Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

Genetic and epigenetic bases of long-term adverse effects of childhood cancer therapy

Over the past decade, genome-scale molecular profiling of large childhood cancer survivorship cohorts has led to unprecedented advances in our understanding of the genetic and epigenetic bases of therapy-related adverse health outcomes in this vulnerable population. To facilitate the integration of knowledge generated from these studies into formulating next-generation precision care for survivors of childhood cancer, we summarize key findings of genetic and epigenetic association studies of long-term therapy-related adverse effects including subsequent neoplasms and cardiomyopathies among others. We also discuss therapy-related genotoxicities including clonal haematopoiesis and DNA methylation, which may underlie accelerated molecular ageing. Finally, we highlight enhanced risk prediction models for survivors of childhood cancer that incorporate both genetic factors and treatment exposures, aiming to achieve enhanced accuracy in predicting risks for this population. These new insights will hopefully inspire future studies that harness both expanding omics resources and evolving data science methodology to accelerate the translation of precision medicine for survivors of childhood cancer.

The dual function of autophagy in doxorubicin-induced cardiotoxicity: Mechanism and natural products

Doxorubicin (DOX) is an anthracycline antitumor drug discovered in 1969, which can care for leukemia, breast cancer, lymphoma, and sarcoma. However, cardiotoxicity induced by DOX seriously limits its clinical value. The etiopathogenesis and therapeutic strategies are not unified. Autophagy is a critical mechanism in the progression of DOX-induced cardiotoxicity (DIC), autophagy intervention is a potential therapeutic strategy for DIC. Natural product has been considered as a complementary and alternative approach to treat cardiovascular disease. In this review, we summarize the pathology of autophagy in DIC and the natural products for DIC therapy.

Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines

BACKGROUND

Anthracyclines are essential in pediatric cancer treatment, but patients are at risk cancer therapy-related cardiac dysfunction (CTRCD). Standardized definitions by the International Cardio-Oncology Society (IC-OS) aim to enhance precision in risk assessment.

OBJECTIVES

Categorize distinct phenotypes among pediatric patients undergoing anthracycline chemotherapy using unsupervised machine learning.

METHODS

Pediatric cancer patients undergoing anthracycline chemotherapy at our institution were retrospectively included. Clinical and echocardiographic data at baseline, along with follow-up data, were collected from patient records. Unsupervised machine learning was performed, involving dimensionality reduction using principal component analysis and K-means clustering to identify different phenotypic clusters. Identified phenogroups were analyzed for associations with CTRCD, defined following contemporary IC-OS definitions, and hypertensive response.

RESULTS

A total of 187 patients (63.1% male, median age 15.5 years [10.4-18.7]) were included and received anthracycline chemotherapy with a median treatment duration of 0.66 years [0.35-1.92]. Median follow-up duration was 2.78 years [1.31-4.21]. Four phenogroups were identified with following distribution: Cluster 0 (32.6%, n = 61), Cluster 1 (13.9%, n = 26), Cluster 2 (24.6%, n = 46), and Cluster 3 (28.9%, n = 54). Cluster 0 showed the highest risk of moderate CTRCD (HR: 3.10 [95% CI: 1.18-8.16], P = 0.022) compared to other clusters. Cluster 3 demonstrated a protective effect against hypertensive response (HR: 0.30 [95% CI: 0.13- 0.67], P = 0.003) after excluding baseline hypertensive patients. Longitudinal assessments revealed differences in global longitudinal strain and systolic blood pressure among phenogroups.

CONCLUSIONS

Unsupervised machine learning identified distinct phenogroups among pediatric cancer patients undergoing anthracycline chemotherapy, offering potential for personalized risk assessment.

Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy

BACKGROUND

Despite routine echocardiographic surveillance for childhood cancer survivors, the ability to predict cardiomyopathy risk in individual patients is limited. We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors using serial echocardiograms from five centers.

METHODS

We designed a series of deep convolutional neural networks (DCNNs) for prediction of cardiomyopathy (shortening fraction ≤ 28% or ejection fraction ≤ 50% on two occasions) for at-risk survivors ≥ 1-year post initial cancer therapy. We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. We used holdout subsets in a 10-fold cross-validation framework and standard metrics to assess model performance (e.g., F1-score, area under the precision-recall curve [AUPRC]). Performance of the input formats was compared using a combined 5 × 2 cross-validation F-test.

RESULTS

The dataset included 542 pairs of montages: 171 montage pairs from 45 cases at time of cardiomyopathy diagnosis or pre-diagnosis and 371 pairs from 70 at-risk survivors who didn't develop cardiomyopathy during follow-up (non-case). The DCNN trained to distinguish between non-case and time of cardiomyopathy diagnosis or pre-diagnosis case montages achieved an AUROC of 0.89 ± 0.02, AUPRC 0.83 ± 0.03, and F1-score: 0.76 ± 0.04. When limited to smaller subsets of case data (e.g., ≥ 1 or 2 years pre-diagnosis), performance worsened. Model input format did not impact performance accuracy across models.

CONCLUSIONS

This methodology is a promising first step toward development of a DCNN capable of accurately differentiating pre-diagnosis versus non-case echocardiograms to predict survivors more likely to develop cardiomyopathy.

Making sense of artificial intelligence and large language models-including ChatGPT-in pediatric hematology/oncology

ChatGPT and other artificial intelligence (AI) systems have captivated the attention of healthcare providers and researchers for their potential to improve care processes and outcomes. While these technologies hold promise to automate processes, increase efficiency, and reduce cognitive burden, their use also carries risks. In this commentary, we review basic concepts of AI, outline some of the capabilities and limitations of currently available tools, discuss current and future applications in pediatric hematology/oncology, and provide an evaluation and implementation framework that can be used by pediatric hematologist/oncologists considering the use of AI in clinical practice.

Pediatric Cardio-Oncology: Screening, Risk Stratification, and Prevention of Cardiotoxicity Associated with Anthracyclines

Anthracyclines have significantly improved the survival of children with malignant tumors, but the associated cardiotoxicity, an effect now under the purview of pediatric cardio-oncology, due to its cumulative and irreversible effects on the heart, limits their clinical application. A systematic screening and risk stratification approach provides the opportunity for early identification and intervention to mitigate, reverse, or prevent myocardial injury, remodeling, and dysfunction associated with anthracyclines. This review summarizes the risk factors, surveillance indexes, and preventive strategies of anthracycline-related cardiotoxicity to improve the safety and efficacy of anthracyclines.

Variants in structural cardiac genes in patients with cancer therapy-related cardiac dysfunction after anthracycline chemotherapy: a case control study

BACKGROUND

Variants in cardiomyopathy genes have been identified in patients with cancer therapy-related cardiac dysfunction (CTRCD), suggesting a genetic predisposition for the development of CTRCD. The diagnostic yield of genetic testing in a CTRCD population compared to a cardiomyopathy patient cohort is not yet known and information on which genes should be assessed in this population is lacking.

METHODS

We retrospectively included 46 cancer patients with a history of anthracycline induced CTRCD (defined as a decrease in left ventricular ejection fraction (LVEF) to < 50% and a ≥ 10% reduction from baseline by echocardiography). Genetic testing was performed for 59 established cardiomyopathy genes. Only variants of uncertain significance and (likely) pathogenic variants were included. Diagnostic yield of genetic testing was compared with a matched cohort of patients with dilated cardiomyopathy (DCM, n = 46) and a matched cohort of patients without cardiac disease (n = 111).

RESULTS

Average LVEF at time of CTRCD diagnosis was 30.1 ± 11.0%. Patients were 52.9 ± 14.6 years old at time of diagnosis and 30 (65.2%) were female. Most patients were treated for breast cancer or lymphoma, with a median doxorubicin equivalent dose of 300 mg/m2 [112.5-540.0]. A genetic variant, either pathogenic, likely pathogenic or of uncertain significance, was identified in 29/46 (63.0%) of patients with CTRCD, which is similar to the DCM cohort (34/46, 73.9%, p = 0.262), but significantly higher than in the negative control cohort (47/111, 39.6%, p = 0.018). Variants in TTN were the most prevalent in the CTRCD cohort (43% of all variants). All (likely) pathogenic variants identified in the CTRCD cohort were truncating variants in TTN. There were no significant differences in severity of CTRCD and in recovery rate in variant-harbouring individuals versus non-variant harbouring individuals.

CONCLUSIONS

In this case-control study, cancer patients with anthracycline-induced CTRCD have an increased burden of genetic variants in cardiomyopathy genes, similar to a DCM cohort. If validated in larger prospective studies, integration of genetic data in risk prediction models for CTRCD may guide cancer treatment. Moreover, genetic results have important clinical impact, both for the patient in the setting of precision medicine, as for the family members that will receive genetic counselling.

Association between genetic variants of transmembrane transporters and susceptibility to anthracycline-induced cardiotoxicity: Current understanding and existing evidence

Anthracyclines remain the cornerstone of numerous chemotherapeutic protocols, with beneficial effects against haematological malignancies and solid tumours. Unfortunately, the clinical usefulness of anthracyclines is compromised by the development of cardiotoxic side effects, leading to dose limitations or treatment discontinuation. There is no absolute linear correlation between the incidence of cardiotoxicity and the threshold dose, suggesting that genetic factors may modify the association between anthracyclines and cardiotoxicity risk. And the majority of single nucleotide polymorphisms (SNPs) associated with anthracycline pharmacogenomics were identified in the ATP-binding cassette (ABC) and solute carrier (SLC) transporters, generating increasing interest in the pharmacogenetic implications of their genetic variations for anthracycline-induced cardiotoxicity (AIC). This review focuses on the influence of SLC and ABC polymorphisms on AIC and highlights the prospects and clinical significance of pharmacogenetics for individualised preventive approaches.

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline

A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases

BACKGROUND

The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases.

METHODS

We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis.

FINDINGS

Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations.

CONCLUSIONS

ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases.

Advancing Cardio-Oncology in Asia

Cardio-oncology is an emerging multi-disciplinary field, which aims to reduce morbidity and mortality of cancer patients by preventing and managing cancer treatment-related cardiovascular toxicities. With the exponential growth in cancer and cardiovascular diseases in Asia, there is an emerging need for cardio-oncology awareness among physicians and country-specific cardio-oncology initiatives. In this state-of-the-art review, we sought to describe the burden of cancer and cardiovascular disease in Asia, a region with rich cultural and socio-economic diversity. From describing the uniqueness and challenges (such as socio-economic disparity, ethnical and racial diversity, and limited training opportunities) in establishing cardio-oncology in Asia, and outlining ways to overcome any barriers, this article aims to help advance the field of cardio-oncology in Asia.

Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

Pharmacogenetics of chemotherapy treatment response and -toxicities in patients with osteosarcoma: a systematic review

BACKGROUND

Osteosarcoma is the most common bone tumor in children and adolescents. Despite multiagent chemotherapy, only 71% of patients survives and these survivors often experience long-term toxicities. The main objective of this systematic review is to provide an overview of the discovery of novel associations of germline polymorphisms with treatment response and/or chemotherapy-induced toxicities in osteosarcoma.  METHODS: MEDLINE and Embase were systematically searched (2010-July 2022). Genetic association studies were included if they assessed > 10 germline genetic variants in > 5 genes in relevant drug pathways or if they used a genotyping array or other large-scale genetic analysis. Quality was assessed using adjusted STrengthening the REporting of Genetic Association studies (STREGA)-guidelines. To find additional evidence for the identified associations, literature was searched to identify replication studies.

RESULTS

After screening 1999 articles, twenty articles met our inclusion criteria. These range from studies focusing on genes in relevant pharmacokinetic pathways to whole genome sequencing. Eleven articles reported on doxorubicin-induced cardiomyopathy. For seven genetic variants in CELF4, GPR35, HAS3, RARG, SLC22A17, SLC22A7 and SLC28A3, replication studies were performed, however without consistent results. Ototoxicity was investigated in one study. Five small studies reported on mucosistis or bone marrow, nephro- and/or hepatotoxicity. Six studies included analysis for treatment efficacy. Genetic variants in ABCC3, ABCC5, FasL, GLDC, GSTP1 were replicated in studies using heterogeneous efficacy outcomes.

CONCLUSIONS

Despite that results are promising, the majority of associations were poorly reproducible due to small patient cohorts. For the future, hypothesis-generating studies in large patient cohorts will be necessary, especially for cisplatin-induced ototoxicity as these are largely lacking. In order to form large patient cohorts, national and international collaboration will be essential.

Predicting the effect of sirolimus on disease activity in patients with systemic lupus erythematosus using machine learning

WHAT IS KNOWN AND OBJECTIVES

The present study aimed to predict the effect of sirolimus on disease activity in patients with systemic lupus erythematosus (SLE) using machine learning and to recommend appropriate sirolimus dosage regimen for patients with SLE.

METHODS

The E_max model was selected for machine learning, where the evaluation indicator was the change rate of systemic lupus erythematosus disease activity index from baseline value.

RESULTS

A total 103 patients with SLE were included for modelling, where the E_max , ET₅₀ were -53.9%, 1.53 months in the final model respectively, and the evaluation of the final model was good. Further simulation found that the follow-up time to achieve 25%, 50%, 75% and 80% (plateau) E_max of sirolimus effecting on disease activity in patients with SLE were 0.51, 1.53, 4.59 and 6.12 months, respectively. In addition, the sirolimus dosage was flexible and adjusted according to drug concentration, where the intersection of sirolimus concentration range included in this study was about 8-10 ng/ml.

WHAT IS NEW AND CONCLUSIONS

This study was the first time to predict the effect of sirolimus on disease activity in patients with SLE and in order to achieve better therapeutic effect maintaining a concentration of 8-10 ng/ml sirolimus for at least 6.12 months was necessary.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Cardiovascular Imaging in Cardio-Oncology: The Role of Echocardiography and Cardiac MRI in Modern Cardio-Oncology

Cardiovascular (CV) events are an increasingly common limitation of effective anticancer therapy. Over the last decade imaging has become essential to patients receiving contemporary cancer therapy. Herein we discuss the current state of CV imaging in cardio-oncology. We also provide a practical apparatus for the use of imaging in everyday cardiovascular care of oncology patients to improve outcomes for those at risk for cardiotoxicity, or with established cardiovascular disease. Finally, we consider future directions in the field given the wave of new anticancer therapies.

Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data

Cancer mortality has improved due to earlier detection via screening, as well as due to novel cancer therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitions. However, similarly to older cancer therapies such as anthracyclines, these therapies have also been documented to cause cardiotoxic events including cardiomyopathy, myocardial infarction, myocarditis, arrhythmia, hypertension, and thrombosis. Imaging modalities such as echocardiography and magnetic resonance imaging (MRI) are critical in monitoring and evaluating for cardiotoxicity from these treatments, as well as in providing information for the assessment of function and wall motion abnormalities. MRI also allows for additional tissue characterization using T1, T2, extracellular volume (ECV), and delayed gadolinium enhancement (DGE) assessment. Furthermore, emerging technologies may be able to assist with these efforts. Nuclear imaging using targeted radiotracers, some of which are already clinically used, may have more specificity and help provide information on the mechanisms of cardiotoxicity, including in anthracycline mediated cardiomyopathy and checkpoint inhibitor myocarditis. Hyperpolarized MRI may be used to evaluate the effects of oncologic therapy on cardiac metabolism. Lastly, artificial intelligence and big data of imaging modalities may help predict and detect early signs of cardiotoxicity and response to cardioprotective medications as well as provide insights on the added value of molecular imaging and correlations with cardiovascular outcomes. In this review, the current imaging modalities used to assess for cardiotoxicity from cancer treatments are discussed, in addition to ongoing research on targeted molecular radiotracers, hyperpolarized MRI, as well as the role of artificial intelligence (AI) and big data in imaging that would help improve the detection and prognostication of cancer-treatment cardiotoxicity.

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.

The genetic underpinnings of anthracycline-induced cardiomyopathy predisposition

Anthracyclines, chemotherapeutic agents that have contributed to significant improvements in cancer survival, also carry risk of both acute and chronic cardiotoxicity. This has led to significantly elevated risks of cardiac morbidity and mortality among cancer survivors treated with these agents. Certain treatment related, demographic, and medical factors increase an individual's risk of anthracycline induced cardiotoxicity; however, significant variability among those affected suggests that there is an underlying genetic predisposition to anthracycline induced cardiotoxicity. The current narrative review seeks to summarize the literature to date that has identified genetic variants associated with anthracycline induced cardiotoxicity. These include variants found in genes that encode proteins associated with anthracycline transportation and metabolism, those that encode proteins associated with the generation of reactive oxygen species, and those known to be associated with cardiac disease. While there is strong evidence that susceptibility to anthracycline induced cardiotoxicity has genetic underpinnings, the majority of work to date has been candidate gene analyses. Future work should focus on genome-wide analyses including genome-wide association and sequencing-based studies to confirm and expand these findings.

Heart Failure With Targeted Cancer Therapies: Mechanisms and Cardioprotection

Oncology has seen growing use of newly developed targeted therapies. Although this has resulted in dramatic improvements in progression-free and overall survival, challenges in the management of toxicities related to longer-term treatment of these therapies have also become evident. Although a targeted approach often exploits the differences between cancer cells and noncancer cells, overlap in signaling pathways necessary for the maintenance of function and survival in multiple cell types has resulted in systemic toxicities. In particular, cardiovascular toxicities are of important concern. In this review, we highlight several targeted therapies commonly used across a variety of cancer types, including HER2 (human epidermal growth factor receptor 2)+ targeted therapies, tyrosine kinase inhibitors, immune checkpoint inhibitors, proteasome inhibitors, androgen deprivation therapies, and MEK (mitogen-activated protein kinase kinase)/BRAF (v-raf murine sarcoma viral oncogene homolog B) inhibitors. We present the oncological indications, heart failure incidence, hypothesized mechanisms of cardiotoxicity, and potential mechanistic rationale for specific cardioprotective strategies.