Cardiac and renal function interactions in heart failure with reduced ejection fraction: A mathematical modeling analysis

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study's goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains-DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NFκB factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/β-catenin pathway, the renin-angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein-protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments.

Understanding heterogeneous mechanisms of heart failure with preserved ejection fraction through cardiorenal mathematical modeling

In contrast to heart failure (HF) with reduced ejection fraction (HFrEF), effective interventions for HF with preserved ejection fraction (HFpEF) have proven elusive, in part because it is a heterogeneous syndrome with incompletely understood pathophysiology. This study utilized mathematical modeling to evaluate mechanisms distinguishing HFpEF and HFrEF. HF was defined as a state of chronically elevated left ventricle end diastolic pressure (LVEDP > 20mmHg). First, using a previously developed cardiorenal model, sensitivities of LVEDP to potential contributing mechanisms of HFpEF, including increased myocardial, arterial, or venous stiffness, slowed ventricular relaxation, reduced LV contractility, hypertension, or reduced venous capacitance, were evaluated. Elevated LV stiffness was identified as the most sensitive factor. Large LV stiffness increases alone, or milder increases combined with either decreased LV contractility, increased arterial stiffness, or hypertension, could increase LVEDP into the HF range without reducing EF. We then evaluated effects of these mechanisms on mechanical signals of cardiac outward remodeling, and tested the ability to maintain stable EF (as opposed to progressive EF decline) under two remodeling assumptions: LV passive stress-driven vs. strain-driven remodeling. While elevated LV stiffness increased LVEDP and LV wall stress, it mitigated wall strain rise for a given LVEDP. This suggests that if LV strain drives outward remodeling, a stiffer myocardium will experience less strain and less outward dilatation when additional factors such as impaired contractility, hypertension, or arterial stiffening exacerbate LVEDP, allowing EF to remain normal even at high filling pressures. Thus, HFpEF heterogeneity may result from a range of different pathologic mechanisms occurring in an already stiffened myocardium. Together, these simulations further support LV stiffening as a critical mechanism contributing to elevated cardiac filling pressures; support LV passive strain as the outward dilatation signal; offer an explanation for HFpEF heterogeneity; and provide a mechanistic explanation distinguishing between HFpEF and HFrEF.

Salsolinol improves angiotensin II‑induced myocardial fibrosis in vitro via inhibition of LSD1 through regulation of the STAT3/Notch‑1 signaling pathway

The clinical incidence of congestive heart failure (CHF) is very high and it poses a significant threat to the health of patients. The traditional Chinese medicine monomer salsolinol is widely used to treat similar symptoms of CHF. However, there have been no reports on the effect of salsolinol for the management of CHF and its effects on myocardial fibrosis. In the present study, salsolinol was used to treat angiotensin II (AngII)-induced human cardiac fibroblasts (HCFs) and cell proliferation and migration were assessed using a CCK-8, EdU staining assay and wound healing assay. Subsequently, immunofluorescence, western blotting and other techniques were used to detect indicators associated with cell fibrosis and relevant kits were used to detect markers of cellular inflammation and reactive oxygen species (ROS) production. Molecular docking analysis was used to predict the relationship between salsolinol and lysine-specific histone demethylase 1A (LSD1). Subsequently, the expression of LSD1 in the serum of CHF patients was detected by reverse transcription-quantitative PCR. Finally, LSD1 was overexpressed in cells to explore the regulatory mechanism of salsolinol in AngII-induced HFCs. Salsolinol reduced the proliferation and migration. Salsolinol reduced the expression of fibrosis marker proteins α-smooth muscle actin, Collagen I and Collagen III in a concentration-dependent manner, thereby reducing cell fibrosis. In addition, salsolinol reduced the levels of TNF-α and IL-6 in the cell supernatant and ROS production following AngII induction. Salsolinol inhibited LSD1 expression and regulated the STAT3/Notch-1 signaling pathway. Upregulation of LSD1 reversed the effects of salsolinol on AngII-induced HCFs. Salsolinol inhibited LSD1 via regulation of the STAT3/Notch-1 signaling pathway to improve Ang II-induced myocardial fibrosis in vitro.

A systematic review of cardiac in-silico clinical trials

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

Understanding the Mechanisms and Treatment of Heart Failure: Quantitative Systems Pharmacology Models with a Focus on SGLT2 Inhibitors and Sex-Specific Differences

Heart failure (HF), which is a major clinical and public health challenge, commonly develops when the myocardial muscle is unable to pump an adequate amount of blood at typical cardiac pressures to fulfill the body's metabolic needs, and compensatory mechanisms are compromised or fail to adjust. Treatments consist of targeting the maladaptive response of the neurohormonal system, thereby decreasing symptoms by relieving congestion. Sodium-glucose co-transporter 2 (SGLT2) inhibitors, which are a recent antihyperglycemic drug, have been found to significantly improve HF complications and mortality. They act through many pleiotropic effects, and show better improvements compared to others existing pharmacological therapies. Mathematical modeling is a tool used to describe the pathophysiological processes of the disease, quantify clinically relevant outcomes in response to therapies, and provide a predictive framework to improve therapeutic scheduling and strategies. In this review, we describe the pathophysiology of HF, its treatment, and how an integrated mathematical model of the cardiorenal system was built to capture body fluid and solute homeostasis. We also provide insights into sex-specific differences between males and females, thereby encouraging the development of more effective sex-based therapies in the case of heart failure.

Suppression of Cardiogenic Edema with Sodium-Glucose Cotransporter-2 Inhibitors in Heart Failure with Reduced Ejection Fraction: Mechanisms and Insights from Pre-Clinical Studies

In heart failure with reduced ejection fraction (HFrEF), cardiogenic edema develops from impaired cardiac function, pathological remodeling, chronic inflammation, endothelial dysfunction, neurohormonal activation, and altered nitric oxide-related pathways. Pre-clinical HFrEF studies have shown that treatment with sodium-glucose cotransporter-2 inhibitors (SGLT-2i) stimulates natriuretic and osmotic/diuretic effects, improves overall cardiac function, attenuates maladaptive cardiac remodeling, and reduces chronic inflammation, oxidative stress, and endothelial dysfunction. Here, we review the mechanisms and effects of SGLT-2i therapy on cardiogenic edema in various models of HFrEF. Overall, the data presented suggest a high translational importance of these studies, and pre-clinical studies show that SGLT-2i therapy has a marked effect on suppressing the progression of HFrEF through multiple mechanisms, including those that affect the development of cardiogenic edema.

Sodium-Glucose Cotransporter-2 Inhibitors Improve Heart Failure with Reduced Ejection Fraction Outcomes by Reducing Edema and Congestion

Pathological sodium-water retention or edema/congestion is a primary cause of heart failure (HF) decompensation, clinical symptoms, hospitalization, reduced quality of life, and premature mortality. Sodium-glucose cotransporter-2 inhibitors (SGLT-2i) based therapies reduce hospitalization due to HF, improve functional status, quality, and duration of life in patients with HF with reduced ejection fraction (HFrEF) independently of their glycemic status. The pathophysiologic mechanisms and molecular pathways responsible for the benefits of SGLT-2i in HFrEF remain inconclusive, but SGLT-2i may help HFrEF by normalizing salt-water homeostasis to prevent clinical edema/congestion. In HFrEF, edema and congestion are related to compromised cardiac function. Edema and congestion are further aggravated by renal and pulmonary abnormalities. Treatment of HFrEF patients with SGLT-2i enhances natriuresis/diuresis, improves cardiac function, and reduces natriuretic peptide plasma levels. In this review, we summarize current clinical research studies related to outcomes of SGLT-2i treatment in HFrEF with a specific focus on their contribution to relieving or preventing edema and congestion, slowing HF progression, and decreasing the rate of rehospitalization and cardiovascular mortality.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps-impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.

Predicted Cardiac Functional Responses to Renal Actions of SGLT2i in the DAPACARD Trial Population: A Mathematical Modeling Analysis

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) have been shown to reduce the risk of worsening heart failure (HF) in subjects with HF and a reduced ejection fraction (HFrEF) in multiple clinical trials. The DAPACARD clinical trial was conducted to examine the effects of DAPAgliflozin on CARDiac substrate uptake, myocardial efficiency, and myocardial contractile work in type 2 diabetes mellitus (T2DM) subjects. As a complement to the clinical study, a mechanistic mathematical model of cardiorenal physiology was used to quantify the influence of established natriuretic/diuretic effects of SGLT2i on cardiac function (myocardial efficiency and global longitudinal strain). Virtual participants reflecting the participant-level characteristics in the DAPACARD trial were produced by varying model parameters over physiologically plausible ranges. A second virtual population was generated by inducing a state of HFrEF in the DAPACARD T2DM virtual participants (DAPACARD-HFrEF virtual participants) for comparison. Cardiac responses to placebo and SGLT2i were simulated over 42 days. Cardiac hemodynamic improvements were predicted in DAPACARD-HFrEF virtual participants but not in DAPACARD virtual participants. In particular, the natriuresis/diuresis induced by SGLT2i improved the global longitudinal strain and myocardial efficiency in DAPACARD-HFrEF virtual participants within the first 14 days (change from baseline: global longitudinal strain: -0.95% and myocardial efficiency: 0.34%), whereas the global longitudinal strain and myocardial efficiency in DAPACARD virtual participants were slightly worse (change from baseline: global longitudinal strain: 0.35% and myocardial efficiency: -0.01%). The results of the DAPACARD virtual participants modeling were in line with the clinical data but do not preclude additional effects from other mechanisms of SGLT2i. This article is protected by copyright. All rights reserved.

Higher extracellular water to total body water ratio was associated with chronic kidney disease progression in type 2 diabetes

AIM

We studied the association between extracellular volume status and chronic kidney disease (CKD) progression; and the role of extracellular volume excess as a potential mediator in the relationship between matrix metalloproteinases (MMP)-2 and CKD progression in Type 2 diabetes mellitus (T2DM).

METHODS

We conducted a prospective cohort study of 1079 T2DM patients. Bioelectrical impedance analysis (BIA) was performed to assess body fluid status.

RESULTS

After up to 8.6 years of follow-up, 471 (43.7%) patients experienced CKD progression. In the fully adjusted model, extracellular water (ECW)/ total body water (TBW)ratios 0.39-0.40 and > 0.40 were associated with 45% and 78% higher risk of CKD progression respectively. Patients with an increase in ECW/TBW ratio had 40% higher risk of CKD progression compared to those with no change or reduction of ECW/TBW ratio. Higher ECW/TBW ratio accounted for 17.4% of the relationship between MMP-2 and CKD progression in T2DM (p = 0.026).

CONCLUSIONS

Extracellular volume excess was independently associated with CKD progression in T2DM. Higher ECW/TBW ratio mediated the positive association between MMP-2 and CKD progression. Further studies are needed to elucidate the role of extracellular volume excess in deterioration of renal function.

Cardiorenal Systems Modeling: Left Ventricular Hypertrophy and Differential Effects of Antihypertensive Therapies on Hypertrophy Regression

Cardiac and renal function are inextricably connected through both hemodynamic and neurohormonal mechanisms, and the interaction between these organ systems plays an important role in adaptive and pathophysiologic remodeling of the heart, as well as in the response to renally acting therapies. Insufficient understanding of the integrative function or dysfunction of these physiological systems has led to many examples of unexpected or incompletely understood clinical trial results. Mathematical models of heart and kidney physiology have long been used to better understand the function of these organs, but an integrated model of renal function and cardiac function and cardiac remodeling has not yet been published. Here we describe an integrated cardiorenal model that couples existing cardiac and renal models, and expands them to simulate cardiac remodeling in response to pressure and volume overload, as well as hypertrophy regression in response to angiotensin receptor blockers and beta-blockers. The model is able to reproduce different patterns of hypertrophy in response to pressure and volume overload. We show that increases in myocyte diameter are adaptive in pressure overload not only because it normalizes wall shear stress, as others have shown before, but also because it limits excess volume accumulation and further elevation of cardiac stresses by maintaining cardiac output and renal sodium and water balance. The model also reproduces the clinically observed larger LV mass reduction with angiotensin receptor blockers than with beta blockers. We further provide a mechanistic explanation for this difference by showing that heart rate lowering with beta blockers limits the reduction in peak systolic wall stress (a key signal for myocyte hypertrophy) relative to ARBs.