The American Diabetes Association (ADA) “Standards of Medical Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Glycemic control is assessed by the A1C measurement, continuous glucose monitoring (CGM) using either time in range (TIR) and/or glucose management indicator (GMI), and blood glucose monitoring (BGM). A1C is the metric used to date in clinical trials demonstrating the benefits of improved glycemic control. Individual glucose monitoring (discussed in detail in Section 7, “Diabetes Technology,” https://doi.org/10.2337/dc22-S007) is a useful tool for diabetes self-management, which includes meals, exercise, and medication adjustment, particularly in individuals taking insulin. CGM serves an increasingly important role in the management of the effectiveness and safety of treatment in many patients with type 1 diabetes and in selected patients with type 2 diabetes. Individuals on a variety of insulin regimens can benefit from CGM with improved glucose control, decreased hypoglycemia, and enhanced self-efficacy (Section 7, “Diabetes Technology,” https://doi.org/10.2337/dc22-S007) (1).

Glycemic Assessment

Recommendations

  • 6.1 Assess glycemic status (A1C or other glycemic measurement such as time in range or glucose management indicator) at least two times a year in patients who are meeting treatment goals (and who have stable glycemic control). E

  • 6.2 Assess glycemic status at least quarterly and as needed in patients whose therapy has recently changed and/or who are not meeting glycemic goals. E

A1C reflects average glycemia over approximately 3 months. The performance of the test is generally excellent for National Glycohemoglobin Standardization Program (NGSP)-certified assays (see www.ngsp.org). The test is the primary tool for assessing glycemic control and has a strong predictive value for diabetes complications (24). Thus, A1C testing should be performed routinely in all patients with diabetes at initial assessment and as part of continuing care. Measurement approximately every 3 months determines whether patients’ glycemic targets have been reached and maintained. A 14-day CGM assessment of TIR and GMI can serve as a surrogate for A1C for use in clinical management (59). The frequency of A1C testing should depend on the clinical situation, the treatment regimen, and the clinician’s judgment. The use of point-of-care A1C testing or CGM-derived TIR and GMI may provide an opportunity for more timely treatment changes during encounters between patients and providers. People with type 2 diabetes with stable glycemia well within target may do well with A1C testing or other glucose assessment only twice per year. Unstable or intensively managed patients or people not at goal with treatment adjustments may require testing more frequently (every 3 months with interim assessments as needed for safety) (10). CGM parameters can be tracked in the clinic or via telemedicine to optimize diabetes management.

A1C Limitations

The A1C test is an indirect measure of average glycemia and, as such, is subject to limitations. As with any laboratory test, there is variability in the measurement of A1C. Although A1C variability is lower on an intraindividual basis than that of blood glucose measurements, clinicians should exercise judgment when using A1C as the sole basis for assessing glycemic control, particularly if the result is close to the threshold that might prompt a change in medication therapy. For example, conditions that affect red blood cell turnover (hemolytic and other anemias, glucose-6-phosphate dehydrogenase deficiency, recent blood transfusion, use of drugs that stimulate erythropoesis, end-stage kidney disease, and pregnancy) may result in discrepancies between the A1C result and the patient’s true mean glycemia. Hemoglobin variants must be considered, particularly when the A1C result does not correlate with the patient’s CGM or BGM levels. However, most assays in use in the U.S. are accurate in individuals who are heterozygous for the most common variants (see www.ngsp.org/interf.asp). Other measures of average glycemia such as fructosamine and 1,5-anhydroglucitol are available, but their translation into average glucose levels and their prognostic significance are not as clear as for A1C and CGM. Though some variability in the relationship between average glucose levels and A1C exists among different individuals, in general the association between mean glucose and A1C within an individual correlates over time (11).

A1C does not provide a measure of glycemic variability or hypoglycemia. For patients prone to glycemic variability, especially patients with type 1 diabetes or type 2 diabetes with severe insulin deficiency, glycemic control is best evaluated by the combination of results from BGM/CGM and A1C. Discordant results between BGM/CGM and A1C can be the result of the conditions outlined above or glycemic variability, with BGM missing the extremes.

Correlation Between BGM and A1C

Table 6.1 shows the correlation between A1C levels and mean glucose levels based on the international A1C-Derived Average Glucose (ADAG) study, which assessed the correlation between A1C and frequent BGM and CGM in 507 adults (83% non-Hispanic White) with type 1, type 2, and no diabetes (12), and an empirical study of the average blood glucose levels at premeal, postmeal, and bedtime associated with specified A1C levels using data from the ADAG trial (13). The American Diabetes Association (ADA) and the American Association for Clinical Chemistry have determined that the correlation (r = 0.92) in the ADAG trial is strong enough to justify reporting both the A1C result and the estimated average glucose (eAG) result when a clinician orders the A1C test. Clinicians should note that the mean plasma glucose numbers in Table 6.1 are based on ∼2,700 readings per A1C in the ADAG trial. In a recent report, mean glucose measured with CGM versus central laboratory–measured A1C in 387 participants in three randomized trials demonstrated that A1C may underestimate or overestimate mean glucose in individuals (11). Thus, as suggested, a patient’s BGM or CGM profile has considerable potential for optimizing his or her glycemic management (12).

Table 6.1

Estimated average glucose (eAG)

A1C (%)mg/dL*mmol/L
97 (76–120) 5.4 (4.2–6.7) 
126 (100–152) 7.0 (5.5–8.5) 
154 (123–185) 8.6 (6.8–10.3) 
183 (147–217) 10.2 (8.1–12.1) 
212 (170–249) 11.8 (9.4–13.9) 
10 240 (193–282) 13.4 (10.7–15.7) 
11 269 (217–314) 14.9 (12.0–17.5) 
12 298 (240–347) 16.5 (13.3–19.3) 
A1C (%)mg/dL*mmol/L
97 (76–120) 5.4 (4.2–6.7) 
126 (100–152) 7.0 (5.5–8.5) 
154 (123–185) 8.6 (6.8–10.3) 
183 (147–217) 10.2 (8.1–12.1) 
212 (170–249) 11.8 (9.4–13.9) 
10 240 (193–282) 13.4 (10.7–15.7) 
11 269 (217–314) 14.9 (12.0–17.5) 
12 298 (240–347) 16.5 (13.3–19.3) 

Data in parentheses are 95% CI. A calculator for converting A1C results into eAG, in either mg/dL or mmol/L, is available at professional.diabetes.org/eAG.

*

These estimates are based on ADAG data of ∼2,700 glucose measurements over 3 months per A1C measurement in 507 adults with type 1, type 2, or no diabetes. The correlation between A1C and average glucose was 0.92 (12,13). Adapted from Nathan et al. (12).

A1C Differences in Ethnic Populations and Children

In the ADAG study, there were no significant differences among racial and ethnic groups in the regression lines between A1C and mean glucose, although the study was underpowered to detect a difference and there was a trend toward a difference between the African and African American and the non-Hispanic White cohorts, with higher A1C values observed in Africans and African Americans compared with non-Hispanic Whites for a given mean glucose. Other studies have also demonstrated higher A1C levels in African Americans than in Whites at a given mean glucose concentration (14,15). In contrast, a recent report in Afro-Caribbeans found lower A1C relative to glucose values (16). Taken together, A1C and glucose parameters are essential for the optimal assessment of glycemic status.

A1C assays are available that do not demonstrate a statistically significant difference in individuals with hemoglobin variants. Other assays have statistically significant interference, but the difference is not clinically significant. Use of an assay with such statistically significant interference may explain a report that for any level of mean glycemia, African Americans heterozygous for the common hemoglobin variant HbS had lower A1C by about 0.3 percentage points when compared with those without the trait (17,18). Another genetic variant, X-linked glucose-6-phosphate dehydrogenase G202A, carried by 11% of African Americans, was associated with a decrease in A1C of about 0.8% in hemizygous men and 0.7% in homozygous women compared with those without the trait (19).

A small study comparing A1C to CGM data in children with type 1 diabetes found a highly statistically significant correlation between A1C and mean blood glucose, although the correlation (r = 0.7) was significantly lower than in the ADAG trial (20). Whether there are clinically meaningful differences in how A1C relates to average glucose in children or in different ethnicities is an area for further study (14,21,22). Until further evidence is available, it seems prudent to establish A1C goals in these populations with consideration of individualized CGM, BGM, and A1C results. Limitations in perfect alignment between glycemic measurements do not interfere with the usefulness of BGM/CGM for insulin dose adjustments.

Glucose Assessment by Continuous Glucose Monitoring

Recommendations

  • 6.3 Standardized, single-page glucose reports from continuous glucose monitoring (CGM) devices with visual cues, such as the ambulatory glucose profile, should be considered as a standard summary for all CGM devices. E

  • 6.4 Time in range is associated with the risk of microvascular complications and can be used for assessment of glycemic control. Additionally, time below target and time above target are useful parameters for the evaluation of the treatment regimen (Table 6.2). C

CGM is rapidly improving diabetes management. As stated in the recommendations, time in range (TIR) is a useful metric of glycemic control and glucose patterns, and it correlates well with A1C in most studies (2328). New data support the premise that increased TIR correlates with the risk of complications. The studies supporting this assertion are reviewed in more detail in Section 7, “Diabetes Technology” (http://doi.org/10.2337/dc22-S007); they include cross-sectional data and cohort studies (2931) demonstrating TIR as an acceptable end point for clinical trials moving forward and that it can be used for assessment of glycemic control. Additionally, time below target (<70 and <54 mg/dL [3.9 and 3.0 mmol/L]) and time above target (>180 mg/dL [10.0 mmol/L]) are useful parameters for insulin dose adjustments and reevaluation of the treatment regimen.

Table 6.2

Standardized CGM metrics for clinical care

1. Number of days CGM device is worn (recommend 14 days)  
2. Percentage of time CGM device is active (recommend 70% of data from 14 days)  
3. Mean glucose  
4. Glucose management indicator  
5. Glycemic variability (%CV) target ≤36%*  
6. TAR: % of readings and time >250 mg/dL (>13.9 mmol/L) Level 2 hyperglycemia 
7. TAR: % of readings and time 181–250 mg/dL (10.1–13.9 mmol/L) Level 1 hyperglycemia 
8. TIR: % of readings and time 70–180 mg/dL (3.9–10.0 mmol/L) In range 
9. TBR: % of readings and time 54–69 mg/dL (3.0–3.8 mmol/L) Level 1 hypoglycemia 
10. TBR: % of readings and time <54 mg/dL (<3.0 mmol/L) Level 2 hypoglycemia 
1. Number of days CGM device is worn (recommend 14 days)  
2. Percentage of time CGM device is active (recommend 70% of data from 14 days)  
3. Mean glucose  
4. Glucose management indicator  
5. Glycemic variability (%CV) target ≤36%*  
6. TAR: % of readings and time >250 mg/dL (>13.9 mmol/L) Level 2 hyperglycemia 
7. TAR: % of readings and time 181–250 mg/dL (10.1–13.9 mmol/L) Level 1 hyperglycemia 
8. TIR: % of readings and time 70–180 mg/dL (3.9–10.0 mmol/L) In range 
9. TBR: % of readings and time 54–69 mg/dL (3.0–3.8 mmol/L) Level 1 hypoglycemia 
10. TBR: % of readings and time <54 mg/dL (<3.0 mmol/L) Level 2 hypoglycemia 

CGM, continuous glucose monitoring; CV, coefficient of variation; TAR, time above range; TBR, time below range; TIR, time in range.

*

Some studies suggest that lower %CV targets (<33%) provide additional protection against hypoglycemia for those receiving insulin or sulfonylureas. Adapted from Battelino et al. (34).

For many people with diabetes, glucose monitoring is key for achieving glycemic targets. Major clinical trials of insulin-treated patients have included BGM as part of multifactorial interventions to demonstrate the benefit of intensive glycemic control on diabetes complications (32). BGM is thus an integral component of effective therapy of patients taking insulin. In recent years, CGM is now a standard method for glucose monitoring for most adults with type 1 diabetes (33). Both approaches to glucose monitoring allow patients to evaluate individual responses to therapy and assess whether glycemic targets are being safely achieved. The international consensus on TIR provides guidance on standardized CGM metrics (see Table 6.2) and considerations for clinical interpretation and care (34). To make these metrics more actionable, standardized reports with visual cues, such as the ambulatory glucose profile (see Fig. 6.1), are recommended (34) and may help the patient and the provider better interpret the data to guide treatment decisions (23,26). BGM and CGM can be useful to guide medical nutrition therapy and physical activity, prevent hypoglycemia, and aid medication management. While A1C is currently the primary measure to guide glucose management and a valuable risk marker for developing diabetes complications, the CGM metrics TIR (with time below range and time above range) and GMI provide the insights for a more personalized diabetes management plan. The incorporation of these metrics into clinical practice is in evolution, and remote access to these data can be critical for telemedicine. A rapid optimization and harmonization of CGM terminology and remote access is occurring to meet patient and provider needs (3537). The patient’s specific needs and goals should dictate BGM frequency and timing and consideration of CGM use. Please refer to Section 7, “Diabetes Technology” (http://doi.org/10.2337/dc22-SPPC), for a more complete discussion of the use of BGM and CGM.

Figure 6.1

Key points included in standard ambulatory glucose profile (AGP) report. Reprinted from Holt et al. (33).

Figure 6.1

Key points included in standard ambulatory glucose profile (AGP) report. Reprinted from Holt et al. (33).

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With the advent of new technology, CGM has evolved rapidly in both accuracy and affordability. As such, many patients have these data available to assist with self-management and their providers’ assessment of glycemic status. Reports can be generated from CGM that will allow the provider and person with diabetes to determine TIR, calculate GMI, and assess hypoglycemia, hyperglycemia, and glycemic variability. As discussed in a recent consensus document, a report formatted as shown in Fig. 6.1 can be generated (34). Published data suggest a strong correlation between TIR and A1C, with a goal of 70% TIR aligning with an A1C of ∼7% in two prospective studies (8,25). Note the goals of therapy next to each metric in Fig. 6.1 (e.g., low, <4%; very low, <1%) as values to guide changes in therapy.

For glycemic goals in older adults, please refer to Section 13, “Older Adults” (http://doi.org/10.2337/dc22-S013). For glycemic goals in children, please refer to Section 14, “Children and Adolescents” (http://doi.org/10.2337/dc22-S014). For glycemic goals in pregnant women, please refer to Section 15, “Management of Diabetes in Pregnancy” (http://doi.org/10.2337/dc22-S015). Overall, regardless of the population being served, it is critical for the glycemic targets to be woven into the overall patient-centered strategy. For example, in a very young child, safety and simplicity may outweigh the need for perfect control in the short run. Simplification may decrease parental anxiety and build trust and confidence, which could support further strengthening of glycemic targets and self-efficacy. Similarly, in healthy older adults, there is no empiric need to loosen control. However, the provider needs to work with an individual and should consider adjusting targets or simplifying the regimen if this change is needed to improve safety and adherence.

Recommendations

  • 6.5a An A1C goal for many nonpregnant adults of <7% (53 mmol/mol) without significant hypoglycemia is appropriate. A

  • 6.5b If using ambulatory glucose profile/glucose management indicator to assess glycemia, a parallel goal for many nonpregnant adults is time in range of >70% with time below range <4% and time <54 mg/dL <1% (Fig. 6.1 and Table 6.2). B

  • 6.6 On the basis of provider judgment and patient preference, achievement of lower A1C levels than the goal of 7% may be acceptable and even beneficial if it can be achieved safely without significant hypoglycemia or other adverse effects of treatment. B

  • 6.7 Less stringent A1C goals (such as <8% [64 mmol/mol]) may be appropriate for patients with limited life expectancy or where the harms of treatment are greater than the benefits. B

  • 6.8 Reassess glycemic targets based on the individualized criteria in Fig. 6.2. E

A1C and Microvascular Complications

Hyperglycemia defines diabetes, and glycemic control is fundamental to diabetes management. The Diabetes Control and Complications Trial (DCCT) (32), a prospective randomized controlled trial of intensive (mean A1C about 7% [53 mmol/mol]) versus standard (mean A1C about 9% [75 mmol/mol]) glycemic control in patients with type 1 diabetes, showed definitively that better glycemic control is associated with 50–76% reductions in rates of development and progression of microvascular (retinopathy, neuropathy, and diabetic kidney disease) complications. Follow-up of the DCCT cohorts in the Epidemiology of Diabetes Interventions and Complications (EDIC) study (38,39) demonstrated persistence of these microvascular benefits over two decades despite the fact that the glycemic separation between the treatment groups diminished and disappeared during follow-up.

Figure 6.2

Patient and disease factors used to determine optimal glycemic targets. Characteristics and predicaments toward the left justify more stringent efforts to lower A1C; those toward the right suggest less stringent efforts. A1C 7% = 53 mmol/mol. Adapted with permission from Inzucchi et al. (68).

Figure 6.2

Patient and disease factors used to determine optimal glycemic targets. Characteristics and predicaments toward the left justify more stringent efforts to lower A1C; those toward the right suggest less stringent efforts. A1C 7% = 53 mmol/mol. Adapted with permission from Inzucchi et al. (68).

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The Kumamoto Study (40) and UK Prospective Diabetes Study (UKPDS) (41,42) confirmed that intensive glycemic control significantly decreased rates of microvascular complications in patients with short-duration type 2 diabetes. Long-term follow-up of the UKPDS cohorts showed enduring effects of early glycemic control on most microvascular complications (43).

Therefore, achieving A1C targets of <7% (53 mmol/mol) has been shown to reduce microvascular complications of type 1 and type 2 diabetes when instituted early in the course of disease (2,44). Epidemiologic analyses of the DCCT (32) and UKPDS (45) demonstrate a curvilinear relationship between A1C and microvascular complications. Such analyses suggest that, on a population level, the greatest number of complications will be averted by taking patients from very poor control to fair/good control. These analyses also suggest that further lowering of A1C from 7% to 6% [53 mmol/mol to 42 mmol/mol] is associated with further reduction in the risk of microvascular complications, although the absolute risk reductions become much smaller. The implication of these findings is that there is no need to deintensify therapy for an individual with an A1C between 6% and 7% in the setting of low hypoglycemia risk with a long life expectancy. There are now newer agents that do not cause hypoglycemia, making it possible to maintain glucose control without the risk of hypoglycemia (see Section 9, “Pharmacologic Approaches to Glycemic Treatment,” https://doi.org/10.2337/dc22-S009).

Given the substantially increased risk of hypoglycemia in type 1 diabetes and with polypharmacy in type 2 diabetes, the risks of lower glycemic targets may outweigh the potential benefits on microvascular complications. Three landmark trials (Action to Control Cardiovascular Risk in Diabetes [ACCORD], Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation [ADVANCE], and Veterans Affairs Diabetes Trial [VADT]) were conducted to test the effects of near normalization of blood glucose on cardiovascular outcomes in individuals with long-standing type 2 diabetes and either known cardiovascular disease (CVD) or high cardiovascular risk. These trials showed that lower A1C levels were associated with reduced onset or progression of some microvascular complications (4648).

The concerning mortality findings in the ACCORD trial discussed below and the relatively intense efforts required to achieve near euglycemia should also be considered when setting glycemic targets for individuals with long-standing diabetes, such as those populations studied in ACCORD, ADVANCE, and VADT. Findings from these studies suggest caution is needed in treating diabetes to near-normal A1C goals in people with long-standing type 2 diabetes with or at significant risk of CVD.

These landmark studies need to be considered with an important caveat; glucagon-like peptide 1 (GLP-1) receptor agonists and sodium–glucose cotransporter 2 (SGLT2) inhibitors were not approved at the time of these trials. As such, these agents with established cardiovascular and renal benefits appear to be safe and beneficial in this group of individuals at high risk for cardiorenal complications. Prospective randomized clinical trials examining these agents for cardiovascular safety were not designed to test higher versus lower A1C; therefore, beyond post hoc analysis of these trials, we do not have evidence that it is the glucose lowering by these agents that confers the CVD and renal benefit (49). As such, on the basis of physician judgment and patient preferences, select patients, especially those with little comorbidity and a long life expectancy, may benefit from adopting more intensive glycemic targets if they can achieve them safely and without hypoglycemia or significant therapeutic burden.

A1C and Cardiovascular Disease Outcomes

Cardiovascular Disease and Type 1 Diabetes

CVD is a more common cause of death than microvascular complications in populations with diabetes. There is evidence for a cardiovascular benefit of intensive glycemic control after long-term follow-up of cohorts treated early in the course of type 1 diabetes. In the DCCT, there was a trend toward lower risk of CVD events with intensive control. In the 9-year post-DCCT follow-up of the EDIC cohort, participants previously randomized to the intensive arm had a significant 57% reduction in the risk of nonfatal myocardial infarction (MI), stroke, or cardiovascular death compared with those previously randomized to the standard arm (50). The benefit of intensive glycemic control in this cohort with type 1 diabetes has been shown to persist for several decades (51) and to be associated with a modest reduction in all-cause mortality (52).

Cardiovascular Disease and Type 2 Diabetes

In type 2 diabetes, there is evidence that more intensive treatment of glycemia in newly diagnosed patients may reduce long-term CVD rates. In addition, data from the Swedish National Diabetes Registry (53) and the Joint Asia Diabetes Evaluation (JADE) demonstrate greater proportions of people with diabetes being diagnosed at <40 years of age and a demonstrably increased burden of heart disease and years of life lost in people diagnosed at a younger age (5457). Thus, to prevent both microvascular and macrovascular complications of diabetes, there is a major call to overcome therapeutic inertia and treat to target for an individual patient (57,58). During the UKPDS, there was a 16% reduction in CVD events (combined fatal or nonfatal MI and sudden death) in the intensive glycemic control arm that did not reach statistical significance (P = 0.052), and there was no suggestion of benefit on other CVD outcomes (e.g., stroke). Similar to the DCCT/EDIC, after 10 years of observational follow-up, those originally randomized to intensive glycemic control had significant long-term reductions in MI (15% with sulfonylurea or insulin as initial pharmacotherapy, 33% with metformin as initial pharmacotherapy) and in all-cause mortality (13% and 27%, respectively) (43).

ACCORD, ADVANCE, and VADT suggested no significant reduction in CVD outcomes with intensive glycemic control in participants followed for shorter durations (3.5–5.6 years) and who had more advanced type 2 diabetes and CVD risk than the UKPDS participants. All three trials were conducted in relatively older participants with a longer known duration of diabetes (mean duration 8–11 years) and either CVD or multiple cardiovascular risk factors. The target A1C among intensive-control subjects was <6% (42 mmol/mol) in ACCORD, <6.5% (48 mmol/mol) in ADVANCE, and a 1.5% reduction in A1C compared with control subjects in VADT, with achieved A1C of 6.4% vs. 7.5% (46 mmol/mol vs. 58 mmol/mol) in ACCORD, 6.5% vs. 7.3% (48 mmol/mol vs. 56 mmol/mol) in ADVANCE, and 6.9% vs. 8.4% (52 mmol/mol vs. 68 mmol/mol) in VADT. Details of these studies are reviewed extensively in the joint ADA position statement “Intensive Glycemic Control and the Prevention of Cardiovascular Events: Implications of the ACCORD, ADVANCE, and VA Diabetes Trials” (58).

The glycemic control comparison in ACCORD was halted early due to an increased mortality rate in the intensive compared with the standard treatment arm (1.41% vs. 1.14% per year; hazard ratio 1.22 [95% CI 1.01–1.46]), with a similar increase in cardiovascular deaths. Analysis of the ACCORD data did not identify a clear explanation for the excess mortality in the intensive treatment arm (59).

Longer-term follow-up has shown no evidence of cardiovascular benefit, or harm, in the ADVANCE trial (60). The end-stage renal disease rate was lower in the intensive treatment group over follow-up. However, 10-year follow-up of the VADT cohort (61) did demonstrate a reduction in the risk of cardiovascular events (52.7 [control group] vs. 44.1 [intervention group] events per 1,000 person-years) with no benefit in cardiovascular or overall mortality. Heterogeneity of mortality effects across studies was noted, which may reflect differences in glycemic targets, therapeutic approaches, and, importantly, population characteristics (62).

Mortality findings in ACCORD (59) and subgroup analyses of VADT (63) suggest that the potential risks of intensive glycemic control may outweigh its benefits in higher-risk individuals. In all three trials, severe hypoglycemia was significantly more likely in participants who were randomly assigned to the intensive glycemic control arm. Those patients with a long duration of diabetes, a known history of hypoglycemia, advanced atherosclerosis, or advanced age/frailty may benefit from less aggressive targets (64,65).

As discussed further below, severe hypoglycemia is a potent marker of high absolute risk of cardiovascular events and mortality (66). Therefore, providers should be vigilant in preventing hypoglycemia and should not aggressively attempt to achieve near-normal A1C levels in people in whom such targets cannot be safely and reasonably achieved. As discussed in Section 9, “Pharmacologic Approaches to Glycemic Treatment” (http://doi.org/10.2337/dc22-S009), addition of specific SGLT2 inhibitors or GLP-1 receptor agonists that have demonstrated CVD benefit is recommended in patients with established CVD, chronic kidney disease, and heart failure. As outlined in more detail in Section 9, “Pharmacologic Approaches to Glycemic Treatment” (http://doi.org/10.2337/dc22-S009) and Section 10, “Cardiovascular Disease and Risk Management” (https://doi.org/10.2337/dc22-S010), the cardiovascular benefits of SGLT2 inhibitors or GLP-1 receptor agonists are not contingent upon A1C lowering; therefore, initiation can be considered in people with type 2 diabetes and CVD independent of the current A1C or A1C goal or metformin therapy. Based on these considerations, the following two strategies are offered (67):

  1. If already on dual therapy or multiple glucose-lowering therapies and not on an SGLT2 inhibitor or GLP-1 receptor agonist, consider switching to one of these agents with proven cardiovascular benefit.

  2. Introduce SGLT2 inhibitors or GLP-1 receptor agonists in people with CVD at A1C goal (independent of metformin) for cardiovascular benefit, independent of baseline A1C or individualized A1C target.

Setting and Modifying A1C Goals

Numerous factors must be considered when setting glycemic targets. The ADA proposes general targets appropriate for many people but emphasizes the importance of individualization based on key patient characteristics. Glycemic targets must be individualized in the context of shared decision-making to address individual needs and preferences and consider characteristics that influence risks and benefits of therapy; this approach will optimize engagement and self-efficacy.

The factors to consider in individualizing goals are depicted in Fig. 6.2. This figure is not designed to be applied rigidly but to be used as a broad construct to guide clinical decision-making (68) and engage people with type 1 and type 2 diabetes in shared decision-making. More aggressive targets may be recommended if they can be achieved safely and with an acceptable burden of therapy and if life expectancy is sufficient to reap the benefits of stringent targets. Less stringent targets (A1C up to 8% [64 mmol/mol]) may be recommended if the patient’s life expectancy is such that the benefits of an intensive goal may not be realized, or if the risks and burdens outweigh the potential benefits. Severe or frequent hypoglycemia is an absolute indication for the modification of treatment regimens, including setting higher glycemic goals.

Diabetes is a chronic disease that progresses over decades. Thus, a goal that might be appropriate for an individual early in the course of their diabetes may change over time. Newly diagnosed patients and/or those without comorbidities that limit life expectancy may benefit from intensive control proven to prevent microvascular complications. Both DCCT/EDIC and UKPDS demonstrated metabolic memory, or a legacy effect, in which a finite period of intensive control yielded benefits that extended for decades after that control ended. Thus, a finite period of intensive control to near-normal A1C may yield enduring benefits even if control is subsequently deintensified as patient characteristics change. Over time, comorbidities may emerge, decreasing life expectancy and thereby decreasing the potential to reap benefits from intensive control. Also, with longer disease duration, diabetes may become more difficult to control, with increasing risks and burdens of therapy. Thus, A1C targets should be reevaluated over time to balance the risks and benefits as patient factors change.

Recommended glycemic targets for many nonpregnant adults are shown in Table 6.3. The recommendations include blood glucose levels that appear to correlate with achievement of an A1C of <7% (53 mmol/mol). Pregnancy recommendations are discussed in more detail in Section 15, “Management of Diabetes in Pregnancy” (https://doi.org/10.2337/dc22-S015).

Table 6.3

Summary of glycemic recommendations for many nonpregnant adults with diabetes

A1C <7.0% (53 mmol/mol)*# 
Preprandial capillary plasma glucose 80–130 mg/dL* (4.4–7.2 mmol/L) 
Peak postprandial capillary plasma glucose <180 mg/dL* (10.0 mmol/L) 
A1C <7.0% (53 mmol/mol)*# 
Preprandial capillary plasma glucose 80–130 mg/dL* (4.4–7.2 mmol/L) 
Peak postprandial capillary plasma glucose <180 mg/dL* (10.0 mmol/L) 
*

More or less stringent glycemic goals may be appropriate for individual patients.

#

CGM may be used to assess glycemic target as noted in Recommendation 6.5b and Fig. 6.1. Goals should be individualized based on duration of diabetes, age/life expectancy, comorbid conditions, known CVD or advanced microvascular complications, hypoglycemia unawareness, and individual patient considerations (as per Fig. 6.2).

Postprandial glucose may be targeted if A1C goals are not met despite reaching preprandial glucose goals. Postprandial glucose measurements should be made 1–2 h after the beginning of the meal, generally peak levels in patients with diabetes.

The issue of preprandial versus postprandial BGM targets is complex (69). Elevated postchallenge (2-h oral glucose tolerance test) glucose values have been associated with increased cardiovascular risk independent of fasting plasma glucose in some epidemiologic studies, whereas intervention trials have not shown postprandial glucose to be a cardiovascular risk factor independent of A1C. In people with diabetes, surrogate measures of vascular pathology, such as endothelial dysfunction, are negatively affected by postprandial hyperglycemia. It is clear that postprandial hyperglycemia, like preprandial hyperglycemia, contributes to elevated A1C levels, with its relative contribution being greater at A1C levels that are closer to 7% (53 mmol/mol). However, outcome studies have shown A1C to be the primary predictor of complications, and landmark trials of glycemic control such as the DCCT and UKPDS relied overwhelmingly on preprandial BGM. Additionally, a randomized controlled trial in patients with known CVD found no CVD benefit of insulin regimens targeting postprandial glucose compared with those targeting preprandial glucose (70). Therefore, it is reasonable to check postprandial glucose in individuals who have premeal glucose values within target but A1C values above target. In addition, when intensifying insulin therapy, measuring postprandial plasma glucose 1–2 h after the start of a meal (using BGM or CGM) and using treatments aimed at reducing postprandial plasma glucose values to <180 mg/dL (10.0 mmol/L) may help to lower A1C.

An analysis of data from 470 participants in the ADAG study (237 with type 1 diabetes and 147 with type 2 diabetes) found that the glucose ranges highlighted in Table 6.1 are adequate to meet targets and decrease hypoglycemia (13,71). These findings support that premeal glucose targets may be relaxed without undermining overall glycemic control as measured by A1C. These data prompted the revision in the ADA-recommended premeal glucose target to 80–130 mg/dL (4.4–7.2 mmol/L) but did not affect the definition of hypoglycemia.

Recommendations

  • 6.9 Occurrence and risk for hypoglycemia should be reviewed at every encounter and investigated as indicated. C

  • 6.10 Glucose (approximately 15–20 g) is the preferred treatment for the conscious individual with blood glucose <70 mg/dL (3.9 mmol/L), although any form of carbohydrate that contains glucose may be used. Fifteen minutes after treatment, if blood glucose monitoring (BGM) shows continued hypoglycemia, the treatment should be repeated. Once the BGM or glucose pattern is trending up, the individual should consume a meal or snack to prevent recurrence of hypoglycemia. B

  • 6.11 Glucagon should be prescribed for all individuals at increased risk of level 2 or 3 hypoglycemia, so that it is available should it be needed. Caregivers, school personnel, or family members providing support to these individuals should know where it is and when and how to administer it. Glucagon administration is not limited to health care professionals. E

  • 6.12 Hypoglycemia unawareness or one or more episodes of level 3 hypoglycemia should trigger hypoglycemia avoidance education and reevaluation and adjustment of the treatment regimen to decrease hypoglycemia. E

  • 6.13 Insulin-treated patients with hypoglycemia unawareness, one level 3 hypoglycemic event, or a pattern of unexplained level 2 hypoglycemia should be advised to raise their glycemic targets to strictly avoid hypoglycemia for at least several weeks in order to partially reverse hypoglycemia unawareness and reduce risk of future episodes. A

  • 6.14 Ongoing assessment of cognitive function is suggested with increased vigilance for hypoglycemia by the clinician, patient, and caregivers if impaired or declining cognition is found. B

Hypoglycemia is the major limiting factor in the glycemic management of type 1 and type 2 diabetes. Recommendations regarding the classification of hypoglycemia are outlined in Table 6.4 (7277). Level 1 hypoglycemia is defined as a measurable glucose concentration <70 mg/dL (3.9 mmol/L) but ≥54 mg/dL (3.0 mmol/L). A blood glucose concentration of 70 mg/dL (3.9 mmol/L) has been recognized as a threshold for neuroendocrine responses to falling glucose in people without diabetes. Because many people with diabetes demonstrate impaired counterregulatory responses to hypoglycemia and/or experience hypoglycemia unawareness, a measured glucose level <70 mg/dL (3.9 mmol/L) is considered clinically important (independent of the severity of acute hypoglycemic symptoms). Level 2 hypoglycemia (defined as a blood glucose concentration <54 mg/dL [3.0 mmol/L]) is the threshold at which neuroglycopenic symptoms begin to occur and requires immediate action to resolve the hypoglycemic event. If a patient has level 2 hypoglycemia without adrenergic or neuroglycopenic symptoms, they likely have hypoglycemia unawareness (discussed further below). This clinical scenario warrants investigation and review of the medical regimen (7882). Lastly, level 3 hypoglycemia is defined as a severe event characterized by altered mental and/or physical functioning that requires assistance from another person for recovery.

Table 6.4

Classification of hypoglycemia

Glycemic criteria/description
Level 1 Glucose <70 mg/dL (3.9 mmol/L) and ≥54 mg/dL (3.0 mmol/L) 
Level 2 Glucose <54 mg/dL (3.0 mmol/L) 
Level 3 A severe event characterized by altered mental and/or physical status requiring assistance for treatment of hypoglycemia 
Glycemic criteria/description
Level 1 Glucose <70 mg/dL (3.9 mmol/L) and ≥54 mg/dL (3.0 mmol/L) 
Level 2 Glucose <54 mg/dL (3.0 mmol/L) 
Level 3 A severe event characterized by altered mental and/or physical status requiring assistance for treatment of hypoglycemia 

Reprinted from Agiostratidou et al. (72).

Symptoms of hypoglycemia include, but are not limited to, shakiness, irritability, confusion, tachycardia, and hunger. Hypoglycemia may be inconvenient or frightening to patients with diabetes. Level 3 hypoglycemia may be recognized or unrecognized and can progress to loss of consciousness, seizure, coma, or death. Hypoglycemia is reversed by administration of rapid-acting glucose or glucagon. Hypoglycemia can cause acute harm to the person with diabetes or others, especially if it causes falls, motor vehicle accidents, or other injury. Recurrent level 2 hypoglycemia and/or level 3 hypoglycemia is an urgent medical issue and requires intervention with medical regimen adjustment, behavioral intervention, and, in some cases, use of technology to assist with hypoglycemia prevention and identification (73,8285). A large cohort study suggested that among older adults with type 2 diabetes, a history of level 3 hypoglycemia was associated with greater risk of dementia (86). Conversely, in a substudy of the ACCORD trial, cognitive impairment at baseline or decline in cognitive function during the trial was significantly associated with subsequent episodes of level 3 hypoglycemia (87). Evidence from DCCT/EDIC, which involved adolescents and younger adults with type 1 diabetes, found no association between frequency of level 3 hypoglycemia and cognitive decline (88).

Studies of rates of level 3 hypoglycemia that rely on claims data for hospitalization, emergency department visits, and ambulance use substantially underestimate rates of level 3 hypoglycemia (89) yet reveal a high burden of hypoglycemia in adults over 60 years of age in the community (90). African Americans are at substantially increased risk of level 3 hypoglycemia (90,91). In addition to age and race, other important risk factors found in a community-based epidemiologic cohort of older Black and White adults with type 2 diabetes include insulin use, poor or moderate versus good glycemic control, albuminuria, and poor cognitive function (90). Level 3 hypoglycemia was associated with mortality in participants in both the standard and the intensive glycemia arms of the ACCORD trial, but the relationships between hypoglycemia, achieved A1C, and treatment intensity were not straightforward. An association of level 3 hypoglycemia with mortality was also found in the ADVANCE trial (92). An association between self-reported level 3 hypoglycemia and 5-year mortality has also been reported in clinical practice (93). Glucose variability is also associated with an increased risk for hypoglycemia (94).

Young children with type 1 diabetes and the elderly, including those with type 1 and type 2 diabetes (86,95), are noted as particularly vulnerable to hypoglycemia because of their reduced ability to recognize hypoglycemic symptoms and effectively communicate their needs. Individualized glucose targets, patient education, dietary intervention (e.g., bedtime snack to prevent overnight hypoglycemia when specifically needed to treat low blood glucose), exercise management, medication adjustment, glucose monitoring, and routine clinical surveillance may improve patient outcomes (96). CGM with automated low glucose suspend and hybrid closed-loop systems have been shown to be effective in reducing hypoglycemia in type 1 diabetes (97). For patients with type 1 diabetes with level 3 hypoglycemia and hypoglycemia unawareness that persists despite medical treatment, human islet transplantation may be an option, but the approach remains experimental (98,99).

In 2015, the ADA changed its preprandial glycemic target from 70–130 mg/dL (3.9–7.2 mmol/L) to 80–130 mg/dL (4.4–7.2 mmol/L). This change reflects the results of the ADAG study, which demonstrated that higher glycemic targets corresponded to A1C goals (13). An additional goal of raising the lower range of the glycemic target was to limit overtreatment and provide a safety margin in patients titrating glucose-lowering drugs such as insulin to glycemic targets.

Hypoglycemia Treatment

Providers should continue to counsel patients to treat hypoglycemia with fast-acting carbohydrates at the hypoglycemia alert value of 70 mg/dL (3.9 mmol/L) or less. This should be reviewed at each patient visit. Hypoglycemia treatment requires ingestion of glucose- or carbohydrate-containing foods (100102). The acute glycemic response correlates better with the glucose content of food than with the carbohydrate content of food. Pure glucose is the preferred treatment, but any form of carbohydrate that contains glucose will raise blood glucose. Added fat may retard and then prolong the acute glycemic response. In type 2 diabetes, ingested protein may increase insulin response without increasing plasma glucose concentrations (103). Therefore, carbohydrate sources high in protein should not be used to treat or prevent hypoglycemia. Ongoing insulin activity or insulin secretagogues may lead to recurrent hypoglycemia unless more food is ingested after recovery. Once the glucose returns to normal, the individual should be counseled to eat a meal or snack to prevent recurrent hypoglycemia.

Glucagon

The use of glucagon is indicated for the treatment of hypoglycemia in people unable or unwilling to consume carbohydrates by mouth. Those in close contact with, or having custodial care of, people with hypoglycemia-prone diabetes (family members, roommates, school personnel, childcare providers, correctional institution staff, or coworkers) should be instructed on the use of glucagon, including where the glucagon product is kept and when and how to administer it. An individual does not need to be a health care professional to safely administer glucagon. In addition to traditional glucagon injection powder that requires reconstitution prior to injection, intranasal glucagon and ready-to-inject glucagon preparations for subcutaneous injection are available. Care should be taken to ensure that glucagon products are not expired.

Hypoglycemia Prevention

Hypoglycemia prevention is a critical component of diabetes management. BGM and, for some patients, CGM are essential tools to assess therapy and detect incipient hypoglycemia. Patients should understand situations that increase their risk of hypoglycemia, such as when fasting for laboratory tests or procedures, when meals are delayed, during and after the consumption of alcohol, during and after intense exercise, and during sleep. Hypoglycemia may increase the risk of harm to self or others, such as when driving. Teaching people with diabetes to balance insulin use and carbohydrate intake and exercise are necessary, but these strategies are not always sufficient for prevention (82,104106). Formal training programs to increase awareness of hypoglycemia and to develop strategies to decrease hypoglycemia have been developed, including the Blood Glucose Awareness Training Programme, Dose Adjusted for Normal Eating (DAFNE), and DAFNEplus. Conversely, some individuals with type 1 diabetes and hypoglycemia who have a fear of hyperglycemia are resistant to relaxation of glycemic targets (78,80). Regardless of the factors contributing to hypoglycemia and hypoglycemia unawareness, this represents an urgent medical issue requiring intervention.

In type 1 diabetes and severely insulin-deficient type 2 diabetes, hypoglycemia unawareness (or hypoglycemia-associated autonomic failure) can severely compromise stringent diabetes control and quality of life. This syndrome is characterized by deficient counterregulatory hormone release, especially in older adults, and a diminished autonomic response, which are both risk factors for and caused by hypoglycemia. A corollary to this “vicious cycle” is that several weeks of avoidance of hypoglycemia has been demonstrated to improve counterregulation and hypoglycemia awareness in many patients (107). Hence, patients with one or more episodes of clinically significant hypoglycemia may benefit from at least short-term relaxation of glycemic targets and availability of glucagon (108). Any person with recurrent hypoglycemia or hypoglycemia unawareness should have their glucose management regimen adjusted.

Use of CGM Technology in Hypoglycemia Prevention

With the advent of CGM and CGM-assisted pump therapy, there has been a promise of alarm-based prevention of hypoglycemia (109,110). To date, there have been a number of randomized controlled trials in adults with type 1 diabetes and studies in adults and children with type 1 diabetes using real-time CGM (see Section 7, “Diabetes Technology,” https://doi.org/10.2337/dc22-S007). These studies had differing A1C at entry and differing primary end points and thus must be interpreted carefully. Real-time CGM studies can be divided into studies with elevated A1C with the primary end point of A1C reduction and studies with A1C near target with the primary end point of reduction in hypoglycemia (100,110125). In people with type 1 and type 2 diabetes with A1C above target, CGM improved A1C between 0.3% and 0.6%. For studies targeting hypoglycemia, most studies demonstrated a significant reduction in time spent between 54 and 70 mg/dL. A recent report in people with type 1 diabetes over the age of 60 years revealed a small but statistically significant decrease in hypoglycemia (126). No study to date has reported a decrease in level 3 hypoglycemia. In a single study using intermittently scanned CGM, adults with type 1 diabetes with A1C near goal and impaired awareness of hypoglycemia demonstrated no change in A1C and decreased level 2 hypoglycemia (116). For people with type 2 diabetes, studies examining the impact of CGM on hypoglycemic events are limited; a recent meta-analysis does not reflect a significant impact on hypoglycemic events in type 2 diabetes (127), whereas improvements in A1C were observed in most studies (127133). Overall, real-time CGM appears to be a useful tool for decreasing time spent in a hypoglycemic range in people with impaired awareness. For type 2 diabetes, other strategies to assist patients with insulin dosing can improve A1C with minimal hypoglycemia (134,135).

For further information on management of patients with hyperglycemia in the hospital, see Section 16, “Diabetes Care in the Hospital” (https://doi.org/10.2337/dc22-S016).

Stressful events (e.g., illness, trauma, surgery, etc.) may worsen glycemic control and precipitate diabetic ketoacidosis or nonketotic hyperglycemic hyperosmolar state, life-threatening conditions that require immediate medical care to prevent complications and death. Any condition leading to deterioration in glycemic control necessitates more frequent monitoring of blood glucose; ketosis-prone patients also require urine or blood ketone monitoring. If accompanied by ketosis, vomiting, or alteration in the level of consciousness, marked hyperglycemia requires temporary adjustment of the treatment regimen and immediate interaction with the diabetes care team. The patient treated with noninsulin therapies or medical nutrition therapy alone may require insulin. Adequate fluid and caloric intake must be ensured. Infection or dehydration are more likely to necessitate hospitalization of individuals with diabetes versus those without diabetes.

A physician with expertise in diabetes management should treat the hospitalized patient. For further information on the management of diabetic ketoacidosis and the nonketotic hyperglycemic hyperosmolar state, please refer to the ADA consensus report “Hyperglycemic Crises in Adult Patients With Diabetes” (135).

*

A complete list of members of the American Diabetes Association Professional Practice Committee can be found at http://doi.org/10.2337/dc22-SPPC.

Suggested citation: American Diabetes Association Professional Practice Committee. 6. Glycemic targets: Standards of Medical Care in Diabetes—2022. Diabetes Care 2022;45(Suppl. 1):S83–S96

1
Deshmukh
H
,
Wilmot
EG
,
Gregory
R
, et al
.
Effect of flash glucose monitoring on glycemic control, hypoglycemia, diabetes-related distress, and resource utilization in the Association of British Clinical Diabetologists (ABCD) nationwide audit
.
Diabetes Care
2020
;
43
:
2153
2160
2
Laiteerapong
N
,
Ham
SA
,
Gao
Y
, et al
.
The legacy effect in type 2 diabetes: impact of early glycemic control on future complications (the Diabetes & Aging Study)
.
Diabetes Care
2019
;
42
:
416
426
3
Stratton
IM
,
Adler
AI
,
Neil
HAW
, et al
.
Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study
.
BMJ
2000
;
321
:
405
412
4
Little
RR
,
Rohlfing
CL
;
National Glycohemoglobin Standardization Program (NGSP) Steering Committee
.
Status of hemoglobin A1c measurement and goals for improvement: from chaos to order for improving diabetes care
.
Clin Chem
2011
;
57
:
205
214
5
Valenzano
M
,
Cibrario Bertolotti
I
,
Valenzano
A
,
Grassi
G
.
Time in range-A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study
.
BMJ Open Diabetes Res Care
2021
;
9
:
e001045
6
Fabris
C
,
Heinemann
L
,
Beck
R
,
Cobelli
C
,
Kovatchev
B
.
Estimation of hemoglobin A1c from continuous glucose monitoring data in individuals with type 1 diabetes: is time in range all we need?
Diabetes Technol Ther
2020
;
22
:
501
508
7
Ranjan
AG
,
Rosenlund
SV
,
Hansen
TW
,
Rossing
P
,
Andersen
S
,
Nørgaard
K
.
Improved time in range over 1 year is associated with reduced albuminuria in individuals with sensor-augmented insulin pump-treated type 1 diabetes
.
Diabetes Care
2020
;
43
:
2882
2885
8
Beck
RW
,
Bergenstal
RM
,
Cheng
P
, et al
.
The relationships between time in range, hyperglycemia metrics, and HbA1c
.
J Diabetes Sci Technol
2019
;
13
:
614
626
9
Šoupal
J
,
Petruželková
L
,
Grunberger
G
, et al
.
Glycemic outcomes in adults with T1D are impacted more by continuous glucose monitoring than by insulin delivery method: 3 years of follow-up from the COMISAIR study
.
Diabetes Care
2020
;
43
:
37
43
10
Jovanovič
L
,
Savas
H
,
Mehta
M
,
Trujillo
A
,
Pettitt
DJ
.
Frequent monitoring of A1C during pregnancy as a treatment tool to guide therapy
.
Diabetes Care
2011
;
34
:
53
54
11
Beck
RW
,
Connor
CG
,
Mullen
DM
,
Wesley
DM
,
Bergenstal
RM
.
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
.
Diabetes Care
2017
;
40
:
994
999
12
Nathan
DM
,
Kuenen
J
,
Borg
R
,
Zheng
H
,
Schoenfeld
D
;
A1c-Derived Average Glucose Study Group
.
Translating the A1C assay into estimated average glucose values
.
Diabetes Care
2008
;
31
:
1473
1478
13
Wei
N
,
Zheng
H
,
Nathan
DM
.
Empirically establishing blood glucose targets to achieve HbA1c goals
.
Diabetes Care
2014
;
37
:
1048
1051
14
Selvin
E
.
Are there clinical implications of racial differences in HbA1c? A difference, to be a difference, must make a difference
.
Diabetes Care
2016
;
39
:
1462
1467
15
Bergenstal
RM
,
Gal
RL
,
Connor
CG
, et al.;
T1D Exchange Racial Differences Study Group
.
Racial differences in the relationship of glucose concentrations and hemoglobin A1c levels
.
Ann Intern Med
2017
;
167
:
95
102
16
Khosla
L
,
Bhat
S
,
Fullington
LA
,
Horlyck-Romanovsky
MF
.
HbA1c performance in African descent populations in the United States with normal glucose tolerance, prediabetes, or diabetes: a scoping review
.
Prev Chronic Dis
2021
;
18
:
E22
17
Lacy
ME
,
Wellenius
GA
,
Sumner
AE
, et al
.
Association of sickle cell trait with hemoglobin A1c in African Americans
.
JAMA
2017
;
317
:
507
515
18
Rohlfing
C
,
Hanson
S
,
Little
RR
.
Measurement of hemoglobin A1c in patients with sickle cell trait
.
JAMA
2017
;
317
:
2237
19
Wheeler
E
,
Leong
A
,
Liu
C-T
, et al.;
EPIC-CVD Consortium
;
EPIC-InterAct Consortium
;
Lifelines Cohort Study
.
Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis
.
PLoS Med
2017
;
14
:
e1002383
20
Diabetes Research in Children Network (DirecNet) Study Group
.
Relationship of A1C to glucose concentrations in children with type 1 diabetes: assessments by high-frequency glucose determinations by sensors
.
Diabetes Care
2008
;
31
:
381
385
21
Buse
JB
,
Kaufman
FR
,
Linder
B
,
Hirst
K
,
El Ghormli
L
;
HEALTHY Study Group
.
Diabetes screening with hemoglobin A1c versus fasting plasma glucose in a multiethnic middle-school cohort
.
Diabetes Care
2013
;
36
:
429
435
22
Kamps
JL
,
Hempe
JM
,
Chalew
SA
.
Racial disparity in A1C independent of mean blood glucose in children with type 1 diabetes
.
Diabetes Care
2010
;
33
:
1025
1027
23
Advani
A
.
Positioning time in range in diabetes management
.
Diabetologia
2020
;
63
:
242
252
24
Avari
P
,
Uduku
C
,
George
D
,
Herrero
P
,
Reddy
M
,
Oliver
N
.
Differences for percentage times in glycemic range between continuous glucose monitoring and capillary blood glucose monitoring in adults with type 1 diabetes: analysis of the REPLACE-BG dataset
.
Diabetes Technol Ther
2020
;
22
:
222
227
25
Vigersky
RA
,
McMahon
C
.
The relationship of hemoglobin A1C to time-in-range in patients with diabetes
.
Diabetes Technol Ther
2019
;
21
:
81
85
26
Kröger
J
,
Reichel
A
,
Siegmund
T
,
Ziegler
R
.
Clinical recommendations for the use of the ambulatory glucose profile in diabetes care
.
J Diabetes Sci Technol
2020
;
14
:
586
594
27
Livingstone
R
,
Boyle
JG
,
Petrie
JR
.
How tightly controlled do fluctuations in blood glucose levels need to be to reduce the risk of developing complications in people with type 1 diabetes?
Diabet Med
2020
;
37
:
513
521
28
Messer
LH
,
Berget
C
,
Vigers
T
, et al
.
Real world hybrid closed-loop discontinuation: predictors and perceptions of youth discontinuing the 670G system in the first 6 months
.
Pediatr Diabetes
2020
;
21
:
319
327
29
Mayeda
L
,
Katz
R
,
Ahmad
I
, et al
.
Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease
.
BMJ Open Diabetes Res Care
2020
;
8
:
e000991
30
Yoo
JH
,
Choi
MS
,
Ahn
J
, et al
.
Association between continuous glucose monitoring-derived time in range, other core metrics, and albuminuria in type 2 diabetes
.
Diabetes Technol Ther
2020
;
22
:
768
776
31
Lu
J
,
Ma
X
,
Shen
Y
, et al
.
Time in range is associated with carotid intima-media thickness in type 2 diabetes
.
Diabetes Technol Ther
2020
;
22
:
72
78
32
Diabetes Control and Complications Trial Research Group
;
Nathan
DM
,
Genuth
S
,
Lachin
J
, et al
.
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
33
Holt
RIG
,
DeVries
JH
,
Hess-Fischl
A
, et al
.
The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
.
Diabetes Care
2021
;
44
:
2589
2625
34
Battelino
T
,
Danne
T
,
Bergenstal
RM
, et al
.
Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range
.
Diabetes Care
2019
;
42
:
1593
1603
35
Tchero
H
,
Kangambega
P
,
Briatte
C
,
Brunet-Houdard
S
,
Retali
G-R
,
Rusch
E
.
Clinical effectiveness of telemedicine in diabetes mellitus: a meta-analysis of 42 randomized controlled trials
.
Telemed J E Health
2019
;
25
:
569
583
36
Salabelle
C
,
Ly Sall
K
,
Eroukhmanoff
J
, et al
.
COVID-19 pandemic lockdown in young people with type 1 diabetes: positive results of an unprecedented challenge for patients through telemedicine and change in use of continuous glucose monitoring
.
Prim Care Diabetes
2021
;
15
:
884
886
37
Prabhu Navis
J
,
Leelarathna
L
,
Mubita
W
, et al
.
Impact of COVID-19 lockdown on flash and real-time glucose sensor users with type 1 diabetes in England
.
Acta Diabetol
2021
;
58
:
231
237
38
Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group
;
Lachin
JM
,
White
NH
,
Hainsworth
DP
, et al
.
Effect of intensive diabetes therapy on the progression of diabetic retinopathy in patients with type 1 diabetes: 18 years of follow-up in the DCCT/EDIC
.
Diabetes
2015
;
64
:
631
642
39
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group
;
Lachin
JM
,
Genuth
S
,
Cleary
P
,
Davis
MD
,
Nathan
DM
.
Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy
.
N Engl J Med
2000
;
342
:
381
389
40
Ohkubo
Y
,
Kishikawa
H
,
Araki
E
, et al
.
Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study
.
Diabetes Res Clin Pract
1995
;
28
:
103
117
41
UK Prospective Diabetes Study (UKPDS) Group
.
Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34)
.
Lancet
1998
;
352
:
854
865
42
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
43
Holman
RR
,
Paul
SK
,
Bethel
MA
,
Matthews
DR
,
Neil
HAW
.
10-year follow-up of intensive glucose control in type 2 diabetes
.
N Engl J Med
2008
;
359
:
1577
1589
44
Lind
M
,
Pivodic
A
,
Svensson
A-M
,
Ólafsdóttir
AF
,
Wedel
H
,
Ludvigsson
J
.
HbA1c level as a risk factor for retinopathy and nephropathy in children and adults with type 1 diabetes: Swedish population based cohort study
.
BMJ
2019
;
366
:
l4894
45
Adler
AI
,
Stratton
IM
,
Neil
HAW
, et al
.
Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (UKPDS 36): prospective observational study
.
BMJ
2000
;
321
:
412
419
46
Duckworth
W
,
Abraira
C
,
Moritz
T
, et al.;
VADT Investigators
.
Glucose control and vascular complications in veterans with type 2 diabetes
.
N Engl J Med
2009
;
360
:
129
139
47
Patel
A
,
MacMahon
S
,
Chalmers
J
, et al.;
ADVANCE Collaborative Group
.
Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes
.
N Engl J Med
2008
;
358
:
2560
2572
48
Ismail-Beigi
F
,
Craven
T
,
Banerji
MA
, et al.;
ACCORD trial group
.
Effect of intensive treatment of hyperglycaemia on microvascular outcomes in type 2 diabetes: an analysis of the ACCORD randomised trial
.
Lancet
2010
;
376
:
419
430
49
Buse
JB
,
Bain
SC
,
Mann
JFE
, et al.;
LEADER Trial Investigators
.
Cardiovascular risk reduction with liraglutide: an exploratory mediation analysis of the LEADER trial
.
Diabetes Care
2020
;
43
:
1546
1552
50
Nathan
DM
,
Cleary
PA
,
Backlund
J-YC
, 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
51
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
52
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
53
Emerging Risk Factors Collaboration
;
Di Angelantonio
E
,
Kaptoge
S
,
Wormser
D
, et al
.
Association of cardiometabolic multimorbidity with mortality
.
JAMA
2015
;
314
:
52
60
54
Yeung
RO
,
Zhang
Y
,
Luk
A
, et al
.
Metabolic profiles and treatment gaps in young-onset type 2 diabetes in Asia (the JADE programme): a cross-sectional study of a prospective cohort
.
Lancet Diabetes Endocrinol
2014
;
2
:
935
943
55
Sattar
N
,
Rawshani
A
,
Franzén
S
, et al
.
Age at diagnosis of type 2 diabetes mellitus and associations with cardiovascular and mortality risks
.
Circulation
2019
;
139
:
2228
2237
56
Zabala
A
,
Darsalia
V
,
Holzmann
MJ
, et al
.
Risk of first stroke in people with type 2 diabetes and its relation to glycaemic control: a nationwide observational study
.
Diabetes Obes Metab
2020
;
22
:
182
190
57
Zoungas
S
,
Woodward
M
,
Li
Q
, et al.;
ADVANCE Collaborative group
.
Impact of age, age at diagnosis and duration of diabetes on the risk of macrovascular and microvascular complications and death in type 2 diabetes
.
Diabetologia
2014
;
57
:
2465
2474
58
Skyler
JS
,
Bergenstal
R
,
Bonow
RO
, et al.;
American Diabetes Association
;
American College of Cardiology Foundation
;
American Heart Association
.
Intensive glycemic control and the prevention of cardiovascular events: implications of the ACCORD, ADVANCE, and VA diabetes trials. A position statement of the American Diabetes Association and a scientific statement of the American College of Cardiology Foundation and the American Heart Association
.
Diabetes Care
2009
;
32
:
187
192
59
Gerstein
HC
,
Miller
ME
,
Byington
RP
, et al.;
Action to Control Cardiovascular Risk in Diabetes Study Group
.
Effects of intensive glucose lowering in type 2 diabetes
.
N Engl J Med
2008
;
358
:
2545
2559
60
Zoungas
S
,
Chalmers
J
,
Neal
B
, et al.;
ADVANCE-ON Collaborative Group
.
Follow-up of blood-pressure lowering and glucose control in type 2 diabetes
.
N Engl J Med
2014
;
371
:
1392
1406
61
Hayward
RA
,
Reaven
PD
,
Wiitala
WL
, et al.;
VADT Investigators
.
Follow-up of glycemic control and cardiovascular outcomes in type 2 diabetes
.
N Engl J Med
2015
;
372
:
2197
2206
62
Turnbull
FM
,
Abraira
C
,
Anderson
RJ
, et al
.
Intensive glucose control and macrovascular outcomes in type 2 diabetes
.
Diabetologia
2009
;
52
:
2288
2298
63
Duckworth
WC
,
Abraira
C
,
Moritz
TE
, et al.;
Investigators of the VADT
.
The duration of diabetes affects the response to intensive glucose control in type 2 subjects: the VA Diabetes Trial
.
J Diabetes Complications
2011
;
25
:
355
361
64
Lipska
KJ
,
Ross
JS
,
Miao
Y
,
Shah
ND
,
Lee
SJ
,
Steinman
MA
.
Potential overtreatment of diabetes mellitus in older adults with tight glycemic control
.
JAMA Intern Med
2015
;
175
:
356
362
65
Vijan
S
,
Sussman
JB
,
Yudkin
JS
,
Hayward
RA
.
Effect of patients’ risks and preferences on health gains with plasma glucose level lowering in type 2 diabetes mellitus
.
JAMA Intern Med
2014
;
174
:
1227
1234
66
Lee
AK
,
Warren
B
,
Lee
CJ
, et al
.
The association of severe hypoglycemia with incident cardiovascular events and mortality in adults with type 2 diabetes
.
Diabetes Care
2018
;
41
:
104
111
67
Davies
MJ
,
D’Alessio
DA
,
Fradkin
J
, et al
.
Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
.
Diabetes Care
2018
;
41
:
2669
2701
68
Inzucchi
SE
,
Bergenstal
RM
,
Buse
JB
, et al
.
Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes
.
Diabetes Care
2015
;
38
:
140
149
69
American Diabetes Association
.
Postprandial blood glucose
.
Diabetes Care
2001
;
24
:
775
778
70
Raz
I
,
Wilson
PWF
,
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
71
Albers
JW
,
Herman
WH
,
Pop-Busui
R
, et al.;
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group
.
Effect of prior intensive insulin treatment during the Diabetes Control and Complications Trial (DCCT) on peripheral neuropathy in type 1 diabetes during the Epidemiology of Diabetes Interventions and Complications (EDIC) Study
.
Diabetes Care
2010
;
33
:
1090
1096
72
Agiostratidou
G
,
Anhalt
H
,
Ball
D
, et al
.
Standardizing clinically meaningful outcome measures beyond HbA1c for type 1 diabetes: a consensus report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange
.
Diabetes Care
2017
;
40
:
1622
1630
73
Lamounier
RN
,
Geloneze
B
,
Leite
SO
, et al.;
HAT Brazil study group
.
Hypoglycemia incidence and awareness among insulin-treated patients with diabetes: the HAT study in Brazil
.
Diabetol Metab Syndr
2018
;
10
:
83
74
Li
P
,
Geng
Z
,
Ladage
VP
,
Wu
J
,
Lorincz
I
,
Doshi
JA
.
Early hypoglycaemia and adherence after basal insulin initiation in a nationally representative sample of Medicare beneficiaries with type 2 diabetes
.
Diabetes Obes Metab
2019
;
21
:
2486
2495
75
Shivaprasad
C
,
Aiswarya
Y
,
Kejal
S
, et al
.
Comparison of CGM-derived measures of glycemic variability between pancreatogenic diabetes and type 2 diabetes mellitus
.
J Diabetes Sci Technol
2021
;
15
:
134
140
76
Hendrieckx
C
,
Ivory
N
,
Singh
H
,
Frier
BM
,
Speight
J
.
Impact of severe hypoglycaemia on psychological outcomes in adults with Type 2 diabetes: a systematic review
.
Diabet Med
2019
;
36
:
1082
1091
77
Yang
W
,
Ma
J
,
Yuan
G
, et al
.
Determining the optimal fasting glucose target for patients with type 2 diabetes: Results of the multicentre, open-label, randomized-controlled FPG GOAL trial
.
Diabetes Obes Metab
2019
;
21
:
1973
1977
78
Polonsky
WH
,
Fortmann
AL
,
Price
D
,
Fisher
L
.
“Hyperglycemia aversiveness”: investigating an overlooked problem among adults with type 1 diabetes
.
J Diabetes Complications
2021
;
35
:
107925
79
Al Hayek
A
,
Robert
AA
,
Al Dawish
M
.
Impact of the FreeStyle Libre flash glucose monitoring system on diabetes self-management practices and glycemic control among patients with type 2 diabetes in Saudi Arabia: A prospective study
.
Diabetes Metab Syndr
2021
;
15
:
557
563
80
Brennan
MC
,
Albrecht
MA
,
Brown
JA
,
Leslie
GD
,
Ntoumanis
N
.
Self-management group education to reduce fear of hypoglycemia as a barrier to physical activity in adults living with type 1 diabetes: a pilot randomized controlled trial
.
Can J Diabetes
2021
;
45
:
619
628
81
Mannucci
E
,
Naletto
L
,
Vaccaro
G
, et al
.
Efficacy and safety of glucose-lowering agents in patients with type 2 diabetes: a network meta-analysis of randomized, active comparator-controlled trials
.
Nutr Metab Cardiovasc Dis
2021
;
31
:
1027
1034
82
Amiel
SA
,
Choudhary
P
,
Jacob
P
, et al
.
Hypoglycaemia Awareness Restoration Programme for People with Type 1 Diabetes and Problematic Hypoglycaemia Persisting Despite Optimised Self-care (HARPdoc): protocol for a group randomised controlled trial of a novel intervention addressing cognitions
.
BMJ Open
2019
;
9
:
e030356
83
Harris
SM
,
Joyce
H
,
Miller
A
,
Connor
C
,
Amiel
SA
,
Mulnier
H
.
The attitude of healthcare professionals plays an important role in the uptake of diabetes self-management education: analysis of the Barriers to Uptake of Type 1 Diabetes Education (BUD1E) study survey
.
Diabet Med
2018
;
35
:
1189
1196
84
Choudhary
P
,
Amiel
SA
.
Hypoglycaemia in type 1 diabetes: technological treatments, their limitations and the place of psychology
.
Diabetologia
2018
;
61
:
761
769
85
Hopkins
D
,
Lawrence
I
,
Mansell
P
, et al
.
Improved biomedical and psychological outcomes 1 year after structured education in flexible insulin therapy for people with type 1 diabetes: the U.K. DAFNE experience
.
Diabetes Care
2012
;
35
:
1638
1642
86
Whitmer
RA
,
Karter
AJ
,
Yaffe
K
,
Quesenberry
CP
Jr
,
Selby
JV
.
Hypoglycemic episodes and risk of dementia in older patients with type 2 diabetes mellitus
.
JAMA
2009
;
301
:
1565
1572
87
Punthakee
Z
,
Miller
ME
,
Launer
LJ
, et al.;
ACCORD Group of Investigators
;
ACCORD-MIND Investigators
.
Poor cognitive function and risk of severe hypoglycemia in type 2 diabetes: post hoc epidemiologic analysis of the ACCORD trial
.
Diabetes Care
2012
;
35
:
787
793
88
Jacobson
AM
,
Musen
G
,
Ryan
CM
, et al.;
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study Research Group
.
Long-term effect of diabetes and its treatment on cognitive function
.
N Engl J Med
2007
;
356
:
1842
1852
89
Karter
AJ
,
Moffet
HH
,
Liu
JY
,
Lipska
KJ
.
Surveillance of hypoglycemia-limitations of emergency department and hospital utilization data
.
JAMA Intern Med
2018
;
178
:
987
988
90
Lee
AK
,
Lee
CJ
,
Huang
ES
,
Sharrett
AR
,
Coresh
J
,
Selvin
E
.
Risk factors for severe hypoglycemia in black and white adults with diabetes: the Atherosclerosis Risk in Communities (ARIC) Study
.
Diabetes Care
2017
;
40
:
1661
1667
91
Karter
AJ
,
Lipska
KJ
,
O’Connor
PJ
, et al.;
SUPREME-DM Study Group
.
High rates of severe hypoglycemia among African American patients with diabetes: the surveillance, prevention, and Management of Diabetes Mellitus (SUPREME-DM) network
.
J Diabetes Complications
2017
;
31
:
869
873
92
Zoungas
S
,
Patel
A
,
Chalmers
J
, et al.;
ADVANCE Collaborative Group
.
Severe hypoglycemia and risks of vascular events and death
.
N Engl J Med
2010
;
363
:
1410
1418
93
McCoy
RG
,
Van Houten
HK
,
Ziegenfuss
JY
,
Shah
ND
,
Wermers
RA
,
Smith
SA
.
Increased mortality of patients with diabetes reporting severe hypoglycemia
.
Diabetes Care
2012
;
35
:
1897
1901
94
Cahn
A
,
Zuker
I
,
Eilenberg
R
, et al
.
Machine learning based study of longitudinal HbA1c trends and their association with all-cause mortality: analyses from a National Diabetes Registry
.
Diabetes Metab Res Rev
.
7 July 2021 [Epub ahead of print]. DOI: 10.1002/dmrr.3485
95
DuBose
SN
,
Weinstock
RS
,
Beck
RW
, et al
.
Hypoglycemia in older adults with type 1 diabetes
.
Diabetes Technol Ther
2016
;
18
:
765
771
96
Seaquist
ER
,
Anderson
J
,
Childs
B
, et al
.
Hypoglycemia and diabetes: a report of a workgroup of the American Diabetes Association and the Endocrine Society
.
Diabetes Care
2013
;
36
:
1384
1395
97
Bergenstal
RM
,
Klonoff
DC
,
Garg
SK
, et al.;
ASPIRE In-Home Study Group
.
Threshold-based insulin-pump interruption for reduction of hypoglycemia
.
N Engl J Med
2013
;
369
:
224
232
98
Hering
BJ
,
Clarke
WR
,
Bridges
ND
, et al.;
Clinical Islet Transplantation Consortium
.
Phase 3 trial of transplantation of human islets in type 1 diabetes complicated by severe hypoglycemia
.
Diabetes Care
2016
;
39
:
1230
1240
99
Harlan
DM
.
Islet transplantation for hypoglycemia unawareness/severe hypoglycemia: caveat emptor
.
Diabetes Care
2016
;
39
:
1072
1074
100
McTavish
L
,
Wiltshire
E
.
Effective treatment of hypoglycemia in children with type 1 diabetes: a randomized controlled clinical trial
.
Pediatr Diabetes
2011
;
12
:
381
387
101
McTavish
L
,
Corley
B
,
Weatherall
M
,
Wiltshire
E
,
Krebs
JD
.
Weight-based carbohydrate treatment of hypoglycaemia in people with type 1 diabetes using insulin pump therapy: a randomized crossover clinical trial
.
Diabet Med
2018
;
35
:
339
346
102
Georgakopoulos
K
,
Katsilambros
N
,
Fragaki
M
, et al
.
Recovery from insulin-induced hypoglycemia after saccharose or glucose administration
.
Clin Physiol Biochem
1990
;
8
:
267
272
103
Layman
DK
,
Clifton
P
,
Gannon
MC
,
Krauss
RM
,
Nuttall
FQ
.
Protein in optimal health: heart disease and type 2 diabetes
.
Am J Clin Nutr
2008
;
87
:
1571S
1575S
104
Stanton-Fay
SH
,
Hamilton
K
,
Chadwick
PM
, et al.;
DAFNEplus study group
.
The DAFNEplus programme for sustained type 1 diabetes self management: intervention development using the Behaviour Change Wheel
.
Diabet Med
2021
;
38
:
e14548
105
Farrell
CM
,
McCrimmon
RJ
.
Clinical approaches to treat impaired awareness of hypoglycaemia
.
Ther Adv Endocrinol Metab
2021
;
12
:
20420188211000248
106
Cox
DJ
,
Gonder-Frederick
L
,
Julian
DM
,
Clarke
W
.
Long-term follow-up evaluation of blood glucose awareness training
.
Diabetes Care
1994
;
17
:
1
5
107
Cryer
PE
.
Diverse causes of hypoglycemia-associated autonomic failure in diabetes
.
N Engl J Med
2004
;
350
:
2272
2279
108
Mitchell
BD
,
He
X
,
Sturdy
IM
,
Cagle
AP
,
Settles
JA
.
Glucagon prescription patterns in patients with either type 1 or 2 diabetes with newly prescribed insulin
.
Endocr Pract
2016
;
22
:
123
135
109
Hermanns
N
,
Heinemann
L
,
Freckmann
G
,
Waldenmaier
D
,
Ehrmann
D
.
Impact of CGM on the management of hypoglycemia problems: overview and secondary analysis of the HypoDE study
.
J Diabetes Sci Technol
2019
;
13
:
636
644
110
Heinemann
L
,
Freckmann
G
,
Ehrmann
D
, et al
.
Real-time continuous glucose monitoring in adults with type 1 diabetes and impaired hypoglycaemia awareness or severe hypoglycaemia treated with multiple daily insulin injections (HypoDE): a multicentre, randomised controlled trial
.
Lancet
2018
;
391
:
1367
1377
111
Beck
RW
,
Riddlesworth
T
,
Ruedy
K
, et al.;
DIAMOND Study Group
.
Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: the DIAMOND randomized clinical trial
.
JAMA
2017
;
317
:
371
378
112
Sequeira
PA
,
Montoya
L
,
Ruelas
V
, et al
.
Continuous glucose monitoring pilot in low-income type 1 diabetes patients
.
Diabetes Technol Ther
2013
;
15
:
855
858
113
Tumminia
A
,
Crimi
S
,
Sciacca
L
, et al
.
Efficacy of real-time continuous glucose monitoring on glycaemic control and glucose variability in type 1 diabetic patients treated with either insulin pumps or multiple insulin injection therapy: a randomized controlled crossover trial
.
Diabetes Metab Res Rev
2015
;
31
:
61
68
114
Bolinder
J
,
Antuna
R
,
Geelhoed-Duijvestijn
P
,
Kröger
J
,
Weitgasser
R
.
Novel glucose-sensing technology and hypoglycaemia in type 1 diabetes: a multicentre, non-masked, randomised controlled trial
.
Lancet
2016
;
388
:
2254
2263
115
Hermanns
N
,
Schumann
B
,
Kulzer
B
,
Haak
T
.
The impact of continuous glucose monitoring on low interstitial glucose values and low blood glucose values assessed by point-of-care blood glucose meters: results of a crossover trial
.
J Diabetes Sci Technol
2014
;
8
:
516
522
116
Reddy
M
,
Jugnee
N
,
El Laboudi
A
,
Spanudakis
E
,
Anantharaja
S
,
Oliver
N
.
A randomized controlled pilot study of continuous glucose monitoring and flash glucose monitoring in people with type 1 diabetes and impaired awareness of hypoglycaemia
.
Diabet Med
2018
;
35
:
483
490
117
Riddlesworth
T
,
Price
D
,
Cohen
N
,
Beck
RW
.
Hypoglycemic event frequency and the effect of continuous glucose monitoring in adults with type 1 diabetes using multiple daily insulin injections
.
Diabetes Ther
2017
;
8
:
947
951
118
van Beers
CAJ
,
DeVries
JH
,
Kleijer
SJ
, et al
.
Continuous glucose monitoring for patients with type 1 diabetes and impaired awareness of hypoglycaemia (IN CONTROL): a randomised, open-label, crossover trial
.
Lancet Diabetes Endocrinol
2016
;
4
:
893
902
119
Battelino
T
,
Conget
I
,
Olsen
B
, et al.;
SWITCH Study Group
.
The use and efficacy of continuous glucose monitoring in type 1 diabetes treated with insulin pump therapy: a randomised controlled trial
.
Diabetologia
2012
;
55
:
3155
3162
120
Deiss
D
,
Bolinder
J
,
Riveline
J-P
, et al
.
Improved glycemic control in poorly controlled patients with type 1 diabetes using real-time continuous glucose monitoring
.
Diabetes Care
2006
;
29
:
2730
2732
121
Tamborlane
WV
,
Beck
RW
,
Bode
BW
, et al.;
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group
.
Continuous glucose monitoring and intensive treatment of type 1 diabetes
.
N Engl J Med
2008
;
359
:
1464
1476
122
O’Connell
MA
,
Donath
S
,
O’Neal
DN
, et al
.
Glycaemic impact of patient-led use of sensor-guided pump therapy in type 1 diabetes: a randomised controlled trial
.
Diabetologia
2009
;
52
:
1250
1257
123
Beck
RW
,
Hirsch
IB
,
Laffel
L
, et al.;
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group
.
The effect of continuous glucose monitoring in well-controlled type 1 diabetes
.
Diabetes Care
2009
;
32
:
1378
1383
124
Battelino
T
,
Phillip
M
,
Bratina
N
,
Nimri
R
,
Oskarsson
P
,
Bolinder
J
.
Effect of continuous glucose monitoring on hypoglycemia in type 1 diabetes
.
Diabetes Care
2011
;
34
:
795
800
125
Ludvigsson
J
,
Hanas
R
.
Continuous subcutaneous glucose monitoring improved metabolic control in pediatric patients with type 1 diabetes: a controlled crossover study
.
Pediatrics
2003
;
111
:
933
938
126
Pratley
RE
,
Kanapka
LG
,
Rickels
MR
, et al.;
Wireless Innovation for Seniors With Diabetes Mellitus (WISDM) Study Group
.
Effect of continuous glucose monitoring on hypoglycemia in older adults with type 1 diabetes: a randomized clinical trial
.
JAMA
2020
;
323
:
2397
2406
127
Dicembrini
I
,
Mannucci
E
,
Monami
M
,
Pala
L
.
Impact of technology on glycemic control in type 2 diabetes: a meta-analysis of randomized trials on continuous glucose monitoring and continuous subcutaneous insulin infusion
.
Diabetes Obes Metab
2019
;
21
:
2619
2625
128
Beck
RW
,
Riddlesworth
TD
,
Ruedy
K
, et al.;
DIAMOND Study Group
.
Continuous glucose monitoring versus usual care in patients with type 2 diabetes receiving multiple daily insulin injections: a randomized trial
.
Ann Intern Med
2017
;
167
:
365
374
129
Ehrhardt
NM
,
Chellappa
M
,
Walker
MS
,
Fonda
SJ
,
Vigersky
RA
.
The effect of real-time continuous glucose monitoring on glycemic control in patients with type 2 diabetes mellitus
.
J Diabetes Sci Technol
2011
;
5
:
668
675
130
Haak
T
,
Hanaire
H
,
Ajjan
R
,
Hermanns
N
,
Riveline
J-P
,
Rayman
G
.
Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulin-treated type 2 diabetes: a multicenter, open-label randomized controlled trial
.
Diabetes Ther
2017
;
8
:
55
73
131
Yoo
HJ
,
An
HG
,
Park
SY
, et al
.
Use of a real time continuous glucose monitoring system as a motivational device for poorly controlled type 2 diabetes
.
Diabetes Res Clin Pract
2008
;
82
:
73
79
132
Garg
S
,
Zisser
H
,
Schwartz
S
, et al
.
Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial
.
Diabetes Care
2006
;
29
:
44
50
133
New
JP
,
Ajjan
R
,
Pfeiffer
AFH
,
Freckmann
G
.
Continuous glucose monitoring in people with diabetes: the randomized controlled Glucose Level Awareness in Diabetes Study (GLADIS)
.
Diabet Med
2015
;
32
:
609
617
134
Bergenstal
RM
,
Johnson
M
,
Passi
R
, et al
.
Automated insulin dosing guidance to optimise insulin management in patients with type 2 diabetes: a multicentre, randomised controlled trial
.
Lancet
2019
;
393
:
1138
1148
135
Kitabchi
AE
,
Umpierrez
GE
,
Miles
JM
,
Fisher
JN
.
Hyperglycemic crises in adult patients with diabetes
.
Diabetes Care
2009
;
32
:
1335
1343
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