Therapeutic futility and phenotypic heterogeneity in heart failure with preserved ejection fraction: what is the role of bionic learning?

Kao, David; Purohit, Suneet; Jhund, Pardeep S.

doi:10.1002/ejhf.1658

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…As the clinical end point of numerous genetic and environmental conditions, HF constitutes a multifaceted and often intractable syndrome ( 45 , 46 ). Owing to its widespread clinical heterogeneity that impedes the development of effective therapies for HF ( 8 ), the current multi-cohort study sought to identify the distinct features of end-stage HF that reflect differences in the cardiac epigenome.…”

Section: Discussionmentioning

confidence: 99%

Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure

Pepin

Potter

et al. 2021

American Journal of Physiology-Heart and Circulatory Physiology

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Heart Failure (HF) is a multifactorial syndrome that remains a leading cause of worldwide morbidity. Despite its high prevalence, only half of HF patients respond to guideline-directed medical management, prompting therapeutic efforts to confront the molecular underpinnings of its heterogeneity. In the current study, we examined epigenetics as a yet unexplored source of heterogeneity among patients with end-stage HF. Specifically, a multicohort-based study was designed to quantify cardiac genome-wide cytosine-p-guanine (CpG) methylation of cardiac biopsies from male patients undergoing left ventricular assist device (LVAD) implantation. In both pilot (n = 11) and testing (n = 31) cohorts, unsupervised multidimensional scaling of genome-wide myocardial DNA methylation exhibited a bimodal distribution of CpG methylation found largely to occur in the promoter regions of metabolic genes. Among the available patient attributes, only categorical self-identified patient race could delineate this methylation signature, with African American (AA) and Caucasian American (CA) samples clustering separately. Because race is a social construct, and thus a poor proxy of human physiology, extensive review of medical records was conducted, but ultimately failed to identify co-variates of race at the time of LVAD surgery. By contrast, retrospective analysis exposed a higher all-cause mortality among AA (56.3%) relative to CA (16.7%) patients at 2 years following LVAD placement (P=0.03). Geocoding-based approximation of patient demographics uncovered disparities in income levels among AA relative to CA patients. Therefore, although additional studies are needed, the current analysis implicates cardiac DNA methylation as a previously unrecognized indicator of socioeconomic disparity in human heart failure outcomes.

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Section: Discussionmentioning

confidence: 99%

Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure

Pepin

Potter

et al. 2021

American Journal of Physiology-Heart and Circulatory Physiology

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“…Most CVDs are heterogeneous, 38 which means patients with the same CVD may have distinct etiologies, clinical characteristics, auxiliary examination results, outcomes, and therapeutic responses. Therefore, there is an urgent need to integrate data from different sources to make the classification of a disease more accurate.…”

Section: Ai-assisted Classification Of Cvdsmentioning

confidence: 99%

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

Shu

Ren

Song

2021

Circ J

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With the rapid development of artificial intelligence (AI) and machine learning (ML), as well as the arrival of the big data era, technological innovations have occurred in the field of cardiovascular medicine. First, the diagnosis of cardiovascular diseases (CVDs) is highly dependent on assistive examinations, the interpretation of which is time consuming and often limited by the knowledge level and clinical experience of doctors; however, AI could be used to automatically interpret the images obtained in auxiliary examinations. Second, some of the predictions of the incidence and prognosis of CVDs are limited in clinical practice by the use of traditional prediction models, but there may be occasions when AI-based prediction models perform well by using ML algorithms. Third, AI has been used to assist precise classification of CVDs by integrating a variety of medical data from patients, which helps better characterize the subgroups of heterogeneous diseases. To help clinicians better understand the applications of AI in CVDs, this review summarizes studies relating to AI-based diagnosis, prediction, and classification of CVDs. Finally, we discuss the challenges of applying AI to cardiovascular medicine.

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“…Epidemiologic studies have identified an array of genetic, 2,3 lifestyle and environmental 4 factors which ultimately lead to HFpEF as a clinical endpoint. [5][6][7] They have been shown to disrupt mitochondrial homeostasis in HFpEF, 8 ultimately yielding a metabolic signature that is distinct from HFrEF. 9 Preclinical models of HFpEF seek to reproduce the individual -and synergistic -myocardial consequences of obesity, 10 diabetes mellitus, 11 hypertension, 12 and aging.…”

Section: Introductionmentioning

confidence: 99%

Genetic Loss of Nicotinamide Nucleotide Transhydrogenase Prevents from Cardiometabolic Heart Failure with Preserved Ejection Fraction

Pepin

Nazir

Konrad

et al. 2023

Preprint

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RationaleHeart failure with preserved ejection fraction (HFpEF) represents a common clinical endpoint of cardiometabolic diseases which impair myocardial diastolic relaxation. Although myocardial redox perturbations are known to accompany HFpEF, the specific role of mitochondrial oxidative stress has not been demonstrated yet.ObjectiveBased on an observation that C57BL6/N – but not C57BL6/J – mice develop diastolic dysfunction when provided anad libitumhigh-fat and 0.5% N(ω)-nitro-L-arginine methyl ester (HFD+L-NAME) diet, we conducted a multi-cohort murine study to determine whether the loss of Nicotinamide Nucleotide Transhydrogenase (NNT), a mitochondrial transhydrogenase that couples NADPH:NADP+to NADH:NAD+homeostasis, protects mice from developing cardiometabolic alterations.Methods and ResultsTwo cohorts of 12-week-old male and female mice possessing wild-type (Nnt+/+) or deleted (Nnt-/-) NNT were challenged by HFD+L-NAME for 9 weeks (n = 6-10). MaleNnt+/+mice developed obesity (23.2% Δ,P= 0.003), arterial hypertension (24 ± 5 Δ mmHg,P= 0.023), impaired glucose tolerance (P= 0.006), and reduced maximal treadmill running distance (−172 ± 73.1 Δ m,P= 0.006) following 9 weeks HFD+L-NAME, whereas maleNnt-/-mice did not. Female mice were protected from cardiometabolic dysfunction regardless ofNntgenotype. Cardiac functional and morphologic characterization revealed similar NNT-dependent and sex-specific increases in E/e’ (42.8 vs. 21.5,P< 0.001) and E/A (2.3 vs 1.4,P= 0.007) ratios, diastolic stiffness (0.09 vs 0.04 mmHg/μL,P= 0.02), and myocardial fibrosis (P= 0.02). Unsupervised transcriptomic analysis identified distinct genetic and dietary signatures, whereinNnt+/+exhibited disproportionate perturbations in various mitochondrial oxidative pathways following HFD+L-NAME. Our search for putative transcriptional regulators identified NNT-dependent suppression of NAD+ dependent deacetylaseSirt3.ConclusionsTaken together, these observations support that the genetic disruption ofNntprotects against both cardiac and metabolic consequences of HFD+L-NAME, thus highlighting a novel etiology-specific avenue for HFpEF therapeutics.

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Therapeutic futility and phenotypic heterogeneity in heart failure with preserved ejection fraction: what is the role of bionic learning?

Cited by 4 publications

References 14 publications

Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure

Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

Genetic Loss of Nicotinamide Nucleotide Transhydrogenase Prevents from Cardiometabolic Heart Failure with Preserved Ejection Fraction

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