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HbA1c and risk of severe hypoglycemia in type 2 diabetes: The Diabetes and Aging Study
... Concurrence of themes between articles resulted in the determination of key patient factors where individualised HbA 1c targets were beneficial. These factors were: > presence of established cardiovascular disease [5][6][7][8][9][10][11][12][13][14] > advancing age and diabetes duration 6,11,12,[14][15][16][17][18][19][20][21][22][23][24][25] > presence of frailty, disability, cognitive impairment or comorbidity 6,11,12,14,[26][27][28][29][30][31][32][33][34][35][36] > presence of problematic hypoglycaemia 18,20,[37][38][39][40][41][42][43][44][45] > presence of psychosocial, social or economic issues. 15,[46][47][48][49][50][51][52] Individualised HbA 1c targets and established cardiovascular disease Evidence from the UK Prospective Diabetes Study (UKPDS) in people with type 2 diabetes showed that, in their patient population, early intensive glycaemic control resulted in improved microvascular and macrovascular outcomes. ...
... 39 Risk of severe hypoglycaemia in type 2 diabetes is highest in those achieving near-normal glycaemia (HbA 1c <42 mmol/mol (<6.0%)) or with very poor glycaemic control (HbA 1c ≥75 mmol/mol (≥9.0%)). 43 Since the data presented by ACCORD and ORIGIN trials, it is recognised that glycaemic targets for patients with hypoglycaemia unawareness or preceding severe hypoglycaemia should be individualised to avoid hypoglycaemia at the expense of a relaxed HbA 1c target. Special care should be taken in the management of comorbid patients and patients with longer diabetes durations, such as the demographics of the patients in the ACCORD study. ...
... 64 While this may be useful at reducing population-level risk, population-level HbA 1c performance indicators do not adequately consider the additional costs and adverse effects of a uniform glycaemic target to a heterogeneous diabetes population. 43 Furthermore, these indicators are not consistent with widely published evidence-based guidelines encouraging individualised approaches. 54 We suggest implementation of additional quality indicators; for example, flagging low HbA 1c values in patients with frailty as a marker of over-treatment to encourage appropriate glycaemic target individualisation. ...
Background:
Evidence and guidelines increasingly support an individualised approach to care for people with type 2 diabetes and individualisation of glycaemic targets in response to patient factors.
Methods:
We undertook a scoping review of the literature for evidence of factors impacting upon glycated haemoglobin target individualisation in adults with type 2 diabetes. Data were analysed thematically with the themes inductively derived from article review.
Findings:
Evidence suggests that presence of cardiovascular disease, hypoglycaemia unawareness, severe hypoglycaemia, limited life expectancy, advanced age, long diabetes duration, frailty, cognitive impairment, disability, extensive comorbidity, diabetes distress and patient preference should inform the setting of glycaemic targets.
Conclusion:
The management of people with diabetes is complex. In clinical practice, many patients will have a variety of factors that should be considered when personalising their care. Approaches to personalised care and glycaemic treatment targets should be undertaken as part of a shared decision-making process between physician and patient. Use of electronic records might enable greater efficiency and more widespread use of personalised care plans for people with diabetes.
... In recent years, treatment guidelines have focused on patient-centred care and individualised treatments, including consideration of patients' clinical characteristics and comorbidities [4]. Evidence suggests that these clinical characteristics in heterogeneous populations of adults with type 2 diabetes (T2D) can impact patients' therapeutic response to treatment [5][6][7]. Such parameters include glycaemic control (measured by HbA1c level), age, duration of disease, and background antidiabetic medication [5][6][7][8]. ...
... Evidence suggests that these clinical characteristics in heterogeneous populations of adults with type 2 diabetes (T2D) can impact patients' therapeutic response to treatment [5][6][7]. Such parameters include glycaemic control (measured by HbA1c level), age, duration of disease, and background antidiabetic medication [5][6][7][8]. ...
... For example, increased body mass index (BMI) has been associated with poor glycaemic control and insulin resistance; weight loss in individuals with a high BMI can lead to lower insulin requirements [9]. A further consideration is the risk of hypoglycaemia, which is higher in people with poor glycaemic control (HbA1c C 9.0%) or lower HbA1c levels (\ 6.0%) [6], and in older people [10]. ...
Aims:
To investigate the efficacy and safety of fast-acting insulin aspart (faster aspart) compared with insulin aspart (IAsp) in participants with type 2 diabetes (T2D) across different subgroups.
Methods:
We report on a post hoc analysis of onset 9, a 16-week trial of participants with T2D randomised to faster aspart (n = 546) or IAsp (n = 545). Participants were grouped by baseline HbA1c (< 7.0%, ≥ 7.0%), meal test bolus insulin dose (≤ 10 units [U], > 10 U to ≤ 20 U, > 20 U), body mass index (< 30 kg/m2, ≥ 30 to < 35 kg/m2, ≥ 35 kg/m2), and age (< 65 years, ≥ 65 years). Outcomes assessed were change from baseline in HbA1c and in 1-h postprandial glucose (PPG) increment, and severe or blood glucose (BG)-confirmed hypoglycaemia.
Results:
Faster aspart provided reductions in HbA1c comparable to IAsp across all subgroups, with improved 1-h PPG control compared with IAsp in several subgroups. Faster aspart had comparable or improved rates of severe or BG-confirmed hypoglycaemia versus IAsp, particularly in participants with good glycaemic control (HbA1c < 7.0%), the elderly (≥ 65 years old), and those with insulin resistance (> 20 U meal test bolus insulin dose).
Conclusions:
Faster aspart provides effective overall glycaemic control, with improved early PPG control compared with IAsp across a range of patient characteristics.
Clinical trial registration:
NCT03268005.
... or ≥9% appeared to be at highest risk for severe hypoglycaemia. 87 Hypoglycaemia can be associated with unfavourable health outcomes including a higher risk of falls, fractures, cardiovascular events and mortality. 87 A recent cross-sectional study in patients with type 2 diabetes has shown patients on insulin and sulfonylureas, with a HbA1c <7%, had a significantly higher TAR and TBR compared with those not treated with those agents. ...
... 87 Hypoglycaemia can be associated with unfavourable health outcomes including a higher risk of falls, fractures, cardiovascular events and mortality. 87 A recent cross-sectional study in patients with type 2 diabetes has shown patients on insulin and sulfonylureas, with a HbA1c <7%, had a significantly higher TAR and TBR compared with those not treated with those agents. 86 This provides a further advantage in using TIR as an outcome measure, especially for these hypoglycaemia-inducing agents, given other CGM metrics including TBR are always to be reported along with it. ...
Glycated haemoglobin (HbA1c) is currently the gold standard outcome measure for type 2 diabetes trials. Time in range is a continuous glucose monitoring (CGM) metric defined as the proportion of time in euglycemia (3.9–10.0 mmol/L) and may be valuable not only in type 1 diabetes clinical trials but also as an endpoint in type 2 diabetes trials. This narrative review aimed to assess the relative merits of time in range versus HbA1c as outcome measures for type 2 diabetes studies. It reviews the strengths and limitations of time in range as an outcome measure and evaluates studies in type 2 diabetes that have used time in range as a primary or secondary outcome measure. A literature search was conducted on PubMed and MEDLINE databases using key terms “time in range” AND “diabetes” OR “type 2 diabetes mellitus”. Further evidence was obtained from relevant references of retrieved articles. Literature search identified 247 papers, of which 110 were included in this review. These included a broad range of articles, including 45 randomized trials using time in range as an outcome measure in patients with type 2 diabetes, as well as papers validating time in range. Time in range provides valuable and clinically relevant information and should be used as an important endpoint in type 2 diabetes in clinical trial settings, in conjunction with HbA1c.
... Increasing the HbA1c target does not eliminate the risk entirely with the risk severe hypoglycaemia having a u-shaped relationship to HbA1c, that is, risk is higher when HbA1c is very low or very high. 28,29 In addition, increasing HbA1c target range may increase the risk of both macrovascular and microvascular complications associated with hyperglycaemia such as diabetic retinopathy, nephropathy and neuropathy. 28,29 Therefore, adjusting target HbA1c range alone may be a blunt tool to prevent severe hypoglycaemia in older adults with diabetes while providing modest benefit for long-term complication risk reduction -this is where the additional glycaemic insights provided by CGM or flash glucose monitoring can be of benefit. ...
... 28,29 In addition, increasing HbA1c target range may increase the risk of both macrovascular and microvascular complications associated with hyperglycaemia such as diabetic retinopathy, nephropathy and neuropathy. 28,29 Therefore, adjusting target HbA1c range alone may be a blunt tool to prevent severe hypoglycaemia in older adults with diabetes while providing modest benefit for long-term complication risk reduction -this is where the additional glycaemic insights provided by CGM or flash glucose monitoring can be of benefit. Although there is some evidence that CGM reduces hypoglycaemia 23 and glycaemic variability in older patients living with either type 1 or type 2 diabetes, 30 there has not been extensive work examining the impact of isCGM in older adults living with diabetes. ...
Objective
Intermittently scanned continuous glucose monitoring (isCGM) has revolutionised the care of people with diabetes but its uptake and benefits in older adults are not well known. We examined the impact of isCGM (Freestyle Libre, FSL) on glycaemic outcomes in younger (⩽65 years) and older adults (>65 years) with diabetes.
Design and methods
In total, 2260 adult patients registered on the Libreview account at University Hospitals Birmingham NHS Foundation Trust, UK, were included. Inclusion criteria: all patients with type 1 and type 2 diabetes aged >18 years, use of isCGM >6 months, scanning at least 6 times/day. Demographics, diabetes history and glycaemic outcomes (time in range (TIR), time above range and time below range (TBR), estimated HbA1c, HbA1c at start and at end of study) were collected by accessing electronic patient records and Libreview. Outcomes were compared between age groups ⩽65 or >65 years old.
Results
Most patients were of Caucasian ethnicity (⩽65 years 68%, >65 years 73%) and had type 1 diabetes. Mean duration of diabetes was 19.5 years (range 0–65 years) and 34.5 years (range 0–79 years) for ⩽65 and >65 years, respectively. Only a quarter of those ⩽65 years achieved (219/943; 23.2%) their age specific TIR target compared to 69% (78/113) of those >65 years cohort, while 70.1% (663/946) of ⩽65 years and 40.7% (46/113) of >65 years achieved their age-specific TBR target. When the less strict ⩽65 years TBR target was applied, 75% (85/113) of >65 years cohort achieved this.
Conclusion
FSL use was associated with improved glycaemic outcomes across all age groups. Individualised targets may be needed to improve TBR in those aged >65 years.
... Hypoglycemia, generally categorized by the American Diabetes Association (ADA) (21) as level 1 (blood glucose $54 and ,70 mg/dL), level 2 (blood glucose ,54 mg/dL), or level 3 (severe event characterized by altered mental and/or physical status requiring assistance for treatment of hypoglycemia) may occur in up to 50% of people with type 2 diabetes and is associated with multiple safety concerns and unfavorable health outcomes (22). Hypoglycemia not only increases the risk of falls, fall-related fractures, and cardiovascular events, but also increases the risk for dementia and ultimately leads to poor healthrelated quality of life and increased mortality (23)(24)(25)(26)(27)(28). ...
... patients with type 2 diabetes added to the controversy. Although patients with an A1C ,6 or $9% were at highest risk for self-reported, severe hypoglycemia, hypoglycemia was observed at all levels of A1C and glycemic control (23). ...
The A1C metric has been the gold standard for assessing glycemia for decades. This biologic assay, based on averaging, is fraught with limitations and may be giving way to more holistic approaches. This article reviews glycemic time in range as the new standard for assessing patients with continuous glucose monitoring data. Information from the International Consensus Group on Time in Range will be summarized.
... People with well-controlled diabetes (HbA 1c ≤ 7%) more often than those with poorly-controlled diabetes (HbA 1c > 7%) are willing to prove that "a person can live a normal life with diabetes" (65.4% (70) and 41.4% (12), respectively, p = 0.019). on the other hand, patients with the level of HbA 1c > 7% more often than patients with HbA 1c ≤ 7% inform about their disease for safety reasons, in order to obtain help in the case of hypoglycaemia (65.5% (19) and 44.9% (48), respectively, p = 0.048) (Figure 2). ...
... on the other hand, unsatisfactory HbA 1c values may occur in patients with unstable diabetes, or with frequent episodes of hypoglycaemia and hyperglycaemia. lipska et al. showed that severe hypoglycaemia was common in patients with type 2 diabetes at all levels of glycemic control, and the risk was usually higher in patients with almost normal glycemia or very poor glycemic control [19]. ...
Background. Type 2 diabetes is a chronic disease that can influence the relationship between patients and their social environment. Some diabetics are afraid of discrimination because of their illness. Objectives. Understanding who of their social circle those afflicted with type 2 diabetes inform of the course of their disease. Material and methods. 136 patients with type 2 diabetes, including 71 women and 65 men (age – median: 62.5, min–max: 40–84) were subjected to a survey study which included, firstly, questions on who they inform about their affliction, secondly, the degree to which they admit to the affliction as compared with selected carbohydrate metabolism parameters of their illness (HbA1c, fasting glucose). Results. Regarding their affliction, patients with type 2 diabetes most often inform their family members of their state of being, especially those who live with them (99.1%; 111), as well as those who do not live with them (86%; 117), then other people with diabetes (80.1%; 109), friends (72.8%; 99) and neighbours (63.2%; 86). In contrast, every second employed respondent did not inform their employer. The reason for admission to being type 2 diabetic was primarily motivated by a desire to prove that they can live a normal life while diabetic (60.3%; 86). There is a negative correlation between the level of HbA1c and a willingness to reveal that the afflicted can live a normal life despite their diabetes (p < 0,05). Conclusions. Our research shows that type 2 diabetics do not always inform certain people within their social environment about their illness. This may have negative consequences. The reasons for this behavior require further research.
... Regarding predictive variables, HbA1c has consistently been identi ed as a crucial factor in hypoglycemia research. Prior studies have reported that both excessively high HbA1c levels (> 9%) and overly stringent glycemic control (HbA1c < 7%) are associated with increased hypoglycemia risk [11] . Additionally, insulin use within these HbA1c ranges further exacerbates hypoglycemia risk. ...
Objective: To identify risk factors for hypoglycemia in hospitalized patients with type 2 diabetes mellitus (T2DM) and develop predictive models for hypoglycemia severity based on machine learning algorithms. Methods: Adult non-pregnant hospitalized patients diagnosed with T2DM were retrospectively enrolled from the electronic medical record system of the Affiliated Hospital of Qingdao University. Patients were categorized into hypoglycemia groups (mild, moderate-to-severe) or a non-hypoglycemia group based on inpatient venous plasma glucose levels. After data preprocessing, univariate and multivariate analyses were conducted to identify significant predictors. Three predictive models (XGBoost, Random Forest [RF], and Logistic Regression) were subsequently constructed and validated to evaluate their predictive performances. Results: From an initial cohort of 8,947 patients, 1,798 patients were included after data screening. Among the evaluated models, the RF model demonstrated the highest predictive accuracy (93.3%) and Kappa coefficient (0.873), followed by XGBoost (accuracy: 92.6%, Kappa: 0.860). Logistic regression exhibited comparatively lower performance (accuracy: 83.8%, Kappa: 0.685). The macro-average area under the ROC curve (AUC) values for RF, XGBoost, and logistic regression were 0.960, 0.955, and 0.788, respectively, highlighting the superior discriminative capability of the RF model. While both XGBoost and RF models identified glycemic control metrics and glucose variability as core predictors for hypoglycemia, the RF model additionally emphasized medication usage, whereas XGBoost prioritized basal metabolic parameters. Conclusions: The RF model outperformed XGBoost and conventional logistic regression in predicting hypoglycemia severity among hospitalized T2DM patients. The results emphasize the importance of closely monitoring glucose levels and glucose variability during diabetes management to prevent hypoglycemia. The developed model provides a foundation for implementing preventive strategies to reduce hypoglycemia occurrence in hospitalized patients with T2DM.
... These results support current clinical advice to reduce glycemic variability 20 had the highest risk of severe self-reported hypoglycemia. 24 Although we attempted to remove the influence of severe hypoglycemic events (requiring hospital admission) from our analyses, it remains likely that those with low HbA 1c concentrations frequently entered less severe hypoglycemic ranges that did not require hospital admission. ...
INTRODUCTION
Although poor glycemic control is associated with dementia, it is unknown if variability in glycemic control, even in those with optimal glycosylated hemoglobin A1c (HbA1c) levels, increases dementia risk.
METHODS
Among 171,964 people with type 2 diabetes, we evaluated the hazard of dementia association with long‐term HbA1c variability using five operationalizations, including standard deviation (SD), adjusting for demographics and comorbidities.
RESULTS
The mean baseline age was 61 years (48% women). Greater HbA1c SD was associated with greater dementia hazard (adjusted hazard ratio = 1.15 [95% confidence interval: 1.12, 1.17]). In stratified analyses, higher HbA1c SD quintiles were associated with greater dementia hazard among those with a mean HbA1c < 6% (P = 0.0004) or 6% to 8% (P < 0.0001) but not among those with mean HbA1c ≥ 8% (P = 0.42).
DISCUSSION
Greater HbA1c variability is associated with greater dementia risk, even among those with HbA1c concentrations at ideal clinical targets. These findings add to the importance and clinical impact of recommendations to minimize glycemic variability.
Highlights
We observed a cohort of 171,964 people with type 2 diabetes (mean age 61 years).
This cohort was based in Northern California between 1996 and 2018.
We examined the association between glycosylated hemoglobin A1c (HbA1c) variability and dementia risk.
Greater HbA1c variability was associated with greater dementia hazard.
This was most evident among those with normal–low mean HbA1c concentrations.
... As illustrated by these cases, A1C measurements do not reflect daily or short-term variations in glucose levels, and glycemic excursions may persist even as A1C improves (1)(2)(3)(4)(5)(6). TIR, defined as the percentage of time glucose levels are between 70 and 180 mg/dL, is a marker of glycemic control (7). ...
... Using two or more injections of insulin per day was a predictor of increased hypoglycemia risk [14]. Finally, Pazos-Couselo et al. evaluated 63 insulin-treated patients with type 2 diabetes and showed that CGM over 1 week identified more hypoglycemia than was found on 8 weeks of monitoring with SMBG. ...
We are describing our experience of introducing Continuous Glucose Monitoring (CGM) for the first time as far as we know not in specialized endocrine clinic, but in Internal Medicine Residency Clinic in USA. The 25- patients we included in the trial were with type 2 Diabetes Mellitus (DM 2) -85% and Type 1 DM (DM1)-15%. They were treated with multiple injections of Insulin per day and were self-Monitoring their blood glucose (SMBG) 4 times a day. Their HbA1c was in the beginning of the trial was 9.5-to 14%. Before the introduction of the CGM the patients were spending 1 hour and 14 minutes a day having mild hypoglycemia- between 69-54 mg/dl-4.75% and more significant hypoglycemia- less than 54 mg/dl 29 min a day-3.01%. The CGM was started in the Clinic by the CGM team. In the CGM team were actively participating a10- Internal medicine and Transitional Year Residents under the supervision of board-Certified Endocrinologist who was a member of the clinic also. The CGM used was Dexcom G6. The goal of the project was to show that not only in specialized centers, but in General Internal Medicine Residency clinic we can not only improve the control of DM type 1 and type 2 in those 25- patients but most importantly reduce the time they had hypoglycemia. The Internal Medicine and Transitional year Residents were actively involved in the project. They were educating the patients before starting the CGM on their die and how to adjust their Insulin at home based on written instruction materials and treat their low blood sugar. The patients were called at least once a week by Internal Medicine Clinic representative of the CGM team with instructions how to adjust their Insulin, treat their hypoglycemia and to counsel them about their diet and physical activity. The patients had scheduled appointment to the clinic once a month. After the glucotoxicity from the initial high blood sugar was overcomed by using the appropriate dose of Insulin the control of the diabetes was achieved with reduction of HbA1c to 7.04% mean from 11.21% before the introduction of the CGM. With the help of the CGM the time spent by the patients with BS less than 70 mg/dl decreased from 1 hour and 14 minutes per day-4.75% to 11 minutes per day-0.78% and the time spend with blood sugar less than 54 mg/dl per day decreased from 29 minutes-3.01% to 3 minute per day-0.25%. Both values after the introduction of CGM were within the American Diabetic Association (ADA) standards- mild hypoglycemia goal of less than 4 % and more significant hypoglycemia goal of less than 1 % per day. Four of the patients were able to have excellent control of their DM 2 without any low blood glucose only on per oral antidiabetic medications and or GLP1-RAG. Our experience showed that introduction of CGM instead of SMBG in General Internal Medicine residency Clinic can help a lot of patients with DM type 1 and type 2 on multiple injections of insulin per day to reduce mild and more serious hypoglycemia and improve their blood sugar control with Insulin. Also, we showed that this can be done safely in General Internal Residency clinic and not only in specialized endocrine clinics which can be adopted in other Internal Medicine residency Clinics in USA. This also significantly improves the education and experience with the CGM devise of our Internal Medicine and Transitional Year Medical Residents.
... Obesity is a well-known risk factor for T2D and the associations of blood cell parameters and T2D were also reported in many studies [40][41][42] . HbA1c was also identified by MR-CUE in both populations and its association with hypoglycemia was reported in a previous study 43 . Some established T2D risk factors, including insulin resistance, insulin-resistance-related factors, and other obesity factors, have genetic-association summary statistics in only the European population, and thus the cross-population comparison was not presented. ...
Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on outcome using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in shared genetic etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations.
... Glycosylated hemoglobin (HbA1c) is a product of the covalent binding of glucose and hemoglobin, which is often used to reflect the glycometabolism function [18]. Studies have shown that [19] HbA1c levels are closely related to many chronic diseases, including hypertension and coronary heart disease, and can be used to predict the incidence rate of various cardiovascular and cerebrovascular diseases. Hempe et al. [5] analyzed the data from the famous American Diabetes cardiovascular disease risk study and found that the incidence of cardiovascular complications was higher in the high-HGI group compared with the low-HGI group. ...
Objective:
To investigate the relationship between glycosylated hemoglobin variant index and cardiovascular disease in patients with type 2 diabetes.
Methods:
A total of 120 patients with type 2 diabetes who were admitted to the Department of Endocrinology in Chun'an Branch of Zhejiang Provincial People's Hospital from January 2014 to January 2017 were enrolled. The clinical data, fasting blood glucose, and glycosylated hemoglobin levels of the patients were collected, and HGI was obtained by calculating the FPG level into the formula. Follow-up for three years was performed to observe the cardiovascular disease (including coronary heart disease and ischemic stroke) in patients. The occurrence of CVD was analyzed in patients with different levels of HGI. Multivariate logistics regression analysis was used to analyze the risk factors of CVD in patients with T2DM.
Results:
After three years of follow-up, 8 cases of 120 patients were lost to follow-up. In the end, 24 cases of CVD occurred in 112 patients, with an incidence rate of 21.43%. Comparing the clinical data of CVD patients and non-CVD patients, it was found that the proportion of age, FPG, HbA1c, HGI, and insulin control in the CVD group was higher than that of the non-CVD group, and the difference was statistically significant (P < 0.05). After grouping according to different HGI levels, it was found that with the increase of HGI level, the proportion of HbA1c, FPG, TC, CVD, and insulin use showed an upward trend (P < 0.05). Multivariate logistic regression analysis showed that high HGI level (OR = 4.660), older age (OR = 4.815), and higher FPG level (OR = 1.717) are independent risk factors that affect T2DM patients with cardiovascular disease (P < 0.05).
Conclusion:
High HGI is independently associated with CVD events in patients with type 2 diabetes. HGI testing is helpful for clinical assessment of personalized assessment and prediction of cardiovascular risk in patients with diabetes.
... Contrary to the conventional wisdom that hypoglycemia occurs with a much lower HbA1c, our findings showed that more than 80% of subjects had HbA1c >6.5 and >7.0%, respectively ( Table 2). The large-scale Diabetes and Aging Study that had a study population whose age was similar with our study cohort [25], showed that poorly controlled diabetes (HbA1c ≥9%) independently predicted the risk of severe hypoglycemia. The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial also reported an increased hypoglycemia risk among those with a poorer glycemic control [26]. ...
Aim: This study investigated the incidence of sulfonylurea-induced hypoglycemia and its predictors in Type 2 diabetes (T2D) patients. Patients & methods: In this prospective, observational study, T2D patients on maximal sulfonylurea-metformin therapy >1 year were enrolled. Hypoglycemia was defined as having symptoms or a blood glucose level <3.9 mmol/l. Results: Of the 401 patients, 120 (29.9%) developed sulfonylurea-induced hypoglycemia during the 12-month follow-up. The ABCC8 rs757110, KCNJ11 rs5219, CDKAL1 rs7756992 and KCNQ1 rs2237892 gene polymorphisms were not associated with sulfonylurea-induced hypoglycemia (p > 0.05). Prior history of hypoglycemia admission (odds ratio = 16.44; 95% CI: 1.74–154.33, p = 0.014) independently predicted its risk. Conclusion: Sulfonylurea-treated T2D patients who experienced severe hypoglycemia are at increased risk of future hypoglycemia episodes.
... In a study by Lipska et al., T2D patients who achieved normal glycemia (<6% HbA1c) and those with persistent elevated glucose ( ‡9% HbA1c) were at the highest risk for severe hypoglycemia. 38 Rising health care resource utilization In addition to the clinical consequences of suboptimally managed diabetes is the economic impact on health care systems. The global prevalence of diabetes is expected to increase, from 9.3% in 2019 to 10.2% by 2030 39 with associated costs estimated to be *$2.25 trillion. ...
Numerous studies have demonstrated the clinical benefits of continuous glucose monitoring (CGM) use in individuals with type 1 diabetes and type 2 diabetes (T2D) who are treated with intensive insulin therapy. A growing body of evidence suggests that CGM use may also confer similar glycemic benefits in T2D individuals who are treated with less-intensive therapies. Investigators are also exploring the potential use of CGM as an aid in weight management. This article reviews the continuing evolution of CGM, focusing on how CGM may be used to improve glycemic control and promote adoption of desired health behaviors within broader T2D and prediabetes populations.
... However, more recent evidence in type 2 diabetes, including a post hoc analysis of the ACCORD (Action to Control Cardiovascular Risk in Diabetes) trial, have reported that patients with higher A1C values have a greater risk of an acute hypoglycemic event (26). Data from our study generally indicate a U-shaped association, with an increased risk at lower and higher A1C levels ( Table 2 and Figure 1), although associations were nonsignificant in multivariate models; this pattern was also observed in a recent observational study (31). ...
This retrospective study examined changes in medication orders as a risk factor for future acute hypoglycemic events. The investigators identified factors associated with acute hypoglycemic events resulting in emergency department visits or inpatient admissions. Non-Hispanic Black race, chronic kidney disease, insulin at baseline, and nonprivate insurance were associated with higher risk of an acute hypoglycemic event, whereas age, sex, and A1C were not. After adjustment for other risk factors, changes in insulin orders after A1C measurement were associated with a 1.5 times higher risk of an acute hypoglycemia (adjusted hazard ratio 1.48, 95% CI 1.08-2.03). These results further understanding of risk factors and clinical processes relevant to predicting and preventing acute hypoglycemia.
... 53-63 mmol/mol (7.0%-7.9%) and 64-74 mmol/mol (8.0%-8.9%).51 While the current study is hypothetical in nature, the use of data from the NDR represents a key strength. ...
Aims:
Achieving glycemic control represents a key goal of type 2 diabetes (T2D) management. However, many individuals fail to achieve glycemic targets due to therapeutic inertia, and remaining in poor glycemic control can lead to increased risk of diabetes-related complications over the short and long term. The present study aimed to evaluate the economic and clinical burden associated with poor glycemic control in Sweden, in people with T2D initiating first-line glucose-lowering therapy.
Materials and methods:
Population data were obtained from Swedish national registers. Immediate glycemic control was compared with delays in achieving control of 1 and 3 years, with outcomes projected over 3, 10 and 50 years in the validated IQVIA CORE Diabetes Model. Glycemic control was defined as glycated hemoglobin targets of 52, 48 and 42 mmol/mol, as recommended in Swedish guidelines, according to age and disease duration. Costs (expressed in 2019 Swedish krona [SEK]) were accounted from a Swedish societal perspective.
Results:
Immediate glycemic control was associated with population-level cost savings of up to SEK 279 million and SEK 673 million versus delays of 1 and 3 years, respectively, as well as small population-level life expectancy benefits of up to 1,350 and 2,590 life years gained. Reduced levels of burden were due to lower incidences and delayed times to onset of diabetes-related complications.
Conclusions:
Even in people with T2D initiating first-line glucose-lowering therapy, the economic burden of poor glycemic control in Sweden is substantial, but could be reduced by early and effective treatment to achieve glycemic targets. This article is protected by copyright. All rights reserved.
... Finally, HbA1c measurements are higher for patients predicted for hyper/ hypoglycemia than other complications. It has been shown that patients with diabetes with an out-of-control glycemia are at higher risk of severe hypoglycemia 50 . We emphasize that these findings in Fig. 4c may not fully be relevant in a clinical setting, but reflect attributes of the training data. ...
Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7–77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.
... Severe hypoglycemia is a serious acute complication of diabetes that affects many more patients with T2D than we generally recognize 39-42 . Patients with elevated blood glucose are not protected from hypoglycemia, as studies seeking to identify which patients have elevated risk of hypoglycemia found that blood glucose variability was a strong predictor 43,44 . In a study of 108 patients with T2D on a mix of insulin and non-insulin agents Gehlaut et al. ...
The role of continuous glucose monitoring (CGM) in type 1 diabetes is well established in improving glycemic control and reducing hypoglycemia. Type 2 diabetes (T2D) is more prevalent than type 1 diabetes and management of type 2 diabetes is more heterogeneous, requiring treatment ranging from lifestyle modification to oral medications to intensive insulin therapy. Recent randomized controlled trials in intensively insulin treated type 2 diabetes demonstrated efficacy and safety of rtCGM in reducing glycated hemoglobin without increasing hypoglycemia. Though evidence is limited, early studies have indicated a role for rtCGM in selected patients with non-insulin requiring T2D to improve glycemic control and/or reduce hypoglycemia. Based on literature review, we summarized current data on the use of rtCGM in T2D management, and provided future research direction to generate more evidence on the utility of CGM in this population.
... First, we used a series of age-and sexadjusted and unadjusted models to examine the associations of BACE1 with glycemic indices of diabetes: fasting blood glucose (FBG), glycosylated hemoglobin (GHb), and HbA1c. 48 In both models, BACE1 levels were associated with GHb and HbA1c, but not FBG, and BACE1 cleavage activity for INSR-β and sINSR levels were associated with all three indices (Table 2). However, only an association of sINSR levels with total triglycerides was observed in the adjusted model. ...
Introduction:
Patients with type 2 diabetes mellitus (T2DM) are at a high risk of cognitive impairment, with insulin resistance playing a pivotal role. β-site amyloid precursor protein cleaving enzyme 1 (BACE1) is considered a predictor of Alzheimer's disease. However, the potential roles of BACE1 in insulin resistance and the risk of cognitive impairment in T2DM remain unclear.
Methods:
We measured plasma BACE1 levels, BACE1 cleavage activities for Swedish mutant amyloid precursor protein (APPsw) and insulin receptor β subunit (INSR-β), and soluble INSR (sINSR) levels in a clinical cohort study.
Results:
T2DM patients with or without cognitive impairment exhibited elevated plasma BACE1 levels and BACE1 enzymatic activities for APPsw and INSR-β, and sINSR levels. Moreover, the glycemic status correlated with elevated BACE1 levels and BACE1-mediated INSR cleavage, which was associated with insulin resistance.
Discussion:
The elevated BACE1 levels in T2DM may contribute to increasing the cognitive impairment risk through both amyloidogenesis and insulin resistance.
... In contrast, the potential impact of a low mean glucose level on the incidence of hypoglycemia should be considered 29 . Lipska et al. 30 showed that an increased risk of hypoglycemia was associated with near-normal HbA 1c level (HbA 1c <6%). In the present study, we found the %CV cut-offs increased with ascending HbA 1c levels ( Table 5). ...
Aims/Introduction
To define the target value for the percentage coefficient of variation for glucose (%CV) as a measure of glycemic variability (GV) in Chinese diabetes patients.
Materials and Methods
This retrospective study included 3,007 diabetes patients who underwent continuous glucose monitoring for 3 days. Type 2 diabetes was divided into groups according to the received treatment: group 1, non‐insulinotropic agent (n = 138); group 2, insulinotropic agent (n = 761); group 3, basal insulin therapy (n = 100); group 4, premixed insulin (n = 784); and group 5, intensive insulin therapy (n = 612). Type 1 diabetes patients were included as group 6 (n = 612). %CV and percentage of time per day within, below (3.9mmol/L; TBR3.9) and above (10.0 mmol/L) the target glucose range (3.9–10.0 mmol/L) were computed. TBR3.9 ≥4% was defined as excessive hypoglycemia.
Results
Type 2 diabetes with a premixed or intensive insulin regimen had an increased %CV compared with those receiving oral therapy or basal insulin. The upper limit of %CV in group 1 was 33%, which was adopted as the threshold to define excessive GV. For each treatment group, the percentage of people with TBR3.9 ≥4% was significantly greater in the subgroup with %CV >33% than ≤33% (P < 0.001). In participants who achieved TBR3.9 <4%, the time per day spent within the target glucose range of 3.9–10.0 mmol/L > 70% and time per day above 10.0 mmol/L <25%, the 95th percentile of %CV was 32.70%. Further receiver operating characteristic curve analysis showed that the cut‐off values of %CV for predicting TBR3.9 ≥4% varied by the type of diabetes and glycated hemoglobin categories.
Conclusions
A %CV of 33% was set as the threshold for excess glucose variability in Chinese diabetes patients. Meanwhile, glycated hemoglobin and the type of diabetes should be considered for the goal‐setting of %CV.
... A recent study suggested that increasing the target HbA1c level may not effectively prevent hypoglycemia in older adults. 12 In addition, a previous report has indicated that polypharmacy is an important factor in diabetes-related comorbidity; 13 thus, we should determine the individual target of HbA1c levels by evaluating cognitive function as the JDS/JGS Joint Committee recommended. ...
Assessing cognitive function and the risk of hypoglycemia among older individuals with diabetes is an ongoing challenge. Although the Japan Diabetes Society/Japan Geriatrics Society Joint Committee has already provided recommendations for glycemic control in older individuals with diabetes, its usefulness in clinical settings remains unclear.
A retrospective, single-center study was conducted on 616 outpatients aged over 65 years at Osaka Red Cross Hospital, Japan. They were assessed for glycemic control and cognitive function using the Dementia Assessment Sheet for Community-based Integrated Care System 21-items (DASC-21). Patients were categorized into three groups based on cognitive function, and each group was divided
into six subcategories based on recommended therapeutic regimens.
Ninety-eight patients treated with insulin, sulfonylurea, or glinide were identified using DASC-21 and classified into categories IIB and IIIB. The number of hypoglycemic events was divided according to the lower limit of the recommended glycohemoglobin (HbA1c) value. However, the results did not significantly differ. Notably, in 7 of 9 IIIB patients who with hypoglycemic events, their DASC-21 scores reached up to 36. This suggests that the physicians had not identified the risk of dementia before conducting the assessment using DASC-21, which might result in continuous therapy for diabetes including daily multiple insulin injections.
Physicians can overlook the risk of hypoglycemia and cognitive impairment thereby failing to optimize diabetic therapies among older individuals if DASC-21 is not used during assessments in daily diabetic care.
... Risk factors for HG in DM [22][23][24][25][26][27][28][29][30] include basal and bolus insulin 20,31 , SU drugs 3,5,32 , chronic kidney disease 18 , and certain combinations of medications for DM 6,9,33 . Since clinicians should regularly assess the risk of HG in patients with DM 12,31 , an automated, point-of-care approach to estimating risk may help clinicians to save time and identify strategies to limit risk of HG among their patients. ...
Objective. Hypoglycemia (HG) occurs in up to 60% of patients with diabetes mellitus (DM) each year. Our objective was to assess a HG alert tool in an electronic health record system and determine the tool's effect on clinical practice and outcomes.
Methods. The tool applied a previously developed logistic-regression model to provide patient-specific information about HG risk. We randomized academic outpatient primary-care providers (PCPs) to see or not see the alerts. Adult patients were assigned to study group according to the first PCP seen during four months. We assessed five months' prescriptions, diagnostic testing, and HG. Categorical variables were compared by multinomial model, binary variables by logistic model, and continuous variables by linear model.
Results. A total of 3350 patients visited 123 intervention PCPs; 3395 patients visited 220 control PCPs. Intervention PCPs were shown 18,645 alerts (mean of 152 per PCP). Patients' mean age was 55 years, with 61% female, 49% black, and 49% Medicaid recipients. Mean baseline A1c (8.7%) and body mass index (35.2 kg/m2) were similar between groups. During follow-up, the number of A1c and glucose tests and number of new, refilled, changed, or discontinued insulin prescriptions were highest for patients with highest risk. Per 100 patients on average, the intervention group had significantly fewer sulfonylurea refills (6 versus 8; p<0.05) and outpatient encounters (470 versus 502; p<0.05), though the change in encounters was not significant. Frequency of A1c testing and HG events was unchanged.
Conclusions. Informing PCPs about risk of HG led to fewer sulfonylurea refills and visits. Longer-term studies are needed to assess the potential for long-term benefits of the alert.
... Up to 95% of severe hypoglycemic events do not result in an ED or hospital encounter and are treated outside of the medical system. [14][15][16] Hypoglycemic events that require ED-level or hospital-level care are likely to be severe, burdensome to patients (by nature of requiring a higher level of care), associated with great cost, and potentially affect the most vulnerable patients (ie, those unable or unsafe to remain at home after an event). These data may help clinicians and health systems better assess the risk of hypoglycemia-related ED visits and hospitalizations and proactively modify treatment regimens to reduce that risk. ...
Importance
Severe hypoglycemia is a serious and potentially preventable complication of diabetes, with some of the most severe episodes requiring emergency department (ED) care or hospitalization. A variety of health conditions increase the risk of hypoglycemia. People with diabetes often have multiple comorbidities, and the association of such multimorbidity with hypoglycemia risk in the context of other risk factors is uncertain.
Objective
To examine the associations of age, cumulative multimorbidity, glycated hemoglobin (HbA1c) level, and use of glucose level–lowering medication with hypoglycemia-related ED visits and hospitalizations.
Design, Setting, and Participants
Cohort study of claims and laboratory data from OptumLabs Data Warehouse, an administrative claims database of commercially insured and Medicare Advantage beneficiaries in the United States. Participants were adults (aged ≥18 years) with diabetes who had an available HbA1c level result in 2015. Data from January 1, 2014, to December 31, 2016, were analyzed. Final analyses were conducted from December 2017 to September 2018.
Main Outcomes and Measures
This study calculated rates of hypoglycemia-related ED visits and hospitalizations during the year after the index HbA1c level was obtained, stratified by patient demographic characteristics, diabetes type, comorbidities (from 16 guideline-specified high-risk conditions), index HbA1c level, and glucose level–lowering medication use. The association of each variable with hypoglycemia-related ED and hospital care was examined using multivariable Poisson regression analysis overall and by diabetes type.
Results
The study cohort was composed of 201 705 adults with diabetes (mean [SD] age, 65.8 [12.1] years; 102 668 [50.9%] women; 118 804 [58.9%] white; mean [SD] index HbA1c level, 7.2% [1.5%]). Overall, there were 9.06 (95% CI, 8.64-9.47) hypoglycemia-related ED visits and hospitalizations per 1000 persons per year. The risk of hypoglycemia-related ED visits and hospitalizations was increased by age 75 years or older (incidence rate ratio [IRR], 1.56 [95% CI, 1.23-2.02] vs 18-44 years), black race/ethnicity (IRR, 1.30 [95% CI, 1.16-1.46] vs white race/ethnicity), lower annual household income (IRR, 0.63 [95% CI, 0.53-0.74] for ≥40 000), number of comorbidities (increasing from IRR of 1.66 [95% CI, 1.42-1.95] in the presence of 2 comorbidities to IRR of 4.12 [95% CI, 3.07-5.51] with ≥8 comorbidities compared with ≤1), prior hypoglycemia-related ED visit or hospitalization (IRR, 6.60 [95% CI, 5.77-7.56]), and glucose level–lowering treatment regimen (IRR, 6.73 [95% CI, 4.93-9.22] for sulfonylurea; 12.53 [95% CI, 8.90-17.64] for basal insulin; and 27.65 [95% CI, 20.32-37.63] for basal plus bolus insulin compared with other medications). Independent of these factors, having type 1 diabetes was associated with a 34% increase in the risk of hypoglycemia-related ED visits or hospitalizations (IRR, 1.34 [95% CI, 1.15-1.55]). The index HbA1c level was associated with hypoglycemia-related ED visits and hospitalizations when both low (IRR, 1.45 [95% CI, 1.12-1.87] for HbA1c level ≤5.6% vs 6.5%-6.9%) and high (IRR, 1.24 [95% CI, 1.02-1.50] for HbA1c level ≥10%).
Conclusions and Relevance
In this cohort study of adults with diabetes, the risk of an ED visit or hospitalization for hypoglycemia appeared to be highest among patients with type 1 diabetes, multiple comorbidities, prior severe hypoglycemia, and sulfonylurea and/or insulin use. At-risk patients may benefit from individualized treatment regimens to decrease their risk of hypoglycemia.
... Observational studies show an inconsistent relationship between glycaemic control and hypoglycaemia rates in patients with diabetes. [17][18][19] These inconsistencies most probably reflect inter-individual differences in hypoglycaemia propensity, 20 in combination with varying patient tolerance and awareness thresholds of hypoglycaemic events and a tendency to titrate insulin to this individual threshold of hypoglycaemia (rather than to a glycaemic target). ...
Aims:
Treat-to-target, randomized controlled trials have confirmed lower rates of hypoglycaemia at equivalent glycaemic control with insulin degludec (degludec) versus insulin glargine 100 units/mL (glargine U100) in patients with type 1 (T1D) or type 2 diabetes (T2D). Treat-to-target trials are designed to enable comparisons of safety and tolerability at a similar HbA1c level. In this post hoc analysis of the SWITCH 1 and 2 trials, we utilised a patient-level modelling approach to compare how glycaemic control might differ between basal insulins at a similar rate of hypoglycaemia.
Materials and methods:
Data for HbA1c and symptomatic hypoglycaemia from the SWITCH 1 and SWITCH 2 trials were analysed separately for patients with T1D and T2D, respectively. The association between the individual patient-level risk of hypoglycaemia and HbA1c was investigated using a Poisson regression model and used to estimate potential differences in glycaemic control with degludec versus glargine U100, at the same rate of hypoglycaemia.
Results:
Improvements in glycaemic control increased the incidence of hypoglycaemia with both basal insulins across diabetes types. Our analysis suggests that patients could achieve a mean HbA1c reduction of 0.70 [0.05; 2.20]95% CI (for T1D) or 0.96 [0.39; 1.99]95% CI (for T2D) percentage points (8 [1; 24]95% CI or 10 [4; 22]95% CI mmol/mol, respectively) further with degludec than with glargine U100 before incurring an equivalent risk of hypoglycaemia.
Conclusion:
Our findings suggest that patients in clinical practice may be able to achieve lower glycaemia targets with degludec versus glargine U100, before incurring an equivalent risk of hypoglycaemia This article is protected by copyright. All rights reserved.
... Data for medical history (Table 3) was comparable to other studies in type 2 diabetes for cardiovascular disease [17], previous myocardial infarction [9], renal complications [18], depression [19], neuropathy and peripheral vascular disease [20]. Duration of insulin therapy use and recorded medical history are typical of established type 2 diabetes. ...
Introduction:
The impact of flash glucose monitoring technology on HbA1c in type 2 diabetes managed by basal bolus insulin is uncertain. Three parallel European retrospective non-interventional chart review studies collected data reported in medical records. Each country's study aim was to determine the effectiveness of the device on HbA1c when used by their population for 3-6 months as their standard of care for management of glycaemia in a real-world setting.
Methods:
Medical records were eligible for adult patients with type 2 diabetes, on a basal bolus insulin regimen for 1 year or more, device use for 3 months or more before the start of the study, an HbA1c concentration up to 3 months prior to starting device use (patients were using blood glucose monitoring for self-management) between 64 and 108 mmol/mol (8.0-12.0%) plus an HbA1c determination 3-6 months after commencing flash glucose monitoring use.
Results:
Records were analysed from 18 medical centres in Austria (n = 92), France (n = 88) and Germany (n = 183). Baseline HbA1c results, recorded up to 90 days before the start of device use, were comparable across the three countries and were reduced significantly by 9.6 ± 8.8 mmol/mol mean ± SD (Austria [0.9 ± 0.8%], p < 0.0001), 8.9 ± 12.5 mmol/mol (France [0.8% ± 1.1], p < 0.0001) and 10.1 ± 12.2 mmol/mol (Germany [0.9% ± 1.1], p < 0.0001). No significant differences were detected between age group, sex, BMI or duration of insulin use.
Conclusions:
Three European real-world, chart review studies in people with type 2 diabetes managed using basal bolus insulin therapy each concluded that HbA1c was significantly reduced after changing to use of flash glucose monitoring for 3-6 months in a real-world setting.
... Eine aktuelle detaillierte Über- sicht über die jeweiligen Programme geben Iqbal und Hel- ler[25]. ▶Tab. 2 Risikofaktoren für Hypoglykämien bei Diabetes mellitus[7,8,11,[16][17][18][19][20][21]. ...
ZUSAMMENFASSUNG
Schwere Hypoglykämien führen häufig zu Verletzungen und sind mit einer mehr als 2-fach erhöhten kardiovaskulären Mortalität und Morbidität assoziiert. Hauptrisikofaktoren für Hypoglykämien bei Typ-1-Diabetes sind Störungen der Hypoglykämie-Wahrnehmung bzw. rekurrente Hypoglykämien, während bei Typ-2-Diabetes komplexe geriatrische Multimorbidität und kognitive Einschränkungen dominieren. Individuelle (Re-)Schulungen der Patienten bilden die Grundlage der Prävention. Therapieziele sollten unter Wertung des Patientenwunsches, der Komorbiditäten, des Hypoglykämierisikos, der Lebenserwartung u.a. individuell festgelegt werden. Bei Typ-2-Diabetes und Hypoglykämie-Vulnerabilität sind Antidiabetika ohne intrinsisches Hypoglykämierisiko (DPP-4-Hemmer, GLP-1-Analoga, SGLT-2-Hemmer) in der Zweitlinien-Therapie nach Metformin zu bevorzugen. Moderne CGM-Technologien tragen dazu bei, insbesondere gefährliche nächtliche Hypoglykämien zu reduzieren und die Hypoglykämie-Wahrnehmung zu verbessern. Auch CSII und wahrscheinlich der Einsatz moderner Basalinsulinanaloga senken das Hypoglykämierisiko bei Typ-1- bzw. Typ-2-Diabetes. Die simultane Transplantation von Pankreas und Niere, die isolierte Pankreastransplantation und Inseltransplantation führen nicht nur zur (temporären) Insulinunabhängigkeit, sondern vermeiden auch Hypoglykämien. Die primäre Therapie bei bewusstseinsgestörten Patienten mit schwerer Hypoglykämie besteht in der streng intravenösen Gabe von 50 ml 40 %iger Glukose als Bolus. Alternativ kann die Gabe von 1 mg Glukagon intramuskulär erfolgen. Ältere komorbide oder verletzte Diabetespatienten, Sulfonylharnstoff-Hypoglykämien und Hypoglykämien unter Alkoholkonsum oder Analgetika-/Sedativa-Medikation bedürfen obligat der stationären Überwachung.
... Although it is often assumed that the risk of hypoglycaemia is increased with low baseline HbA1c levels, evidence to suggest high baseline HbA1c as a risk factor for hypoglycaemia is accumulating, 204,205 and a U-shaped relationship is likely. 206 Results of our sensitivity analyses using different risk measures and effect models were exploratory in nature and not adjusted for multiplicity. Nevertheless, more significant results were observed when risk difference was used and moreover when zero event studies were excluded. ...
Objectives:
For patients with type 2 diabetes, newer antihyperglycaemic agents (AHA), including the dipeptidyl peptidase IV inhibitors (DPP4i), glucagon-like peptide-1 receptor agonists (GLP1RA) and sodium glucose co-transporter 2 inhibitors (SGLT2i) offer a lower risk of hypoglycaemia relative to sulfonylurea or insulin. However, it is not clear how AHA compare to placebo on risk of any hypoglycaemia. This study evaluates the risk of any and severe hypoglycaemia with AHA and metformin relative to placebo.
Design:
A systematic review and meta-analysis was conducted of randomized, placebo-controlled trials ≥12 weeks in duration. MEDLINE, Embase and the Cochrane Library were searched up to April 16, 2019. Studies allowing use of other diabetes medications were excluded. Mantel-Haenszel risk ratio with 95% confidence intervals were used to pool estimates based on class of AHA and number of concomitant therapies used.
Patients:
Eligible studies enrolled patients with type 2 diabetes ≥18 years of age.
Results:
144 studies met our inclusion criteria. Any hypoglycaemia was not increased with AHA when used as monotherapy (DPP4i (RR 1.12; 95% CI 0.81-1.56), GLP1RA (1.77; 0.91-3.46), SGLT2i (1.34; 0.83-2.15)), or as add-on to metformin (DPP4i (0.95; 0.67-1.35), GLP1RA (1.24; 0.80-1.91), SGLT2i (1.29; 0.91-1.83)) or as triple therapy (1.13; 0.67-1.91). However, metformin monotherapy (1.73; 1.02-2.94) and dual therapy initiation (3.56; 1.79-7.10) was associated with an increased risk of any hypoglycaemia. Severe hypoglycaemia was rare not increased for any comparisons.
Conclusions:
Metformin and the simultaneous initiation of dual therapy, but not AHA used alone or as single add-on combination therapy, was associated with an increased risk of any hypoglycaemia relative to placebo.
... Risk tended to be higher in patients with either near-normal glycaemia or very poor glycemic control. 26 It is well known that not only lower average glucose levels, but also higher glucose variability indicate a greater hypoglycemia risk. 27 Additionally, taking into consideration some authors' observations, indicating that HbA1c levels may increase with age For personal use only. ...
Purpose
We assessed changes in diabetes mellitus (DM) overtreatment prevalence in geriatric patients that had taken place after the introduction of the rule of therapy individualization in the Polish diabetes treatment guidelines.
Patients and methods
This time-trend assessment comprised two retrospective cross-sectional cohort studies of type 2 DM patients admitted to a geriatric ward in 2009–2010 (1st round) and in 2014–2015 (2nd round). A high-risk group was defined as patients on antihyperglycemic medications prior to admission, who were 80+ years old, diagnosed with dementia, end-stage renal disease, or had a history of macrovascular complications. The primary outcome measure was glycosylated A1C hemoglobin (HbA1C) ≤7.0% (53 mmol/mol).
Results
213 patients in the 1st round and 83 in the 2nd round were included. Groups did not differ in age, gender, health and functional characteristics. The percentage of dementia (36.1% versus 18.8%, P=0.002) and of the high-risk cases (79.3% versus 67.7%, P=0.05) was higher in the 2nd round of the study. During the study, tight glycemic control prevalence in the high-risk group decreased significantly from 73.1% to 58.5%, P=0.04 (odds ratio 0.68, 95% CI 0.47–0.97), and the median value of HbA1c increased significantly from 6.4%, IQR 5.7–7.3 (46 mmol/mol, IQR 39–56) to 6.7%, IQR 6.1–7.9 (50 mmol/mol, IQR 43–63), P=0.03.
Conclusion
Despite the principle of individualization of DM therapy that was in force, after a five-year observation, the problem of DM overtreatment still concerned a large percentage of geriatric patients, although a positive trend was noted in this respect.
... That acknowledged, inclusion of hypoglycemia in the present study would have had little or no impact on the estimates of economic burden as the risk of severe hypoglycemia would be unlikely to change significantly over the HbA1c levels in the present analysis. Lipska et al. 32 reported comparable risk of severe hypoglycemia in patients with HbA1c between 6.0 and 6.9% (42 and 52 mmol/mol), 7.0 and 7.9% (53 and 63 mmol/mol), 8.0 and 8.9% (64 and 74 mmol/mol) in an analysis of over 9,000 type 2 diabetes patients in the US. Hypoglycemia risk was only elevated in patients with low HbA1c levels (<6%, 42 mmol/ mol) and high HbA1c levels (!9%, 75 mmol/mol). ...
Background and aims: Effective glycemic control is the cornerstone of successful type 2 diabetes management. However, many patients fail to reach glycemic control targets and therapeutic inertia (failure to intensify therapy to address poor glycemic control in a timely manner) has been widely reported. The aim of the present study is to evaluate the economic burden associated with diabetes-related complications due to poor glycemic control for patients with type 2 diabetes in the UK.
Methods: A validated long-term model of type 2 diabetes (IQVIA CORE Diabetes Model) was used to project cost outcomes for a UK population with type 2 diabetes, based on data from The Health Improvement Network primary care database, at different levels of glycemic control. Costs associated with diabetes-related complications were accounted in 2017 Pounds Sterling (GBP). Life expectancy and complication costs were estimated for populations achieving different glycated hemoglobin (HbA1c) targets, in a number of delayed treatment intensification scenarios, and across a range of time horizons.
Results: For patients with an HbA1c level of 8.2% (66 mmol/mol), 7 years in poor control could increase mean costs associated with diabetes-related complications by over GBP 690 per patient and lead to costs of over GBP 1,500 in lost workplace productivity compared with achieving good glycemic control (HbA1c 7.0%, 53 mmol/mol) over a 10-year time horizon. Based on published estimates of the proportion of type 2 diabetes patients failing to meet glycemic targets in the UK, this corresponds to an additional economic burden of approximately GBP 2,600 million (complication costs plus lost productivity costs).
Conclusions: The economic burden of poor glycemic control in type 2 diabetes in the UK is substantial. Efforts to avoid clinical inertia could substantially reduce diabetes-related complication costs even in the short-term.
... Although previous work over decades has identified many risk factors for HG, this is the first model that combines nearly all known risk factors among US outpatients in a primary-care setting, which may afford opportunities to change long-term practice, and introduce educational or self-management strategies for patients, to lower the risk of HG. Several other studies that identified risk factors for HG have not used statistical models 49 or focused primarily on subgroups of patients with diabetes, such as patients with Medicaid 50 , hospitalization 34,38 , additional cardiovascular risk factors 51 , self-reported HG 35 , severe HG 40 , symptomatic HG 25 and use of only injectable drugs for diabetes 20 . One of our own previous studies examined the association between HG, inpatient death and inpatient length of stay among patients treated with insulin 52 . ...
Objective: Hypoglycemia occurs in 20% to 60% of patients with diabetes mellitus. Identifying at-risk patients can facilitate interventions to lower risk. We sought to develop a hypoglycemia prediction model.
Methods: In this retrospective cohort study, urban adults prescribed a diabetes drug between 2004 and 2013 were identified. Demographic and clinical data were extracted from an electronic medical record (EMR). Laboratory tests, diagnostic codes, and natural language processing (NLP) identified hypoglycemia. We compared multiple logistic regression, classification and regression trees (CART), and Random Forest. Models were evaluated on an independent test set or through cross-validation.
Results: 38,780 patients had mean age 57 years; 56% were female, 40% African-American, and 39% uninsured. Hypoglycemia occurred in 8,128 (539 identified only by NLP). In logistic regression, factors positively associated with hypoglycemia included infection, non-long-acting insulin, dementia, and recent hypoglycemia. Negatively associated factors included long-acting insulin plus sulfonylurea, and age 75 or older. Models' area under curve was similar (logistic regression, 89%; CART, 88%; Random Forest, 90%, with 10-fold cross-validation).
Conclusions: NLP improved identification of hypoglycemia. Non-long-acting insulin was an important risk factor. Decreased risk with age may reflect treatment or diminished awareness of HG. More complex models did not improve prediction.
... Významně se riziko těžké hypo glykemie zvyšuje u pacientů starších než 75 let a též při léčbě více typy antidiabetik současně [47,48,58]. Křivka rizika hypoglykemie má ve vztahu k hodnotám HbA 1c tvar písmene "U", tedy stoupá směrem k nižším hodno tám i k hodnotám vyšším [59]. Riziko hypoglykemie je tedy vyšší i mezi pacienty s vyšším HbA 1c (≥ 9 %), zřejmě díky vyšší míře nedisciplinovanosti, kterou hodnota HbA 1c pravděpodobně symbolizuje. ...
Hypoglycemia particularly severe one is important side effect of diabetes therapy with impact on patient´s quality of life and mortality. The highest risk of hypoglycemia is connected with insulin therapy followed by derivates of sulfonylurea (SU) and glinides. The risk of hypoglycemia found in observational studies were 2-3 times higher in patients treated with SU and 3-4 higher when treated with insulin compared with other types of antidiabetics. The risk of hypoglycemia is increased in patients over 75 years of age, with longer period of treatment with insulin and in those treated with several types of antidiabetics.
... glucose, patients with low HbA1c, young patients, insulin users, and in elderly patients (contrary to the abovementioned report) and patients with long history of diabetes [8][9][10]. Another study reported that episodes of hypoglycemia are independent of HbA1c level [11]. To our knowledge, there is currently little or no information on the relation between hypoglycemia and HbA1c level or the factors associated with hypoglycemia. ...
Background
Hypoglycemia is associated with cardiovascular diseases, increased risk of death. Therefore, it is important to avoid hypoglycemia. The aim of this study was to characterize hypoglycemia according to glycated hemoglobin (HbA1c) level and determine the contributing factors in type 2 diabetes mellitus (T2DM), using continuous glucose monitoring (CGM).
Methods
T2DM patients (n = 293) receiving inpatient care were divided into five groups according to HbA1c level on admission (Group 1: ≥ 6 to < 7%, Group 2: ≥ 7 to < 8%, Group 3: ≥ 8 to < 9%, Group 4: ≥ 9 to < 10%, and Group 5: ≥ 10%). The frequency of hypoglycemia and factors associated with hypoglycemia were analyzed.
Results
Hypoglycemia occurred in 15 patients (5.1%), including 4 (8%), 4 (6%), and 7 (10%) patients of Groups 1, 2, and 3, respectively, but in none of groups 4 and 5. Patients with hypoglycemia of Groups 1 had low insulin secretion and were high among insulin users, those of Groups 2 had low homeostasis model assessment of insulin resistance (HOMA-IR). Those of Group 2 and 3 had significantly lower mean blood glucose levels, those of Group 3 only had significantly lower maximum blood glucose level and percentage of AUC > 180 mg/dL. In any of the HbA1c groups, variations in blood glucose level were significantly larger in patients with hypoglycemia than without.
Conclusions
Hypoglycemia occurred in patients with a wide range of HbA1c on admission (range 6–9%), suggesting that prediction of hypoglycemia based on HbA1c alone is inappropriate. Among patients with low HbA1c, strict control sometimes induce hypoglycemia. Among patients with high HbA1c, the possibility of hypoglycemia should be considered if there is a marked discrepancy between HbA1c and randomly measured blood glucose level. Larger variations in blood glucose level induce hypoglycemia in any of the HbA1c groups. The treatment to reduce variations in blood glucose level is important to prevent hypoglycemia.
... Our results for SUs are consistent with previous observational studies that have examined the association between hypoglycemia and achieved on-therapy HbA1c (rather than HbA1c response). 22,23 In the ACCORD trial, participants with the greatest HbA1c response at 4 months had a reduced rather than increased risk of hypoglycemia, although this can be explained by the fact that, in ACCORD, the participants with least initial response were more likely to be on insulin, the therapy with by far the strongest association with hypoglycemia. 24 In this study, we found an unexpected association between greater response to TZD therapy and an increased risk of hypoglycemia, but no evidence of an association with MFN response, which would have indicated a positive association between increased drug response and increased risk of hypoglycemia was a more general characteristic of glucoselowering therapy. ...
Objective
Precision medicine drug therapy seeks to maximize efficacy and minimize harm for individual patients. This will be difficult if drug response and side effects are positively associated, meaning that patients likely to respond best are at increased risk of side effects. We applied joint longitudinal–survival models to evaluate associations between drug response (longitudinal outcome) and the risk of side effects (survival outcome) for patients initiating type 2 diabetes therapy.
Study design and setting
Participants were randomized to metformin (MFN), sulfonylurea (SU), or thiazolidinedione (TZD) therapy in the A Diabetes Outcome Progression Trial (ADOPT) drug efficacy trial (n=4,351). Joint models were parameterized for 1) current HbA1c response (change from baseline in HbA1c) and 2) cumulative HbA1c response (total HbA1c change).
Results
With MFN, greater HbA1c response did not increase the risk of gastrointestinal events (HR per 1% absolute greater current response 0.82 [95% CI 0.67, 1.01]; HR per 1% higher cumulative response 0.90 [95% CI 0.81, 1.00]). With SU, greater current response was associated with an increased risk of hypoglycemia (HR 1.41 [95% CI 1.04, 1.91]). With TZD, greater response was associated with an increased risk of edema (current HR 1.45 [95% CI 1.05, 2.01]; cumulative 1.22 [95% CI 1.07, 1.38]) but not fracture.
Conclusion
Joint modeling provides a useful framework to evaluate the association between response to a drug and the risk of developing side effects. There may be great potential for widespread application of joint modeling to evaluate the risks and benefits of both new and established medications.
... 15 The newer International Hypoglycaemia Study Group guidelines highlighted the importance of blood glucose concentrations <v3.0 mmol/L, 19 but the lower number of such events prevents analysis of data using the current model. It should be noted, however, that population analyses have shown no association between risk of severe hypoglycaemia and HbA1c, 20 while analysis of clinical trials reveals differing hypoglycaemic rate reductions with Gla-100 vs NPH insulin, dependent on adjustment for baseline, endpoint or change in HbA1c. 18 Clearly, HbA1c is not the sole determinant of hypoglycaemia risk, which is dependent upon a number of factors including hypoglycaemia definition. ...
Basal insulin therapy often involves a compromise between achieving glycaemic targets and avoiding hypoglycaemia, dependent on how intensively insulin is titrated. In the phase 3a EDITION 1, 2 and 3 studies, insulin glargine 300 U/mL (Gla‐300) provided equivalent glycaemic control to insulin glargine 100 U/mL (Gla‐100) with less hypoglycaemia in people with type 2 diabetes mellitus (T2DM). The current study evaluated the rates of confirmed (≤3.9 mmol/L [≤70 mg/dL]) or severe hypoglycaemia over 6 months of treatment with Gla‐300 or Gla‐100 in these EDITION studies, as a function of HbA1c. Analysis was performed on patient‐level data pooled from the three EDITION studies, and annualized hypoglycaemia rate as a function of HbA1c at month 6 was fitted using a negative binomial regression model. People treated with Gla‐300 experienced a consistently lower rate of confirmed (≤3.9 mmol/L [≤70 mg/dL]) or severe hypoglycaemia versus those treated with Gla‐100, regardless of HbA1c at month 6. The results suggest that treatment with Gla‐300 versus Gla‐100 could allow people with T2DM to achieve equivalent glycaemic control with less hypoglycaemia.
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... In FDS1 we found that, in those who were likely to experience recurrent hypoglycaemia, there was a positive relationship with HbA 1c [8]. This has also been observed in other studies [23,56] and may reflect unstable control which includes unpredictable swings but a tendency to higher overall blood glucose levels as an attempt at compensatory self-protection. The fact that we did not observe this relationship in FDS2 may reflect the increased availability of medications (incretin-based therapies, SGLT2 inhibitors, and especially novel basal insulin formulations) that may be particularly useful in these patients. ...
Aims
Since recent changes in therapies and management may have influenced the risk of severe hypoglycaemia complicating type 2 diabetes, we determined its incidence and predictors in community‐based patients studied between 2008 and 2013 and compared them with those in a cohort with type 2 diabetes from the same geographical area assessed a decade earlier.
Methods
We studied 1,551 participants (mean age 65.7 years, 51.9% males) with type 2 diabetes from the longitudinal observational Fremantle Diabetes Study Phase II (FDS2). Severe hypoglycaemia was ascertained as that requiring ambulance attendance, Emergency Department services, and/or hospitalisation. Cox proportional hazards modelling determined predictors of first episode, and negative binomial regression identified predictors of frequency.
Results
Sixty‐three patients (4.1%) experienced 83 episodes representing an incidence of 1.34 (95% CI: 1.08‐1.67) /100 patient‐years (versus 1.67 (1.31‐2.13) /100 patient‐years in FDS Phase 1; P=0.18). Those experiencing severe hypoglycaemia had 1‐4 episodes in both cohorts. Independent predictors of incident severe hypoglycaemia in FDS2 were older age, higher educational attainment, alcohol consumption, current smoking, sulfonylurea/insulin treatment, prior severe hypoglycaemia, renal impairment and plasma N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP). The same variables except smoking were associated with frequency of severe hypoglycaemia. Most of these risk factors paralleled those in Phase 1 but current smoking and plasma NT‐proBNP were novel.
Conclusions
The incidence and frequency of severe hypoglycaemia did not change between FDS Phases but novel risk factors including plasma NT‐proBNP were observed in FDS2.
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... Recent research has indicated that reduction of hypoglycemic risk is strongly associated with a reduction in glycemic variability (1,2). Severe hypoglycemia is not uncommon among patients with type 2 diabetes (T2D) across all levels of glycemic control and tends to be higher in patients with either near-normal glycemia or very poor glycemic control (3). ...
IN BRIEF Glucose variability is a potential independent risk factor of poor clinical outcome among people with diabetes, with adequate measurement technically difficult and cumbersome. For this study, a novel 14-day continuousensor was used to assess glucose variability among people with type 2 diabetes (T2D). The aim was to characterize glucose profiles for up to 2 weeks in T2D and to survey device utilization in a standard clinical setting and its potential to collect clinically meaningful data.
... The risk tends to be higher in patients with either near-normal glycaemia or very poor glycaemic control. 43 ...
We queried PubMed and other internet databases to identify studies, meta-analyses, review articles and other data sources regarding hypoglycaemia incidence/costs/impacts and continuous glucose monitoring (CGM) use. Our analysis of the evidence showed that hypoglycaemia remains a significant health concern and a primary obstacle to optimal adherence to prescribed diabetes treatment. In addition to its adverse clinical consequences, hypoglycaemia negatively impacts quality of life and places additional financial burdens on patients, patient families, employers and healthcare payers. Clinical trials have shown that the use of CGM can reduce the incidence and duration of hypoglycaemic episodes. This article reviews relevant CGM studies, discusses the prevalence and clinical/financial implications of hypoglycaemia, and explores the strengths and limitations of current CGM systems in minimising the burden of hypoglycaemia.
Application of continuous glucose monitoring (CGM) has moved diabetes care from a reactive to a proactive process, in which a person with diabetes can prevent episodes of hypoglycemia or hyperglycemia, rather than taking action only once low and high glucose are detected. Consequently, CGM devices are now seen as the standard of care for people with type 1 diabetes mellitus (T1DM). Evidence now supports the use of CGM in people with type 2 diabetes mellitus (T2DM) on any treatment regimen, not just for those on insulin therapy. Expanding the application of CGM to include all people with T1DM or T2DM can support effective intensification of therapies to reduce glucose exposure and lower the risk of complications and hospital admissions, which are associated with high healthcare costs. All of this can be achieved while minimizing the risk of hypoglycemia and improving quality of life for people with diabetes. Wider application of CGM can also bring considerable benefits for women with diabetes during pregnancy and their children, as well as providing support for acute care of hospital inpatients who experience the adverse effects of hyperglycemia following admission and surgical procedures, as a consequence of treatment-related insulin resistance or reduced insulin secretion. By tailoring the application of CGM for daily or intermittent use, depending on the patient profile and their needs, one can ensure the cost-effectiveness of CGM in each setting. In this article we discuss the evidence-based benefits of expanding the use of CGM technology to include all people with diabetes, along with a diverse population of people with non-diabetic glycemic dysregulation.
Recent studies have demonstrated the clinical utility of continuous glucose monitoring (CGM) use in type 2 diabetes (T2D) who are treated with intensive insulin management. Large retrospective database analyses of T2D patients treated with less-intensive therapies have also shown that CGM use was associated with significant reductions in HbA1c levels and health resource utilization, including diabetes-related hospitalizations/emergency room care. Despite the growing body of evidence supporting CGM use in the broader T2D population, current eligibility criteria required by public and many private insurers are denying millions of individuals with T2D access to this valuable technology. In this article, we discuss an evidence-based rationale for modifying current eligibility requirements for CGM coverage.
Objective
To investigate the prevalence, outcomes, and factors associated with potential glycemic overtreatment and undertreatment of type 2 diabetes mellitus (T2DM) in long-term care facilities (LTCFs).
Design
Systematic review.
Setting and Participants
Residents with T2DM and aged ≥60 years living in LTCFs.
Measures
Articles published between January 2000 and September 2020 were retrieved following a systematic search of MEDLINE, EMBASE, Cochrane Library, CINAHL plus, and gray literature. Inclusion criteria were the reporting of (1) potential overtreatment and undertreatment quantitatively defined (implicitly or explicitly) based on hemoglobin A1c (HbA1c) and/or blood glucose; (2) prevalence, outcomes, and associated factors of potential glycemic overtreatment and undertreatment; and (3) the study involved residents of LTCFs.
Results
Fifteen studies were included. Prevalence of potential overtreatment (5%–86%, n = 15 studies) and undertreatment (1.4%–35%, n = 8 studies) varied widely among facilities and geographical locations, and according to definitions used. Prevalence of potential overtreatment was 16%–74% when defined as treatment with a glucose-lowering medication in a resident with ≥1 hypoglycemia risk factor or serious comorbidity, together with a HbA1c <7% (n = 10 studies). Potential undertreatment was commonly defined as residents on glucose-lowering medication having HbA1c >8.5% and the prevalence 1.4%–14.8% (n = 6 studies). No studies prospectively measured resident health outcomes from overtreatment and undertreatment. Potential overtreatment was positively associated with use of oral glucose-lowering medications, dementia diagnosis or dementia severity, and/or need for assistance with activities of daily living (n = 2 studies). Negative association was found between potential overtreatment and use of insulin/combined insulin and oral glucose-lowering medication. No studies reported factors associated with potential undertreatment.
Conclusions and Implications
The prevalence of potential glycemic overtreatment and undertreatment varied widely among residents with T2DM depending on the definition(s) used in each study. Longitudinal studies examining associations between glycemic management and health outcomes, and the use of consensus definitions of overtreatment and undertreatment are required to establish findings about actual glycemic overtreatment and undertreatment in LTCFs.
Aims:
In people with type 2 diabetes (T2D) requiring intensification beyond glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and oral antihyperglycaemic drugs (OADs), switching to iGlarLixi was shown to be efficacious and well-tolerated in the LixiLan-G trial. This exploratory analysis of LixiLan-G assessed the efficacy and safety of switching to iGlarLixi versus continuing GLP-1 RA therapy, stratified by screening HbA1c level (≥7.0-≤7.5 %; >7.5-≤8.0 %; >8.0-≤9.0 %) and previous GLP-1 RA regimen at screening (once/twice daily or once weekly).
Materials and methods:
Endpoints for all subgroups included: change in HbA1c , achievement of HbA1c <7 % and hypoglycaemia events. Adverse events and changes in fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (PPG), 2-hour PPG excursion and weight were analysed by previous GLP-1 RA regimen.
Results:
Switching to iGlarLixi in all subgroups resulted in significantly greater reductions in HbA1c and proportions of participants reaching HbA1c <7 % (including with no documented hypoglycaemia) at Week 26 compared with continued GLP-1 RA. Switching to iGlarLixi also led to significantly greater reductions in FPG, 2-hour PPG, and 2-hour PPG excursion, irrespective of previous GLP-1 RA regimen. Rates of hypoglycaemia were low, but slightly higher in those who switched to iGlarLixi for all subgroups. Modest weight gain was seen with iGlarLixi, irrespective of previous GLP-1 RA regimen.
Conclusions:
Switching to iGlarLixi improved glycaemic control, regardless of screening HbA1c or previous GLP-1 RA type, offering a simple, efficacious and well-tolerated treatment intensification option for people with T2D inadequately controlled by GLP-1 RAs and OADs. This article is protected by copyright. All rights reserved.
Objective
To assess incidence trends of first hospitalization for hypoglycaemia in Denmark and to examine HbA1c levels and glucose‐lowering drug use before and after hospitalization among individuals with type 1 or type 2 diabetes.
Research Design and Methods
We performed a population‐based study linking diagnosis, prescription and laboratory data. Standardized incidence of first hospitalization for hypoglycaemia in Denmark was assessed for each calendar year 1997–2017. HbA1c and glucose‐lowering drug use was compared with age‐ and sex‐matched diabetes comparisons without hospitalization for hypoglycaemia.
Results
The annual age‐ and sex‐standardized incidence rate of first hospitalization for hypoglycaemia per 100,000 person‐years increased during 1997–2003 (from 17.7 to 30.3 per 100,000 person‐years), remained stable until 2010 (30.4) and gradually declined until 2017 (22.0). During this period, we identified 3,479 people with type 1 diabetes and 15,329 people with type 2 diabetes experiencing first hospitalization for hypoglycaemia. Both diabetes groups experienced a mean HbA1c decrease of ~12%–15% in the months preceding first hospitalization, followed by a gradually increasing HbA1c afterwards. People with type 1 diabetes and hospitalization used similar insulin therapies as those without hospitalization. People with type 2 diabetes and hospitalization more often received insulin (55%) than comparisons (45%), and 45% discontinued insulin or stopped all glucose‐lowering therapy after first hospitalization.
Conclusions
Incidence of hospitalizations for hypoglycaemia has declined by one fourth the last decade in the Danish population. A HbA1c decrease precedes first hospitalization for hypoglycaemia in individuals with diabetes, and profound changes in glucose‐lowering drug therapy for type 2 diabetes occur after hospitalization.
Objectives
To access the adequacy of treatment decisions in accordance with current recommendations for individualizing glycemic targets in primary and tertiary care.
Methods
This multicenter cross-sectional study was conducted with a cohort of older type 2 diabetes patients from southern Brazil. Inclusion criteria were age over 65 years, having a previous diagnosis of type 2 diabetes (according to ADA criteria) and having at least two consultations registered in the medical records within one year The primary outcome was the adequacy of treatment decisions according to pre-established HbA1c targets, which was compared with the complexity of care. The ideal HbA1c targets were: (1) 7-7.5% for an estimated life expectancy >10 years; (2) 7.5-8% for a life expectancy of 5-10 years; (3) 8-8.5% for a life expectancy <5 years. For analysis, the chi-square test was used for categorical variables and the t-test was used for continuous variables.
Results
Overall, 49.1% and 50.3% of the patients in the primary and tertiary care groups, received inadequate management. In patients whose HbA1c level was over target, the treatment was intensified in 46.3% and 51.2% of the primary and tertiary care groups, respectively (p = 0.57). In patients whose HbA1c level was under target, treatment was de-intensified in 5.9% and 26.2% in the primary and tertiary care groups, respectively (p <0.01).
Conclusion
Treatment changes based on individualized glycemic targets do occur in a minority of patients, which reflects the need for new strategies to facilitate individualized treatment targets and optimize the treatment adequacy in older adults.
After participating in this activity on chronic obstructive pulmonary disease (COPD), family physicians will be better able to: Identify symptomatic patients at increased risk of COPD to prompt early diagnostic evaluation. Individualize evidence-based therapy with the goal of reducing COPD exacerbations and improving patient outcomes. Identify the role of fixed triple-combination inhalers as part of individualized therapy.
Background
Previous meta-analysis investigating the incidence and prevalence of hypoglycaemia in both types of diabetes is limited. The purpose of this review is to conduct a systematic review and meta-analysis of the existing literature which investigates the incidence and prevalence of hypoglycaemia in individuals with diabetes.
Methods
PubMed, Embase and Cochrane library databases were searched up to October 2018. Observational studies including individuals with diabetes of all ages and reporting incidence and/or prevalence of hypoglycaemia were included. Two reviewers independently screened articles, extracted data and assessed the quality of included studies. Meta-analysis was performed using a random effects model with 95% confidence interval (CI) to estimate the pooled incidence and prevalence of hypoglycaemia in individuals with diabetes.
Results
Our search strategy generated 35,007 articles, of which 72 studies matched the inclusion criteria and were included in the meta-analysis. The prevalence of hypoglycaemia ranged from 0.074% to 73.0%, comprising a total of 2,462,810 individuals with diabetes. The incidence rate of hypoglycaemia ranged from 0.072 to 42,890 episodes per 1,000 person-years: stratified by type of diabetes, it ranged from 14.5 to 42,890 episodes per 1,000 person-years and from 0.072 to 16,360 episodes per 1,000-person years in type 1 and type 2 diabetes, respectively.
Conclusion
Hypoglycaemia is very common among individuals with diabetes. Further studies are needed to investigate hypoglycaemia-associated risk factors.
Context
Hypoglycemia in the outpatient setting has a significant financial impact on the health care system and negative impact on a person’s quality of life. Primary care physicians must address a multitude of issues in a visit with a person with Type 2 diabetes, often leaving little time to ask about hypoglycemia.
Objective
To develop quality measures that focus on outpatient hypoglycemia episodes for patients 65 and older with Type 2 diabetes that facilitates a clinician’s ability to identify opportunities to improve the quality of care and reduce hypoglycemic episodes.
Results
A technical expert panel established by the Endocrine Society in March 2019, which includes endocrinologists, primary care physicians, a diabetes educator/pharmacist, and a patient, developed three outpatient hypoglycemia quality measures. The measure set is intended to improve quality of care for patients with Type 2 diabetes who are at greatest risk for hypoglycemia. The measures were available for public comment in July 2019. A fourth measure on shared decision-making was removed from the final measure set based on public feedback.
Conclusion
A lack of outpatient hypoglycemia measures focused on older adults with Type 2 diabetes is a barrier to improving care of people with diabetes and reducing hypoglycemic episodes. This paper provides measure specifications for three measures that may be used to focus quality improvement efforts on patients at greatest risk for hypoglycemia.
Objective:
As part of a multidisciplinary team managing patients with type-2 diabetes, pharmacists need a consistent approach of identifying and prioritizing patients at highest risk of adverse outcomes. Our objective was to identify which predictors of adverse outcomes among type-2 diabetes patients were significant and common across 7 outcomes and whether these predictors improved the performance of risk prediction models. Identifying such predictors would allow pharmacists and other health care providers to prioritize their patient panels.
Research design and methods:
Our study population included 120,256 adults aged 65 years or older with type-2 diabetes from a large integrated health system. Through an observational retrospective cohort study design, we assessed which risk factors were associated with 7 adverse outcomes (hypoglycemia, hip fractures, syncope, emergency department visit or hospital admission, death, and 2 combined outcomes). We split (50:50) our study cohort into a test and training set. We used logistic regression to model outcomes in the test set and performed k-fold validation (k=5) of the combined outcome (without death) within the validation set.
Results:
The most significant predictors across the 7 outcomes were: age, number of medicines, prior history of outcome within the past 2 years, chronic kidney disease, depression, and retinopathy. Experiencing an adverse outcome within the prior 2 years was the strongest predictor of future adverse outcomes (odds ratio range: 4.15-7.42). The best performing models across all outcomes included: prior history of outcome, physiological characteristics, comorbidities and pharmacy-specific factors (c-statistic range: 0.71-0.80).
Conclusions:
Pharmacists and other health care providers can use models with prior history of adverse event, number of medicines, chronic kidney disease, depression and retinopathy to prioritize interventions for elderly patients with type-2 diabetes.
The number of elderly persons is increasing dramatically in the world, especially in East Asia, and the average age of patients with diabetes becomes older in this area. Elderly patients with diabetes are easily impaired quality of life because of frailty and developing physical disability. One of the major cause of these conditions is treatment‐related severe hypoglycemia, which becomes a trigger of incidental falls, irreversible brain damage, cognitive dysfunction, cardiovascular events, and fetal arrhythmia.
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