Daily Cardiology Research Analysis
Three advances span mechanisms, imaging-guided therapy, and population health. A time-resolved transcriptomic study shows circular RNAs rise during vascular cell differentiation and can serve as blood biomarkers for atherosclerotic disease. In complex coronary artery disease, intravascular imaging–guided PCI lowers target vessel failure, with benefits increasing alongside lesion complexity. Street-level deep learning of built environments links greenspace and sidewalks to reduced major adverse c
Summary
Three advances span mechanisms, imaging-guided therapy, and population health. A time-resolved transcriptomic study shows circular RNAs rise during vascular cell differentiation and can serve as blood biomarkers for atherosclerotic disease. In complex coronary artery disease, intravascular imaging–guided PCI lowers target vessel failure, with benefits increasing alongside lesion complexity. Street-level deep learning of built environments links greenspace and sidewalks to reduced major adverse cardiovascular events.
Research Themes
- Mechanistic biomarkers (circular RNAs) for vascular disease
- Intravascular imaging-guided PCI outcomes in complex CAD
- AI analysis of built environment and cardiovascular risk
Selected Articles
1. Circular RNAs increase during vascular cell differentiation and are biomarkers for vascular disease.
Daily RNA-seq across iPSC differentiation showed a global rise in circularization tied to MYC downregulation and reduced splicing factors. In patients, select circRNAs fell in atherosclerotic tissue and PBMCs, and a small panel (COL4A1/2, HSPG2, YPEL2) discriminated disease with AUC 0.79 in tissue and 0.73 in blood.
Impact: Links developmental circRNA dynamics to disease signatures and provides a tangible, minimal circRNA panel with cross-tissue diagnostic potential.
Clinical Implications: CircRNA panels could evolve into noninvasive blood tests for atherosclerosis detection or risk stratification; assays require standardization and prospective validation.
Key Findings
- Global circRNA levels increased during EC and SMC maturation with 397 and 214 circRNAs upregulated >2-fold (adjusted P<0.05).
- MYC decreased during maturation, paralleled by downregulation of splicing factors (e.g., SRSF1, SRSF2) and altered circRNA levels.
- In patients, circRNAs decreased in atherosclerotic tissue and PBMCs; a panel from COL4A1/COL4A2/HSPG2/YPEL2 discriminated disease (tissue AUC 0.79; PBMC AUC 0.73).
Methodological Strengths
- Time-resolved high-throughput RNA-seq with de novo circRNA detection across differentiation trajectory
- External validation in human arterial tissue and PBMCs with machine-learning classification
Limitations
- Clinical validation cohorts and outcomes were limited; no prospective prognostic evaluation
- Causality between MYC/splicing changes and circRNA biogenesis not proven in vivo
Future Directions: Prospective, multi-center validation of circRNA panels, assay standardization, and mechanistic in vivo studies linking circRNA biogenesis to vascular remodeling and outcomes.
AIMS: The role of circular RNAs (circRNAs) and their regulation in health and disease are poorly understood. Here, we systematically investigated the temporally resolved transcriptomic expression of circRNAs during differentiation of human induced pluripotent stem cells (iPSCs) into vascular endothelial cells (ECs) and smooth muscle cells (SMCs) and explored their potential as biomarkers for human vascular disease. METHODS AND RESULTS: Using high-throughput RNA sequencing and a de novo circRNA detection pipeline, we quantified the daily levels of 31 369 circRNAs in a 2-week differentiation trajectory from human stem cells to proliferating mesoderm progenitors to quiescent, differentiated EC and SMC. We detected a significant global increase in RNA circularization, with 397 and 214 circRNAs up-regulated greater than two-fold (adjusted P < 0.05) in mature EC and SMC, compared with undifferentiated progenitor cells. This global increase in circRNAs was associated with up-regulation of host genes and their promoters and a parallel down-regulation of splicing factors. Underlying this switch, the proliferation-regulating transcription factor MYC decreased as vascular cells matured, and inhibition of MYC led to down-regulation of splicing factors such as SRSF1 and SRSF2 and changes in vascular circRNA levels. Examining the identified circRNAs in arterial tissue samples and in peripheral blood mononuclear cells (PBMCs) from patients, we found that circRNA levels decreased in atherosclerotic disease, in contrast to their increase during iPSC maturation into EC and SMC. Using machine learning, we determined that a set of circRNAs derived from COL4A1, COL4A2, HSPG2, and YPEL2 discriminated atherosclerotic from healthy tissue with an area under the receiver operating characteristic curve (AUC) of 0.79. circRNAs from HSPG2 and YPEL2 in blood PBMC samples detected atherosclerosis with an AUC of 0.73. CONCLUSION: Time-resolved transcriptional profiling of linear and circRNA species revealed that circRNAs provide granular molecular information for disease profiling. The identified circRNAs may serve as blood biomarkers for atherosclerotic vascular disease.
2. Outcomes of intravascular imaging-guided percutaneous coronary intervention according to lesion complexity.
Across 4,611 patients from an RCT and registry, IVI-guided PCI reduced 3-year target vessel failure versus angiography guidance regardless of complexity, with HR 0.49 in patients with ≥3 complexity features and HR 0.72 in those with <3. Absolute risk reduction increased with each added complexity feature.
Impact: Strengthens the clinical case for routine IVUS/OCT guidance, especially in highly complex lesions, by quantifying the gradient of benefit.
Clinical Implications: In complex PCI, default IVI guidance should be prioritized; the greater the lesion complexity, the larger the expected event reduction and potential cost-effectiveness.
Key Findings
- Patients with ≥3 complex lesion features had higher TVF risk than those with <3 (11.0% vs 7.2%; HR 1.59, 95% CI 1.28-1.96).
- IVI-guided PCI reduced 3-year TVF versus angiography-guided PCI in both strata (≥3: 7.4% vs 14.4%, HR 0.49; <3: 5.7% vs 8.1%, HR 0.72).
- Absolute risk reduction in TVF increased with accumulating complexity features (interaction p=0.048).
Methodological Strengths
- Large sample combining randomized trial and real-world registry
- Prespecified, hard composite endpoint with 3-year follow-up
Limitations
- Pooled analysis includes non-randomized registry data with potential residual confounding
- Heterogeneity in imaging modalities (IVUS vs OCT) and operator practice not fully controlled
Future Directions: Head-to-head trials stratified by complexity, cost-effectiveness analyses, and modality-specific (IVUS vs OCT) optimization strategies.
BACKGROUND: Recent trials have shown that intravascular imaging (IVI)-guided percutaneous coronary intervention (PCI) improves clinical outcome, as compared to angiography-guided PCI, in complex coronary artery lesions. However, it is unclear whether this benefit is affected by overall lesion complexity in each patient. AIMS: The present study sought to investigate the impact of overall lesion complexity on the benefit of IVI-guided PCI. METHODS: A total of 4,611 patients with complex coronary artery lesions from the RENOVATE-COMPLEX-PCI trial (n=1,639) and the institutional registry of the Samsung Medical Center (n=2,972) were classified according to the number of complex lesion features found in each patient. The primary outcome was target vessel failure (TVF) at 3 years, a composite of cardiac death, target vessel myocardial infarction, or target vessel revascularisation. RESULTS: The cutoff value for the number of complex lesion features to predict TVF, determined using the maximally selected log-rank test, was 3. Patients with ≥3 complex lesion features had a higher risk of TVF than those with <3 complex lesion features (11.0% vs 7.2%, hazard ratio [HR] 1.59, 95% confidence interval [CI]: 1.28-1.96; p<0.001). IVI-guided PCI significantly reduced the risk of TVF compared with angiography-guided PCI in both groups (≥3 complex lesion features: 7.4% vs 14.4%, HR 0.49, 95% CI: 0.35-0.69; p<0.001; <3 complex lesion features: 5.7% vs 8.1%, HR 0.72, 95% CI: 0.53-0.98; p=0.039). The benefit of IVI-guided PCI tended to increase as the number of complex lesion features increased (absolute risk reduction for TVF: -0.012 vs -0.027 vs -0.055 vs -0.077, respectively, for 1 vs 2 vs 3 vs ≥4 complex lesion features; interaction p=0.048). CONCLUSIONS: In patients with complex coronary artery lesions, IVI-guided PCI showed a lower risk of TVF across all degrees of lesion complexity. The prognostic benefit of IVI-guided PCI tended to increase as patients had more complex lesion features. (RENOVATE-COMPLEX-PCI [ClinicalTrials.gov: NCT03381872]; Institutional cardiovascular catheterisation database of the Samsung Medical Center [ClinicalTrials.gov: NCT03870815]).
3. Deep Learning Analysis of Google Street View to Assess Residential Built Environment and Cardiovascular Risk in a U.S. Midwestern Retrospective Cohort.
In 49,887 adults with 2,083 MACE over 26.86 months, higher visible vertical greenspace (tree-sky index) and sidewalk presence around residences were independently associated with lower MACE risk (HR 0.95 and 0.91, respectively). Deep learning of street-level images enables scalable environmental risk profiling.
Impact: Demonstrates a scalable, low-cost approach to quantify built environments and link them to cardiovascular outcomes, informing public health and urban planning.
Clinical Implications: Incorporate environmental risk context into prevention strategies; clinicians and systems could target high-risk neighborhoods for tailored interventions and advocate for walkability and greenspace.
Key Findings
- Among 49,887 individuals, 2,083 MACE occurred over a median 26.86 months.
- Higher tree-sky index near residences associated with lower MACE risk (HR 0.95, 95% CI 0.91–0.99).
- Sidewalk presence associated with lower MACE risk (HR 0.91, 95% CI 0.87–0.96), independent of greenspace and other covariates.
Methodological Strengths
- Very large cohort with comprehensive adjustment for demographic, socioeconomic, environmental, and clinical covariates
- Novel application of deep learning on street-level imagery to derive exposure metrics (greenspace, sidewalks)
Limitations
- Retrospective, single-region cohort limits causal inference and generalizability
- Potential exposure misclassification from imagery and unmeasured neighborhood confounders
Future Directions: Prospective, multi-city replication; interventional studies on urban design; integration with personal mobility/wearable data to clarify mechanisms.
AIMS: Cardiovascular disease (CVD) is a leading global cause of mortality. Environmental factors are increasingly recognized as influential determinants of cardiovascular health. Nevertheless, a finer-grained understanding of the effects of the built environment remains crucial for comprehending CVD. We sought to investigate the relationship between built environment features, including residential greenspace and sidewalks, and cardiovascular risk using street-level imagery and deep learning techniques. METHODS: This study employed Google Street View (GSV) imagery and deep learning techniques to analyze built environment features around residences in relation to major adverse cardiovascular events (MACE) risk. Data from a Northeast Ohio cohort were utilized. Various covariates, including socioeconomic and environmental factors, were incorporated in Cox Proportional Hazards models. RESULTS: Of 49,887 individuals included, 2,083 experienced MACE over a median follow-up of 26.86 months. Higher tree-sky index and sidewalk presence were associated with reduced MACE risk (HR: 0.95, 95% CI: 0.91-0.99, and HR: 0.91, 95% CI: 0.87-0.96, respectively), even after adjusting for demographic, socioeconomic, environmental, and clinical factors. CONCLUSIONS: Visible vertical greenspace and sidewalks, as discerned from street-level images using deep learning, demonstrated potential associations with cardiovascular risk. This innovative approach highlights the potential of deep learning to analyze built environments at scale, offering new avenues for public health research. Future research is needed to validate these associations and better understand the underlying mechanisms. This study examined how features of the built environment, such as greenspace and walkability, influence cardiovascular health by analyzing Google Street View images using advanced deep learning techniques. Higher levels of greenspace (Tree-Sky Index) around residences were associated with a 5% lower risk of major adverse cardiovascular events (MACE).Walkable neighborhoods (Pavement Index) were linked to a 9% reduction in cardiovascular risk, independently of greenspace.