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5 Steps to Lower HbA1c Fast! Bariability A. Critchley wakefulness in infants, Iain M. CareyTess HarrisStephen DeWildeDerek G. Cook; Variability in Glycated Hemoglobin and Risk of Poor Outcomes Among People With Type 2 Diabetes in a Large Primary Care Cohort Study. Diabetes Care 1 December ; 42 12 : —HbAc variability -
Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS Erratum In: BMJ Jan 2; ADVANCE Collaborative Group; Patel A, MacMahon S, Chalmers J, Neal B, Billot L, Woodward M, Marre M, Cooper M, Glasziou P, Grobbee D, Hamet P, Harrap S, Heller S, Liu L, Mancia G, Mogensen CE, Pan C, Poulter N, Rodgers A, Williams B, Bompoint S, de Galan BE, Joshi R, Travert F.
Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. Duckworth W, Abraira C, Moritz T, Reda D, Emanuele N, Reaven PD, Zieve FJ, Marks J, Davis SN, Hayward R, Warren SR, Goldman S, McCarren M, Vitek ME, Henderson WG, Huang GD; VADT Investigators.
Glucose control and vascular complications in veterans with type 2 diabetes. Epub Dec Erratum In: N Engl J Med. Home P. Contributions of basal and post-prandial hyperglycaemia to micro- and macrovascular complications in people with type 2 diabetes. Curr Med Res Opin. Raz I, Wilson PW, Strojek K, Kowalska I, Bozikov V, Gitt AK, Jermendy G, Campaigne BN, Kerr L, Milicevic Z, Jacober SJ.
Effects of prandial versus fasting glycemia on cardiovascular outcomes in type 2 diabetes: the HEART2D trial. Mean blood glucose compared with HbA1c in the prediction of cardiovascular disease in patients with type 1 diabetes.
Epub Nov Waden J, Forsblom C, Thorn LM, Gordin D, Saraheimo M, Groop PH; Finnish Diabetic Nephropathy Study Group. A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes.
Epub Aug 3. Marcovecchio ML, Tossavainen PH, Dunger DB. Status and rationale of renoprotection studies in adolescents with type 1 diabetes. Pediatr Diabetes. Epub Jun 2. Sugawara A, Kawai K, Motohashi S, Saito K, Kodama S, Yachi Y, Hirasawa R, Shimano H, Yamazaki K, Sone H.
HbA 1c variability and the development of microalbuminuria in type 2 diabetes: Tsukuba Kawai Diabetes Registry 2. Epub May Erratum In: Diabetologia. Hirakawa Y, Arima H, Zoungas S, Ninomiya T, Cooper M, Hamet P, Mancia G, Poulter N, Harrap S, Woodward M, Chalmers J.
Impact of visit-to-visit glycemic variability on the risks of macrovascular and microvascular events and all-cause mortality in type 2 diabetes: the ADVANCE trial. Epub May 8.
Effect of intensive therapy on the microvascular complications of type 1 diabetes mellitus. Holman RR, Paul SK, Bethel MA, Neil HA, Matthews DR. Long-term follow-up after tight control of blood pressure in type 2 diabetes.
Epub Sep Ihnat MA, Thorpe JE, Ceriello A. Hypothesis: the 'metabolic memory', the new challenge of diabetes. Diabet Med. Molitch ME, Steffes MW, Cleary PA, Nathan DM.
Baseline analysis of renal function in the Diabetes Control and Complications Trial. The Diabetes Control and Complications Trial Research Group [corrected]. Kidney Int. Erratum In: Kidney Int May;43 5 Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, Hadden D, Turner RC, Holman RR.
Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes UKPDS 35 : prospective observational study. Molyneaux LM, Constantino MI, McGill M, Zilkens R, Yue DK. Better glycaemic control and risk reduction of diabetic complications in Type 2 diabetes: comparison with the DCCT.
Diabetes Res Clin Pract. Dehghan M, Al Hamad N, Yusufali A, Nusrath F, Yusuf S, Merchant AT. Development of a semi-quantitative food frequency questionnaire for use in United Arab Emirates and Kuwait based on local foods.
Nutr J. Azmi S, Ferdousi M, Petropoulos IN, Ponirakis G, Fadavi H, Tavakoli M, Alam U, Jones W, Marshall A, Jeziorska M, Boulton AJ, Efron N, Malik RA. Corneal confocal microscopy shows an improvement in small-fiber neuropathy in subjects with type 1 diabetes on continuous subcutaneous insulin infusion compared with multiple daily injection.
Jayagopal V, Kilpatrick ES, Jennings PE, Holding S, Hepburn DA, Atkin SL. The biological variation of sex hormone-binding globulin in type 2 diabetes: implications for sex hormone-binding globulin as a surrogate marker of insulin resistance.
Senn S. Testing for baseline balance in clinical trials. Stat Med. Lancaster GA, Dodd S, Williamson PR. Design and analysis of pilot studies: recommendations for good practice.
J Eval Clin Pract. Matthews JN, Altman DG, Campbell MJ, Royston P. Analysis of serial measurements in medical research. HbA1c variability Diabetes Mellitus Type 2 Diabetes microvascular complications Diabetes macrovascular complications.
Layout table for MeSH terms Diabetes Mellitus Diabetes Mellitus, Type 2 Glucose Metabolism Disorders Metabolic Diseases Endocrine System Diseases Insulin Metformin Pioglitazone Sitagliptin Phosphate Dapagliflozin Liraglutide Insulin, Globin Zinc Gliclazide Hypoglycemic Agents Physiological Effects of Drugs Incretins Hormones Hormones, Hormone Substitutes, and Hormone Antagonists Dipeptidyl-Peptidase IV Inhibitors Protease Inhibitors Enzyme Inhibitors Molecular Mechanisms of Pharmacological Action Sodium-Glucose Transporter 2 Inhibitors.
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Recruitment Status : Active, not recruiting First Posted : August 25, Last Update Posted : December 14, Diabetes Mellitus Type 2. Drug: Metformin Drug: Gliclazide Drug: Sitagliptin Drug: Liraglutide Drug: Pioglitazone Drug: Dapagliflozin Drug: human insulin.
Not Applicable. Study Type :. Interventional Clinical Trial. Estimated Enrollment :. This is an randomized open label clinical trial.
Does Glycated Hemoglobin Variability in Type 2 Diabetes Differ Depending on the Diabetes Treatment Threshold Used in the Qatari Population: Implication on Diabetes Complication Risk?
Study Start Date :. Actual Primary Completion Date :. HbA1c is also known by other names: A1c, glycohemoglobin, glycated hemoglobin , Decoding HbA1c Test for Blood Sugar, glycosylated hemoglobin. Difference Between Mean HbA1c and HbA1c Variability In this study, the average of 3 to 5 HbA1c measures was taken over a 2-year period before enrollment from 9 centers that included patients.
This average was used to calculate average HbA1c written as HbA1c â MEAN. Diabetes Type 1, Type 2 - Causes Symptoms Diagnosis and Treatment FAQs A comprehensive article on diabetes - both Type 1 and Type 2 diabetes, including : causes, signs, symptoms, diagnosis, treatment, facts and a glossary on diabetes.
Earlier research had suggested that variability in the levels of blood glucose of T2D patients could be the cause of adverse outcomes in them. Study Patient data for the study was collected from an observational, prospective, cohort study, designed initially to determine the effect of estimated glomerular filtration rate on morbidity and mortality in patients with T2D.
Advertisement Decoding HbA1c Test for Blood Sugar The HbA1c assay is the gold-standard measurement of chronic glycemia and measures the amount of glucose that binds to hemoglobin over a period of 3 months There were 19 centers in total, and the patients were monitored from to The average HbA1c and the HbA1c variability were measured using a subset of this group, from 9 participating centers as mentioned before.
The results were obtained for HbA1c-SD was a more powerful predictor of all-cause death than HbA1c-MEAN. Also, mortality risk increased above the median value and decreased below the median value of HbA1c-SD.
âHbA1c variability is a strong, independent predictor of all-cause death in [T2D] and appears to be even more powerful than average HbA1c in predicting mortality,â concluded Emanuela Orsi, M.
âFurther studies are required to understand whether HbA1c variability acts as a mediator or [an] innocent bystander in this relationship. â Advertisement HbA1c or A1c Calculator for Blood Glucose HbA1c calculator calculates average plasma and whole blood glucose levels.
A1c test tells your average blood sugar level in the past 3 months and helps check whether your diabetes is in control. Reference: Orsi E, Solini A, Bonora E, et al. âHaemoglobin A1c variability is a strong, independent predictor of all-cause mortality in patients with type 2 diabetesâ.
Diabetes Obes Metab. Suchitra Chari. Hemoglobin A1c Variability is Associated With All-cause Mortality in Type 2 Diabetes. Feb 14, accessed Feb 14, Annual data on personal income were provided by Statistics Denmark The cause of death, classified by ICD codes, can be obtained from the Danish Register of Causes of Death In accordance with Danish law, no approval from an ethics committee is needed in a registry-based study without any active participation from study subjects.
The use of deidentified registry data was approved by the Danish Data Protection Agency record number We defined hypertension from discharge diagnosis or if a subject prior to baseline was treated simultaneously with at least two types of antihypertensive drugs Supplementary Tables 1 and 2.
The Charlson Comorbidity Index CCI was used to account for the general morbidity burden The CCI takes multiple comorbidities into account, including chronic obstructive pulmonary disease, various cancer diseases, liver diseases, renal disease, and cardiovascular diseases Supplementary Table 3.
The score has been reported to have high discrimination in predicting mortality 21 , We separately ascertained contacts for obesity- and smoking-related disorders 23 and used these as covariates in a sensitivity analysis as described below. Smoking was defined from discharge diagnosis for smoking or chronic obstructive pulmonary disease, or pharmacological or nonpharmacological treatment for smoking cessation.
We defined socioeconomic status as the individual mean annual gross income during a 5-year period prior to baseline Income was categorized into tertiles. The end points of interest were incident MACE, death from all causes, and type 2 diabetes.
MACE represented a composite end point of myocardial infarction, unstable angina pectoris, ischemic stroke, peripheral vascular disease, or cardiovascular death, whichever came first. The diagnoses of myocardial infarction and ischemic stroke have previously been validated with high predicted values of Information on cause of death was extracted from the Danish Register of Causes of Death The algorithms and registry diagnoses used for covariates and outcomes are listed in Supplementary Tables 1 and 2.
The primary exposure variable of interest was visit-to-visit HbA 1c variability defined as the intraindividual variability in HbA 1c levels. The variability was defined as the SD of the residuals obtained from a linear regression analysis of the three HbA 1c measurements of each individual, similar to what has been done previously From the linear regression analysis, we used the intercept, the slope, and the SD of the residuals to characterize the patients according to index level, trend, and variability, respectively.
These cutoffs were chosen based on manual inspection of histograms showing the population distributions of index HbA 1c and slope, respectively, to obtain groups with roughly equal sizes.
We subsequently tested other cutoffs in a sensitivity analysis see below. Time zero for all time-to-event analyses was set at the date of the third HbA 1c measurement baseline.
Individual follow-up ended in case of the event of interest, death, emigration, or at 31 December , whichever occurred first. Multiple cause-specific Cox regression was used to evaluate the association between HbA 1c variability and the hazard rates of MACE, all-cause mortality, and type 2 diabetes.
Cause-specific hazard ratios HRs were reported for a 1-SD increase in variability Supplementary Fig. To test the linearity assumption, we used restricted cubic splines plots to assess the functional relationship between HbA 1c variability and the end points Supplementary Fig.
The 5-year risk of all-cause mortality was predicted based on the Cox regression model for the hazard rate of all-cause mortality. By combining a Cox regression model for the competing risk of noncardiovascular mortality with the Cox regression model for the hazard rate of MACE, we predicted the 5-year absolute risks of MACE Three risk profiles were defined based on the combination of risk factor values associated with the lowest, median, and highest absolute 5-year risks.
We also constructed a risk chart that displays 5-year absolute risks of MACE and all-cause mortality for a moderate index HbA 1c , stable trend, and all combinations of the other risk factors. The potential effect modifications of association between variability and the outcome hazard rates by age, sex, CCI, hypertension, index HbA 1c , and trend were evaluated with likelihood ratio tests comparing the main model to a model with the interaction term.
To test the robustness of our analyses, we conducted multiple sensitivity analyses. First, patients who increased in CCI from first HbA 1c measurement to last HbA 1c measurement were excluded.
This was done to assess whether HbA 1c variability is merely a reflection of increased morbidity. This is also of importance, since, for example, severe renal diseases and compromised bone marrow function are associated with altered red blood cell production and life span, affecting the validity of HbA 1c as a marker of long-term blood glucose level.
Third, to assess whether the effect of HbA 1c variability on the hazard rate of MACE and all-cause mortality could be confounded by weight and smoking patterns, we adjusted for separately ascertained registry diagnoses of obesity and smoking-related disorders.
Fourth, to assess whether a more liberal time-related inclusion criterion might influence the results, we included individuals with three measurements spaced annually ±4 months. Fifth, to assess whether the results are dependent on the number of measurements used in the analyses, we conducted a separate analysis including individuals with four measurements spaced annually ±3 months.
Sixth, to assess whether the results could be reproduced using another marker for glucose levels, we used random blood glucose variability as exposure. From the CGPL database, we identified all individuals who had three or more measurements of random blood glucose, taken in the nonfasting state and annually spaced ±3 months.
All other exclusion criteria, variability measurements, intercept, and trend were obtained using the same methods as described for the main analyses.
Seventh, we also used a different index for HbA 1c variability, recently published by Forbes et al. Eighth, we used different cutoffs for categorization of the slope estimate to test whether the position of the cutoff could influence the results.
The greater region of Copenhagen has a current population of 1. At CGPL, , adults underwent HbA 1c testing. Of the individuals referred for HbA 1c testing, , individuals had at least three measurements of HbA 1c taken between and A total of 6, individuals were eligible for inclusion. Supplementary Fig.
Baseline clinical characteristics are summarized in Table 1. Variability is defined as the SD of the residuals from the linear regression. Average annual income corresponds to mean annual gross income during a 5-year period prior to baseline.
The median follow-up period was 6. In total, individuals experienced a MACE during the follow-up period. We found an association between higher HbA 1c variability and incident MACE Table 2 HR 1. We calculated absolute 5-year risks of MACE for selected combinations of age-, sex-, and disease-specific risk factors Fig.
Shown are effects of HbA 1c variability, interaction effects of index HbA 1c , and trend categories.
HbA 1c variability is reported in HR per SD increase in variability. The Cox model was additionally adjusted for age-groups, sex, hypertension, and CCI. A total of individuals died during follow-up. We found an association between higher HbA 1c variability and the hazard of all-cause mortality HR 1.
For example, the HR for a decreasing trend compared with a stable trend was 1. We calculated absolute 5-year risks of all-cause mortality for selected combinations of age-, sex-, and disease-specific risk factors Supplementary Fig.
During follow-up, 1, individuals developed type 2 diabetes. We observed no statistically significant association between HbA 1c variability and type 2 diabetes HR 1.
We found no statistically significant interactions between HbA 1c variability and age-groups, sex, hypertension, CCI, index HbA 1c , and trend for outcome MACE, all-cause mortality, and type 2 diabetes.
We conducted eight sensitivity analyses to test the robustness of our results. As shown in Fig. Sensitivity analyses with the main model included for comparison.
S1 includes only individuals who did not increase in CCI score between the first and third HbA 1c measurement. S2 includes only individuals with a CCI of 0 at baseline. S3 is the main model additionally adjusted for registry diagnoses of obesity and smoking-related disorders or contacts.
S4 includes individuals with three annual measurements ±4 months. S5 includes individuals with four annual measurements ±3 months.
S6 includes random blood glucose variability. All models were adjusted as described in Table 2. We are, to the best of our knowledge, the first to describe an association between glycemic variability and the risk of incident MACE and all-cause mortality in a population without diabetes.
We found that HbA 1c variability, measured as the SD of the residuals, is significantly associated with increased risks of incident MACE and all-cause mortality, independent of traditional cardiovascular risk factors, index HbA 1c , and trend. The same effects were observed for random blood glucose variability.
We did not observe an association between HbA 1c variability and incident type 2 diabetes, but as expected, combinations of index HbA 1c and HbA 1c trend were significantly associated with incident type 2 diabetes. Our findings suggest that glycemic variability may contain valuable prognostic information for the outcomes of MACE and mortality, even in individuals with HbA 1c levels within normal range.
Several pathophysiological mechanisms may be involved in the observed association between visit-to-visit glycemic variability and MACE and all-cause mortality. Mechanistic animal and human studies have mainly focused on short-term variability in blood glucose, and studies on long-term variability, as reflected in HbA 1c levels, have not been conducted.
However, intermittent high blood glucose exposure, rather than chronic hyperglycemia, has been shown to have deleterious effects on endothelial function, mediated through oxidative stress 14 , In vitro studies have shown that intermittent high glucose levels stimulate overproduction of reactive oxygen species, which leads to increased cellular apoptosis in human umbilical vein endothelial cells compared with a constant high-glucose environment 14 , Fluctuations in blood glucose concentrations have also been associated with an increase in circulating inflammatory cytokines and accelerated macrophage adhesion to endothelial cells, stimulating the progression and formation of fibrotic atherosclerotic lesions 32 , The association between HbA 1c variability and cardiovascular events that we report could reflect that HbA 1c is a proxy for other systemic conditions that increase cardiovascular risk.
Such systemic conditions could in theory lead to generalized frailty, in which higher variability in numerous biological parameters confer risk through parallel pathological pathways. We have tried to account for this through various sensitivity analyses.
First, we performed a sensitivity analysis including only individuals without significant diseases. Hence, subjects with severe systemic conditions such as cancer and kidney and liver disorders were excluded at baseline.
Second, we performed a sensitivity analysis where individuals who increased in CCI during the inclusion period were excluded. As such, we accounted for individuals with an a priori increased risk of cardiovascular events due to a more rapid decline in overall health status.
Although our study design does not allow for casual inference, both of these sensitivity analyses gave results that were very similar to the main analyses Fig.
Cardiovascular Diabetology volume 19Article number: Cite variabilihy article. Metrics details. To assess Enhance cognitive decision-making skills associations vzriability various HbA1c measures, varaibility a single baseline HbA1c value, overall mean, yearly updated variabilitty, standard deviation Wakefulness in infantscoefficient of variation HbA1c-CVand HbAAc variability score HVSwith microvascular disease MVD risk in patients with type 2 diabetes. The primary outcome was the composite MVD events retinopathy, nephropathy, or neuropathy occurring during the study follow-up. In the models without adjustment for baseline HbA1c, all HbA1c variability and mean measures were significantly associated with MVD risk, except HVS. With adjustment for baseline HbA1c, HbA1c-CV had the strongest association with MVD risk. For every unit of increase in HbA1c-CV, the MVD risk significantly increased by 3.
HbAc variability -
The Clinical Practice Research Datalink CPRD is a large primary care database representative of the U. population 21 , This study is based on general practices in England only with anonymous linkage to Hospital Episode Statistics and Office for National Statistics death registration data Hospital Episode Statistics records clinical and administrative information on all National Health Service—funded inpatient episodes and allows for identification of method of admission e.
Linkage is also available to the Index of Multiple Deprivation IMD , the official measure for small area deprivation in the U. IMD combines data from seven domains income, employment, education skills and training, health and disability, crime, barriers to housing and services, and living environment , ranking local areas from the most deprived 1 to the least deprived 32, We carried out a further analysis of individuals with DM from a previously published retrospective matched cohort study 7 , DM type was classified using a combination of DM Read Codes and prescribing of anti-DM medication Read Codes are a primary care clinical terminology used extensively in the U.
They have a hierarchical structure similar to ICD codes, and cross-mapping is possible between systems From this group, we then restricted to 58, All patients were then followed for outcomes from 1 January until the earliest date of the following: death, de-registration from practice, practice leaving CPRD, or 31 December We measured the following primary outcomes during follow-up: all-cause mortality and first emergency hospitalization defined as an admission that was unpredictable and at short notice because of clinical need.
We subsequently subdivided CVD into those deaths related to CAD I20—I These causes were chosen based on a prior study demonstrating strong associations with average HbA 1c We included infection using previously defined codes 8 , 25 due to strong associations with hyperglycemia and since infections may also promote HbA 1c variability 7 , Using all recorded HbA 1c measurements from to , we estimated, for each patient, the following: Average HbA 1c using the mean of annual means in each year, variability in HbA 1c using the coefficient of variation CoV , and trajectory in HbA 1c estimated from the individual patient annual slope from a linear regression model.
Patients had a minimum of four recorded HbA 1c measurements to be included one per year in the main analysis or four at any time in a less restrictive sensitivity analysis. We summarized the impact of average, variability, and trajectory of HbA 1c by creating six categories for each measure using the 10th, 25th, 50th, and 75—90th percentiles as cut points [see Supplementary Fig.
These categories are not of equal size because we wanted to be able to investigate extremes. However, using the same percentiles for each measure ensures a fair comparison of the importance of each of these three HbA 1c summary measures. In our primary analyses, we adjusted for age, sex, practice, smoking status, BMI, duration of DM, and deprivation IMD.
In secondary analyses, we further adjusted for baseline 1 January comorbidities, hypoglycemic episodes, anti-DM medications, and medications to reduce cardiovascular risk statins, antihypertensives.
For comorbidities, we searched the primary care record for any Read Code denoting a history of atrial fibrillation, metastatic cancer, chronic obstructive pulmonary disease, dementia, epilepsy, heart failure, psychosis, schizophrenia or bipolar disorder, stroke, or transient ischemic attack Hypoglycemic episodes were similarly identified using Read Codes and, additionally, ICD codes on the linked hospital record.
We categorized use of anti-DM medications in the baseline period — into five mutually exclusive hierarchical categories: any use of insulin, sulfonylureas without insulin , biguanides without insulin or sulfonylureas , other anti-DM medications with or without biguanides , and none.
Cox regression was used to estimate hazard ratios HRs for all-cause mortality and time to first emergency hospitalization during follow-up, with adjustment for age, age 2 , sex, practice, smoking status, BMI, durations of DM, and deprivation IMD.
We then compared the impact of average, variability, and trajectory of HbA 1c by separately fitting the comparable categories described above to the models. Subsequently, we fitted mutually adjusted models, which included two and then all three of these HbA 1c summaries.
In sensitivity analyses, we further adjusted for additional confounders including a history of significant hypoglycemic episodes, comorbidities using a score 29 validated for use with U. primary care data, and medication both for DM and for reducing cardiovascular risk antihypertensives, statins , as described above.
Our main analyses were carried out with baseline — HbA 1c measures. We then carried out a number of sensitivity analyses. Finally, we fitted a model with time-dependent HbA 1c summaries, where we updated each of the three main parameters average, variability, and trajectory on an annual basis — by including the most recent year of data into the 4-year run-in period and dropping the earliest year.
We assessed whether the pattern of relationships among average, variability, and trajectory of HbA 1c was similar for different cause-specific outcome measures CVD, CAD, IS, and infection mortality or admissions. All analyses were performed in SAS, version 9. The mean age of the 58, eligible patients was Over the 4-year run-in period, eligible patients averaged 7.
Higher average levels, increasing variability, and positive or negative trajectories were all associated with younger age and obesity, while longer duration of DM was only related to increasing average level Table 1 and Supplementary Fig.
Type of DM treatment had a significant impact on all HbA 1c measures, with those on insulin having higher average levels, more variability, and positive or negative trajectories Supplementary Fig.
Summary of HbA 1c average, trajectory, and variability CoV by baseline patient characteristics. Average of the previous four annual means , , , and CoV derived from the mean and SD of all measurements in the previous 4 years.
Note that all cutoffs correspond to the following percentiles: 10th, 25th, 50th, 75th, and 90th. Adjustment for CoV explained virtually all the effect of trajectory Supplementary Table 2. By contrast, a graded increase in mortality risk was seen with increasing variability, ranging from HR 1.
Further adjustment for history of hypoglycemic events attenuated the impact of variability, but variability still maintained a stronger and more consistent association with mortality than average HbA 1c Table 2.
Sensitivity analyses adjusting for comorbidities did not affect the estimates of mortality risk associated with any of the HbA 1c measures, while adjustment for DM treatment category explained the greater risk associated with highest average level but not any of the associations with variability Supplementary Table 3.
Results were similar for older and younger groups Supplementary Fig. Including more patients by relaxing the inclusion criteria to four HbA 1c measurements at any time did not significantly alter any patterns of risk Supplementary Table 5.
All models mutually adjust for HbA 1c measures unless indicated plus age, age 2 , sex, duration of DM, index of multiple deprivation, smoking, and BMI.
The impact of variability on mortality risk was seen at both the highest and lowest levels of average HbA 1c Fig.
Stratified analyses demonstrating the effect of HbA 1c variability at high and low values of average HbA 1c and of average HbA 1c at high and low levels of variability. A and B : Effects of HbA 1c variability on the risk of all-cause mortality stratified by high and low average HbA 1c A and the effects of HbA 1c average on the risk of all-cause mortality stratified by high and low variability B.
HbA 1c of 6. In time-updated Cox models, CoV became a stronger predictor of mortality risk HR 2. However, the pattern of variability being more strongly associated than average level was somewhat altered when the cause of death was CAD and IS Supplementary Table 8. Associations with variability were still present but slightly weaker for CAD and IS deaths HR 1.
Both average and CoV HbA 1c showed statistically positive associations with time to first emergency hospitalization, while trajectory was not related Table 3. Overall, and for infection and all CVD hospitalizations, the magnitude of the association was more comparable between average level and variability, especially at extreme levels.
For CAD and IS hospitalizations the pattern was different; a stronger graded association with average HbA 1c was now seen. Further, associations with rising CoV were no longer statistically significant Table 3.
Trajectory was not independently associated with hospitalization. All models mutually adjust for HbA 1c measures plus age, age 2 , sex, duration of DM, index of multiple deprivation, smoking, and BMI.
Increasing variability and raised average level of HbA 1c were both associated with higher risks of mortality. Trajectory of trend in HbA 1c was not associated after adjustment for variability.
A steeper and more monotonic relationship was observed between variability and mortality, with even small rises in CoV increasing risk. Associations with variability were also consistent, being present at both higher and lower levels of average HbA 1c and among both younger and older people with T2DM.
This was particularly evident after we carried out time-updated analyses, or adjusted for treatment category, when the highest levels of variability almost trebled the risk of mortality, and average HbA 1c was no longer associated with mortality at all.
The magnitude of associations with variability was attenuated slightly after adjustment for severe hypoglycemic episodes; adjustments for a comorbidity score or use of key medications had little effect on any measure.
However, with CAD and IS as the outcome, these associations were altered. For mortality, associations with average HbA 1c became stronger than CoV, and for first emergency hospitalization, associations with average HbA 1c were further strengthened, and the relationship with CoV was no longer statistically significant.
Recent systematic reviews have identified a range of potential risks associated with HbA 1c variability but have had great difficulty in reaching clear conclusions about the magnitude of these risks and how they interplay with average HbA 1c 12 — This uncertainty may be due to the lack of standard approach to summarizing HbA 1c variability or agreement about how much might be clinically significant.
Many studies use a relative measure e. Nevertheless, our results are broadly similar to two other recent studies: one a cohort study from Italy Renal Insufficiency And Cardiovascular Events [RIACE] 30 and the other an analysis of U.
data from a different primary care data set The Italian study 15, T2DM patients shared many similar conclusions, particularly that HbA 1c variability was a stronger predictor of all-cause mortality than mean HbA 1c and that trajectory was not associated with mortality after adjustment for variability.
They also found an impact of variability at both higher and lower levels of mean HbA 1c , although, unlike our results, they found no J-shape association between average HbA 1c and risk.
Unlike our study, these authors also reported independent associations between HbA 1c trajectory and mortality. Our study also showed that variability is important in younger people with T2DM as well as older people.
Our results feature key areas of disagreement with other recent studies. A large cohort study of U. This study included mostly older white men mean age 65 years , not representative of broader populations, and could not adjust for some key confounders such as DM duration, strongly related to average HbA 1c in our data set, and did not use statistically comparable categories to compare average and variability in HbA 1c.
The somewhat conflicting results of these key studies have possibly also led to some inertia in developing guidelines that more explicitly address HbA 1c stability in T2DM patients. The key strengths of our study are the large and representative data set that was used.
We included both younger and older people with prevalent T2DM and measured variability, average, and trajectory of HbA 1c over a 4-year time period using comparable categories before assessing outcomes. This is important in assessing causality, since many DM complications e. While we designed our study to ensure that HbA 1c variables were measured prior to the occurrence of any key outcome, the limitation here is that these measurements become out-of-date over the lengthy follow-up up to 6 years.
To address this, we carried out a sensitivity analysis that incorporated time-updated HbA 1c values. This strengthened the importance of variability as a predictor of all-cause mortality, with average HbA 1c no longer showing an effect. While time-updated analyses appear more credible, they also run a greater risk of reverse causality; i.
However, in an analysis of individuals with at least 2-year survival after baseline, we found no evidence of reverse causality. We did not find strong evidence of an impact of trajectory direction of trends in HbA 1c on mortality or hospitalization risks after adjustment for variability.
However, our study design only measured trajectory over a 4-year time period, which may be insufficient to fully characterize this for most people. Our results were robust to adjustment for key confounders measured at baseline, and we were able to adjust for more potential confounders than previous studies.
However, residual confounding remains a potential explanation for our findings. In particular, we were unable to adjust for other lifestyle factors that might be important e.
Most of our covariates are likely to be relatively stable over the study period, but medication use may vary, and therefore reported associations based on baseline usage may be attenuated.
We were unable to consider newer classes of anti-DM medications e. Our primary analyses excluded a significant number of patients who did not have at least one measurement of HbA 1c in each year of the 4-year baseline period. This was done in order to develop a more robust measurement of variability and to avoid biasing estimates of variability toward patients who may have had a lot of measurements taken close to a health event e.
As there were virtually no other missing data, we believe our findings are likely representative of most patients with T2DM.
However, more severe hypoglycemia requiring medical care would be expected to present in secondary care either through accident and emergency attendance or hospital admission and have been reported to primary care, and so captured in our data set, and may also be more strongly associated with poor outcomes.
Most previous larger studies using HbA 1c to assess variability have not been able to adjust for hypoglycemia. Finally, our article is based entirely on observational data and so cannot consider the extent to which any risks might be reversible if variability were reduced.
A detailed analysis of mechanisms by which longer-term variability might increase mortality risk is beyond the scope of this study. Adjustments for severe hypoglycemia did not affect estimates of the strength of associations between poor outcomes and average HbA 1c but somewhat attenuated the magnitude of our variability estimates, though they remained statistically and clinically significant.
Associations between mortality and average HbA 1c were attenuated after adjustment for treatment, but this was not the case for CoV, which may suggest different mechanisms of action. Elevated average HbA 1c was more strongly associated with CAD and IS deaths, and particularly CAD and IS hospitalizations, where the association with CoV was completely attenuated and only average levels appeared predictive.
Strong associations of average HbA 1c with CAD and myocardial infarction were also observed recently in the UK Biobank data 28 and the Veterans Affairs study Few other studies have been sufficiently powered to assess associations among HbA 1c average, variability, and CVD subcodes, but this suggests that a focus on CVD as an outcome could be incomplete.
HbA 1c has known associations with both preprandial glucose levels and atherosclerosis but is poorly correlated with postprandial glucose 33 , 34 and provides an incomplete measure of acute glucose excursions. Other measures including blood glucose variability may therefore be important to support DM management better 33 , 34 , though HbA 1c measurements are the mainstay of DM management in primary care in the U.
Higher levels of HbA 1c variability could potentially reflect many different patient and service level factors. Our study identified that higher variability was associated with many patient characteristics that might be related to patient adherence with DM management, such as smoking, higher BMI, male sex, younger age, and higher levels of socioeconomic deprivation.
However, we are not aware of evidence that HbA 1c variability is directly related to treatment adherence and could not assess this. Nevertheless, variability in HbA 1c could be easily measured in U. primary care, and likely elsewhere, since multiple measurements of HbA 1c are available in routine practice for most patients.
They could thus inform decisions based on finer assessments of future risk. There were already evidence, guidelines, and analyses supporting more relaxed targets for average HbA 1c among older people 37 and also for people with significant comorbidity or frailty 16 , 38 — Our results may suggest this could also be appropriate for younger people, but this requires confirmation in RCTs.
We included mainly prevalent cases of DM, some of whom had already been diagnosed with DM for many years, and considered only mortality and unplanned hospital admissions over a relatively short term. Importantly, smaller elevations in average HbA 1c increased the risk of CAD and IS hospitalizations and mortality in our data, and only average was predictive of first hospitalization for CAD and IS.
These findings also strongly support a focus on average chronic levels. Our study highlights the need for individualized targets but suggests a need to focus on stability as well as a lower target level for many people living with T2DM.
Variability in HbA 1c was more important than average level in predicting mortality among people with prevalent T2DM in U. primary care. Average level remained important, though, particularly at higher levels of HbA 1c e.
Current guidelines promote both lower levels of HbA 1c and stability of HbA 1c , but tend to prioritize the former, while our analyses generally suggest that more importance should be given to stability for many patients. Measurements of variability could be incorporated into primary care consultations to guide risk assessment also.
This unfunded study is based in part on data from the CPRD obtained under license from the U. Medicines and Healthcare products Regulatory Agency.
The data are provided by patients and collected by the National Health Service as part of patient care and support. The protocol no.
Duality of Interest. Background: HbA1c variability has emerged as risk factor for cardiovascular diseases in diabetes. However, the impact of HbA1c variability on cardiovascular diseases in subjects within the recommended HbA1c target has been relatively unexplored.
Methods: Using data from a large database, we studied , people with type 2 diabetes without cardiovascular diseases. HbA1c variability was expressed as quartiles of the standard deviation of HbA1c during three years exposure phase.
The primary composite outcome included non-fatal myocardial infarction, non-fatal stroke, all-cause mortality and was assessed during five years following the first three years of exposure to HbA1c variability longitudinal phase.
Cox models were adjusted for a large range of risk factors and results were expressed as adjusted hazard ratios.
Study record managers: refer to the Data Element Definitions if submitting registration or results information. There are numerous HbAf reasons HbAc variability it vsriability be Sustainable fat loss goals that HbA1c variability may affect variabiljty risk. Of interest are the concepts that varkability laboratory and clinic evidence suggests that periods variabiligy sustained hyperglycemia are 'remembered' varixbility memorySupplements for improving nutrient absorption and utilization in the body HAbc turn is recognized to place patients at greater long-term risk of complications. As such it can be speculated that the detrimental effect of variability in HbA1c may be mediated via the same mechanism as 'metabolic memory' phenomenon. Aims: To determine whether treatment to one of 2 threshold levels will result in one group of type 2 diabetes patients having the same mean HbA1c but with differing HbA1c variability to that of another and related to markers of oxidative stress, inflammation and microvascular complications. To determine whether a difference in HbA1c variability between the 2 groups will reflect in changes in small nerve fibers assessed with the sensitive method of corneal confocal microscopy and cardiac autonomic function testing.
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