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• W4382199627 abstract "Related Article, p. 267 Related Article, p. 267 Primacy is often given to time-averaged mean glycemia as indicated by glycated hemoglobin (HbA1c) when considering the risks of diabetic complications from hyperglycemia. However, emerging evidence suggests that glycemic variability (GV), simply defined as the degree of short-term or long-term glucose fluctuations between peaks and nadirs, may represent an additional risk factor beyond static HbA1c in patients with diabetes.1Hirsch I.B. Glycemic variability and diabetes complications: does it matter? Of course it does!.Diabetes Care. 2015; 38: 1610-1614https://doi.org/10.2337/dc14-2898Crossref PubMed Scopus (186) Google Scholar,2Ceriello A. Monnier L. Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications.Lancet Diabetes Endocrinol. 2019; 7: 221-230https://doi.org/10.1016/S2213-8587(18)30136-0Abstract Full Text Full Text PDF PubMed Scopus (292) Google Scholar GV has been implicated in endothelial dysfunction, oxidative stress, and inflammation—providing potential mechanistic links to adverse clinical outcomes such as diabetic kidney disease or cardiovascular events.2Ceriello A. Monnier L. Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications.Lancet Diabetes Endocrinol. 2019; 7: 221-230https://doi.org/10.1016/S2213-8587(18)30136-0Abstract Full Text Full Text PDF PubMed Scopus (292) Google Scholar Short-term GV represents within-day and between-day glucose fluctuations, traditionally calculated from self-monitoring of blood glucose, and now increasingly assessed by continuous glucose monitoring (CGM).2Ceriello A. Monnier L. Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications.Lancet Diabetes Endocrinol. 2019; 7: 221-230https://doi.org/10.1016/S2213-8587(18)30136-0Abstract Full Text Full Text PDF PubMed Scopus (292) Google Scholar In contrast, long-term GV represents glucose fluctuations over months to years, typically evaluated using HbA1c variability based on serial HbA1c measurements, although occasionally it is assessed using serial fasting and postprandial glucose values determined by self-monitoring of blood glucose.3Ceriello A. Glucose variability and diabetic complications: is it time to treat?.Diabetes Care. 2020; 43: 1169-1171https://doi.org/10.2337/dci20-0012Crossref PubMed Scopus (42) Google Scholar Heterogeneity in GV definitions and measurements poses a challenge in clinical practice and research owing to the inability to readily compare studies and the lack of consensus about gold-standard metrics. Ideal GV metrics should be easily obtainable and interpretable. Although the coefficient of variation (CV) of glucose readings is now routinely reported as a key short-term GV metric in the ambulatory glucose profile from CGM,4Danne T. Nimri R. Battelino T. et al.International consensus on use of continuous glucose monitoring.Diabetes Care. 2017; 40: 1631-1640https://doi.org/10.2337/dc17-1600Crossref PubMed Scopus (1192) Google Scholar measures of HbA1c variability remain a matter of debate. Most studies use variance-based metrics like standard deviation or CV,5Gorst C. Kwok C.S. Aslam S. et al.Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis.Diabetes Care. 2015; 38: 2354-2369https://doi.org/10.2337/dc15-1188Crossref PubMed Scopus (352) Google Scholar which are simple to compute yet less intuitive for clinical interpretation. Consequently, researchers have introduced new HbA1c variability metrics, such as the “HbA1c variability score” (HVS),6Forbes A. Murrells T. Mulnier H. Sinclair A.J. Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study.Lancet Diabetes Endocrinol. 2018; 6: 476-486https://doi.org/10.1016/S2213-8587(18)30048-2Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar indicating the percentage of HbA1c measurements with absolute changes ≥0.5% compared to the previous value, and “HbA1c time in range,”7Prentice J.C. Mohr D.C. Zhang L. et al.Increased hemoglobin A1c time in range reduces adverse health outcomes in older adults with diabetes.Diabetes Care. 2021; 44: 1750-1756https://doi.org/10.2337/dc21-0292Crossref PubMed Scopus (0) Google Scholar reflecting the percentage of days HbA1c measurements fall within a prespecified target range. Table 1 highlights some widely used HbA1c variability metrics. Higher HbA1c variability, regardless of the definition, has been shown to be significantly associated with increased risks of microvascular and macrovascular complications and all-cause mortality in both type 1 and type 2 diabetes across several cohort studies and post hoc analyses of clinical trials.5Gorst C. Kwok C.S. Aslam S. et al.Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis.Diabetes Care. 2015; 38: 2354-2369https://doi.org/10.2337/dc15-1188Crossref PubMed Scopus (352) Google Scholar, 6Forbes A. Murrells T. Mulnier H. Sinclair A.J. Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study.Lancet Diabetes Endocrinol. 2018; 6: 476-486https://doi.org/10.1016/S2213-8587(18)30048-2Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar, 7Prentice J.C. Mohr D.C. Zhang L. et al.Increased hemoglobin A1c time in range reduces adverse health outcomes in older adults with diabetes.Diabetes Care. 2021; 44: 1750-1756https://doi.org/10.2337/dc21-0292Crossref PubMed Scopus (0) Google Scholar, 8Sheng C.S. Tian J. Miao Y. et al.Prognostic significance of long-term HbA1c variability for all-cause mortality in the ACCORD Trial.Diabetes Care. 2020; 43: 1185-1190https://doi.org/10.2337/dc19-2589Crossref PubMed Scopus (55) Google ScholarTable 1Commonly Used HbA1c Variability MetricsMetricComputationInterpretationAdvantagesLimitationsHbA1c SDSquare root of the average squared differences between each HbA1c measurement and the mean HbA1c valueVariation of HbA1c fluctuations around the meanEasy to computeLess intuitive for clinical interpretation; unable to distinguish between downward and upward HbA1c trajectoriesHbA1c CVRatio of HbA1c SD to the mean HbA1c valueMagnitude of HbA1c variability relative to the meanEasy to compute; adjusted on the mean HbA1cLess intuitive for clinical interpretation; unable to distinguish between downward and upward HbA1c trajectoriesHVSRatio of the number of times successive HbA1c measurements differed by 0.5% or higher to the number of comparisonsPercentage of HbA1c measurements with absolute changes ≥0.5%Easy to compute; intuitive for clinical interpretationUnable to distinguish between downward and upward HbA1c trajectoriesHbA1c TIRPercentage of time each patient’s HbA1c measurements fall within a prespecified target rangeHbA1c stability within patient-level target ranges adjusted to comorbidities, complications, and life expectancyIntuitive for clinical interpretation; individualized target for each patientNot accounting for the number of HbA1c measurements; HbA1c target may be unclear for some patients (e.g., CKD)Abbreviations: CKD, chronic kidney disease; CV, coefficient of variation; HVS, HbA1c variability score; SD, standard deviation; TIR, time in range. Open table in a new tab Abbreviations: CKD, chronic kidney disease; CV, coefficient of variation; HVS, HbA1c variability score; SD, standard deviation; TIR, time in range. In this issue of AJKD, Xu et al9Xu Y. Dong S. Fu E.L. et al.Long-term visit-to-visit variability in hemoglobin A1c and kidney-related outcomes in persons with diabetes.Am J Kidney Dis. 2023; 82: 267-278Abstract Full Text Full Text PDF Scopus (1) Google Scholar make a significant contribution to the expanding body of GV literature by examining data from 93,598 adults with type 1 or type 2 diabetes undergoing routine care in Stockholm, Sweden. The authors demonstrated that higher HbA1c variability, represented by HVS, was significantly associated with the risk of adverse kidney outcomes, including chronic kidney disease (CKD) progression, worsening of albuminuria, and acute kidney injury. The study employed a second measure of HbA1c variability—HbA1c CV—in a sensitivity analysis and yielded concordant results. Notable strengths of this study included a large dataset involving real-world contemporary patients from a country with universal health care coverage, and consistent findings across multiple sensitivity analyses and subgroups. Their findings align with previous studies linking HbA1c variability to risks of CKD progression and worsening albuminuria,5Gorst C. Kwok C.S. Aslam S. et al.Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis.Diabetes Care. 2015; 38: 2354-2369https://doi.org/10.2337/dc15-1188Crossref PubMed Scopus (352) Google Scholar while also revealing a novel association with acute kidney injury risk. This study bolsters the case for GV’s significance as a risk factor for adverse outcomes in diabetes care. Although the study by Xu et al provides valuable insights, better clarity is required to translate such findings into clinical practice. For example, does high GV resulting from intensifying glycemic control and appropriately lowering HbA1c in patients with above-goal levels have the same implications as high GV in patients experiencing wide swings in blood glucose levels, whether owing to nonadherence, unique physiological conditions, or other factors? Indeed, the present study found that high HbA1c variability was associated with adverse kidney outcomes, regardless of the direction of HbA1c changes over time or baseline HbA1c levels. It is counterintuitive that both downward and upward HbA1c trajectories carry equal risk, and neither HVS nor CV can differentiate HbA1c trajectories. HbA1c time in range may serve as a favorable measure of HbA1c stability, better accounting for time spent in individualized HbA1c target ranges, but does not consider the number of HbA1c measurements, which is a limitation of this metric.7Prentice J.C. Mohr D.C. Zhang L. et al.Increased hemoglobin A1c time in range reduces adverse health outcomes in older adults with diabetes.Diabetes Care. 2021; 44: 1750-1756https://doi.org/10.2337/dc21-0292Crossref PubMed Scopus (0) Google Scholar The authors did show in supplemental analyses that stratification by the proportion of HbA1c measurements in the target range (or not) yielded similar risks across HVS categories. Additional practical issues emerge when contemplating the authors’ HVS calculation example: a patient with 5 HbA1c values ranging from 6.4% to 7.5% yielded a variability score of 60%, indicating moderate risk. The feasibility of achieving tighter long-term HbA1c control and lower HVS in such patients is unclear. Another important consideration is what HbA1c variability truly measures, independently. Unmeasured confounding factors, such as overall quality of care or medication adherence,3Ceriello A. Glucose variability and diabetic complications: is it time to treat?.Diabetes Care. 2020; 43: 1169-1171https://doi.org/10.2337/dci20-0012Crossref PubMed Scopus (42) Google Scholar,10Ceriello A. Rossi M.C. De Cosmo S. et al.Overall quality of care predicts the variability of key risk factors for complications in type 2 diabetes: an observational, longitudinal retrospective study.Diabetes Care. 2019; 42: 514-519https://doi.org/10.2337/dc18-1471Crossref PubMed Scopus (25) Google Scholar may associate HbA1c variability with adverse outcomes, regardless of any causal relationship. Indeed, the authors found that contact with the health care system tended to be lower in patients with high HbA1c variability. Asserting GV as an independent risk factor from HbA1c also requires disentangling the risks of HbA1c itself versus HbA1c variability on clinical outcomes, which is challenging owing to their intrinsic relationship. Furthermore, designing an interventional trial to study the impact of reduction in GV on clinical outcomes without simultaneously reducing HbA1c to a similar extent may prove difficult.3Ceriello A. Glucose variability and diabetic complications: is it time to treat?.Diabetes Care. 2020; 43: 1169-1171https://doi.org/10.2337/dci20-0012Crossref PubMed Scopus (42) Google Scholar Despite these challenges, the associations of GV and adverse outcomes make it a glycemic marker worthy of attention. Combining CGM with lifestyle modifications presents a promising strategy for lowering short-term GV.11Taylor P.J. Thompson C.H. Luscombe-Marsh N.D. Wycherley T.P. Wittert G. Brinkworth G.D. Efficacy of real-time continuous glucose monitoring to improve effects of a prescriptive lifestyle intervention in type 2 diabetes: a pilot study.Diabetes Ther. 2019; 10: 509-522https://doi.org/10.1007/s13300-019-0572-zCrossref PubMed Scopus (24) Google Scholar Recent breakthrough therapies, such as sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RA), have also been shown to decrease short-term GV in patients with diabetes and provide promising cardiorenal protection for those with CKD.12Lee H. Park S.E. Kim E.Y. Glycemic variability impacted by SGLT2 inhibitors and GLP 1 agonists in patients with diabetes mellitus: a systematic review and meta-analysis.J Clin Med. 2021; 10: 4078https://doi.org/10.3390/jcm10184078Crossref PubMed Scopus (10) Google Scholar Indeed, it would be interesting to investigate the interplay between these newer medications, long-term GV, and adverse kidney outcomes. Xu’s study used a dataset collected from 2006 to 2019, which probably explains the very low rate of SGLT2 inhibitors (0.4%) and GLP-1 RA (1.2%) use in the study population. As a result, their study could not explore the effect of these medications on HbA1c variability and kidney outcomes. Further research with more recent data that includes these newer therapies would be valuable. Specifically, understanding these newer medications’ impact on GV, which may in part explain their potent cardiorenal protective effect,13Rangaswami J. Bhalla V. de Boer I.H. et al.Cardiorenal protection with the newer antidiabetic agents in patients with diabetes and chronic kidney disease: a scientific statement from the American Heart Association.Circulation. 2020; 142: e265-e286https://doi.org/10.1161/CIR.0000000000000920Crossref PubMed Scopus (85) Google Scholar is an important area of exploration. As the authors astutely point out, glycemic metrics are even more complex in the context of CKD, as HbA1c is considered less reliable as CKD advances.14Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work GroupKDIGO 2022 clinical practice guideline for diabetes management in chronic kidney disease.Kidney Int. 2022; 102: S1-S127https://doi.org/10.1016/j.kint.2022.06.008Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar Concerns include CKD-related anemia with reduced erythrocyte lifespans leading to less glycation accumulation, and competition from another post-translational protein modification, carbamylation (nonenzymatic, spontaneous binding of urea-derived isocyanate), acting on the same amino groups as glycation.15Tang M. Berg A. Rhee E.P. et al.The impact of carbamylation and anemia on HbA1c’s association with renal outcomes in patients with diabetes and chronic kidney disease.Diabetes Care. 2022; 46: 130-137https://doi.org/10.2337/dc22-1399Crossref Scopus (3) Google Scholar Moreover, contributions to high GV in patients with CKD may include altered insulin and glucose metabolism, which exacerbates HbA1c’s shortcomings in wholly capturing glycemic profiles. Given these limitations, CGM is an appealing option for providing a more comprehensive glycemic profile, including quantifying short-term GV in CKD patients. To effectively incorporate GV into clinical practice, it will be essential to simplify and standardize GV metrics, and attain the evidence basis through further research that targeted efforts to reduce GV will result in improved clinical outcomes. Achieving such goals, however, may be a challenging and complex process, as noted above. Nevertheless, the study by Xu et al emphasizes the importance of considering additional glycemic metrics beyond single HbA1c readings in diabetes and CKD care, and highlights how much more work needs to be done in this field. Mengyao Tang, MD, MPH, and Sahir Kalim, MD, MMSc. Dr Tang is supported by a grant from the American Heart Association (AHA23POST1010825). The funder had no role in defining the content of the manuscript. The authors declare that they have no relevant financial interests. Received April 30, 2023, in response to an invitation from the journal. Direct editorial input from an Associate Editor and a Deputy Editor. Accepted in revised form June 8, 2023. Long-term Visit-to-Visit Variability in Hemoglobin A1c and Kidney-Related Outcomes in Persons With DiabetesAmerican Journal of Kidney DiseasesVol. 82Issue 3PreviewTo characterize associations between long-term visit-to-visit variability of hemoglobin A1c (HbA1c) and risk of adverse kidney outcomes in patients with diabetes. Full-Text PDF" @default.
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• W4382199627 title "Long-term Glycemic Variability: A Variable Glycemic Metric Entangled With Glycated Hemoglobin" @default.
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