Clinical and Biomarker Predictors of Expanded Heart Failure Outcomes in Patients With Type 2 Diabetes Mellitus After a Recent Acute Coronary Syndrome: Insights From the EXAMINE Trial

Utilizing synchronous care to improve cardiovascular and renal health among patients with type 2 diabetes: Proof-of-concept results from the DECIDE-CV clinical programme

AIM

The management of patients with type 2 diabetes is asynchronous, i.e. not coordinated in time, resulting in delayed access to care and low use of guideline-directed medical therapy (GDMT).

METHODS

We retrospectively analysed consecutive patients assessed in the 'synchronized' DECIDE-CV clinic. In this outpatient clinic, patients with type 2 diabetes and cardiovascular or chronic kidney disease are simultaneously assessed by an endocrinologist, cardiologist and nephrologist in the same visit. The primary outcome was use of GDMT before and after the assessment in the clinic, including sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, renin-angiotensin system blockers and mineralocorticoid receptor antagonists. Secondary outcomes included the baseline-to-last-visit change in surrogate laboratory biomarkers.

RESULTS

The first 232 patients evaluated in the clinic were included. The mean age was 67 ± 12 years, 69% were men and 92% had diabetes. In total, 73% of patients had atherosclerotic cardiovascular disease, 65% heart failure, 56% chronic kidney disease and 59% had a urinary albumin-to-creatinine ratio ≥30 mg/g. There was a significant increase in the use of GDMT:sodium-glucose cotransporter 2 inhibitors (from 44% to 87% of patients), glucagon-like peptide 1 receptor agonists (from 8% to 45%), renin-angiotensin system blockers (from 77% to 91%) and mineralocorticoid receptor antagonists (from 25% to 45%) (p < .01 for all). Among patients with paired laboratory data, glycated haemoglobin, urinary albumin-to-creatinine ratio and N-terminal proB-type natriuretic peptide levels significantly dropped from baseline (p < .05 for all).

CONCLUSIONS

Joint assessment of patients with diabetes in a synchronized cardiometabolic clinic holds promise for enhancing GDMT use and has led to significant reductions in surrogate cardiovascular and renal laboratory biomarkers.

Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome

Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.

Impact of age on the predictive value of NT-proBNP in patients with diabetes mellitus stabilised after an acute coronary syndrome

AIMS

To assess the impact of age on the prognostic value of NT-proBNP concentration in patients with type-2 diabetes mellitus (T2DM) stabilised after an Acute Coronary Syndrome (ACS).

METHODS

The AleCardio study compared aleglitazar with placebo in 7226 patients with T2DM and recent ACS. Patients with heart failure were excluded. Median follow-up was 104 weeks. Baseline NT-proBNP plasma concentration was measured centrally. Multivariable Cox regression was used to determine the mortality predictive information provided by NT-proBNP across age groups.

RESULTS

Median age was 61y (IQR 54, 67). NT-proBNP concentration increased by quartile (Q) of age (median 264, 318, 391, and 588 pg/ml). Compared to Q1, patients in Q4 of NT-proBNP had higher (p < 0.001) adjusted HR for all-cause (aHR 6.9; 95 % CI 4.0-12) and cardiovascular (11; 5.4-23) death. Within each age Q, baseline NT-proBNP in patients who died was 3 times higher than in survivors (all p < 0.001). When age and NT-proBNP levels were modeled as continuous variables, their interaction term was nonsignificant. The relative prognostic information provided by NT-proBNP (percent of total X2) increased from 38 % in age Q1 to 75 % in age Q4 for mortality, and from 50 % to 88 % for CV death.

CONCLUSIONS

Among patients with T2DM stabilised after an ACS, NT-proBNP level predicts death irrespective of age.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Prognostic value of high-sensitivity cardiac troponin for major adverse cardiovascular events in patients with diabetes: a systematic review and meta-analysis

Background

High-sensitivity cardiac troponin (hs-cTn) is associated with cardiovascular outcomes in the general population, but the prognostic value of hs-cTn in the diabetic population remains inconclusive. This study aimed to systematically review current evidence regarding the association between hs-cTn and the prognosis of diabetic patients.

Methods

MEDLINE, Embase, and the Cochrane Database were searched from inception to May, 2023. Observational studies that investigated the prognostic value of hs-cTn in diabetic patients were included in this meta-analysis. Studies were excluded if they did not report outcomes of interest, or urine hs-cTn were measured. Two independent investigators extracted and analyzed the data according to the PRISMA guidelines. The primary outcome was long-term major adverse cardiovascular events (MACE).

Results

We included 30 cohort studies of 62,419 diabetic patients. After a median follow-up of 5 (4.1-9.5) years, the pooled results suggested elevation of hs-cTn was associated with a significantly increased risk of MACE (adjusted hazard ratio (HR) per standard deviation (SD) change 1.15, 95% CI [1.06-1.25], I2 = 0%) and heart failure (adjusted HR per SD change 1.33, 95% CI [1.08-1.63], I2 = 0%) in patients with diabetes. No significant association was found regarding the association between elevation of hs-cTn and risk of all-cause mortality (adjusted HR per SD change 1.24, 95% CI [0.98-1.57], I2 = 0%). The results of sensitivity analyses were similar in prospective cohort studies, high-quality studies, or population without major cardiovascular comorbidities at baseline. hs-cTn may represent a strong and independent predictor of MACE and heart failure in diabetic patients. Future research is warranted to determine the appropriate cutoff value for hs-cTn with different comorbidities, for instance, diabetic nephropathy, peripheral artery diseases, etc.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Predicting and preventing heart failure in type 2 diabetes

The burden of heart failure among people with type 2 diabetes is increasing globally. People with comorbid type 2 diabetes and heart failure often have worse outcomes than those with only one of these conditions-eg, higher hospitalisation and mortality rates. Therefore, it is essential to implement optimal heart failure prevention strategies for people with type 2 diabetes. A detailed understanding of the pathophysiology underlying the occurrence of heart failure in type 2 diabetes can aid clinicians in identifying relevant risk factors and lead to early interventions that can help prevent heart failure. In this Review, we discuss the pathophysiology and risk factors of heart failure in type 2 diabetes. We also review the risk assessment tools for predicting heart failure incidence in people with type 2 diabetes as well as the data from clinical trials that have assessed the efficacy of lifestyle and pharmacological interventions. Finally, we discuss the potential challenges in implementing new management approaches and offer pragmatic recommendations to help overcome these challenges.

Biomarker Changes Associated With Both Dulaglutide and Cardiovascular Events in the REWIND Randomized Controlled Trial: A Nested Case-Control Post Hoc Analysis

OBJECTIVE

The glucagon-like peptide-1 receptor agonist dulaglutide reduced MACE in the Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) trial. This article expores the relationship of selected biomarkers to both dulaglutide and major adverse cardiovascular events (MACE).

RESEARCH DESIGN AND METHODS

In this post hoc analysis, stored fasting baseline and 2-year plasma samples from 824 REWIND participants with MACE during follow-up and 845 matched non-MACE participants were analyzed for 2-year changes in 19 protein biomarkers. Two-year changes in 135 metabolites were also analyzed in 600 participants with MACE during follow-up and in 601 matched non-MACE participants. Linear and logistic regression models were used to identify proteins that were associated with both dulaglutide treatment and MACE. Similar models were used to identify metabolites that were associated with both dulaglutide treatment and MACE.

RESULTS

Compared with placebo, dulaglutide was associated with a greater reduction or lesser 2-year rise from baseline in N-terminal prohormone of brain natriuretic peptide (NT-proBNP), growth differentiation factor 15 (GDF-15), high-sensitivity C-reactive protein, and a greater 2-year rise in C-peptide. Compared with placebo, dulaglutide was also associated with a greater fall from baseline in 2-hydroxybutyric acid and a greater rise in threonine (P < 0.001). Increases from baseline in two of the proteins (but neither metabolite) were associated with MACE, including NT-proBNP (OR 1.267; 95% CI 1.119, 1.435; P < 0.001) and GDF-15 (OR 1.937; 95% CI 1.424, 2.634; P < 0.001).

CONCLUSIONS

Dulaglutide was associated with a reduced 2-year rise from baseline of NT-proBNP and GDF-15. Higher rises of these biomarkers were also associated with MACE.

Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach

Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions.

Emerging Biomarkers for Predicting Clinical Outcomes in Patients with Heart Disease

Cardiovascular disease is most frequently caused by the development and progression of atherosclerosis. When coronary arteries are afflicted, and the stenoses caused by atherosclerotic plaques are severe enough, the metabolic supply-and-offer balance is disturbed, leading to myocardial ischemia. If atherosclerotic plaques become unstable and local thrombosis develops, a myocardial infarction occurs. Sometimes, myocardial ischemia and infarction may result in significant and irreversible heart failure. To prevent severe complications, such as acute coronary syndromes and ischemia-related heart failure, extensive efforts have been made for developing biomarkers that would help identify patients at increased risk for cardiovascular events. In this two-part study, we attempted to provide a review of existing knowledge of blood biomarkers that may be used in this setting. The first part of this work was dedicated to conventional biomarkers, which are already used in clinical practice. In the second part, here presented, we discuss emerging biomarkers which have not yet become mainstream.

External validation and extension of the TIMI risk score for heart failure in diabetes for patients with recent acute coronary syndrome: An analysis of the EXAMINE trial

AIMS

The Thrombolysis in Myocardial Infarction Risk Score for Heart Failure (HF) in Diabetes (TRS-HF_DM ) prognosticates HF hospitalization in people with type 2 diabetes (T2D). This study aimed to externally validate and extend its use for those with recent acute coronary syndrome (ACS).

MATERIALS AND METHODS

The TRS-HF_DM was externally validated in the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care (EXAMINE) trial (n = 5380) and extended with natriuretic biomarkers. Missing data were multiply imputed. Initial TRS-HF_DM variables were previous HF (2 points), atrial fibrillation (1 point), coronary artery disease (1 point), estimated glomerular filtration rate <60 ml/min/1.73 m² (1 point), and urine albumin-to-creatinine ratio 30-300 mg/g (1 point) and >300 mg/g (2 points).

RESULTS

In total, HF hospitalization occurred in 193 (3.6%) patients. Based on the TRS-HF_DM , 25% of patients were classified as intermediate risk (1 point), 30% were classified as high risk (2 points), 19% were classified as very-high risk (3 points) and 26% were classified as severe risk (≥4 points). Before model extension, discrimination (C-index 0.76, 95%·CI 0.73-0.80) and calibration (calibration slope 0.82, 95%·CI 0.65-1.0; calibration-in-the-large -0.15, 95%·CI -0.37-0.64) were moderate-to-good in individuals with T2D and recent ACS. The extension of TRS-HF_DM with the addition of N-terminal pro-B-type natriuretic peptide (NT-ProBNP) improved discrimination (C-index 0.82, 95%·CI 0.79-0.85) and calibration (calibration slope 0.84, 95%·CI 0.66-1.02; calibration-in-the-large -0.12, 95%·CI -0.33-0.081) for this higher-risk population.

CONCLUSION

The TRS-HF_DM with the extension of NT-ProBNP improves risk stratification and generalizes the use of the risk score for patients with T2D and ACS. Future validation studies in ACS populations may be warranted.

Conventional Biomarkers for Predicting Clinical Outcomes in Patients with Heart Disease

Atherosclerosis is the main cause of cardiovascular disease worldwide. The progression of coronary atherosclerosis leads to coronary artery disease, with impaired blood flow to the myocardium and subsequent development of myocardial ischemia. Acute coronary syndromes and post-myocardial infarction heart failure are two of the most common complications of coronary artery disease and are associated with worse outcomes. In order to improve the management of patients with coronary artery disease and avoid major cardiovascular events, several risk assessment tools have been developed. Blood and imaging biomarkers, as well as clinical risk scores, are now available and validated for clinical practice, but research continues. The purpose of the current paper is to provide a review of recent findings regarding the use of humoral biomarkers for risk assessment in patients with heart disease.

Effect of Glucose Levels on Cardiovascular Risk

Cardiovascular diseases remain the leading cause of death and disability. The development of cardiovascular diseases is traditionally associated with various risk factors, most of which are somehow related to an unhealthy lifestyle (smoking, obesity, lack of physical activity, etc.). There are also risk factors associated with genetic predisposition, as well as the presence of concomitant diseases, especially chronic ones. One of the most striking examples is, of course, type 2 diabetes. This metabolic disorder is associated with impaired carbohydrate metabolism. The main clinical manifestation of type 2 diabetes is elevated blood glucose levels. The link between diabetes and CVD is well known, so it is logical to assume that elevated glucose levels may be important, to some extent, in the context of heart and vascular disease. In this review, we tried to summarize data on the possible role of blood glucose as a risk factor for the development of CVD.

Protein Biomarkers and Cardiovascular Outcomes in People With Type 2 Diabetes and Acute Coronary Syndrome: The ELIXA Biomarker Study

OBJECTIVE

To use protein biomarkers to identify people with type 2 diabetes at high risk of cardiovascular outcomes and death.

RESEARCH DESIGN AND METHODS

Biobanked serum from 4,957 ELIXA (Evaluation of Lixisenatide in Acute Coronary Syndrome) trial participants was analyzed. Forward-selection Cox models identified independent protein risk factors for major adverse cardiovascular events (MACE) and death that were compared with a previously validated biomarker panel.

RESULTS

NT-proBNP and osteoprotegerin predicted both outcomes. In addition, trefoil factor 3 predicted MACE, and angiopoietin-2 predicted death (C = 0.70 and 0.79, respectively, compared with 0.63 and 0.66 for clinical variables alone). These proteins had all previously been identified and validated. Notably, C statistics for just NT-proBNP plus clinical risk factors were 0.69 and 0.78 for MACE and death, respectively.

CONCLUSIONS

NT-proBNP and other proteins independently predict cardiovascular outcomes in people with type 2 diabetes following acute coronary syndrome. Adding other biomarkers only marginally increased NT-proBNP's prognostic value.

Major Adverse Cardiovascular Events and Mortality Prediction by Circulating GDF-15 in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis

Background: Growth differentiation factor 15 (GDF-15) is a homeostatic cytokine that regulates neural and cardio-metabolic functions, and its release is increased in response to stress, injury, and inflammation. In patients with coronary artery disease and heart failure (HF), three separate meta-analyses have found that elevated circulating GDF-15 was predictive of major adverse cardiovascular events (MACE), but none has evaluated its effects on incident MACE including HF and mortality hazard in type 2 diabetes. Methods: MEDLINE, EMBASE, and Scopus databases were queried. Articles that met the predefined eligibility criteria, including prospective studies that reported adjusted hazard ratios (aHRs), were selected according to the Cochrane Handbook and PRISMA guidelines. Study endpoints were (1) MACE including HF, and (2) all-cause mortality. Different GDF-15 concentration measurements were harmonized using a validated mathematical approach to express log2-transformed values in per standard deviation (SD). Study heterogeneity (I2), quality, and bias were assessed. Results: 19354 patients in 8 prospective studies were included. In 7 studies that reported 4247 MACE among 19200 participants, the incident rate was 22.1% during a median follow-up of 5.6 years. It was found that four of eight studies included HF decompensation or hospitalization as a component of MACE. In 5 studies that reported all-cause mortality, 1893 of 13223 patients died, at an incidence rate of 15.1% over 5.0 years. Of note, each 1 SD increase of log2[GDF-15] was associated with aHRs of 1.12 (1.09−1.15, I2 = 5%, p < 0.000001) and 1.27 (1.11−1.46, I2 = 86%, p = 0.00062) and for MACE and all-cause mortality, respectively. Conclusion: Elevated circulating level of GDF-15 was robustly predictive of MACE in patients with T2D but its prognostic significance in the prediction of mortality requires further studies.

Biomarkers for the prediction of heart failure and cardiovascular events in patients with type 2 diabetes: a position statement from the Heart Failure Association of the European Society of Cardiology

Knowledge on risk predictors of incident heart failure (HF) in patients with type 2 diabetes (T2D) is crucial given the frequent coexistence of the two conditions and the fact that T2D doubles the risk of incident HF. In addition, HF is increasingly being recognized as an important endpoint in trials in T2D. On the other hand, the diagnostic and prognostic performance of established cardiovascular biomarkers may be modified by the presence of T2D. The present position paper, derived by an expert panel workshop organized by the Heart Failure Association of the European Society of Cardiology, summarizes the current knowledge and gaps in evidence regarding the use of a series of different biomarkers, reflecting various pathogenic pathways, for the prediction of incident HF and cardiovascular events in patients with T2D and in those with established HF and T2D.

Clinical Prediction Models for Heart Failure Hospitalization in Type 2 Diabetes: A Systematic Review and Meta-Analysis

Background Clinical prediction models have been developed for hospitalization for heart failure in type 2 diabetes. However, a systematic evaluation of these models' performance, applicability, and clinical impact is absent. Methods and Results We searched Embase, MEDLINE, Web of Science, Google Scholar, and Tufts' clinical prediction registry through February 2021. Studies needed to report the development, validation, clinical impact, or update of a prediction model for hospitalization for heart failure in type 2 diabetes with measures of model performance and sufficient information for clinical use. Model assessment was done with the Prediction Model Risk of Bias Assessment Tool, and meta-analyses of model discrimination were performed. We included 15 model development and 3 external validation studies with data from 999 167 people with type 2 diabetes. Of the 15 models, 6 had undergone external validation and only 1 had low concern for risk of bias and applicability (Risk Equations for Complications of Type 2 Diabetes). Seven models were presented in a clinically useful manner (eg, risk score, online calculator) and 2 models were classified as the most suitable for clinical use based on study design, external validity, and point-of-care usability. These were Risk Equations for Complications of Type 2 Diabetes (meta-analyzed c-statistic, 0.76) and the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (meta-analyzed c-statistic, 0.78), which was the simplest model with only 5 variables. No studies reported clinical impact. Conclusions Most prediction models for hospitalization for heart failure in patients with type 2 diabetes have potential concerns with risk of bias or applicability, and uncertain external validity and clinical impact. Future research is needed to address these knowledge gaps.

Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine

OBJECTIVE

Phenotypic heterogeneity among patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD) is ill defined. We used cluster analysis machine-learning algorithms to identify phenotypes among trial participants with T2DM and ASCVD.

RESEARCH DESIGN AND METHODS

We used data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) study (n = 14,671), a cardiovascular outcome safety trial comparing sitagliptin with placebo in patients with T2DM and ASCVD (median follow-up 3.0 years). Cluster analysis using 40 baseline variables was conducted, with associations between clusters and the primary composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina) assessed by Cox proportional hazards models. We replicated the results using the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial.

RESULTS

Four distinct phenotypes were identified: cluster I included Caucasian men with a high prevalence of coronary artery disease; cluster II included Asian patients with a low BMI; cluster III included women with noncoronary ASCVD disease; and cluster IV included patients with heart failure and kidney dysfunction. The primary outcome occurred, respectively, in 11.6%, 8.6%, 10.3%, and 16.8% of patients in clusters I to IV. The crude difference in cardiovascular risk for the highest versus lowest risk cluster (cluster IV vs. II) was statistically significant (hazard ratio 2.74 [95% CI 2.29-3.29]). Similar phenotypes and outcomes were identified in EXSCEL.

CONCLUSIONS

In patients with T2DM and ASCVD, cluster analysis identified four clinically distinct groups. Further cardiovascular phenotyping is warranted to inform patient care and optimize clinical trial designs.

Prognostic Significance of Preprocedural N-Terminal Pro-B-Type Natriuretic Peptide Assessment in Diabetic Patients With Multivessel Coronary Disease Undergoing Revascularization

Background and Aims: The N-terminal pro-B-type natriuretic peptide (NT-proBNP) may predict adverse cardiovascular outcomes in patients with diabetes. However, its prognostic value in patients with multivessel disease (MVD) undergoing coronary revascularization remains unclear. This study aimed to evaluate the prognostic significance of preprocedural NT-proBNP levels in diabetic patients with MVD undergoing coronary revascularization.

Methods: A total of 886 consecutive diabetic patients with MVD who underwent coronary revascularization were enrolled in this study. Patients were divided into quartiles according to their pre-procedural NT-proBNP levels. Kaplan-Meier curves and Cox regression analyses were performed to evaluate the risk of cardiovascular events, including all-cause death, cardiovascular death, myocardial infarction (MI), stroke, and major adverse cardiovascular events (MACE), according to the NT-proBNP quartiles.

Results: During a median follow-up period of 4.2 years, 111 patients died (with 82 being caused by cardiovascular disease), 133 had MI, 55 suffered from stroke, and 250 experienced MACE. Kaplan-Meier curves demonstrated that NT-proBNP levels were significantly associated with higher incidences of all-cause death, cardiovascular death, MI, and MACE (log-rank test, P < 0.001, respectively). Multivariate Cox regression analysis revealed that NT-proBNP level was an independent predictor of adverse outcomes, including all-cause death (HR, 1.968; 95% CI, 1.377–2.812; P < 0.001), cardiovascular death (HR, 1.940; 95% CI, 1.278–2.945; P = 0.002), MI (HR, 1.722; 95% CI, 1.247–2.380; P = 0.001), and MACE (HR, 1.356; 95% CI, 1.066–1.725; P = 0.013). The role of NT-proBNP in predicting adverse outcomes was similar in patients with stable angina pectoris and acute coronary syndrome. Moreover, preprocedural NT-proBNP alone discriminated against the SYNTAX II score for predicting all-cause death [area under the curve (AUC), 0.662 vs. 0.626, P = 0.269], cardiovascular death (AUC, 0.680 vs. 0.622, P = 0.130), MI (AUC, 0.641 vs. 0.579, P = 0.050), and MACE (AUC, 0.593 vs. 0.559, P = 0.171). The addition of NT-proBNP to the SYNTAX II score showed a significant net reclassification improvement, integrated discrimination improvement, and improved C-statistic (all P < 0.05).

Conclusion: NT-proBNP levels were an independent prognostic marker for adverse outcomes in diabetic patients with MVD undergoing coronary revascularization, suggesting that preprocedural NT-proBNP measurement might help in the risk stratification of high-risk patients.

Dipeptidyl peptidase-4 inhibitors, glucagon-like peptide 1 receptor agonists and sodium-glucose co-transporter-2 inhibitors for people with cardiovascular disease: a network meta-analysis

BACKGROUND

Cardiovascular disease (CVD) is a leading cause of death globally. Recently, dipeptidyl peptidase-4 inhibitors (DPP4i), glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium-glucose co-transporter-2 inhibitors (SGLT2i) were approved for treating people with type 2 diabetes mellitus. Although metformin remains the first-line pharmacotherapy for people with type 2 diabetes mellitus, a body of evidence has recently emerged indicating that DPP4i, GLP-1RA and SGLT2i may exert positive effects on patients with known CVD.

OBJECTIVES

To systematically review the available evidence on the benefits and harms of DPP4i, GLP-1RA, and SGLT2i in people with established CVD, using network meta-analysis.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, and the Conference Proceedings Citation Index on 16 July 2020. We also searched clinical trials registers on 22 August 2020. We did not restrict by language or publication status.

SELECTION CRITERIA

We searched for randomised controlled trials (RCTs) investigating DPP4i, GLP-1RA, or SGLT2i that included participants with established CVD. Outcome measures of interest were CVD mortality, fatal and non-fatal myocardial infarction, fatal and non-fatal stroke, all-cause mortality, hospitalisation for heart failure (HF), and safety outcomes.

DATA COLLECTION AND ANALYSIS

Three review authors independently screened the results of searches to identify eligible studies and extracted study data. We used the GRADE approach to assess the certainty of the evidence. We conducted standard pairwise meta-analyses and network meta-analyses by pooling studies that we assessed to be of substantial homogeneity; subgroup and sensitivity analyses were also pursued to explore how study characteristics and potential effect modifiers could affect the robustness of our review findings. We analysed study data using the odds ratios (ORs) and log odds ratios (LORs) with their respective 95% confidence intervals (CIs) and credible intervals (Crls), where appropriate. We also performed narrative synthesis for included studies that were of substantial heterogeneity and that did not report quantitative data in a usable format, in order to discuss their individual findings and relevance to our review scope.

MAIN RESULTS

We included 31 studies (287 records), of which we pooled data from 20 studies (129,465 participants) for our meta-analysis. The majority of the included studies were at low risk of bias, using Cochrane's tool for assessing risk of bias. Among the 20 pooled studies, six investigated DPP4i, seven studied GLP-1RA, and the remaining seven trials evaluated SGLT2i. All outcome data described below were reported at the longest follow-up duration. 1. DPP4i versus placebo Our review suggests that DPP4i do not reduce any risk of efficacy outcomes: CVD mortality (OR 1.00, 95% CI 0.91 to 1.09; high-certainty evidence), myocardial infarction (OR 0.97, 95% CI 0.88 to 1.08; high-certainty evidence), stroke (OR 1.00, 95% CI 0.87 to 1.14; high-certainty evidence), and all-cause mortality (OR 1.03, 95% CI 0.96 to 1.11; high-certainty evidence). DPP4i probably do not reduce hospitalisation for HF (OR 0.99, 95% CI 0.80 to 1.23; moderate-certainty evidence). DPP4i may not increase the likelihood of worsening renal function (OR 1.08, 95% CI 0.88 to 1.33; low-certainty evidence) and probably do not increase the risk of bone fracture (OR 1.00, 95% CI 0.83 to 1.19; moderate-certainty evidence) or hypoglycaemia (OR 1.11, 95% CI 0.95 to 1.29; moderate-certainty evidence). They are likely to increase the risk of pancreatitis (OR 1.63, 95% CI 1.12 to 2.37; moderate-certainty evidence). 2. GLP-1RA versus placebo Our findings indicate that GLP-1RA reduce the risk of CV mortality (OR 0.87, 95% CI 0.79 to 0.95; high-certainty evidence), all-cause mortality (OR 0.88, 95% CI 0.82 to 0.95; high-certainty evidence), and stroke (OR 0.87, 95% CI 0.77 to 0.98; high-certainty evidence). GLP-1RA probably do not reduce the risk of myocardial infarction (OR 0.89, 95% CI 0.78 to 1.01; moderate-certainty evidence), and hospitalisation for HF (OR 0.95, 95% CI 0.85 to 1.06; high-certainty evidence). GLP-1RA may reduce the risk of worsening renal function (OR 0.61, 95% CI 0.44 to 0.84; low-certainty evidence), but may have no impact on pancreatitis (OR 0.96, 95% CI 0.68 to 1.35; low-certainty evidence). We are uncertain about the effect of GLP-1RA on hypoglycaemia and bone fractures. 3. SGLT2i versus placebo This review shows that SGLT2i probably reduce the risk of CV mortality (OR 0.82, 95% CI 0.70 to 0.95; moderate-certainty evidence), all-cause mortality (OR 0.84, 95% CI 0.74 to 0.96; moderate-certainty evidence), and reduce the risk of HF hospitalisation (OR 0.65, 95% CI 0.59 to 0.71; high-certainty evidence); they do not reduce the risk of myocardial infarction (OR 0.97, 95% CI 0.84 to 1.12; high-certainty evidence) and probably do not reduce the risk of stroke (OR 1.12, 95% CI 0.92 to 1.36; moderate-certainty evidence). In terms of treatment safety, SGLT2i probably reduce the incidence of worsening renal function (OR 0.59, 95% CI 0.43 to 0.82; moderate-certainty evidence), and probably have no effect on hypoglycaemia (OR 0.90, 95% CI 0.75 to 1.07; moderate-certainty evidence) or bone fracture (OR 1.02, 95% CI 0.88 to 1.18; high-certainty evidence), and may have no impact on pancreatitis (OR 0.85, 95% CI 0.39 to 1.86; low-certainty evidence). 4. Network meta-analysis Because we failed to identify direct comparisons between each class of the agents, findings from our network meta-analysis provided limited novel insights. Almost all findings from our network meta-analysis agree with those from the standard meta-analysis. GLP-1RA may not reduce the risk of stroke compared with placebo (OR 0.87, 95% CrI 0.75 to 1.0; moderate-certainty evidence), which showed similar odds estimates and wider 95% Crl compared with standard pairwise meta-analysis. Indirect estimates also supported comparison across all three classes. SGLT2i was ranked the best for CVD and all-cause mortality.

AUTHORS' CONCLUSIONS

Findings from both standard and network meta-analyses of moderate- to high-certainty evidence suggest that GLP-1RA and SGLT2i are likely to reduce the risk of CVD mortality and all-cause mortality in people with established CVD; high-certainty evidence demonstrates that treatment with SGLT2i reduce the risk of hospitalisation for HF, while moderate-certainty evidence likely supports the use of GLP-1RA to reduce fatal and non-fatal stroke. Future studies conducted in the non-diabetic CVD population will reveal the mechanisms behind how these agents improve clinical outcomes irrespective of their glucose-lowering effects.

Permission to prescribe: do cardiologists need permission to prescribe diabetes medications that afford cardiovascular benefit?

PURPOSE OF REVIEW

Antihyperglycemic therapies including sodium glucose contransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) have been demonstrated to confer significant cardiovascular benefit and reduce future events in patients with type 2 diabetes mellitus (T2DM). However, despite positive data from cardiovascular outcome trials, these therapies remain underutilized in a large proportion of patients who have clinical indications and meet coverage guidelines for their initiation. One of the causes of the observed gap between scientific evidence and clinical cardiology practice is therapeutic hesitancy (otherwise known as therapeutic inertia). The purpose of this review is to discuss the contributors to therapeutic hesitancy in the implementation of these evidence-based therapies and, more importantly, provide pragmatic solutions to address these barriers.

RECENT FINDINGS

Recent studies have demonstrated that clinicians may not initiate cardiovascular protective therapies due to a reluctance to overstep perceived interdisciplinary boundaries, concerns about causing harm due to medication side effects, and a sense of unfamiliarity with the optimal choice of therapy amidst a rapidly evolving landscape of T2DM therapies.

SUMMARY

Herein, we describe a multifaceted approach aimed at creating a 'permission to prescribe' culture, developing integrated multidisciplinary models of care, enhancing trainees' experiences in cardiovascular disease prevention, and utilizing technology to motivate change. Taken together, these interventions should increase the implementation of evidence-based therapies and improve the quality of life and cardiovascular outcomes of individuals with T2DM.

Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes

AIMS/HYPOTHESIS

Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies.

METHODS

Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495).

RESULTS

Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts.

CONCLUSIONS/INTERPRETATION

Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.

Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication

Heart failure (HF) and type 2 diabetes mellitus (T2DM) have a synergistic effect on cardiovascular (CV) morbidity and mortality in patients with established CV disease (CVD). The aim of this review is to summarize the knowledge regarding the discriminative abilities of conventional and novel biomarkers in T2DM patients with established HF or at higher risk of developing HF. While conventional biomarkers, such as natriuretic peptides and high-sensitivity troponins demonstrate high predictive ability in HF with reduced ejection fraction (HFrEF), this is not the case for HF with preserved ejection fraction (HFpEF). HFpEF is a heterogeneous disease with a high variability of CVD and conventional risk factors including T2DM, hypertension, renal disease, older age, and female sex; therefore, the extrapolation of predictive abilities of traditional biomarkers on this population is constrained. New biomarker-based approaches are disputed to be sufficient for improving risk stratification and the prediction of poor clinical outcomes in patients with HFpEF. Novel biomarkers of biomechanical stress, fibrosis, inflammation, oxidative stress, and collagen turn-over have shown potential benefits in determining prognosis in T2DM patients with HF regardless of natriuretic peptides, but their role in point-to-care and in routine practice requires elucidation in large clinical trials.

Prediction of heart failure outcomes in patients with type 2 diabetes mellitus: Validation of the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (TRS-HFDM ) in patients in the ACCORD trial

AIM

To investigate the ability of the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (TRS-HF_DM ) to stratify patients with type 2 diabetes mellitus (T2DM) and high cardiovascular risk for heart failure (HF) hospitalization.

MATERIALS AND METHODS

We used data from the control group of the Action to Control Cardiovascular Risk in Diabetes Study Group (ACCORD) trial (n = 5123; mean follow-up 4.8 years). The TRS-HF_DM includes: prior HF (2 points), atrial fibrillation (1 point), coronary artery disease (1 point), estimated glomerular filtration rate <60 mL/min/1.73 m² (1 point), and urine albumin-to-creatinine ratio (>300 mg/g: 2 points; 30-300 mg/g: 1 point). We evaluated the discrimination (Harrell's C-index) and calibration (Nam-D'Agostino calibration statistic) of the TRS-HF_DM with regard to time to HF hospitalization or death due to HF.

RESULTS

The mean age of the participants was 62.8 ± 6.6 years, and 38% were women. The prevalences of TRS-HF_DM 0, 1, 2, 3 and ≥4 were 42.1%, 34.9%, 14.6%, 6.0% and 2.5%, respectively. Increasing TRS-HF_DM corresponded to an increasing HF risk: 1.3 per 1000 person-years for a TRS-HF_DM of 0 to 64.7 per 1000 person-years for TRS-HF_DM of ≥4. The TRS-HF_DM demonstrated robust discrimination of HF outcomes (C-index 0.78). Furthermore, the score was well calibrated for HF outcomes (calibration statistic P = 0.13). Similar results were seen in participants without baseline HF (C-index 0.75).

CONCLUSION

The TRS-HF_DM discriminates HF-specific risk among people with T2DM. The use of TRS-HF_DM to identify those who would maximally benefit from therapies that reduce HF risk warrants evaluation.

Performance of the recommended ESC/EASD cardiovascular risk stratification model in comparison to SCORE and NT-proBNP as a single biomarker for risk prediction in type 2 diabetes mellitus

BACKGROUND

Recently, the European Society of Cardiology (ESC) and European Association for the Society of Diabetes (EASD) introduced a new cardiovascular disease (CVD) risk stratification model to aid further treatment decisions in individuals with diabetes. Our study aimed to investigate the prognostic performance of the ESC/EASD risk model in comparison to the Systematic COronary Risk Evaluation (SCORE) risk model and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in an unselected cohort of type 2 diabetes mellitus (T2DM).

METHODS AND RESULTS

A total of 1690 T2DM patients with a 10-year follow up for fatal CVD and all-cause death and a 5-year follow up for CVD and all-cause hospitalizations were analyzed. According to ESC/EASD risk criteria 25 (1.5%) patients were classified as moderate, 252 (14.9%) high, 1125 (66.6%) very high risk and 288 (17.0%) were not classifiable. Both NT-proBNP and SCORE risk model were associated with 10-year CVD and all-cause death and 5-year CVD and all-cause hospitalizations while the ESC/EASD model was only associated with 10-year all-cause death and 5-year all-cause hospitalizations. NT-proBNP and SCORE showed significantly higher C-indices than the ESC/EASD risk model for CVD death [0.80 vs. 0.53, p < 0.001; 0.64 vs. 0.53, p = 0.001] and all-cause death [0.73, 0.66 vs. 0.52, p < 0.001 for both]. The performance of SCORE improved in a subgroup without CVD aged 40-64 years compared to the unselected cohort, while NT-proBNP performance was robust across all groups.

CONCLUSION

The new introduced ESC/EASD risk stratification model performed limited compared to SCORE and single NT-proBNP assessment for predicting 10-year CVD and all-cause fatal events in individuals with T2DM.

Timing of randomization after an acute coronary syndrome in patients with type 2 diabetes mellitus

BACKGROUND

The timing of enrolment following an acute coronary syndrome (ACS) may influence cardiovascular (CV) outcomes and potentially treatment effect in clinical trials. Understanding the timing and type of clinical events after an ACS will allow for clinicians to better tailor evidence-based treatments to optimize therapeutic effect. Using a large contemporary trial in patients with type 2 diabetes mellitus (T2DM) post-ACS, we examined the impact of timing of enrolment on subsequent CV outcomes.

METHODS

EXAMINE was a randomized trial of alogliptin versus placebo in 5,380 patients with T2DM and a recent ACS from October 2009 to March 2013. The primary outcome was a composite of CV death, nonfatal myocardial infarction (MI), or nonfatal stroke. The median follow-up was 18 months. In this post hoc analysis, we examined the occurrence of subsequent CV events by timing of enrollment divided by tertiles of time from ACS to randomization: 8-34, 35-56, and 57-141 days.

RESULTS

Patients randomized early (compared to the latest times) had less comorbidities at baseline including a history of heart failure (HF; 24.7% vs 33.0%), prior coronary artery bypass graft (9.6% vs 15.9%), or atrial fibrillation (5.9% vs 9.4%). Despite the reduced comorbidity burden, the risk of the primary outcome was highest in patients randomized early compared to the latest time (adjusted hazard ratio 1.47; 95% CI 1.21-1.74). Similarly, patients randomized early had an increased risk of recurrent MI (adjusted hazard ratio 1.51; 95% CI 1.17-1.96) and HF hospitalization (1.49; 95% CI 1.05-2.10).

CONCLUSIONS

In a contemporary cohort of T2DM with a recent ACS, the risk for recurrent CV events including MI and HF hospitalization is elevated early after an ACS. Given the emergence of antihyperglycemic therapies that reduce the risk of MI and HF among patients with T2DM at high CV risk, future studies evaluating the initiation of these therapies in the early period following an ACS are warranted given the large burden of potentially modifiable CV events.

The plasma levels of atrial natriuretic peptide and brain natriuretic peptide in type 2 diabetes treated with sodium-glucose cotransporter-2 inhibitor

PURPOSE

The aim of this study was to determine the levels of atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) after treatment with sodium-glucose cotransporter-2 (SGLT2) inhibitor or dipeptidyl peptidase-4 (DPP4) inhibitor in patients with type-2 diabetes inadequately controlled by insulin, and to determine whether variation in ANP levels can explain favorable cardiovascular outcome.

METHODS

We enrolled 56 patients, aged 18-80years, with type-2 diabetes inadequately controlled by insulin: i.e., HbA1c level 7.5-10.5% despite at least 8weeks' injectable insulin at a stable mean dose of 20-150IU daily, with or without no more than two oral antidiabetic agents.

FINDINGS

The 56 patients were randomized between 3 treatment groups: SGLT2 inhibitor (n=18), DPP4 inhibitor (n=19) and placebo (n=19). Patients who received SGLT2 inhibitor or DPP4 inhibitor treatment all showed significantly lower HbA1c levels, fasting blood glucose (FBG) levels and systolic blood pressure at 24weeks than controls. SGLT2 inhibitor treatment decreased ANP levels, BNP levels, systolic blood pressure and weight compared with placebo. Compared to those receiving DPP4 inhibitor, patients receiving SGLT2 inhibitor showed lower HbA1c levels (7.01 vs. 7.58%; P=0.03), ANP levels (28.41 vs. 43.03 pg/mL; P=0.00) and weight (66.14 vs. 71.76 kg; P=0.04) at 24weeks after adjusting for baseline values. The SGLT2 inhibitor group showed higher sodium concentrations than the placebo and DPP4 inhibitor groups (145.89 vs. 143.89 and 144.79 mmol/L, respectively; P=0.00 and P=0.04) at 24 weeks. ANP and BNP levels did not significantly correlate with HbA1c and blood glucose levels.

IMPLICATIONS

These results indicated that SGLT2 inhibitors may be superior to DPP4 inhibitors in reducing risk of cardiovascular disease in diabetic patients. The major study limitation was the small number of patients per group, which should be enlarged in further research.

Clinical and Biomarker Predictors of Expanded Heart Failure Outcomes in Patients With Type 2 Diabetes Mellitus After a Recent Acute Coronary Syndrome: Insights From the EXAMINE Trial

Background

Improved heart failure (HF) risk stratification after a recent acute coronary syndrome may identify those who can benefit from therapies that reduce HF risk. We aimed to identify clinical and biomarker predictors for expanded HF outcomes in patients with type 2 diabetes mellitus after recent acute coronary syndrome.

Methods and Results

The EXAMINE (Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care) trial was a multicenter, non‐inferiority, double‐masked, placebo‐controlled study which randomized 5380 patients with type 2 diabetes mellitus after recent acute coronary syndrome to alogliptin or placebo. Baseline biomarkers were measured in 5154 patients: NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), high‐sensitivity troponin I, adiponectin, growth‐differentiation‐factor‐15, and galectin‐3. Our primary outcome was cardiovascular) death, HF hospitalization, elevated NT‐proBNP during follow‐up, or loop diuretics initiation. The association between clinical variables, biomarkers, and outcomes were assessed using Cox regression models. In the study population, the median age was 61.0 years, 67.7% were men, and 28.0% had baseline HF (median follow‐up was 18 months). In multivariable analyses, NT‐proBNP had the strongest association with the primary outcome (per log₂, hazard ratio 1.24; Wald χ² 67.4; P<0.0001) followed by a prior HF history (hazard ratio 1.42; Wald χ² 20.8; P<0.0001). A model with clinical variables and biomarkers allowed for risk prediction for expanded HF outcomes (C‐statistic=0.72). Discrimination results were similar for cardiovascular death or HF hospitalization.

Conclusions

Among patients with type 2 diabetes mellitus after recent acute coronary syndrome, the use biomarkers such as N‐terminal pro‐B‐type natriuretic peptide and clinical variables enables risk stratification for expanded HF outcomes.

Clinical Trial Registration

URL: https://www.clinicaltrials.gov/. Unique identifier: NCT00968708.