Deep learning cardiac motion analysis reveals the dynamic pathophysiology and genetic architecture of heart failure with preserved ejection fraction

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Abstract

BackgroundHeart failure with preserved ejection fraction (HFpEF) accounts for approximately half of the more than 64 million heart failure cases globally, yet its pathophysiology is incompletely understood, its molecular determinants are poorly defined, and disease-specific therapies remain limited. Comprehensive characterization of the cardiac motion abnormalities central to HFpEF has not been feasible at the population level. MethodsWe developed a deep learning framework integrating image segmentation with optical flow motion analysis and applied it to standard cine cardiac magnetic resonance images from 83,569 UK Biobank participants, deriving 32 myocardial and inner cavity velocity phenotypes spanning the cardiac cycle, including mid-diastolic velocities not previously quantified at population scale. We examined the prognostic relevance, genetic architecture, and candidate causal mediators of the optical flow velocity phenotypes. ResultsIn a pragmatically defined HFpEF subcohort, mid-diastolic and late-diastolic optical flow velocities were reduced relative to healthy reference participants, while higher systolic left ventricular myocardial velocity was associated with lower all-cause mortality (HR 0.61 per SD; 95% CI 0.45-0.82). Genome-wide association analyses identified 12 risk loci associated with optical flow velocity phenotypes. Phospholamban (PLN), the canonical regulator of sarcoplasmic reticulum calcium reuptake, demonstrated the broadest pleiotropy across all cardiac phases. SOX5, a transcription factor involved in extracellular matrix development, showed significant genome-wide association exclusively with mid-diastolic velocities. Mendelian randomization implicated RABGAP1L as a candidate causal mediator of early diastolic velocity (IVW {beta} = -0.255 per NPX for early diastolic right ventricular inner circumferential velocity; p = 5.49x10-{superscript 2}{superscript 2}), nominating a calcium-handling pathway in diastolic dysfunction. ConclusionGenetic and causal-inference evidence implicates intracellular calcium handling and extracellular matrix remodeling as candidate mechanisms underlying diastolic dysfunction. Together, this work establishes deep-learning-enabled cardiac motion phenotyping as a scalable approach to interrogate the molecular basis of HFpEF and nominates candidate targets for therapeutic development. CLINICAL PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABS[bullet] Population-scale optical flow motion analysis of 83,569 cardiac magnetic resonance images from the UK Biobank allows for the extraction of 32 biventricular myocardial and inner cavity velocity phenotypes, including mid-diastolic (L-wave) velocities. [bullet]In HFpEF participants, mid-diastolic and late-diastolic optical flow velocities are reduced relative to healthy reference participants, while higher systolic left ventricular myocardial velocity is associated with lower all-cause mortality. [bullet]Genetic (PLN, SOX5) and causal-inference (RABGAP1L) signals independently nominate intracellular calcium handling and extracellular matrix remodeling as candidate mechanistic pathways underlying diastolic motion abnormalities. What Are the Clinical Implications?[bullet] Optical flow velocity phenotypes require prospective external validation and establishment of population-based reference ranges before clinical application. [bullet]Optical flow velocity phenotypes offer candidate objective metrics for HFpEF identification and risk-stratification for a syndrome that is inconsistently defined in current clinical practice. [bullet]Convergent genomic and causal-inference evidence implicates intracellular calcium handling and extracellular matrix remodeling as possible mechanistic axes for therapeutic development for the diastolic motion abnormalities central to HFpEF.

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