Daily Cardiology Research Analysis
Three impactful cardiology studies advance precision prevention, imaging-based risk stratification, and mechanistic discovery. Population genomic screening for familial hypercholesterolemia improved lipid management at scale; statistical shape modeling of the systemic right ventricle in HLHS refined prognostication; and a new transcriptome-based method (SALVE) predicted interorgan endocrine signals influencing cardiac protein synthesis.
Summary
Three impactful cardiology studies advance precision prevention, imaging-based risk stratification, and mechanistic discovery. Population genomic screening for familial hypercholesterolemia improved lipid management at scale; statistical shape modeling of the systemic right ventricle in HLHS refined prognostication; and a new transcriptome-based method (SALVE) predicted interorgan endocrine signals influencing cardiac protein synthesis.
Research Themes
- Population genomic screening and cardiovascular prevention
- Shape-based cardiac imaging biomarkers in congenital heart disease
- Computational inference of interorgan endocrine signaling affecting the heart
Selected Articles
1. Population Genomic Screening and Improved Lipid Management in Patients With Familial Hypercholesterolemia.
In a 9-system population genomics program (n=228,602), ≈1 in 198 adults carried a pathogenic FH variant. Return of results, documentation in the EHR, and follow-on care were associated with intensification of lipid-lowering therapy and larger LDL-C reductions, demonstrating that population genomic screening can drive clinically meaningful lipid management in FH.
Impact: This study operationalizes Tier 1 genomic screening at scale and links genetic diagnosis to measurable improvements in therapy and LDL-C, a key causal risk factor for ASCVD.
Clinical Implications: Health systems can integrate exome-based FH screening with EHR workflows to prompt therapy optimization and achieve larger LDL-C reductions. Coding FH in the EHR appears to be a modifiable lever to increase treatment changes.
Key Findings
- ≈1/198 adults (1,155 of 228,602) screened carried a pathogenic FH variant.
- Post-screening, many FH carriers had intensified lipid-lowering therapy with associated LDL-C reductions.
- EHR documentation of an FH diagnosis code correlated with higher likelihood of therapeutic modification and larger LDL-C decrease.
Methodological Strengths
- Very large, multi-system implementation with clinical-grade exome sequencing
- Objective assessment via linked medication and laboratory records across health systems
Limitations
- Observational design without randomized control may introduce confounding by indication and implementation heterogeneity
- Abstract lacks detailed quantitative effect sizes for LDL-C change and adherence persistence
Future Directions: Prospective pragmatic trials to test EHR nudges (e.g., automated FH coding prompts) and cascade screening strategies; evaluation of cardiovascular event reduction following genomic implementation.
BACKGROUND: The Helix Research Network program is a large population genomics initiative that screens an all-comers population of patients for Centers for Disease Control and Prevention Tier 1 genetic conditions, including familial hypercholesterolemia (FH). We evaluated changes in clinical management and low-density lipoprotein cholesterol (LDL-C) levels among patients we identified to have FH. METHODS: Participants across 9 US health systems provided samples that underwent clinical-grade exome sequencing. Individuals with a positive screening result for a Tier 1 condition were offered no-cost genetic counseling through their health system. Using medication and laboratory testing records, we evaluated changes in patients' lipid-lowering therapies and LDL-C levels. RESULTS: Among 228 602 adults enrolled between 2017 to 2025, 1155 (≈1/198) had a pathogenic FH variant in CONCLUSIONS: Following genetic screening, many patients with a pathogenic FH variant experienced improvements in clinical management and LDL-C levels. Electronic health record documentation of the diagnosis code was associated with a greater likelihood of therapeutic modifications, which, in turn, were associated with larger LDL-C reductions. Findings underscore the powerful potential of population genomic screening for supporting optimal lipid management in individuals with FH.
2. Shape Variations in RV 3D Geometry Are Associated With Adverse Outcomes in Hypoplastic Left Heart Syndrome Patients: A Fontan Outcomes Registry Using CMR Examination (FORCE) Study.
In 329 post-Fontan HLHS patients, statistical shape modeling of 3D RV geometry identified phenotypes (eg, circumferential dilation, loss of septal concavity) associated with dysfunction and adverse outcomes. Shape-derived metrics added prognostic information beyond conventional volumes, supporting their use for risk stratification and surgical planning in single-ventricle physiology.
Impact: The work pioneers large-scale, multicenter shape phenotyping of the systemic RV in HLHS and links specific geometry to clinically meaningful outcomes, moving beyond volumes to mechanistically relevant descriptors.
Clinical Implications: Shape-based metrics could refine surveillance intervals, timing of interventions, and surgical decisions (e.g., tricuspid valve strategies) by identifying high-risk RV geometries not captured by volumes alone.
Key Findings
- Statistical shape modeling of 3D RV geometry in 329 post-Fontan HLHS patients identified phenotypes such as circumferential dilation and loss of septal concavity.
- RV end-diastolic volume showed an independent association with composite adverse outcomes (reported odds ratio 6.50).
- Shape-derived metrics provided additive prognostic value beyond conventional volumetric analysis.
Methodological Strengths
- Multicenter cohort with standardized CMR and advanced shape modeling (ShapeWorks, PCA)
- Integration of imaging phenotypes with clinical outcomes including mortality and transplant
Limitations
- Abstract truncation precludes full reporting of effect sizes and confidence intervals
- External validation and translation into actionable clinical thresholds require future studies
Future Directions: Prospective validation of shape risk scores, integration with computational flow and valve mechanics, and testing of shape-guided surgical/interventional strategies.
BACKGROUND: Assessment of the systemic right ventricle (RV) is critical for patients with hypoplastic left heart syndrome (HLHS). Traditional imaging metrics fail to capture the RV's complex geometry and remodeling in HLHS, limiting risk stratification. We aimed to apply statistical shape modeling to a large multicenter cohort of cardiac magnetic resonance data sets to define RV shape variants and evaluate associations with clinical outcomes. METHODS: Cardiac magnetic resonance from the FORCE (Fontan Outcomes Registry Using CMR Examinations) was analyzed for patients with HLHS post-Fontan. Three-dimensional RV models were segmented at end-diastole and processed using statistical shape modeling (ShapeWorks). Shape modes were extracted via principal component analysis and correlated with RV function, tricuspid regurgitation, remnant left ventricular morphology, and clinical outcomes, including mortality, transplant, and a composite adverse outcome including heart failure. RESULTS: The mean RV shape template of 329 patients with HLHS (mean age, 14.7±6.3 years) depicted a circumferentially dilated RV with loss of septal concavity. RV end-diastolic volume was independently associated with composite adverse outcome (odds ratio, 6.50; CONCLUSIONS: Our statistical shape modeling analyses provide novel insights into RV geometric remodeling in HLHS and identify specific shape phenotypes associated with dysfunction and adverse outcomes. Shape-based metrics offer additive prognostic value beyond conventional volumetric analysis, with potential implications for risk stratification and surgical decision-making in single-ventricle physiology.
3. SALVE: prediction of interorgan communication with transcriptome latent space representation.
SALVE introduces latent-space and transfer learning to infer cross-tissue endocrine communication from bulk RNA-seq, recapitulating canonical axes (insulin, adiponectin) and nominating novel factors, including galectin-3 as a regulator of cardiac protein synthesis. Partial validation in human iPSC-cardiomyocytes supports biological plausibility.
Impact: This methodological advance enables discovery of endocrine crosstalk affecting the heart directly from transcriptome consortia data and bridges to experimental validation, accelerating cardiokine target nomination.
Clinical Implications: While preclinical, identifying circulating regulators (e.g., galectin-3) of cardiac protein homeostasis can inform biomarker development and therapeutic targeting of maladaptive remodeling.
Key Findings
- SALVE leverages transcriptome latent space and transfer learning to infer secretome–distal organ associations from RNA-seq.
- Applied to GTEx v8, SALVE recapitulated canonical endocrine signaling (insulin, adiponectin) and predicted new organokines.
- Predictions implicated circulating galectin-3 (LGALS3) in regulating cardiac protein synthesis, partially recapitulated in human iPSC-cardiomyocytes.
Methodological Strengths
- Introduction of latent space representations and transfer learning to increase discovery power
- Cross-tissue prediction coupled with experimental validation in human iPSC-cardiomyocytes
Limitations
- Reliance on bulk RNA-seq may obscure cell-type–specific signals
- Experimental validation is partial; in vivo confirmation and causal mechanisms remain to be established
Future Directions: Single-cell extensions of SALVE, prospective validation of predicted cardiokines in animal models, and clinical correlation of circulating candidates with cardiac phenotypes.
Massive transcriptomics data allow gene relationships to be discovered from their correlated expression. We describe secretome association with latent variables between tissues (SALVE), a method to infer the associations between secretome-encoding transcripts and gene modules in a distal organ from RNA sequencing data. This method builds upon similar bioinformatics approaches by introducing transcriptome latent space representations and transfer learning to simultaneously increase discovery power and predict downstream functional associations. Applied to Genotype-Tissue Expression v8 data, we show that the method readily recapitulates canonical endocrine relationships, including insulin and adiponectin signaling while inferring new candidate organokines and their signaling modality. We also explore its utility for generating new hypotheses on cardiokine candidates and finding distal factors that may affect cardiac protein synthesis and metabolism. The predictions suggest a potential role of circulating galectin-3 (LGALS3) in regulating cardiac protein synthesis and homeostasis, which can be recapitulated in part in human-induced pluripotent stem cell-derived cardiomyocytes. This method may aid in ongoing efforts to delineate interorgan communications and endocrine networks in various areas of study.