The risk of overall mortality in patients with Type 2 diabetes receiving different combinations of sulfonylureas and metformin: a retrospective analysis

Reflections on the sulphonylurea story: A drug class at risk of extinction or a drug class worth reviving?

The role of sulphonylureas (SUs) in modern clinical practice poses ongoing clinical debate. With the advent of newer agents in diabetes management, there is an increasing shift away from the prescribing of SUs, but not necessarily to more effective agents. This review provides a different perspective on the debate, reflecting in depth upon the physiology of SUs, drawing on insights gained from monogenic diabetes to highlight the potential benefit of lower doses of SUs, and the probable benefit of gliclazide over most other, if not all SUs, in terms of sulphonylurea failure and cardiovascular outcomes.

Feature extraction for phenotyping from semantic and knowledge resources

OBJECTIVE

Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data.

METHODS

SEmantics-Driven Feature Extraction (SEDFE) collects medical concepts from online knowledge sources as candidate features and gives them vector-form distributional semantic representations derived with neural word embedding and the Unified Medical Language System Metathesaurus. A number of features that are semantically closest and that sufficiently characterize the target phenotype are determined by a linear decomposition criterion and are selected for the final classification algorithm.

RESULTS

SEDFE was compared with the EHR-based SAFE algorithm and domain experts on feature selection for the classification of five phenotypes including coronary artery disease, rheumatoid arthritis, Crohn's disease, ulcerative colitis, and pediatric pulmonary arterial hypertension using both supervised and unsupervised approaches. Algorithms yielded by SEDFE achieved comparable accuracy to those yielded by SAFE and expert-curated features. SEDFE is also robust to the input semantic vectors.

CONCLUSION

SEDFE attains satisfying performance in unsupervised feature selection for EHR phenotyping. Both fully automated and EHR-independent, this method promises efficiency and accuracy in developing algorithms for high-throughput phenotyping.

Surrogate-assisted feature extraction for high-throughput phenotyping

Objective

Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy.

Methods

The proposed Surrogate-Assisted Feature Extraction (SAFE) method selects candidate features from a pool of comprehensive medical concepts found in publicly available knowledge sources. The target phenotype's International Classification of Diseases, Ninth Revision and natural language processing counts, acting as noisy surrogates to the gold-standard labels, are used to create silver-standard labels. Candidate features highly predictive of the silver-standard labels are selected as the final features.

Results

Algorithms were trained to identify patients with coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis using various numbers of labels to compare the performance of features selected by SAFE, a previously published automated feature extraction for phenotyping procedure, and domain experts. The out-of-sample area under the receiver operating characteristic curve and F -score from SAFE algorithms were remarkably higher than those from the other two, especially at small label sizes.

Conclusion

SAFE advances high-throughput phenotyping methods by automatically selecting a succinct set of informative features for algorithm training, which in turn reduces overfitting and the needed number of gold-standard labels. SAFE also potentially identifies important features missed by automated feature extraction for phenotyping or experts.

Sulfonylureas and the Risks of Cardiovascular Events and Death: A Methodological Meta-Regression Analysis of the Observational Studies

Recent randomized trials have compared the newer antidiabetic agents to treatments involving sulfonylureas, drugs associated with increased cardiovascular risks and mortality in some observational studies with conflicting results. We reviewed the methodology of these observational studies by searching MEDLINE from inception to December 2015 for all studies of the association between sulfonylureas and cardiovascular events or mortality. Each study was appraised with respect to the comparator, the outcome, and study design-related sources of bias. A meta-regression analysis was used to evaluate heterogeneity. A total of 19 studies were identified, of which six had no major design-related biases. Sulfonylureas were associated with an increased risk of cardiovascular events and mortality in five of these studies (relative risks 1.16-1.55). Overall, the 19 studies resulted in 36 relative risks as some studies assessed multiple outcomes or comparators. Of the 36 analyses, metformin was the comparator in 27 (75%) and death was the outcome in 24 (67%). The relative risk was higher by 13% when the comparator was metformin, by 20% when death was the outcome, and by 7% when the studies had design-related biases. The lowest predicted relative risk was for studies with no major bias, comparator other than metformin, and cardiovascular outcome (1.06 [95% CI 0.92-1.23]), whereas the highest was for studies with bias, metformin comparator, and mortality outcome (1.53 [95% CI 1.43-1.65]). In summary, sulfonylureas were associated with an increased risk of cardiovascular events and mortality in the majority of studies with no major design-related biases. Among studies with important biases, the association varied significantly with respect to the comparator, the outcome, and the type of bias. With the introduction of new antidiabetic drugs, the use of appropriate design and analytical tools will provide their more accurate cardiovascular safety assessment in the real-world setting.

Simultaneous determination of glipizide and its four hydroxylated metabolites in human urine using LC-MS/MS and its application in urinary phenotype study

Cytochrome P450 (CYP) 2C9 and CYP2C19 genetic mutant could influence the plasma concentration of glipizide in human subjects, which refers to glipizide safety and adverse effects in clinic practice. A further study to investigate the relationship of the concentrations between glipizide and its metabolites in human with different CYP mutants was valuable. We firstly develop a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous quantification of glipizide and its hydroxylated metabolites in human urine. After simple protein precipitation with methanol including 4'-OH-tolbutamide and gliclazide (both are internal standards), the analytes were chromatographed on a reversed-phased column with a mobile phase of 0.1% formic acid in acetonitrile and 0.1% formic acid in water by a gradient elution. The ion transitions of the precursor to the product ion were principally protonated ions [M+H]⁺ at m/z 446.4→m/z 321.1 for glipizide, m/z 462.2→m/z 321.1 for the four hydroxylated forms of glipizide, m/z 287.2→m/z 188.0 for 4'-OH-tolbutamide, and m/z 324.1→m/z 127.1 for gliclazide. The method was linear over a concentration range of 0.02-20.0ng/mL. The intraday and inter-day variances were less than 9.9%, and accuracy was within ±6.8%. The method was successfully applied to the urinary phenotyping study in volunteers after a single oral administration of 5-mg glipizide tablet, and two new hydroxycyclohexyl metabolites of glipizide (OH-gp), 4-cis-OH-gp and 3-trans-OH-gp, were found in this study.

Is gliclazide a sulfonylurea with difference? A review in 2016

Sulfonylureas (SUs) remain the most commonly prescribed drug after metformin in the treatment of type 2 diabetes (T2DM), despite the availability of several newer agents. The primary reason of SUs being most popular is their quick glycemic response, time-tested experience and least cost. Although SUs are one amongst the several other second line agents after metformin in all major guidelines, the new Dutch type 2 guidelines specifically advise gliclazide as the preferred second line drug instead of SUs as a class. The World Health Organization (WHO) has also included gliclazide in their Model List of Essential Medicines 2013 motivated by its safety data in elderly patients. Specifically advising gliclazide may have been based on emerging evidence suggesting cardiovascular neutrality of gliclazide over other SUs. This prompted us to do a literature review of gliclazide efficacy and safety data compared to other SUs as well as oral anti-diabetic drugs.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

OBJECTIVE

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy.

MATERIALS AND METHODS

Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype.

RESULTS

The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features.

DISCUSSION

Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable.

CONCLUSION

The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.

Dipeptidyl peptidase-4 inhibitors can minimize the hypoglycaemic burden and enhance safety in elderly people with diabetes

The prevalence of type 2 diabetes mellitus (T2DM) among elderly people is increasing. Often associated with disabilities/comorbidities, T2DM lowers the chances of successful aging and is independently associated with frailty and an increased risk of hypoglycaemia, which can be further exacerbated by antihyperglycaemic treatment. From this perspective, the clinical management of T2DM in the elderly is challenging and requires individualization of optimum glycaemic targets depending on comorbidities, cognitive functioning and ability to recognize and self-manage the disease. The lack of solid evidence-based medicine supporting treatment guidelines for older people with diabetes further complicates the matter. Several classes of medicine for the treatment of T2DM are currently available and different drug combinations are often required to achieve individualized glycaemic goals. Many of these drugs, however, carry disadvantages such as the propensity to cause weight gain or hypoglycaemia. Dipeptidyl peptidase-4 (DPP-4) inhibitors, a recent addition to the pharmacological armamentarium, have become widely accepted in clinical practice because of their efficacy, low risk of hypoglycaemia, neutral effect on body weight, and apparently greater safety in patients with kidney failure. Although more information is needed to reach definitive conclusions, growing evidence suggests that DPP-4 inhibitors may become a valuable component in the pharmacological management of elderly people with T2DM. The present review aims to delineate the potential advantages of this pharmacological approach in the treatment of elderly people with T2DM.

Mortality risk among sulfonylureas: a systematic review and network meta-analysis

BACKGROUND

Sulfonylureas are common second-line options for management of type 2 diabetes; however, they are associated with a higher risk of cardiovascular events compared with other antidiabetic drugs. Since tissue selectivity and risk of hypoglycaemia differ among sulfonylureas, we aimed to assess whether mortality and the risk of cardiovascular events also varies.

METHODS

We searched Medline and Embase from inception to June 11, 2014, to identify controlled studies reporting the risk of all-cause mortality, cardiovascular-related mortality, or myocardial infarction for at least two sulfonylureas. We examined differences in cardiovascular event risk among sulfonylureas with random effects models for direct pairwise comparisons and network meta-analyses to incorporate direct and indirect data.

FINDINGS

14 970 (9%) of 167 327 patients in 18 studies died: 841 (4%) of 19 334 gliclazide users, 5482 (11%) of 49 389 glimepiride users, 2106 (15%) of 14 464 glipizide users, 5296 (7%) of 77 169 glibenclamide users, 1066 (17%) of 6187 tolbutamide users, and 179 (23%) of 784 chlorpropamide users. Inconsistency was low for the network meta-analysis of all-cause mortality, and the relative risk of death compared with glibenclamide was 0·65 (95% credible interval 0·53-0·79) for gliclazide, 0·83 (0·68-1·00) for glimepiride, 0·98 (0·80-1·19) for glipizide, 1·13 (0·90-1·42) for tolbutamide, and 1·34 (0·98-1·86) for chlorpropamide. Similar associations were noted for cardiovascular-related mortality: the relative risk compared with glibenclamide was 0·60 (95% credible interval 0·45-0·84) for gliclazide, 0·79 (0·57-1·11) for glimepiride, 1·01 (0·72-1·43) for glipizide, 1·11 (0·79-1·55) for tolbutamide, and 1·45 (0·88-2·44) for chlorpropamide.

INTERPRETATION

Gliclazide and glimepiride were associated with a lower risk of all-cause and cardiovascular-related mortality compared with glibenclamide. Clinicians should consider possible differences in risk of mortality when selecting a sulfonylurea.

FUNDING

None.

Metformin in combination with various insulin secretagogues in type 2 diabetes and associated risk of cardiovascular morbidity and mortality--a retrospective nationwide study

AIMS

Metformin is the first-line treatment for most patients with type 2 diabetes but many patients need additional treatment with insulin secretagogues (IS) to achieve glycemic control. We aimed to compare mortality and cardiovascular risk among users of metformin in combination with pharmacologically different ISs.

METHODS

Using nationwide administrative Danish registries, we followed all individuals without prior stroke or myocardial infarction who initiated metformin and an IS from 1997 through 2009. Rate ratios (RR) of all-cause mortality, cardiovascular death, and a composite of myocardial infarction, stroke, or cardiovascular death were compared between user groups using time-dependent multivariable Poisson regression models. The most common combination, glimepiride+metformin, was used as reference.

RESULTS

A total of 56,827 patients were included, 56% male, the mean age was 61 ± 12.5 years, and median duration of prior monotherapy was 2.2 (inter quartile range 0.5-4.5) years. Crude incidence rates of mortality for combinations of ISs with metformin were; 15.4 (repaglinide), 28.1 (glipizide), 23.7 (glibenclamide), 21.1 (gliclazide), 20.7 (glimepiride), 27.7 (tolbutamide) deaths per 1000 person years. In adjusted analysis, the associated mortality risk was similar for users of gliclazide+metformin (RR=1.01 [0.88-1.15]), repaglinide+metformin (RR=0.81 [0.62-1.05]), glibenclamide+metformin (RR=0.98 [0.87-1.10]), and tolbutamide+metformin (RR=1.04 [0.85-1.28]). Users of glipizide+metformin was associated with increased all-cause mortality (RR=1.16 [1.02-1.32], p=0.02), cardiovascular death (RR=1.21 [1.01-1.46], p=0.04), and the combined endpoint (RR=1.20 [1.06-1.36, p=0.005).

CONCLUSION

Most ISs in combination with metformin were associated with similar mortality and cardiovascular risk. Whether glipizide is associated with increased risk compared with other ISs when used in combinations with metformin warrants further study.

Combination therapy with metformin plus sulphonylureas versus metformin plus DPP-4 inhibitors: association with major adverse cardiovascular events and all-cause mortality

AIMS

To compare the risk of major adverse cardiovascular events (MACE) and mortality for combination therapies with metformin and either sulphonylurea (SU) or dipeptidyl peptidase-4 inhibitor (DPP-4i).

METHODS

Data were from the UK Clinical Practice Research Datalink (CPRD). Patients with type 2 diabetes were selected if initiated with combination therapies comprising metformin plus SU or DPP-4i 2007-2012. The co-primary endpoints were all-cause mortality and MACE (myocardial infarction or stroke). Times to endpoints were compared using Cox proportional hazards models. Additional analyses were performed on subsets matched directly on key characteristics and by propensity score.

RESULTS

A total of 33 983 patients were prescribed SU and 7864 DPP-4i, and 5447 patients in each cohort could be matched directly and 6901 by propensity score. In the main analysis, there were 716 MACE events and 1217 deaths. Crude event rates for MACE were 11.3 events per 1000 person-years (pkpy) for SU, versus 5.3 pkpy for DPP-4i. For all-cause mortality, rates were 16.9 versus 7.3 pkpy, respectively. Following adjustment, there was a significant increase in the adjusted hazard ratio (aHR) for all-cause mortality in those exposed to SU across all analytical models: aHR = 1.357 (95% CI 1.076-1.710) for all subjects, 1.850 (1.245-2.749) directly matched and 1.497 (1.092-2.052) propensity-matched. For MACE, aHR was 1.710 (1.280-2.285) for all subjects, 1.323 (0.832-2.105) directly matched and 1.547 (1.076-2.225) propensity-matched.

CONCLUSIONS

There was a reduction in all-cause mortality for patients treated with metformin combined with DPP-4i versus metformin plus SU, and a similar trend for MACE.

Strategies for handling missing data in electronic health record derived data

Electronic health records (EHRs) present a wealth of data that are vital for improving patient-centered outcomes, although the data can present significant statistical challenges. In particular, EHR data contains substantial missing information that if left unaddressed could reduce the validity of conclusions drawn. Properly addressing the missing data issue in EHR data is complicated by the fact that it is sometimes difficult to differentiate between missing data and a negative value. For example, a patient without a documented history of heart failure may truly not have disease or the clinician may have simply not documented the condition. Approaches for reducing missing data in EHR systems come from multiple angles, including: increasing structured data documentation, reducing data input errors, and utilization of text parsing / natural language processing. This paper focuses on the analytical approaches for handling missing data, primarily multiple imputation. The broad range of variables available in typical EHR systems provide a wealth of information for mitigating potential biases caused by missing data. The probability of missing data may be linked to disease severity and healthcare utilization since unhealthier patients are more likely to have comorbidities and each interaction with the health care system provides an opportunity for documentation. Therefore, any imputation routine should include predictor variables that assess overall health status (e.g. Charlson Comorbidity Index) and healthcare utilization (e.g. number of encounters) even when these comorbidities and patient encounters are unrelated to the disease of interest. Linking the EHR data with other sources of information (e.g. National Death Index and census data) can also provide less biased variables for imputation. Additional methodological research with EHR data and improved epidemiological training of clinical investigators is warranted.

Saxagliptin efficacy and safety in patients with type 2 diabetes mellitus and cardiovascular disease history or cardiovascular risk factors: results of a pooled analysis of phase 3 clinical trials

OBJECTIVE

This post hoc analysis sought to assess the efficacy, safety, and tolerability of saxagliptin in patients with type 2 diabetes mellitus and cardiovascular (CV) risk factors or disease (CVD).

METHODS

Data from 5 randomized controlled trials were pooled to compare saxagliptin 5 mg with placebo: 2 studies of saxagliptin as monotherapy in drug-naïve patients and 1 each of saxagliptin as add-on therapy to metformin, glyburide, or a thiazolidinedione. Analysis was performed according to the following baseline/trial entry criteria: 1) history/no history of CVD; 2) ≥ 2 versus 0 to 1 CV risk factors; 3) statin use versus no statin use; and 4) hypertension versus no hypertension. Change from baseline glycated hemoglobin (HbA1c), fasting plasma glucose, and postprandial glucose levels; and the proportion of patients achieving an HbA1c level < 7% were analyzed (week 24). Safety was assessed by adverse events, hypoglycemia, and body weight.

RESULTS

In total, 882 patients received saxagliptin 5 mg and 799 received placebo. Differences in adjusted mean change from baseline HbA1c (95% CI) were greater with saxagliptin compared with placebo in patients with a history of CVD (-0.64% [-0.90 to -0.38]) and no history of CVD (-0.68% [-0.78 to -0.58]); with ≥ 2 CV risk factors (-0.73% [-0.85 to -0.60]) and 0 to 1 CV risk factor (-0.62% [-0.75 to -0.48]); with statin use (-0.70% [-0.89 to -0.52]) and no statin use (-0.66% [-0.77 to -0.56]); and with hypertension (-0.69% [-0.82 to -0.57]) and no hypertension (-0.66% [-0.80 to -0.52]). Saxagliptin was well tolerated, with similar adverse event rates and types compared with placebo. There was a < 1% rate of confirmed hypoglycemia in all groups except in patients with CV history who received placebo (2.1%).

CONCLUSION

Saxagliptin improved glycemic measures, resulted in low rates of confirmed hypoglycemia, and was well tolerated in patients with or without CVD and CV risk factors.

Reduced capacity of heart rate regulation in response to mild hypoglycemia induced by glibenclamide and physical exercise in type 2 diabetes

OBJECTIVE

Decreased heart rate variability (HRV) is associated with enhanced mortality due to abnormal cardiac rhythm. While hypoglycemic events are increasingly common in the treatment of type 2 diabetes, HRV is part of the counter-regulation against low blood glucose levels. We hypothesized that HRV was impaired in mild hypoglycemia in diabetic individuals.

MATERIALS/METHODS

Hyperinsulinemic-hypoglycemic clamps were performed in twelve type 2 diabetic patients without cardiovascular disease and in non-diabetic subjects matched for age, sex, and weight. In an additional study, hypoglycemic events, induced by either a single morning dose of glibenclamide or physical exercise, were recorded for the subsequent 24h. Blood glucose concentrations and electrocardiograms were continuously monitored. Serum hormone levels, hypoglycemic symptoms, and forearm blood flow were measured at defined time points.

RESULTS

Occurrence of a symptomatic hypoglycemic episode (mean blood glucose 3.1±0.4 mmol/l) attenuated most of the time and frequency domain measurements in both healthy and diabetic individuals. The magnitude of reduction of HRV parameters was significantly lower in diabetic compared to healthy subjects. Glibenclamide taken in the morning enhanced the daily number of mild hypoglycemic events compared with placebo or moderate exercise. Concordantly, 24-h mean HRV measurements were decreased.

CONCLUSION

HRV response to hypoglycemia is impaired in type 2 diabetic subjects resulting in a higher than expected risk for sudden arrhythmia following mild hypoglycemic episodes.

Treatment persistence, hypoglycaemia and clinical outcomes in type 2 diabetes patients with dipeptidyl peptidase-4 inhibitors and sulphonylureas: a primary care database analysis

AIMS

To investigate therapy persistence, frequency of hypoglycaemia and macrovascular outcomes among type 2 diabetes patients with dipeptidyl peptidase-4 (DPP-4) inhibitors (DPP-4) and sulphonylureas (SU).

METHODS

Data from 19,184 DPP-4 (mean age: 64 years; 56% males) and 31,110 SU users (69 years; 51%) with new prescriptions (index date), without additional antidiabetics except metformin, in 1201 general practises in Germany were analysed. Therapy discontinuation (prescription gap >90 days), hypoglycaemia [International Classification of Diseases (ICD-10)] and macrovascular outcomes (ICD-10) (2-year follow-up) were compared adjusting for age, sex, diabetes duration, metformin, previous hypoglycaemia, health insurance, hypertension, hyperlipidaemia, antihypertensives, lipid-lowering and antithrombotic drugs, microvascular complications and Charlson co-morbidity score using logistic or Cox regression models.

RESULTS

Two years after index date, DDP-4 (non-persistence: 39%) were associated with a lower risk of discontinuation compared to SU (49%) [adjusted hazard ratio (HR): 0.74; 95% confidence interval (CI): 0.71-0.76]. Hypoglycaemias (≥1) were documented in 0.18% patients with DPP-4 and in 1.00% with SU [odds ratio (OR): 0.21; 95%CI: 0.08-0.57]. Hypoglycaemias were significantly associated with incident macrovascular complications (HR: 1.6; 95% CI: 1.1-2.2). Risk of macrovascular events was 26% lower in DPP-4 than in SU users.

CONCLUSIONS

Lack of persistence with antidiabetic therapy is frequently found in primary care patients. DPP-4 was associated with lower therapy discontinuation and a fivefold reduced frequency of patients with hypoglycaemia compared to SU. The low absolute numbers of hypoglycaemias are most likely due to the fact that only severe events were documented. DPP-4 treatment was associated with reduced incidence of macrovascular events relative to SU in type 2 diabetes patients in primary care practises.