disease: Taiwan Diabetes Study.
Bancks MP, Carnethon MR, Jacobs DR Jr, Launer LJ, Reis JP, Schreiner PJ, Shah RV, Sidney S, Yaffe K, Yano Y, et al. Fasting glucose variability in young adulthood and cognitive function in middle age: the Coronary Artery Risk Development in Young Adults CARDIA Study.
Saito Y, Noto H, Takahashi O, Kobayashi D. Visit-to-visit hemoglobin A1c variability is associated with later cancer development in patients with diabetes mellitus. Cancer J Sudbury, Mass. Breton MD, Patek SD, Lv D, Schertz E, Robic J, Pinnata J, Kollar L, Barnett C, Wakeman C, Oliveri M, et al.
Continuous glucose monitoring and insulin informed advisory system with automated titration and dosing of insulin reduces glucose variability in type 1 diabetes mellitus. Volcansek S, Lunder M, Janez A.
Acceptability of continuous glucose monitoring in elderly diabetes patients using multiple daily insulin injections. Avari P, Moscardo V, Jugnee N, Oliver N, Reddy M. Glycemic variability and hypoglycemic excursions with continuous glucose monitoring compared to intermittently scanned continuous glucose monitoring in adults with highest risk type 1 diabetes.
Article PubMed PubMed Central Google Scholar. Deiss D, Szadkowska A, Gordon D, Mallipedhi A, Schutz-Fuhrmann I, Aguilera E, Ringsell C, De Block C, Irace C. Clinical practice recommendations on the routine use of eversense, the first long-term implantable continuous glucose monitoring system.
Iga R, Uchino H, Kanazawa K, Usui S, Miyagi M, Kumashiro N, Yoshino H, Ando Y, Hirose T. Glycemic variability in type 1 diabetes compared with Degludec and Glargine on the morning injection: an open-label randomized controlled trial.
Diabetes Ther. Rodbard HW, Peters AL, Slee A, Cao A, Traina SB, Alba M. The Effect of Canagliflozin, a sodium glucose cotransporter 2 inhibitor, on glycemic end points assessed by continuous glucose monitoring and patient-reported outcomes among people with type 1 diabetes.
Mathieu C, Dandona P, Phillip M, Oron T, Lind M, Hansen L, Thoren F, Xu J, Langkilde AM. Glucose variables in type 1 diabetes studies with dapagliflozin: pooled analysis of continuous glucose monitoring data from DEPICT-1 and Henry RR, Strange P, Zhou R, Pettus J, Shi L, Zhuplatov SB, Mansfield T, Klein D, Katz A.
Effects of dapagliflozin on hour glycemic control in patients with type 2 diabetes: a randomized controlled trial. Nishimura R, Osonoi T, Koike Y, Miyata K, Shimasaki Y. A randomized pilot study of the effect of trelagliptin and alogliptin on glycemic variability in patients with type 2 diabetes.
Adv Ther. Bajaj HS, Venn K, Ye C, Patrick A, Kalra S, Khandwala H, Aslam N, Twum-Barima D, Aronson R. Lowest glucose variability and hypoglycemia are observed with the combination of a GLP-1 receptor agonist and basal insulin VARIATION Study.
Frias JP, Nakhle S, Ruggles JA, Zhuplatov S, Klein E, Zhou R, Strange P. Exenatide once weekly improved hour glucose control and reduced glycaemic variability in metformin-treated participants with type 2 diabetes: a randomized, placebo-controlled trial.
Lixisenatide reduces glycaemic variability in insulin-treated patients with type 2 diabetes. Sofizadeh S, Imberg H, Olafsdottir AF, Ekelund M, Dahlqvist S, Hirsch I, Filipsson K, Ahren B, Sjoberg S, Tuomilehto J, et al. Effect of liraglutide on times in glycaemic ranges as assessed by CGM for type 2 diabetes patients treated with multiple daily insulin injections.
Park SE, Lee BW, Kim JH, Lee WJ, Cho JH, Jung CH, Lee SH, Suh S, Hur GC, Kim SH, et al. Effect of gemigliptin on glycaemic variability in patients with type 2 diabetes STABLE study. Kim NH, Kim DL, Kim KJ, Kim NH, Choi KM, Baik SH, Kim SG. Effects of vildagliptin or pioglitazone on glycemic variability and oxidative stress in patients with type 2 diabetes inadequately controlled with metformin monotherapy: a week, randomised, open label, Pilot Study.
Endocrinol Metab Seoul, Korea. Kim G, Oh S, Jin SM, Hur KY, Kim JH, Lee MK. The efficacy and safety of adding either vildagliptin or glimepiride to ongoing metformin therapy in patients with type 2 diabetes mellitus. Expert Opin Pharmacother. Famulla S, Pieber TR, Eilbracht J, Neubacher D, Soleymanlou N, Woerle HJ, Broedl UC, Kaspers S.
Glucose exposure and variability with empagliflozin as adjunct to insulin in patients with type 1 diabetes: continuous glucose monitoring data from a 4-week, randomized, Placebo-Controlled Trial EASE Suzuki D, Yamada H, Yoshida M, Funazaki S, Amamoto M, Morimoto J, Hara K.
Sodium-glucose cotransporter 2 inhibitors improved time-in-range without increasing hypoglycemia in Japanese patients with type 1 diabetes: a retrospective, single-center, pilot study. J Diabetes Invest. Zhang Y, Zhao Z, Wang S, Zhu W, Jiang Y, Sun S, Chen C, Wang K, Mu L, Cao J, et al.
Intensive insulin therapy combined with metformin is associated with reduction in both glucose variability and nocturnal hypoglycaemia in patients with type 2 diabetes. Diabetes Metab Res Rev. Mori Y, Ohta T, Yokoyama J, Utsunomiya K. Blaychfeld-Magnazi M, Reshef N, Zornitzki T, Madar Z, Knobler H.
The effect of a low-carbohydrate high-fat diet and ethnicity on daily glucose profile in type 2 diabetes determined by continuous glucose monitoring.
Eur J Nutr. Article PubMed Google Scholar. Ranjan A, Schmidt S, Damm-Frydenberg C, Holst JJ, Madsbad S, Norgaard K. Short-term effects of a low carbohydrate diet on glycaemic variables and cardiovascular risk markers in patients with type 1 diabetes: a randomized open-label crossover trial.
Ahola AJ, Forsblom C, Harjutsalo V, Groop PH. Dietary carbohydrate intake and cardio-metabolic risk factors in type 1 diabetes. Eiswirth M, Clark E, Diamond M. Low carbohydrate diet and improved glycaemic control in a patient with type one diabetes.
Endocrinol Diabetes Metab Case Rep. Chang CR, Francois ME, Little JP. Restricting carbohydrates at breakfast is sufficient to reduce hour exposure to postprandial hyperglycemia and improve glycemic variability. Am J Clin Nutr. Henry CJ, Kaur B, Quek RYC, Camps SG.
A low glycaemic index diet incorporating isomaltulose is associated with lower glycaemic response and variability, and promotes fat oxidation in Asians. Camps SG, Kaur B, Quek RYC, Henry CJ.
Does the ingestion of a 24 hour low glycaemic index Asian mixed meal diet improve glycaemic response and promote fat oxidation? A controlled, randomized cross-over study. Nutr J. Shukla AP, Dickison M, Coughlin N, Karan A, Mauer E, Truong W, Casper A, Emiliano AB, Kumar RB, Saunders KH, et al.
The impact of food order on postprandial glycaemic excursions in prediabetes. Trico D, Filice E, Trifiro S, Natali A. Manipulating the sequence of food ingestion improves glycemic control in type 2 diabetic patients under free-living conditions.
Nutr Diabetes. Figueira FR, Umpierre D, Casali KR, Tetelbom PS, Henn NT, Ribeiro JP, Schaan BD. Aerobic and combined exercise sessions reduce glucose variability in type 2 diabetes: crossover randomized trial. PLoS ONE. Farabi SS, Carley DW, Smith D, Quinn L. Impact of exercise on diurnal and nocturnal markers of glycaemic variability and oxidative stress in obese individuals with type 2 diabetes or impaired glucose tolerance.
Diabetes Vasc Dis Res. Schein A, Correa A, Casali KR, Schaan BD. Are glucose levels, glucose variability and autonomic control influenced by inspiratory muscle exercise in patients with type 2 diabetes?
Study protocol for a randomized controlled trial. Karstoft K, Clark MA, Jakobsen I, Muller IA, Pedersen BK, Solomon TP, Ried-Larsen M.
The effects of 2 weeks of interval vs continuous walking training on glycaemic control and whole-body oxidative stress in individuals with type 2 diabetes: a controlled, randomised, crossover trial. Paing AC, McMillan KA, Kirk AF, Collier A, Hewitt A, Chastin SFM.
Dose-response between frequency of interruption of sedentary time and fasting glucose, the dawn phenomenon and night-time glucose in Type 2 diabetes. Diabetic Med. Rafiei H, Robinson E, Barry J, Jung ME, Little JP. Short-term exercise training reduces glycaemic variability and lowers circulating endothelial microparticles in overweight and obese women at elevated risk of type 2 diabetes.
Eur J Sport Sci. Nygaard H, Ronnestad BR, Hammarstrom D, Holmboe-Ottesen G, Hostmark AT. Effects of exercise in the fasted and postprandial state on interstitial glucose in hyperglycemic individuals.
J Sport Sci Med. Figueira FR, Umpierre D, Bock PM, Waclawovsky G, Guerra AP, Donelli A, Andrades M, Casali KR, Schaan BD.
Effect of exercise on glucose variability in healthy subjects: randomized crossover trial. Biol Sport. Solomon TPJ, Tarry E, Hudson CO, Fitt AI, Laye MJ. Immediate post-breakfast physical activity improves interstitial postprandial glycemia: a comparison of different activity-meal timings.
Pflugers Arch. Martin CT, Criego AB, Carlson AL, Bergenstal RM. Advanced technology in the management of diabetes: which comes first-continuous glucose monitor or insulin pump? Curr DiabRep. Kovatchev B, Cobelli C. Glucose variability: timing, risk analysis, and relationship to hypoglycemia in diabetes.
Chico A, Aguilera E, Ampudia-Blasco FJ, Bellido V, Cardona-Hernandez R, Escalada FJ, Fernandez D, Gomez-Peralta F, Villar N, Gorgojo JJ, et al.
Clinical approach to flash glucose monitoring: an expert recommendation. Gomez-Peralta F, Dunn T, Landuyt K, Xu Y, Merino-Torres JF. Flash glucose monitoring reduces glycemic variability and hypoglycemia: real-world data from Spain.
Caprnda M, Mesarosova D, Ortega PF, Krahulec B, Egom E, Rodrigo L, Kruzliak P, Mozos I, Gaspar L. Glycemic variability and vascular complications in patients with type 2 diabetes mellitus. Folia Med. Ohara M, Kohata Y, Nagaike H, Koshibu M, Gima H, Hiromura M, Yamamoto T, Mori Y, Hayashi T, Fukui T, et al.
Association of glucose and blood pressure variability on oxidative stress in patients with type 2 diabetes mellitus and hypertension: a cross-sectional study. Diabetol Metab Syndr. Rodrigues R, de Medeiros LA, Cunha LM, Garrote-Filho MDS, Bernardino Neto M, Jorge PT, Resende ES, Penha-Silva N. Correlations of the glycemic variability with oxidative stress and erythrocytes membrane stability in patients with type 1 diabetes under intensive treatment.
Download references. The authors wish to acknowledge Xiaochuan Zhang from the First Affiliated Hospital of Zhengzhou University, China for editing of English grammar and syntax of the manuscript.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Department of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, , China.
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, , China. Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, , China. Department of Pharmacy, The First Hospital of Changsha, Changsha, , China.
You can also search for this author in PubMed Google Scholar. All authors participated in the writing and editing of this review article. All authors read and approved the final manuscript. Correspondence to Chunsheng Zhu or Meng Bian. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Reprints and permissions. Zhou, Z. et al. Glycemic variability: adverse clinical outcomes and how to improve it?. Cardiovasc Diabetol 19 , Download citation. Received : 09 May Accepted : 02 July Published : 04 July Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Glycemic variability: adverse clinical outcomes and how to improve it? Download PDF.
Review Open access Published: 04 July Glycemic variability: adverse clinical outcomes and how to improve it? Abstract Glycemic variability GV , defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice.
Background Glycemic variability GV , referring to oscillations in blood glucose levels, is usually defined by the measurement of fluctuations of glucose or other related parameters of glucose homoeostasis over a given interval of time i.
Table 1 Main types of metric for assessment of GV Full size table. Full size image. GV and adverse clinical outcomes Given that the limitations of HbA1c measurements, growing evidence demonstrated that GV was a significant and clinically meaningful glycemic metric and had drawn attention for its effects on adverse clinical outcomes, including diabetic macrovascular and microvascular complications, hypoglycemia and mortality [ 26 , 27 , 28 , 29 ] Table 2.
Table 2 The effects of GV on adverse clinical outcomes Full size table. Potential beneficial measures There is now cogent evidence for the deleterious effects of GV.
Table 3 Potential beneficial measures for addressing GV Full size table. Conclusion and future perspective We have attempted to summarize the relationships between two categories of GV and the risk for diabetic macrovascular and microvascular complications, hypoglycemia, mortality and other adverse clinical outcomes Fig.
The effects of glycemic variability on the adverse clinical outcomes. Availability of data and materials Not applicable. References Nathan DM, Turgeon H, Regan S. CAS PubMed PubMed Central Google Scholar DeVries JH. CAS PubMed PubMed Central Google Scholar Frontoni S, Di Bartolo P, Avogaro A, Bosi E, Paolisso G, Ceriello A.
CAS PubMed Google Scholar Kovatchev BP. CAS PubMed Google Scholar Bergenstal RM. PubMed Google Scholar Gorst C, Kwok CS, Aslam S, Buchan I, Kontopantelis E, Myint PK, Heatlie G, Loke Y, Rutter MK, Mamas MA. CAS PubMed Google Scholar Lachin JM, Bebu I, Bergenstal RM, Pop-Busui R, Service FJ, Zinman B, Nathan DM.
CAS PubMed PubMed Central Google Scholar Kilpatrick ES, Rigby AS, Atkin SL. PubMed PubMed Central Google Scholar Hill NR, Oliver NS, Choudhary P, Levy JC, Hindmarsh P, Matthews DR.
PubMed PubMed Central Google Scholar Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, Garg S, Heinemann L, Hirsch I, Amiel SA, et al. PubMed PubMed Central Google Scholar Borot S, Benhamou PY, Atlan C, Bismuth E, Bonnemaison E, Catargi B, Charpentier G, Farret A, Filhol N, Franc S, et al.
CAS PubMed Google Scholar Petrie JR, Peters AL, Bergenstal RM, Holl RW, Fleming GA, Heinemann L. PubMed Google Scholar Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF.
CAS PubMed Google Scholar McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ. CAS PubMed Google Scholar Molnar GD, Taylor WF, Ho MM. CAS PubMed Google Scholar Bailey T, Bode BW, Christiansen MP, Klaff LJ, Alva S.
CAS PubMed PubMed Central Google Scholar Hoss U, Budiman ES. PubMed Google Scholar Kovatchev BP, Cox DJ, Gonder-Frederick LA, Clarke W. CAS PubMed Google Scholar Fabris C, Patek SD, Breton MD. PubMed PubMed Central Google Scholar Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen SJ, Dekker JM, Fletcher A, Grauslund J, et al.
PubMed PubMed Central Google Scholar Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, Bosi E, Buckingham BA, Cefalu WT, Close KL, et al. PubMed PubMed Central Google Scholar Sun B, He F, Gao Y, Zhou J, Sun L, Liu R, Xu H, Chen X, Zhou H, Liu Z, et al.
CAS PubMed Google Scholar Hirakawa Y, Arima H, Zoungas S, Ninomiya T, Cooper M, Hamet P, Mancia G, Poulter N, Harrap S, Woodward M, et al. CAS PubMed Google Scholar Folli F, Corradi D, Fanti P, Davalli A, Paez A, Giaccari A, Perego C, Muscogiuri G.
CAS PubMed Google Scholar Bruginski D, Precoma DB, Sabbag A, Olandowski M. PubMed Google Scholar Slieker RC, van der Heijden A, Nijpels G, Elders PJM. CAS PubMed PubMed Central Google Scholar Lu J, Ma X, Zhou J, Zhang L, Mo Y, Ying L, Lu W, Zhu W, Bao Y, Vigersky RA, et al.
CAS PubMed Google Scholar Picconi F, Parravano M, Ylli D, Pasqualetti P, Coluzzi S, Giordani I, Malandrucco I, Lauro D, Scarinci F, Giorno P, et al. CAS PubMed PubMed Central Google Scholar Zinman B, Marso SP, Poulter NR, Emerson SS, Pieber TR, Pratley RE, Lange M, Brown-Frandsen K, Moses A, Ocampo Francisco AM, et al.
PubMed Google Scholar Nusca A, Tuccinardi D, Albano M, Cavallaro C, Ricottini E, Manfrini S, Pozzilli P, Di Sciascio G. Google Scholar Cardoso CRL, Leite NC, Moram CBM, Salles GF. CAS PubMed PubMed Central Google Scholar Takahashi H, Iwahashi N, Kirigaya J, Kataoka S, Minamimoto Y, Gohbara M, Abe T, Okada K, Matsuzawa Y, Konishi M, et al.
CAS PubMed PubMed Central Google Scholar Xia J, Yin C. PubMed Google Scholar Liang S, Yin H, Wei C, Xie L, He H, Liu X. Google Scholar Besch G, Pili-Floury S, Morel C, Gilard M, Flicoteaux G, Mont L, Perrotti A, Meneveau N, Chocron S, Schiele F, et al.
PubMed PubMed Central Google Scholar Benalia M, Zeller M, Mouhat B, Guenancia C, Yameogo V, Greco C, Yao H, Maza M, Verges B, Cottin Y. CAS Google Scholar Ito T, Ichihashi T, Fujita H, Sugiura T, Yamamoto J, Kitada S, Nakasuka K, Kawada Y, Ohte N.
PubMed Google Scholar Pu Z, Lai L, Yang X, Wang Y, Dong P, Wang D, Xie Y, Han Z. CAS PubMed Google Scholar Foreman YD, Brouwers M, Berendschot T, van Dongen M, Eussen S, van Greevenbroek MMJ, Henry RMA, Houben A, van der Kallen CJH, Kroon AA, et al.
PubMed PubMed Central Google Scholar Gerbaud E, Darier R, Montaudon M, Beauvieux MC, Coffin-Boutreux C, Coste P, Douard H, Ouattara A, Catargi B.
PubMed Google Scholar Wang A, Liu X, Xu J, Han X, Su Z, Chen S, Zhang N, Wu S, Wang Y, Wang Y. Google Scholar Zhou JJ, Schwenke DC, Bahn G, Reaven P. CAS PubMed PubMed Central Google Scholar Tang X, Zhong J, Zhang H, Luo Y, Liu X, Peng L, Zhang Y, Qian X, Jiang B, Liu J, et al. PubMed PubMed Central Google Scholar Bancks MP, Carson AP, Lewis CE, Gunderson EP, Reis JP, Schreiner PJ, Yano Y, Carnethon MR.
PubMed PubMed Central Google Scholar Yu JH, Han K, Park S, Lee DY, Nam GE, Seo JA, Kim SG, Baik SH, Park YG, Kim SM, et al. PubMed PubMed Central Google Scholar Takao T, Matsuyama Y, Suka M, Yanagisawa H, Iwamoto Y. PubMed PubMed Central Google Scholar Mo Y, Zhou J, Ma X, Zhu W, Zhang L, Li J, Lu J, Hu C, Bao Y, Jia W.
CAS PubMed Google Scholar Gu J, Fan YQ, Zhang JF, Wang CQ. PubMed Google Scholar Gu J, Pan JA, Fan YQ, Zhang HL, Zhang JF, Wang CQ. CAS PubMed PubMed Central Google Scholar Yokota S, Tanaka H, Mochizuki Y, Soga F, Yamashita K, Tanaka Y, Shono A, Suzuki M, Sumimoto K, Mukai J, et al.
CAS PubMed PubMed Central Google Scholar Akaza M, Akaza I, Kanouchi T, Sasano T, Sumi Y, Yokota T. Google Scholar Pai YW, Lin CH, Lee IT, Chang MH.
CAS PubMed Google Scholar Yang CP, Li CI, Liu CS, Lin WY, Hwang KL, Yang SY, Li TC, Lin CC. CAS PubMed Google Scholar Su JB, Zhao LH, Zhang XL, Cai HL, Huang HY, Xu F, Chen T, Wang XQ. CAS PubMed PubMed Central Google Scholar Rosa L, Zajdenverg L, Souto DL, Dantas JR, Pinto MVR, Salles G, Rodacki M.
Google Scholar Lai YR, Chiu WC, Huang CC, Tsai NW, Wang HC, Lin WC, Cheng BC, Su YJ, Su CM, Hsiao SY, et al. PubMed PubMed Central Google Scholar Lai YR, Huang CC, Chiu WC, Liu RT, Tsai NW, Wang HC, Lin WC, Cheng BC, Su YJ, Su CM, et al.
PubMed PubMed Central Google Scholar Schreur V, van Asten F, Ng H, Weeda J, Groenewoud JMM, Tack CJ, Hoyng CB, de Jong EK, Klaver CCW, Jeroen Klevering B.
CAS PubMed PubMed Central Google Scholar Zhao Q, Zhou F, Zhang Y, Zhou X, Ying C. CAS PubMed Google Scholar Song KH, Jeong JS, Kim MK, Kwon HS, Baek KH, Ko SH, Ahn YB. CAS Google Scholar Ceriello A, De Cosmo S, Rossi MC, Lucisano G, Genovese S, Pontremoli R, Fioretto P, Giorda C, Pacilli A, Viazzi F, et al.
CAS PubMed Google Scholar Lee CL, Chen CH, Wu MJ, Tsai SF. PubMed PubMed Central Google Scholar Viazzi F, Russo GT, Ceriello A, Fioretto P, Giorda C, De Cosmo S, Pontremoli R. CAS PubMed Google Scholar Lee MY, Huang JC, Chen SC, Chiou HC, Wu PY. CAS Google Scholar Rama Chandran S, Tay WL, Lye WK, Lim LL, Ratnasingam J, Tan ATB, Gardner DSL.
CAS Google Scholar Gomez AM, Munoz OM, Marin A, Fonseca MC, Rondon M, Robledo Gomez MA, Sanko A, Lujan D, Garcia-Jaramillo M, Leon Vargas FM. CAS PubMed PubMed Central Google Scholar Gomez AM, Henao DC, Imitola Madero A, Taboada LB, Cruz V, Robledo Gomez MA, Rondon M, Munoz-Velandia O, Garcia-Jaramillo M, Leon Vargas FM.
CAS PubMed Google Scholar DeVries JH, Bailey TS, Bhargava A, Gerety G, Gumprecht J, Heller S, Lane W, Wysham CH, Zinman B, Bak BA, et al. CAS PubMed Google Scholar Klimontov VV, Myakina NE.
PubMed Google Scholar Uemura F, Okada Y, Torimoto K, Tanaka Y. CAS PubMed Google Scholar Zhong VW, Juhaeri J, Cole SR, Shay CM, Gordon-Larsen P, Kontopantelis E, Mayer-Davis EJ.
PubMed Google Scholar Lanspa MJ, Dickerson J, Morris AH, Orme JF, Holmen J, Hirshberg EL. Google Scholar Timmons JG, Cunningham SG, Sainsbury CA, Jones GC. PubMed Google Scholar Lee CL, Sheu WH, Lee IT, Lin SY, Liang WM, Wang JS, Li YF. CAS PubMed Google Scholar Orsi E, Solini A, Bonora E, Fondelli C, Trevisan R, Vedovato M, Cavalot F, Gruden G, Morano S, Nicolucci A, et al.
CAS PubMed Google Scholar Xu D, Fang H, Xu W, Yan Y, Liu Y, Yao B. CAS PubMed PubMed Central Google Scholar Echouffo-Tcheugui JB, Zhao S, Brock G, Matsouaka RA, Kline D, Joseph JJ. PubMed PubMed Central Google Scholar Akirov A, Diker-Cohen T, Masri-Iraqi H, Shimon I.
PubMed Google Scholar Walker GS, Cunningham SG, Sainsbury CAR, Jones GC. CAS Google Scholar Critchley JA, Carey IM, Harris T, DeWilde S, Cook DG. CAS PubMed Google Scholar Sheng CS, Tian J, Miao Y, Cheng Y, Yang Y, Reaven PD, Bloomgarden ZT, Ning G. PubMed Google Scholar Forbes A, Murrells T, Mulnier H, Sinclair AJ.
PubMed Google Scholar Ravona-Springer R, Heymann A, Schmeidler J, Moshier E, Guerrero-Berroa E, Soleimani L, Sano M, Leroith D, Preiss R, Tzukran R, et al. CAS PubMed PubMed Central Google Scholar Li TC, Yang CP, Tseng ST, Li CI, Liu CS, Lin WY, Hwang KL, Yang SY, Chiang JH, Lin CC. CAS PubMed Google Scholar Bancks MP, Carnethon MR, Jacobs DR Jr, Launer LJ, Reis JP, Schreiner PJ, Shah RV, Sidney S, Yaffe K, Yano Y, et al.
PubMed PubMed Central Google Scholar Saito Y, Noto H, Takahashi O, Kobayashi D. CAS Google Scholar Breton MD, Patek SD, Lv D, Schertz E, Robic J, Pinnata J, Kollar L, Barnett C, Wakeman C, Oliveri M, et al.
CAS PubMed PubMed Central Google Scholar Volcansek S, Lunder M, Janez A. CAS PubMed Google Scholar Avari P, Moscardo V, Jugnee N, Oliver N, Reddy M. Article PubMed PubMed Central Google Scholar Deiss D, Szadkowska A, Gordon D, Mallipedhi A, Schutz-Fuhrmann I, Aguilera E, Ringsell C, De Block C, Irace C.
PubMed PubMed Central Google Scholar Iga R, Uchino H, Kanazawa K, Usui S, Miyagi M, Kumashiro N, Yoshino H, Ando Y, Hirose T. CAS PubMed PubMed Central Google Scholar Rodbard HW, Peters AL, Slee A, Cao A, Traina SB, Alba M.
CAS PubMed Google Scholar Mathieu C, Dandona P, Phillip M, Oron T, Lind M, Hansen L, Thoren F, Xu J, Langkilde AM. CAS PubMed Google Scholar Henry RR, Strange P, Zhou R, Pettus J, Shi L, Zhuplatov SB, Mansfield T, Klein D, Katz A.
CAS PubMed PubMed Central Google Scholar Nishimura R, Osonoi T, Koike Y, Miyata K, Shimasaki Y. CAS PubMed PubMed Central Google Scholar Bajaj HS, Venn K, Ye C, Patrick A, Kalra S, Khandwala H, Aslam N, Twum-Barima D, Aronson R.
CAS PubMed Google Scholar Frias JP, Nakhle S, Ruggles JA, Zhuplatov S, Klein E, Zhou R, Strange P. CAS PubMed Google Scholar Sofizadeh S, Imberg H, Olafsdottir AF, Ekelund M, Dahlqvist S, Hirsch I, Filipsson K, Ahren B, Sjoberg S, Tuomilehto J, et al. PubMed PubMed Central Google Scholar Park SE, Lee BW, Kim JH, Lee WJ, Cho JH, Jung CH, Lee SH, Suh S, Hur GC, Kim SH, et al.
CAS PubMed Google Scholar Kim NH, Kim DL, Kim KJ, Kim NH, Choi KM, Baik SH, Kim SG. CAS Google Scholar Kim G, Oh S, Jin SM, Hur KY, Kim JH, Lee MK.
CAS PubMed Google Scholar Famulla S, Pieber TR, Eilbracht J, Neubacher D, Soleymanlou N, Woerle HJ, Broedl UC, Kaspers S. CAS PubMed Google Scholar Suzuki D, Yamada H, Yoshida M, Funazaki S, Amamoto M, Morimoto J, Hara K.
Google Scholar Mori Y, Ohta T, Yokoyama J, Utsunomiya K. CAS PubMed Google Scholar Blaychfeld-Magnazi M, Reshef N, Zornitzki T, Madar Z, Knobler H. Article PubMed Google Scholar Ranjan A, Schmidt S, Damm-Frydenberg C, Holst JJ, Madsbad S, Norgaard K.
CAS PubMed Google Scholar Ahola AJ, Forsblom C, Harjutsalo V, Groop PH. CAS PubMed Google Scholar Eiswirth M, Clark E, Diamond M. PubMed PubMed Central Google Scholar Chang CR, Francois ME, Little JP.
PubMed PubMed Central Google Scholar Henry CJ, Kaur B, Quek RYC, Camps SG. Google Scholar Camps SG, Kaur B, Quek RYC, Henry CJ.
PubMed PubMed Central Google Scholar Shukla AP, Dickison M, Coughlin N, Karan A, Mauer E, Truong W, Casper A, Emiliano AB, Kumar RB, Saunders KH, et al. CAS PubMed Google Scholar Trico D, Filice E, Trifiro S, Natali A. CAS PubMed PubMed Central Google Scholar Figueira FR, Umpierre D, Casali KR, Tetelbom PS, Henn NT, Ribeiro JP, Schaan BD.
CAS PubMed PubMed Central Google Scholar Farabi SS, Carley DW, Smith D, Quinn L. CAS Google Scholar Schein A, Correa A, Casali KR, Schaan BD. PubMed PubMed Central Google Scholar Karstoft K, Clark MA, Jakobsen I, Muller IA, Pedersen BK, Solomon TP, Ried-Larsen M.
CAS PubMed Google Scholar Paing AC, McMillan KA, Kirk AF, Collier A, Hewitt A, Chastin SFM. Gross LS, Li L, Ford ES, Liu S. Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment.
Bhupathiraju SN, Tobias DK, Malik VS, et al. Glycemic index, glycemic load, and risk of type 2 diabetes: results from 3 large US cohorts and an updated meta-analysis. Mosdol A, Witte DR, Frost G, Marmot MG, Brunner EJ. Dietary glycemic index and glycemic load are associated with high-density-lipoprotein cholesterol at baseline but not with increased risk of diabetes in the Whitehall II study.
Sahyoun NR, Anderson AL, Tylavsky FA, et al. Dietary glycemic index and glycemic load and the risk of type 2 diabetes in older adults. Sakurai M, Nakamura K, Miura K, et al.
Dietary glycemic index and risk of type 2 diabetes mellitus in middle-aged Japanese men. Sluijs I, Beulens JW, van der Schouw YT, et al. Dietary glycemic index, glycemic load, and digestible carbohydrate intake are not associated with risk of type 2 diabetes in eight European countries.
van Woudenbergh GJ, Kuijsten A, Sijbrands EJ, Hofman A, Witteman JC, Feskens EJ. Glycemic index and glycemic load and their association with C-reactive protein and incident type 2 diabetes. J Nutr Metab. Villegas R, Liu S, Gao YT, et al. Prospective study of dietary carbohydrates, glycemic index, glycemic load, and incidence of type 2 diabetes mellitus in middle-aged Chinese women.
Arch Intern Med. Greenwood DC, Threapleton DE, Evans CE, et al. Glycemic index, glycemic load, carbohydrates, and type 2 diabetes: systematic review and dose-response meta-analysis of prospective studies.
Diabetes Care. Livesey G, Taylor R, Livesey H, Liu S. Is there a dose-response relation of dietary glycemic load to risk of type 2 diabetes? Meta-analysis of prospective cohort studies. Dyson PA, Kelly T, Deakin T, et al. Diabetes UK evidence-based nutrition guidelines for the prevention and management of diabetes.
Mann JI, De Leeuw I, Hermansen K, et al. Evidence-based nutritional approaches to the treatment and prevention of diabetes mellitus. American Diabetes Association. Prevention or delay of type 2 diabetes. Ma XY, Liu JP, Song ZY. Glycemic load, glycemic index and risk of cardiovascular diseases: meta-analyses of prospective studies.
Dong JY, Zhang YH, Wang P, Qin LQ. Meta-analysis of dietary glycemic load and glycemic index in relation to risk of coronary heart disease.
Am J Cardiol. Fan J, Song Y, Wang Y, Hui R, Zhang W. Dietary glycemic index, glycemic load, and risk of coronary heart disease, stroke, and stroke mortality: a systematic review with meta-analysis. PLoS One. Mirrahimi A, de Souza RJ, Chiavaroli L, et al.
Associations of glycemic index and load with coronary heart disease events: a systematic review and meta-analysis of prospective cohorts. J Am Heart Assoc. Turati F, Dilis V, Rossi M, et al. Glycemic load and coronary heart disease in a Mediterranean population: the EPIC Greek cohort study.
Liu S, Willett WC, Stampfer MJ, et al. A prospective study of dietary glycemic load, carbohydrate intake, and risk of coronary heart disease in US women. 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. Cai X, Wang C, Wang S, et al. Carbohydrate intake, glycemic index, glycemic load, and stroke: a meta-analysis of prospective cohort studies. Asia Pac J Public Health. Rossi M, Turati F, Lagiou P, Trichopoulos D, La Vecchia C, Trichopoulou A.
Relation of dietary glycemic load with ischemic and hemorrhagic stroke: a cohort study in Greece and a meta-analysis.
Eur J Nutr. Buscemi S, Cosentino L, Rosafio G, et al. Effects of hypocaloric diets with different glycemic indexes on endothelial function and glycemic variability in overweight and in obese adult patients at increased cardiovascular risk.
Clin Nutr. Bullo M, Casas R, Portillo MP, et al. Liu S, Manson JE, Buring JE, Stampfer MJ, Willett WC, Ridker PM.
Relation between a diet with a high glycemic load and plasma concentrations of high-sensitivity C-reactive protein in middle-aged women. Jones JL, Park Y, Lee J, Lerman RH, Fernandez ML. A Mediterranean-style, low-glycemic-load diet reduces the expression of 3-hydroxymethylglutaryl-coenzyme A reductase in mononuclear cells and plasma insulin in women with metabolic syndrome.
Nutr Res. Turati F, Galeone C, Gandini S, et al. High glycemic index and glycemic load are associated with moderately increased cancer risk. Mol Nutr Food Res. Aune D, Chan DS, Lau R, et al. Carbohydrates, glycemic index, glycemic load, and colorectal cancer risk: a systematic review and meta-analysis of cohort studies.
Cancer Causes Control. Choi Y, Giovannucci E, Lee JE. Glycaemic index and glycaemic load in relation to risk of diabetes-related cancers: a meta-analysis. Br J Nutr. Mulholland HG, Murray LJ, Cardwell CR, Cantwell MM. Glycemic index, glycemic load, and risk of digestive tract neoplasms: a systematic review and meta-analysis.
Mullie P, Koechlin A, Boniol M, Autier P, Boyle P. Relation between breast cancer and high glycemic index or glycemic load: a meta-analysis of prospective cohort studies.
Crit Rev Food Sci Nutr. Tsai CJ, Leitzmann MF, Willett WC, Giovannucci EL. Dietary carbohydrates and glycaemic load and the incidence of symptomatic gall stone disease in men. Glycemic load, glycemic index, and carbohydrate intake in relation to risk of cholecystectomy in women.
Wang Q, Xia W, Zhao Z, Zhang H. Effects comparison between low glycemic index diets and high glycemic index diets on HbA1c and fructosamine for patients with diabetes: A systematic review and meta-analysis.
Prim Care Diabetes. Evert AB, Boucher JL. New diabetes nutrition therapy recommendations: what you need to know. Diabetes Spectr. Evert AB, Boucher JL, Cypress M, et al.
Nutrition therapy recommendations for the management of adults with diabetes. Louie JC, Markovic TP, Perera N, et al. A randomized controlled trial investigating the effects of a low-glycemic index diet on pregnancy outcomes in gestational diabetes mellitus.
Louie JC, Markovic TP, Ross GP, Foote D, Brand-Miller JC. Effect of a low glycaemic index diet in gestational diabetes mellitus on post-natal outcomes after 3 months of birth: a pilot follow-up study. Matern Child Nutr. Markovic TP, Muirhead R, Overs S, et al. Randomized controlled trial investigating the effects of a low-glycemic index diet on pregnancy outcomes in women at high risk of gestational diabetes mellitus: The GI Baby 3 Study.
Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Kopelman P. Health risks associated with overweight and obesity.
Obes Rev. Hu T, Mills KT, Yao L, et al. Effects of low-carbohydrate diets versus low-fat diets on metabolic risk factors: a meta-analysis of randomized controlled clinical trials.
Am J Epidemiol. Shyam S, Arshad F, Abdul Ghani R, Wahab NA. Low glycaemic index diets improve glucose tolerance and body weight in women with previous history of gestational diabetes: a six months randomized trial. Ebbeling CB, Leidig MM, Feldman HA, Lovesky MM, Ludwig DS.
Effects of a low-glycemic load vs low-fat diet in obese young adults: a randomized trial. Klemsdal TO, Holme I, Nerland H, Pedersen TR, Tonstad S. Effects of a low glycemic load diet versus a low-fat diet in subjects with and without the metabolic syndrome. Juanola-Falgarona M, Salas-Salvado J, Ibarrola-Jurado N, et al.
Effect of the glycemic index of the diet on weight loss, modulation of satiety, inflammation, and other metabolic risk factors: a randomized controlled trial. Schwingshackl L, Hoffmann G. Dietary glycemic index and the regulation of body weight.
Lennerz BS, Alsop DC, Holsen LM, et al. Effects of dietary glycemic index on brain regions related to reward and craving in men. Aller EE, Larsen TM, Claus H, et al. Weight loss maintenance in overweight subjects on ad libitum diets with high or low protein content and glycemic index: the DIOGENES trial month results.
Int J Obes Lond. Wadden TA, Webb VL, Moran CH, Bailer BA. Lifestyle modification for obesity: new developments in diet, physical activity, and behavior therapy. Atkinson FS, Foster-Powell K, Brand-Miller JC.
International tables of glycemic index and glycemic load values: Donate to the MIC. Get Updates from the Institute. The Linus Pauling Institute's Micronutrient Information Center provides scientific information on the health aspects of dietary factors and supplements, food, and beverages for the general public.
The information is made available with the understanding that the author and publisher are not providing medical, psychological, or nutritional counseling services on this site. The information should not be used in place of a consultation with a competent health care or nutrition professional.
The information on dietary factors and supplements, food, and beverages contained on this website does not cover all possible uses, actions, precautions, side effects, and interactions. It is not intended as nutritional or medical advice for individual problems.
Liability for individual actions or omissions based upon the contents of this site is expressly disclaimed. You may not copy, modify, distribute, display, transmit, perform, publish or sell any of the copyrightable material on this website. You may hyperlink to this website but must include the following statement:.
Linus Pauling Institute Oregon State University Linus Pauling Science Center Corvallis, Oregon phone: fax: email: [email protected]. For media contact information. Skip to main content. Toggle menu Go to search page. Search Field. You are here Food and Beverages » Glycemic Index and Glycemic Load.
Glycemic Index and Glycemic Load. Contents Summary Glycemic Index Individual foods Mixed meal or diet Glycemic Load Disease Prevention Type 2 diabetes mellitus Cardiovascular disease Cancer Gallbladder disease Disease Treatment Diabetes mellitus Obesity Lowering Dietary Glycemic Load Authors and Reviewers References.
PubMed 3. PubMed 4. PubMed 5. PubMed 8. PubMed Vitamins Minerals Micronutrient Inadequacies Other Nutrients Dietary Factors Food and Beverages Fruit and Vegetables Cruciferous Vegetables Garlic Legumes Nuts Whole Grains Glycemic Index and Glycemic Load Coffee Tea Alcoholic Beverages Life Stages Health and Disease.
In the past, carbohydrates were classified as simple or complex based on the number of simple sugars varuability the molecule. Carbohydrates Cholesterol lowering diet plan of varjability or two anx sugars like Glycemic load and glycemic variability or sucrose table sugar; a disaccharide composed of G,ycemic molecule of glucose and one molecule Menopause and blood pressure fructose were labeled simple, while starchy foods were labeled complex because starch is composed of long chains of the simple sugar, glucose. Advice to eat less simple and more complex carbohydrates i. This assumption turned out to be too simplistic since the blood glucose glycemic response to complex carbohydrates has been found to vary considerably. The concept of glycemic index GI has thus been developed in order to rank dietary carbohydrates based on their overall effect on postprandial blood glucose concentration relative to a referent carbohydrate, generally pure glucose 2. Llad PDF. CONFLICTS OF INTEREST: No potential Gltcemic of interest Digestion support products to this article Losd reported. T1DM, type 1 variabllity mellitus; HbA1c, losd hemoglobin; T2DM, type 2 diabetes mellitus. Skip Navigation Skip to contents Search Home Current Current issue Ahead-of print Browse Cholesterol lowering diet plan issues Blycemic by category Article by topic Article by Cholesterol lowering diet plan Best paper of the year Most view Most 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.
0 thoughts on “Glycemic load and glycemic variability”