Multi-proteomic approach to predict specific cardiovascular events in patients with diabetes and myocardial infarction: findings from the EXAMINE trial

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study

BACKGROUND

Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D.

METHODS

The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI).

RESULTS

We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465).

CONCLUSIONS

This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

TRAIL or TRAIL-R2 as a Predictive Biomarker for Mortality or Cardiovascular Events: A Systematic Review and Meta-analysis

ABSTRACT

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and TRAIL-receptor-2 (TRAIL-R2) are associated with atherosclerosis. This meta-analysis aimed to investigate the potential association between TRAIL/TRAIL-R2 with mortality or cardiovascular (CV) events. PubMed, Embase, and Cochrane Library were searched for reports published up to May 2021. Reports were included when the association between TRAIL or TRAIL-R2 and mortality or CV events was reported. Considering the heterogeneity between studies, we used the random-effects model for all analyses. Ultimately, the meta-analysis included 18 studies (16,295 patients). The average follow-up ranged from 0.25 to 10 years. Decreased TRAIL levels were negatively associated with all-cause mortality [rank variable, hazard ratio (HR), 95% CI, 2.93, 1.94-4.42; I2 = 0.0%, Pheterogeneity = 0.835]. Increased TRAIL-R2 levels were positively associated with all-cause mortality (continuous variable, HR, 95% CI, 1.43, 1.23-1.65; I2 = 0.0%, Pheterogeneity = 0.548; rank variable, HR, 95% CI, 7.08, 2.70-18.56; I2 = 46.5%, Pheterogeneity = 0.154), CV mortality (continuous variable, HR, 95% CI, 1.33, 1.14-1.57; I2 = 0.0%, Pheterogeneity = 0.435), myocardial infarction (continuous variable, HR, 95% CI, 1.23, 1.02-1.49; rank variable, HR, 95% CI, 1.49, 1.26-1.76; I2 = 0.7%, Pheterogeneity = 0.402), and new-onset heart failure (rank variable, HR, 95% CI, 3.23, 1.32-7.87; I2 = 83.0%, Pheterogeneity = 0.003). In conclusion, decreased TRAIL was negatively associated with all-cause mortality, and increased TRAIL-R2 was positively associated with all-cause mortality, CV mortality, myocardial infarction, and heart failure.

The Prognostic, Diagnostic, and Therapeutic Potential of TRAIL Signalling in Cardiovascular Diseases

TNF-related apoptosis-inducing ligand (TRAIL) was originally discovered, almost 20 years ago, for its ability to kill cancer cells. More recent evidence has described pleiotropic functions, particularly in the cardiovascular system. There is potential for TRAIL concentrations in the circulation to act as prognostic and/or diagnostic factors for cardiovascular diseases (CVD). Pre-clinical studies also describe the therapeutic capacity for TRAIL signals, particularly in the context of atherosclerotic disease and diseases of the myocardium. Because diabetes mellitus significantly contributes to the progression and pathogenesis of CVDs, in this review we highlight recent evidence for the prognostic, diagnostic, and therapeutic potential of TRAIL signals in CVDs, and where relevant, the impact of diabetes mellitus. A greater understanding of how TRAIL signals regulate cardiovascular protection and pathology may offer new diagnostic and therapeutic avenues for patients suffering from CVDs.

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.

Protein biomarkers of cardiac remodeling and inflammation associated with HFpEF and incident events

There is increasing evidence that HFpEF is a heterogeneous clinical entity and distinct molecular pathways may contribute to pathophysiology. Leveraging unbiased proteomics to identify novel biomarkers, this study seeks to understand the underlying molecular mechanisms of HFpEF. The discovery cohort consisted of HFpEF cases and non-HF controls from the CATHGEN study (N = 176); the validation cohort consisted of participants from the TECOS trial of patients with diabetes (N = 109). Proteins associated with HFpEF were included in a LASSO model to create a discriminative multi-protein model and assessed in the validation cohort. Survival models and meta-analysis were used to test the association of proteins with incident clinical outcomes, including HF hospitalization, mortality and HFpEF hospitalization in CATHGEN, TECOS and the Jackson Heart Study. In the derivation set, 190 proteins were associated with HFpEF in univariate analysis, of which 65 remained significant in the multivariate model. Twenty (30.8%) of these proteins validated in TECOS, including LCN2, U-PAR, IL-1ra, KIM1, CSTB and Gal-9 (OR 1.93-2.77, p < 0.01). LASSO regression yielded a 13-protein model which, when added to a clinical model inclusive of NT-proBNP, improved the AUC from 0.82 to 0.92 (p = 1.5 × 10-4). Five proteins were associated with incident HF hospitalization, four with HFpEF hospitalization and eleven with mortality (p < 0.05). We identified and validated multiple circulating biomarkers associated with HFpEF as well as HF outcomes. These biomarkers added incremental discriminative capabilities beyond clinical factors and NT-proBNP.

Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing

Cardiometabolic diseases, such as type 2 diabetes and cardiovascular disease, have a high public health burden. Understanding the genetically-determined regulation of proteins that are dysregulated in disease can help to dissect the complex biology underpinning them. Here, we perform a protein quantitative trait locus (pQTL) analysis of 255 serum proteins relevant to cardiometabolic processes in 2893 individuals. Meta-analysing whole-genome sequencing (WGS) data from two Greek cohorts, MANOLIS (n = 1356; 22.5x WGS) and Pomak (n = 1537; 18.4x WGS), we detect 302 independently-associated pQTL variants for 171 proteins, including 12 rare variants (minor allele frequency [MAF] < 1%). We additionally find 15 pQTL variants that are rare in non-Finnish European populations, but have drifted up in frequency in the discovery cohorts here. We identify proteins causally associated with cardiometabolic traits, including MEP1B for high-density lipoprotein levels; and describe a knock-out Mep1b mouse model. Our findings furnish insights into the genetic architecture of the serum proteome, identify new protein-disease relationships, and demonstrate the importance of isolated populations in pQTL analysis.

A meta-analysis evaluating indirectly GLP-1 receptor agonists and arrhythmias in patients with type 2 diabetes and myocardial infarction

Aims

At present, the effects of Glucagon-Like Peptide 1 Receptor agonists (GLP-1RAs) on arrhythmia in patients with type 2 diabetes mellitus (T2DM) and myocardial infarction (MI) are still unclear. Hence, this systematic review and meta-analysis aimed to investigate this association.

Methods and results

PubMed, Embase, Cochrane Library, and Web of Science were searched from inception to 30 April 2022. Randomized controlled trials (RCTs) that compared GLP-1RAs with placebo and met the critical criterion of a proportion of patients with T2DM and MI > 30% were included to verify our purpose indirectly. The outcomes of interest included atrial arrhythmias, ventricular arrhythmias, atrioventricular block (AVB), sinus arrhythmia, and cardiac arrest. Relative risk (RR) and 95% confidence intervals (CI) were pooled using a random-effects model. We included five RCTs with altogether 31,314 patients. In these trials, the highest proportion of patients with T2DM and MI was 82.6%, while the lowest was 30.7%. Compared to placebo, GLP-1RAs were associated with a lower risk of atrial arrhythmias (RR 0.81, 95% CI 0.70-0.95). There was no significant difference in the risk of ventricular arrhythmias (RR 1.26, 95% CI 0.87-1.80), AVB (RR 0.95, 95% CI 0.63-1.42), sinus arrhythmia (RR 0.62, 95% CI 0.26-1.49), and cardiac arrest (RR 0.97, 95% CI 0.52-1.83) between groups.

Conclusion

GLP-1RAs may be associated with reduced risk for atrial arrhythmias, which seems more significant for patients with T2DM combined with MI. More studies are needed to clarify the definitive anti-arrhythmic role of this drug.

Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand (TRAIL) in Patients after Acute Stroke: Relation to Stroke Severity, Myocardial Injury, and Impact on Prognosis

Background: Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is known to be associated with poor prognosis after cardiovascular events. We aimed to assess the dynamic changes in TRAIL levels and the relation of TRAIL level to stroke severity, its impact on the short-term outcomes, and its association with markers of cardiac injury in patients after acute stroke. Methods: Between August 2020 and August 2021, 120 consecutive patients, 104 after acute ischemic stroke (AIS), 76 receiving reperfusion therapy, and 16 patients after intracerebral hemorrhage (ICH) were enrolled in our study. Blood samples were obtained from patients at the time of admission, 24 h later, and 48 h later to determine the plasma level of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and high-sensitive Troponin I (hs-TnI). Twelve-lead ECGs were obtained at the time of admission, 24 h later, 48 h later, and at the release of the patients. Evaluations were performed using the National Institutes of Health Stroke Scale (NIHSS) at the time of admission and using the modified Rankin Scale (mRS) 90 days following the patient’s discharge from the hospital. Results: We observed a connection between lower TRAIL levels and stroke severity evaluated using the NIHSS (p = 0.044) on the first day. Lower TRAIL showed an association with severe disability and death as evaluated using the mRS at 90 days, both after 24 (p = 0.0022) and 48 h (p = 0.044) of hospitalization. Moreover, we observed an association between lower TRAIL and NT-proBNP elevation at the time of admission (p = 0.039), after 24 (p = 0.043), and after 48 h (p = 0.023) of hospitalization. In the ECG analysis, lower TRAIL levels were associated with the occurrence of premature ventricular extrasystoles (p = 0.043), and there was an association with prolonged QTc interval (p = 0.052). Conclusions: The results show that lower TRAIL is associated with stroke severity, unfavorable functional outcome, and short-term mortality in patients after acute ischemic stroke. Moreover, we described the association with markers of cardiac injury and ECG changes.

Body weight changes in patients with type 2 diabetes and a recent acute coronary syndrome: an analysis from the EXAMINE trial

BACKGROUND

Patients with type 2 diabetes (T2D) may experience frequent body weight changes over time. The prognostic impact of these weight changes (gains or losses) requires further study.

AIMS

To study the associations between changes in body weight (intentional or unintentional) with subsequent outcomes.

METHODS

The EXAMINE trial included 5380 patients with T2D and a recent acute coronary syndrome, who were randomized to alogliptin or placebo. Time-updated Cox models and mixed effects models were used to test the associations between changes in body weight and subsequent outcomes over a median follow-up of 1.6 (1.0-2.1) years.

RESULTS

During the post-randomization follow-up period, 1044 patients (19.4%) experienced a weight loss ≥ 5% of baseline weight, 2677 (49.8%) had a stable weight, and 1659 (30.8%) had a ≥ 5 % weight gain. Patients with weight loss were more frequently women and had more co-morbid conditions. In contrast, patients who gained ≥ 5% weight were more frequently men with less co-morbid conditions. A weight loss ≥ 5% was independently associated with a higher risk of subsequent adverse outcomes, including all-cause mortality: adjusted HR (95% CI) = 1.79 (1.33-2.42), P < 0.001. Similar associations were found for cardiovascular mortality, the composite of cardiovascular mortality or heart failure hospitalization, and the primary outcome. A weight gain ≥ 5% was independently associated with an increase in the risk of subsequent cardiovascular mortality or heart failure hospitalization only: adjusted HR (95% CI) = 1.34 (1.02-1.76), P = 0.033.

CONCLUSIONS

In patients with T2D who had a recent ACS/MI, a ≥ 5% loss of body weight was associated with a higher risk of subsequent cardiovascular events and mortality.

Diabetes and Heart Failure: Multi-Omics Approaches

Diabetes and heart failure, as important global issues, cause substantial expenses to countries and medical systems because of the morbidity and mortality rates. Most people with diabetes suffer from type 2 diabetes, which has an amplifying effect on the prevalence and severity of many health problems such as stroke, neuropathy, retinopathy, kidney injuries, and cardiovascular disease. Type 2 diabetes is one of the cornerstones of heart failure, another health epidemic, with 44% prevalence. Therefore, finding and targeting specific molecular and cellular pathways involved in the pathophysiology of each disease, either in diagnosis or treatment, will be beneficial. For diabetic cardiomyopathy, there are several mechanisms through which clinical heart failure is developed; oxidative stress with mediation of reactive oxygen species (ROS), reduced myocardial perfusion due to endothelial dysfunction, autonomic dysfunction, and metabolic changes, such as impaired glucose levels caused by insulin resistance, are the four main mechanisms. In the field of oxidative stress, advanced glycation end products (AGEs), protein kinase C (PKC), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) are the key mediators that new omics-driven methods can target. Besides, diabetes can affect myocardial function by impairing calcium (Ca) homeostasis, the mechanism in which reduced protein phosphatase 1 (PP1), sarcoplasmic/endoplasmic reticulum Ca2+ ATPase 2a (SERCA2a), and phosphorylated SERCA2a expressions are the main effectors. This article reviewed the recent omics-driven discoveries in the diagnosis and treatment of type 2 diabetes and heart failure with focus on the common molecular mechanisms.

Patient Phenotypes and SGLT-2 Inhibition in Type 2 Diabetes: Insights From the EMPA-REG OUTCOME Trial

OBJECTIVES

Using latent class analysis (LCA) of EMPA-REG OUTCOME (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients), this study identified distinct phenotypes in subjects with type 2 diabetes (T2D) and cardiovascular (CV) disease and explored treatment effects across phenotypes.

BACKGROUND

In the EMPA-REG OUTCOME trial, empagliflozin reduced risk of CV death or hospitalization for heart failure (HHF) by 34% in subjects with T2D and CV disease. Among such subjects, there has been limited evaluation of clinical phenotypes.

METHODS

Overall, 7,020 participants were treated with empagliflozin 25 mg, 10 mg, or placebo. For this post hoc analysis, participants were randomly separated into training (two-thirds of patients) and validation (remaining one-third) sets. LCA identified 3 phenotype groups (n = 6,639 with complete data). The phenotype association with CV death or HHF and empagliflozin treatment effect across groups was explored by Cox regression (in training and validation sets).

RESULTS

In the training set, phenotype group 1 (n = 1,463; 33.1%) included younger patients with shorter T2D duration and the highest estimated glomerular filtration rate (eGFR). Phenotype group 2 (n = 1,172; 26.5%) included more women with non-coronary artery disease. Phenotype group 3 (n = 1,785; 40.4%) included older patients with advanced coronary disease and the lowest eGFR. The risk of CV death varied across phenotypes (group 2 vs. 1: hazard ratio [HR]; 1.83; 95% confidence interval [CI]: 1.23 to 2.71; group 3 vs. 1: HR: 1.86; 95% CI: 1.30 to 2.67) with similar patterns for CV death or HHF. Consistent treatment effects of empagliflozin were seen across phenotypes in the training and validation sets (interaction p > 0.30).

CONCLUSIONS

Among participants with T2D, this study identified 3 phenotypes with varying CV risk. The treatment effect across phenotypes reaffirms the robustness of CV death or HHF reduction with empagliflozin. (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients [EMPA-REG OUTCOME]; NCT01131676).

Proteomics to improve phenotyping in obese patients with heart failure with preserved ejection fraction

AIMS

Recent evidence points towards a distinct obese phenotype among patients with heart failure with preserved ejection fraction (HFpEF). We aimed to identify differentially expressed circulating biomarkers in obese HFpEF patients and link them to disease severity and outcomes.

METHODS AND RESULTS

From the LIFE-Heart study, 999 patients with HFpEF and 999 patients without heart failure (no-HF) were selected and 92 circulating serum biomarkers were measured using a proximity extension assay. Elevation of identified biomarkers was validated in 220 patients from the Aldo-DHF trial with diagnosed HFpEF. HFpEF patients were older and had more comorbidities including coronary artery disease and type 2 diabetes as compared to no-HF patients (P < 0.05 for all). After adjusting for covariates, adrenomedullin (ADM), galectin-9 (Gal-9), thrombospondin-2 (THBS-2), CD4, and tumour necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2) were significantly higher in obese HFpEF patients [body mass index (BMI) ≥30 kg/m² , n = 464] as compared to lean HFpEF (BMI <30 kg/m² , n = 535) and obese no-HF patients (BMI ≥30 kg/m² , n = 387) (P < 0.001 for both); these findings were verified in the Aldo-DHF validation cohort (P < 0.001). Except for CD4 these proteins were associated with increased estimates of left atrial pressure in a linear fashion. Importantly, ADM and CD4 were associated with increased mortality in obese HFpEF patients after adjusting for covariates.

CONCLUSION

Obese HFpEF patients exhibit higher circulating biomarkers of volume expansion (ADM), myocardial fibrosis (THBS-2) and systemic inflammation (Gal-9, CD4) compared to obese non-HFpEF or lean HFpEF patients. These findings support the clinical definition of a distinct obese HFpEF phenotype and might merit further investigation.

Red cell distribution width in patients with diabetes and myocardial infarction: An analysis from the EXAMINE trial

AIM

To determine the clinical correlates of increased red blood cell distribution width (RDW), its potential mechanistic association with multiple circulating biomarkers, and its prognostic value in patients with type 2 diabetes (T2D) who had a recent acute coronary syndrome.

METHODS

We used time-updated Cox models applied to patients enrolled in the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care (EXAMINE) trial.

RESULTS

A total of 5380 patients were included, the median age was 61 years and 32% were women. Patients with higher RDW were older, more frequently women, with a longer diabetes duration and increased co-morbidities. An RDW of more than 16.1% (both baseline and time-updated) was independently associated with the study primary composite outcome of non-fatal myocardial infarction, non-fatal stroke or cardiovascular death (time-updated adjusted HR = 1.36, 95% CI = 1.16-1.61, p < .001), all-cause death (time-updated adjusted HR = 2.01, 95% CI = 1.60-2.53, p < .001), as well as mortality from non-cardiovascular causes (time-updated adjusted HR = 2.67, 95% CI = 1.72-4.15, p < .001). RDW had a weak-to-moderate correlation with haemoglobin and circulating markers that reflected inflammation, apoptosis, fibrosis and congestion. Alogliptin did not alter RDW values.

CONCLUSIONS

RDW is a marker of disease severity associated with a multitude of poor outcomes, including both cardiovascular and non-cardiovascular death. RDW correlated modestly with inflammatory, pro-apoptotic, pro-fibrotic and congestion markers, and its levels were not affected by alogliptin during the course of the trial.

TRAIL and Cardiovascular Disease-A Risk Factor or Risk Marker: A Systematic Review

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a pro-apoptotic protein showing broad biological functions. Data from animal studies indicate that TRAIL may possibly contribute to the pathophysiology of cardiomyopathy, atherosclerosis, ischemic stroke and abdominal aortic aneurysm. It has been also suggested that TRAIL might be useful in cardiovascular risk stratification. This systematic review aimed to evaluate whether TRAIL is a risk factor or risk marker in cardiovascular diseases (CVDs) focusing on major adverse cardiovascular events. Two databases (PubMed and Cochrane Library) were searched until December 2020 without a year limit in accordance to the PRISMA guidelines. A total of 63 eligible original studies were identified and included in our systematic review. Studies suggest an important role of TRAIL in disorders such as heart failure, myocardial infarction, atrial fibrillation, ischemic stroke, peripheral artery disease, and pulmonary and gestational hypertension. Most evidence associates reduced TRAIL levels and increased TRAIL-R2 concentration with all-cause mortality in patients with CVDs. It is, however, unclear whether low TRAIL levels should be considered as a risk factor rather than a risk marker of CVDs. Further studies are needed to better define the association of TRAIL with cardiovascular diseases.

An Overview of FGF-23 as a Novel Candidate Biomarker of Cardiovascular Risk

Fibroblast growth factor-23 (FGF)-23 is a phosphaturic hormone involved in mineral bone metabolism that helps control phosphate homeostasis and reduces 1,25-dihydroxyvitamin D synthesis. Recent data have highlighted the relevant direct FGF-23 effects on the myocardium, and high plasma levels of FGF-23 have been associated with adverse cardiovascular outcomes in humans, such as heart failure and arrhythmias. Therefore, FGF-23 has emerged as a novel biomarker of cardiovascular risk in the last decade. Indeed, experimental data suggest FGF-23 as a direct mediator of cardiac hypertrophy development, cardiac fibrosis and cardiac dysfunction via specific myocardial FGF receptor (FGFR) activation. Therefore, the FGF-23/FGFR pathway might be a suitable therapeutic target for reducing the deleterious effects of FGF-23 on the cardiovascular system. More research is needed to fully understand the intracellular FGF-23-dependent mechanisms, clarify the downstream pathways and identify which could be the most appropriate targets for better therapeutic intervention. This review updates the current knowledge on both clinical and experimental studies and highlights the evidence linking FGF-23 to cardiovascular events. The aim of this review is to establish the specific role of FGF-23 in the heart, its detrimental effects on cardiac tissue and the possible new therapeutic opportunities to block these effects.

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.