There is no argument that improving mean levels of glycemic control as judged by assays for glycated hemoglobin (HbA1c) reduces the risks of microvascular complications and cardiovascular disease events in patients with type 1 and type 2 diabetes. However, observations in some trials have suggested that targeting HbA1c to suggested targets may not always result in improved outcomes for people with long-standing type 2 diabetes. The reasons why the glycemic control strategies that primarily use HbA1c in these studies did not have predicted outcomes are not clear. Thus, controversy remains as to whether there are glycemic metrics beyond HbA1c that can be defined as effective measures that can be used in addition to HbA1c to help in assessing the risk of an individual developing diabetes complications. In this regard, the concept of “glycemic variability” (GV) is one metric that has attracted a lot of attention. GV can be simply defined as the degree to which a patient’s blood glucose level fluctuates between high (peaks) and low (nadir) levels. The best and most precise way to assess GV is also one that is still debated. Thus, while there is universal agreement that HbA1c is the current gold standard for the primary clinical target, there is no consensus as to whether other proposed glycemic metrics hold promise to provide additional clinical data or whether there should be additional targets beyond HbA1c. Therefore, given the current controversy, we provide a Point-Counterpoint debate on this issue. In the preceding point narrative, Dr. Hirsch provides his argument that fluctuations in blood glucose as assessed by GV metrics are deleterious and control of GV should be a primary treatment target. In the counterpoint narrative below, Dr. Bergenstal argues that there are better markers to assess the risk of diabetes than GV and provides his consideration of other concepts.

—William T. Cefalu

Editor in Chief, Diabetes Care

The goal of a diabetes care team is to help individuals with diabetes live a full and meaningful life as the patient and care team strive to prevent or minimize the potentially devastating complications of diabetes. To continue to make progress on preventing diabetes complications, we will need to further explore both the basic causes of complications and ways to implement innovative management strategies to improve risk factors known to cause complications.

Studies have clearly demonstrated that poor glucose control is one risk factor directly linked to microvascular complications in type 1 diabetes (T1D) and type 2 diabetes (T2D) (1,2). Glucose control has also been shown to influence macrovascular disease (3) and, recently, mortality in T1D (4). While it is less clear how much of a primary role glycemic control plays in cardiovascular disease (CVD) development in T2D (compared with smoking cessation, blood pressure control, lipid/statin management, and the use of antiplatelet agents in appropriate high-risk patients), studies do show that good glycemic control early in the T2D disease process appears to positively affect the long-term development of CVD and mortality (5).

I contend that glycemic variability (GV), despite being associated with the activation of proinflammatory proteins and oxidative stress (6), is not ready to be included as a risk factor that, if normalized, will reliably reduce diabetes complications. The key data existing up until 2010 on whether GV, like glycemic control, was a marker of diabetes complications is best summarized by quoting an important article by Hirsch and Brownlee (7). The authors state: “Given the lack of studies specifically aimed at reducing glycemic variability to determine the effect of such reductions on clinical end points, new treatment guidelines targeting glycemic variability per se cannot be justified” (7).

Since 2010, many articles have addressed the possible importance of reducing GV because it seems logical that replicating the smooth and flat 24-h glycemic profile seen in individuals without diabetes (8) should reduce diabetes complications. In 2011, the A1C-Derived Average Glucose (ADAG) study group, which reviewed a vast amount of glucose data (self-monitoring of blood glucose [SMBG] and continuous glucose monitoring [CGM]) correlated with known cardiovascular risk factors, concluded that HbA1c and mean glucose show stronger associations with CV risk factors than do postprandial glycemia or GV in people with diabetes (9).

The best evidence to support GV being linked to diabetes complications would be a randomized control trial showing a therapeutic strategy aimed at reducing GV versus a strategy focusing on basal glucose control resulting in fewer cardiovascular events. Researchers completed such a study in individuals with T2D at high risk for CVD using a prandial versus basal insulin approach. The trial, known as the HEART2D (Hyperglycemia and Its Effect After Acute Myocardial Infarction on Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus) study, failed to demonstrate that reducing GV led to reduced CVD risk (10). The study was discontinued prematurely, however, due to too few CVD events and less-than-expected differences in postprandial glucose values. The HEART2D data were reanalyzed to evaluate if GV had an effect on cardiovascular outcomes (11). Only one of three markers of GV was reduced with the prandial-targeted therapy and this was the newest, least established GV marker. The reduction of this GV marker did not result in a reduction of cardiovascular outcomes (more on the difficulty, but importance, of standardizing the definition of GV below).

An editorial by Monnier, a leader in the GV field, and Colette (12) summarized the data on GV and complications, including the impact of the GV analysis of HEART2D study. They concluded that it is not appropriate to include GV in the list of risk factors for diabetes complications but that further studies are warranted to confirm or refute its role. It turns out that the HEART2D results agree with two retrospective analyses of the Diabetes Control and Complications Trial (DCCT) showing that GV has only a very small contribution to the microvascular complications in T1D (13,14).

Certainly, it makes sense to minimize GV. Although it has not been proved to be linked to diabetes complications, an elevated GV is associated with hypoglycemia (15), reduced patient satisfaction (16), and biochemical abnormalities felt to possibly be linked to diabetes complications (6). Intervening to effectively and safely reduce GV starts with developing a uniform definition or set of definitions of GV so appropriate comparisons can be made (Table 1). While there is no agreed-on “best” GV metric, currently the preferred GV metric for research work is the coefficient of variation (CV or %CV), which is the least influenced by fluctuations in HbA1c or mean glucose level. Most clinicians are more familiar with the standard deviation (SD), and it is often reported on device printouts, but experts in the field have also noted that there are at least eight measures of SD that may have some possible clinical relevance, including the most common and usually reported total SD, as well as the within-day SD and the between-day SD (17). Recognizing that the SD is not regularly distributed around the mean glucose, many have pointed out that the interquartile range (IQR), although strongly correlated with SD, may be a preferred measure of GV (8). Most measures of GV are not well understood by clinicians, and usually the relevance of GV as currently reported is a mystery to people with diabetes. However, because the IQR can be easily visualized on a modal day (standard day or 24-h glucose profile plot) and is part of the proposed international standard or uniform one-page glucose profile report (Ambulatory Glucose Profile [AGP]) (18,19), using IQR as one of the clinical measures of GV can actually have relevance to clinical decision making for the clinician and patient (18,20) (Fig. 1). Another long-used measure of GV is the mean amplitude of glycemic excursions, which is the average of all blood glucose excursions (up or down) that are of a magnitude greater than 1 SD of all glucose measures (21,22). There are many other measures of GV being explored (23). Four additional GV metrics are defined in Table 1, although it is not clear to me if these measures add any helpful clinical decision-making data over the first four more commonly used measures described above and in Table 1. In summary, GV is a glucose metric that deserves clinical attention but has not yet been shown to be strongly linked to complications. How much stronger are the data for HbA1c versus GV as being strongly linked to the development of diabetes complications and thus an essential diabetes management metric today?

Table 1

GV metrics at a glance

GV metricDefinitionClinical implications of GV metric*Normal reference range (2 SD around mean)
SD The amount of variation or dispersion of a data set. The SD of the data set is the square root of its variance. Variation measure most familiar to clinicians and easy to calculate SDT 10–26 mg/dL 
At least eight subtypes of SD: the three most commonly used are 1) overall (total) SD, SDT (SD of all data, all days); 2) within-day SD, SDW (average of SD for each day); and 3) between-day SD, SDdm (SD of the daily means of each day) Most accurate if values are “normally distributed around the mean,” which is often not the case 
%CV The extent of variability in relation to the mean of the population Less influenced when comparing data sets with widely different mean glucose values (or HbA1c19–25% 
100 × SD/mean of the observations Possibly the “best” single research measure to compare GV over time or between data sets 
IQR The spread of values 25% above and 25% below the median, sometimes called the middle fifty Likely the “best” metric for visualizing GV around the median glucose curve 13–29 mg/dL 
Plotting the IQR (around the median glucose curve) on a modal day glucose profile makes it is easy to spot what time of day has the most GV and needs attention 
Mean amplitude of glycemic excursions Average of all blood glucose excursions or swings (peak to trough) that are greater than 1 SD of all measures for a given glucose profile Most common measure of glucose spikes, swings, or excursions as opposed to glucose dispersion 41 and 48 mg/dL# (results from day 1 and 2 for one normal individual using CGM for 48 h) 
Used for many years; can be applied to SMBG or CGM data 
Continuous overall net glycemic action (1–24 h) Intraday (within-day) glycemic variation No clear benefit of these measures compared with the four more commonly used measures listed above Few normal studies 
CONGA (1–24 h) The standard deviation of the differences of glucose readings for a defined period of hours 
Mean of daily differences Interday (between-day) glycemic variation 
The absolute value of the difference between glucose values taken on two consecutive days at the same time 
Mean absolute difference Mean absolute difference of consecutive blood glucose values derived from SMBG data performed five times per 24 h 
Mean absolute glucose The summed differences between sequential 7-point SMBG profiles per 24 h divided by the time in hours between the first and last blood glucose measurement 
GV metricDefinitionClinical implications of GV metric*Normal reference range (2 SD around mean)
SD The amount of variation or dispersion of a data set. The SD of the data set is the square root of its variance. Variation measure most familiar to clinicians and easy to calculate SDT 10–26 mg/dL 
At least eight subtypes of SD: the three most commonly used are 1) overall (total) SD, SDT (SD of all data, all days); 2) within-day SD, SDW (average of SD for each day); and 3) between-day SD, SDdm (SD of the daily means of each day) Most accurate if values are “normally distributed around the mean,” which is often not the case 
%CV The extent of variability in relation to the mean of the population Less influenced when comparing data sets with widely different mean glucose values (or HbA1c19–25% 
100 × SD/mean of the observations Possibly the “best” single research measure to compare GV over time or between data sets 
IQR The spread of values 25% above and 25% below the median, sometimes called the middle fifty Likely the “best” metric for visualizing GV around the median glucose curve 13–29 mg/dL 
Plotting the IQR (around the median glucose curve) on a modal day glucose profile makes it is easy to spot what time of day has the most GV and needs attention 
Mean amplitude of glycemic excursions Average of all blood glucose excursions or swings (peak to trough) that are greater than 1 SD of all measures for a given glucose profile Most common measure of glucose spikes, swings, or excursions as opposed to glucose dispersion 41 and 48 mg/dL# (results from day 1 and 2 for one normal individual using CGM for 48 h) 
Used for many years; can be applied to SMBG or CGM data 
Continuous overall net glycemic action (1–24 h) Intraday (within-day) glycemic variation No clear benefit of these measures compared with the four more commonly used measures listed above Few normal studies 
CONGA (1–24 h) The standard deviation of the differences of glucose readings for a defined period of hours 
Mean of daily differences Interday (between-day) glycemic variation 
The absolute value of the difference between glucose values taken on two consecutive days at the same time 
Mean absolute difference Mean absolute difference of consecutive blood glucose values derived from SMBG data performed five times per 24 h 
Mean absolute glucose The summed differences between sequential 7-point SMBG profiles per 24 h divided by the time in hours between the first and last blood glucose measurement 
*

Author’s assessment/opinion,

per Mazze et al. (8),

#

per Service (22).

Figure 1

AGP of a person without diabetes (normal) and of a person with diabetes. Adapted with permission from Bergenstal et al. (18).

Figure 1

AGP of a person without diabetes (normal) and of a person with diabetes. Adapted with permission from Bergenstal et al. (18).

Close modal

It has been 32 years (1983) since the start of the DCCT and 22 years (1993) since the DCCT findings were published showing that intensive therapy, including a reduction in HbA1c, reduced microvascular complications (1). Follow-up of the DCCT volunteers in the Epidemiology of Diabetes Interventions and Complications (EDIC) trial expanded the evidence to show that early good HbA1c control in T1D is also an established marker of reduced risk for macrovascular disease (3) and a reduced mortality rate (1).

Over that last two decades, HbA1c has become the:

  • Goal standard for assessing the risk of diabetes complications

  • Goal standard for measuring level of overall glycemic control achieved

  • Goal standard as the glycemic target in diabetes algorithms guiding the need for treatment change

  • Key variable in diabetes pay-for-performance and diabetes quality rankings

  • Main outcome marker for U.S. Food and Drug Administration approval (efficacy or safety) of new glucose-lowering drugs and technology for diabetes management

  • New diagnostic criteria for diabetes

The last 22 years could be referred to as the “HbA1c era,” in which HbA1c has been the main glycemic metric for guiding therapy changes and rating the quality of diabetes care. How have patients with diabetes fared over the last 22 years with HbA1c as the main glycemic target? For DCCT/EDIC patients living with diabetes now for at least 30 years, those with good HbA1c control during the DCCT had about half the rate of microvascular and macrovascular complications compared with those with higher HbA1c during the DCCT, and less than 1% of those achieving good glucose control developed blindness, needed a kidney transplant or dialysis, or had an amputation (24). In addition, HbA1c levels, reported by the Centers for Disease Control and Prevention from successive National Health and Nutrition Examination Surveys, improved over time for the U.S. population (those having an HbA1c <7% went from 44.3 to 52.2% from 1999–2002 to 2007–2010, respectively) (25). Complication rates, also recently reported by the Centers for Disease Control and Prevention, are also going down rather dramatically during the HbA1c era (26) (Fig. 2). In summary, HbA1c has been linked to or is predictive of mortality, various diabetes complications, emergency department visits, hospitalizations, and total cost of care, and intensive therapy does not negatively affect quality of life.

Figure 2

Changes in diabetes-related complications in the U.S. ESRD, end-stage renal disease. Adapted with permission from Gregg et al. (26).

Figure 2

Changes in diabetes-related complications in the U.S. ESRD, end-stage renal disease. Adapted with permission from Gregg et al. (26).

Close modal

HbA1c represents an average of the glucose levels an individual is exposed to over approximately a 3-month period. Even though HbA1c appears to be a good marker of the risk of diabetes complications and mortality, it does not define an individual’s overall glucose profile or the minute-to-minute glucose variations or even reveal if a given patient has had many dangerously low or dangerously high blood glucose readings. This reality has led clinicians and researchers to explore whether there are other glucose metrics beyond HbA1c that would be even better at predicting the risk of diabetes complications or facilitating more effective clinical decision making.

Looking for glycemic metrics beyond HbA1c could be defined as trying to find a measure to replace HbA1c or it could mean finding measures in addition to HbA1c that help sort out the risk of an individual developing diabetes complications. I think it makes sense to explore the use of glycemic markers to supplement the use of HbA1c if they have a sufficient evidence base to demonstrate they are linked to the development of diabetes complications, quality of life, or use of health care resources.

Hypoglycemia is the obvious glucose metric that can greatly enhance the clinical interpretation of the HbA1c, is critical for effective clinical decision making, and is clearly linked to diabetes complications (27). Hypoglycemia is well established as the primary barrier to optimizing glucose control (HbA1c) in both T1D and T2D (28). In addition, recent data reveal that annually there are now more emergency department visits and hospital admissions for hypoglycemia than there are for hyperglycemia (29). Hypoglycemia has long been known to cause seizures, coma, and death but is also strongly associated with CVD (30), all-site cancer (31), dementia (32), reduced quality of life, and excess resource utilization (33). While there is strong evidence supporting the link between HbA1c and hypoglycemia with diabetes complications, the current evidence is much weaker for GV being a direct cause of diabetes complications (Table 2). In fact, one of the great pioneers in the field of hypoglycemia, Philip E. Cryer, recently proposed that the selection of a glycemic goal (target) should be linked to the risk of hypoglycemia—and that the appropriate target might actually be the lowest HbA1c that does not cause severe hypoglycemia and little symptomatic or asymptomatic hypoglycemia (34). His approach is an important reinforcement of the concept of a composite measure of glucose control, one that combines HbA1c and some yet-to-be-agreed-on quantification of hypoglycemia. If one uses SMBG as the measure of plasma glucose, the most reliable composite is a combination of HbA1c and the amount of severe hypoglycemia experienced, as it is difficult to accurately record, quantitate, and compare symptomatic and asymptomatic hypoglycemic events but individuals report severe hypoglycemia fairly reliably (particularly if surveyed every 3 months or so).

Table 2

Link between HbA1c, hypoglycemia, and GV and adverse effects

Adverse effectHbA1cHypoglycemiaGV
Mortality Yes Yes No 
Complications Yes Yes Possible minor effect (on microvascular complications) 
Emergency department visits/hospitalizations Yes Yes Unknown 
Increased cost Yes Yes Unknown 
Decreased quality of life or patient satisfaction Neutral Yes Yes (few studies) 
Increased reactive oxygen species Unknown Unknown Yes 
Increased inflammatory proteins Unknown Unknown Yes 
Adverse effectHbA1cHypoglycemiaGV
Mortality Yes Yes No 
Complications Yes Yes Possible minor effect (on microvascular complications) 
Emergency department visits/hospitalizations Yes Yes Unknown 
Increased cost Yes Yes Unknown 
Decreased quality of life or patient satisfaction Neutral Yes Yes (few studies) 
Increased reactive oxygen species Unknown Unknown Yes 
Increased inflammatory proteins Unknown Unknown Yes 

It is now time to use CGM to more accurately measure and quantitate hypoglycemia. We will need to derive a well-validated, clinically meaningful, and standardized measure of CGM-detected hypoglycemia, first in the clinical research setting and then in practice.

Just as the diabetes community needs to agree on the metrics for GV, it also must agree on metrics for CGM-measured quantification of hypoglycemia. One approach would be to use the uniform glucose report (AGP standard glucose report) as a template that breaks down hypoglycemia into the percent of time spent low (<70 mg/dL), very low (<60 mg/dL), or dangerously low (<50 mg/dL) over a 2-week period of CGM readings (18) (Fig. 1). Therefore, assessment of glucose control would be based on 1) the HbA1c, 2) the percent of time spent in these three categories of hypoglycemia, and 3) any episodes of severe hypoglycemia, defined clinically as requiring the assistance of another person to recover. This composite assessment could then be easily compared across clinical trials and in clinical practice settings as a regulatory benchmark. Over time, acceptable metrics for excellent and poor overall glucose control can be determined. In addition to Cryer, others are starting to look at innovative ways to visualize a combined measure of HbA1c and hypoglycemia (35,36).

As clinicians, patients, researchers, regulators, and payers become accustomed to patients using CGM-derived glucose data, the goal-standard HbA1c may give way to a very comparable measure of glucose exposure by CGM (from which one can estimate an HbA1c if necessary), along with the metrics for glucose time in range and several categories of time below range and time above range. These definitions need to be standardized and, ultimately, correlated with complications, so we finally have a measure of glycemic control that patients and clinicians can use to adjust day-to-day therapy, judge the risk of developing complications, evaluate effects on quality of life and patient experience, and assess resource utilization.

In the last three decades, at least four categories of treatment strategies—what may be referred to as the 4Ts—have emerged as critical to improving glycemic control:

  1. Carefully defining targets for glycemic control (population/performance measurement and personalized/individualized targets)

  2. Utilizing teams (patient-centered team care, shared decision making, self-management training, use of the medical home model of care and support)

  3. Developing advanced therapeutics (new oral and injectable glucose-lowering drugs)

  4. Applying new technologies (insulin pumps; continuous glucose monitors; big data/electronic medical records; smartphone apps for tracking glucose, diet, and exercise; and tools for remote communication between the patient and team)

Only when each individual with diabetes has a clearly defined and agreed-on glycemic target can we then effectively use care teams, new therapies, and advanced technology as needed to safely reach the glucose target. One glycemic target does not fit all, but all need a glycemic target.

During the HbA1c era of the past 20 years, using HbA1c as the main target for glucose control along with comprehensive cardiovascular risk factor management has helped dramatically reduce the risk of diabetes complications for a population of people with diabetes. By adding a well-defined set of measures of hypoglycemia, we can now set safe and effective personal HbA1c targets (and eventually glucose time-in-range targets) for individuals and work to reduce the long-term complications while minimizing hypoglycemia. Reducing GV will be one of many means to reduce hypoglycemia.

In this issue of Diabetes Care, the design of the Fluctuation Reduction With Insulin and GLP-1 Added Together (FLAT-SUGAR) study is presented (37). The FLAT-SUGAR study is a randomized trial comparing the ability of prandial insulin versus prandial exenatide together with basal insulin to reduce GV (note that CV is the GV primary outcome metric). If this study succeeds in showing a difference in GV while maintaining comparable HbA1c levels, the authors state the next logical step would be a trial evaluating if GV is a marker of diabetes complications. This methodical approach of gathering data to confirm a hypothesis supports my contention that while there is reason to work to reduce GV, we do not yet have outcomes data to determine if GV is a true marker of diabetes complications.

While the critical studies on GV and complications are being completed, let us work together now on a consensus around glycemic targets that include both HbA1c (or time in range [CGM derived]) and standardized metrics to define the degree of hypoglycemia (CGM derived). It is time to use glucose data to address both population health and personalized care in diabetes management.

Acknowledgments. The author would like to recognize the skilled manuscript and figure editing assistance of Jeanne Mettner and MaryAnn Esh, of the Park Nicollet Institute. The author would like to thank the International Diabetes Center research and patient services staff led by Mary Johnson, RN, CDE, and Janet Davidson, RN, CDE, for helping establish that the evaluation of glucose patterns is essential to diabetes care and Roger Mazze, PhD, Ellie Strock, ANP, David Wesley, BA, Joan Bissen, RD, and Deborah Mullen, PhD, the International Diabetes Center leadership team directing AGP development and implementation.

Funding. Grants from the Leona M. and Harry B. Helmsley Charitable Trust have supported International Diabetes Center work on glucose data collection, analysis, display, and utilization.

Duality of Interest. R.M.B. has served on a scientific advisory board, consulted, or performed clinical research with Abbott Diabetes Care, Bayer, Becton Dickinson, Boehringer Ingelheim, AstraZeneca, Dexcom, Eli Lilly, Halozyme Therapeutics, Hygieia, Johnson & Johnson, Medtronic, Merck, Novo Nordisk, Roche, Sanofi, and Takeda. R.M.B.'s employer, nonprofit Park Nicollet Institute, contracts for his services, and no personal income goes to R.M.B. He has inherited Merck stock. He is a volunteer for the American Diabetes Association and JDRF.

See accompanying articles, pp. 1558 and 1610.

1.
The Diabetes Control and Complications Trial Research Group
.
The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus
.
N Engl J Med
1993
;
329
:
977
986
2.
UK Prospective Diabetes Study (UKPDS) Group
.
Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33)
.
Lancet
1998
;
352
:
837
853
3.
Nathan
DM
,
Cleary
PA
,
Backlund
JY
, et al.;
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group
.
Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes
.
N Engl J Med
2005
;
353
:
2643
2653
4.
Orchard
TJ
,
Nathan
DM
,
Zinman
B
, et al.;
Writing Group for the DCCT/EDIC Research Group
.
Association between 7 years of intensive treatment of type 1 diabetes and long-term mortality
.
JAMA
2015
;
313
:
45
53
5.
Holman
RR
,
Paul
SK
,
Bethel
MA
,
Matthews
DR
,
Neil
HA
.
10-year follow-up of intensive glucose control in type 2 diabetes
.
N Engl J Med
2008
;
359
:
1577
1589
6.
Wentholt
IME
,
Kulik
W
,
Michels
RPJ
,
Hoekstra
JBL
,
DeVries
JH
.
Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes
.
Diabetologia
2008
;
51
:
183
190
7.
Hirsch
IB
,
Brownlee
M
.
Beyond hemoglobin A1c—need for additional markers of risk for diabetic microvascular complications
.
JAMA
2010
;
303
:
2291
2292
8.
Mazze
RS
,
Strock
E
,
Wesley
D
, et al
.
Characterizing glucose exposure for individuals with normal glucose tolerance using continuous glucose monitoring and ambulatory glucose profile analysis
.
Diabetes Technol Ther
2008
;
10
:
149
159
9.
Borg
R
,
Kuenen
JC
,
Carstensen
B
, et al.;
ADAG Study Group
.
HbA1c and mean blood glucose show stronger associations with cardiovascular disease risk factors than do postprandial glycaemia or glucose variability in persons with diabetes: the A1C-Derived Average Glucose (ADAG) study
.
Diabetologia
2011
;
54
:
69
72
10.
Raz
I
,
Wilson
PW
,
Strojek
K
, et al
.
Effects of prandial versus fasting glycemia on cardiovascular outcomes in type 2 diabetes: the HEART2D trial
.
Diabetes Care
2009
;
32
:
381
386
11.
Siegelaar
SE
,
Kerr
L
,
Jacober
SJ
,
Devries
JH
.
A decrease in glucose variability does not reduce cardiovascular event rates in type 2 diabetic patients after acute myocardial infarction: a reanalysis of the HEART2D study
.
Diabetes Care
2011
;
34
:
855
857
12.
Monnier
L
,
Colette
C
.
Glycemic variability: can we bridge the divide between controversies?
Diabetes Care
2011
;
34
:
1058
1059
13.
Kilpatrick
ES
,
Rigby
AS
,
Atkin
SL
.
The effect of glucose variability on the risk of microvascular complications in type 1 diabetes
.
Diabetes Care
2006
;
29
:
1486
1490
14.
Lachin
JM
,
Genuth
S
,
Nathan
DM
,
Zinman
B
,
Rutledge
BN
;
DCCT/EDIC Research Group
.
Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial—revisited
.
Diabetes
2008
;
57
:
995
1001
15.
Qu
Y
,
Jacober
SJ
,
Zhang
Q
,
Wolka
LL
,
DeVries
JH
.
Rate of hypoglycemia in insulin-treated patients with type 2 diabetes can be predicted from glycemic variability data
.
Diabetes Technol Ther
2012
;
14
:
1008
1012
16.
Testa
MA
,
Gill
J
,
Su
M
,
Turner
RR
,
Blonde
L
,
Simonson
DC
.
Comparative effectiveness of basal-bolus versus premix analog insulin on glycemic variability and patient-centered outcomes during insulin intensification in type 1 and type 2 diabetes: a randomized, controlled, crossover trial
.
J Clin Endocrinol Metab
2012
;
97
:
3504
3514
17.
Rodbard
D
.
Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control
.
Diabetes Technol Ther
2009
;
11
(
Suppl. 1
):
S55
S67
18.
Bergenstal
RM
,
Ahmann
AJ
,
Bailey
T
, et al
.
Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the Ambulatory Glucose Profile (AGP)
.
Diabetes Technol Ther
2013
;
15
:
198
211
19.
Matthaei
S
.
Assessing the value of the Ambulatory Glucose Profile in clinical practice
.
Br J Diabetes Vasc Dis
2014
;
14
:
148
152
20.
Matthaei
S
,
DeAlaiz
RA
,
Bosi E, Evans M, Geelhoed-Duijvestijn N, Joubert M. Consensus recommendations for the use of Ambulatory Glucose Profile in clinical practice
.
Br J Diabetes Vasc Dis
2014
;
14
:
153
157
21.
Service
FJ
,
Molnar
GD
,
Rosevear
JW
,
Ackerman
E
,
Gatewood
LC
,
Taylor
WF
.
Mean amplitude of glycemic excursions, a measure of diabetic instability
.
Diabetes
1970
;
19
:
644
655
22.
Service
FJ
.
Glucose variability
.
Diabetes
2013
;
62
:
1398
1404
23.
McDonnell
CM
,
Donath
SM
,
Vidmar
SI
,
Werther
GA
,
Cameron
FJ
.
A novel approach to continuous glucose analysis utilizing glycemic variation
.
Diabetes Technol Ther
2005
;
7
:
253
263
24.
Nathan
DM
,
Zinman
B
,
Cleary
PA
, et al.;
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Research Group
.
Modern-day clinical course of type 1 diabetes mellitus after 30 years’ duration: the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications and Pittsburgh Epidemiology of Diabetes Complications Experience (1983-2005)
.
Arch Intern Med
2009
;
169
:
1307
1316
25.
Ali
MK
,
Bullard
KM
,
Saaddine
JB
,
Cowie
CC
,
Imperatore
G
,
Gregg
EW
.
Achievement of goals in U.S. diabetes care, 1999-2010
.
N Engl J Med
2013
;
368
:
1613
1624
26.
Gregg
EW
,
Li
Y
,
Wang
J
, et al
.
Changes in diabetes-related complications in the United States, 1990-2010
.
N Engl J Med
2014
;
370
:
1514
1523
27.
Cryer
PE
.
Severe hypoglycemia predicts mortality in diabetes
.
Diabetes Care
2012
;
35
:
1814
1816
28.
Cryer
PE
.
Hypoglycaemia: the limiting factor in the glycaemic management of type I and type II diabetes
.
Diabetologia
2002
;
45
:
937
948
29.
Lipska
KJ
,
Ross
JS
,
Wang
Y
, et al
.
National trends in US hospital admissions for hyperglycemia and hypoglycemia among Medicare beneficiaries, 1999 to 2011
.
JAMA Intern Med
2014
;
174
:
1116
1124
30.
Goto
A
,
Arah
OA
,
Goto
M
,
Terauchi
Y
,
Noda
M
.
Severe hypoglycaemia and cardiovascular disease: systematic review and meta-analysis with bias analysis
.
BMJ
2013
;
347
:
f4533
31.
Kong
AP
,
Yang
X
,
Luk
A
, et al
.
Severe hypoglycemia identifies vulnerable patients with type 2 diabetes at risk for premature death and all-site cancer: the Hong Kong diabetes registry
.
Diabetes Care
2014
;
37
:
1024
1031
32.
Yaffe
K
,
Falvey
CM
,
Hamilton
N
, et al.;
Health ABC Study
.
Association between hypoglycemia and dementia in a biracial cohort of older adults with diabetes mellitus
.
JAMA Intern Med
2013
;
173
:
1300
1306
33.
Willis
WD
,
Diago-Cabezudo
JI
,
Madec-Hily
A
,
Aslam
A
.
Medical resource use, disturbance of daily life and burden of hypoglycemia in insulin-treated patients with diabetes: results from a European online survey
.
Expert Rev Pharmacoecon Outcomes Res
2013
;
13
:
123
130
34.
Cryer
PE
.
Glycemic goals in diabetes: trade-off between glycemic control and iatrogenic hypoglycemia
.
Diabetes
2014
;
63
:
2188
2195
35.
Rodbard
D
.
Evaluating quality of glycemic control: graphical displays of hypo- and hyperglycemia, time in target range, and mean glucose
.
J Diabetes Sci Tech
2015
;
9
:
56
62
36.
Vigersky
RA
.
Escaping the hemoglobin A1c-centric world in evaluating diabetes mellitus interventions
.
J Diabetes Sci Technol.
19 February 2015 [Epub ahead of print]
37.
The FLAT-SUGAR Trial Investigators
.
Design of FLAT-SUGAR: randomized trial of prandial insulin versus prandial GLP-1 receptor agonist together with basal insulin and metformin for high-risk type 2 diabetes
.
Diabetes Care
2015
;
38
:
1558
1566