Daily Cardiology Research Analysis
Analyzed 50 papers and selected 3 impactful papers.
Summary
Three impactful cardiology studies stood out today: a phase 3 randomized trial showed inclisiran safely and durably lowers LDL cholesterol in adolescents with heterozygous familial hypercholesterolaemia; mechanistic work linked proton pump inhibitors to accelerated vascular calcification in chronic kidney disease via COX-2-mediated inhibition of PINK1/Parkin mitophagy; and a deep learning ECG model accurately predicted angiographically significant coronary artery disease with strong external validation.
Research Themes
- Pediatric lipid-lowering therapy and RNA interference
- Drug-induced vascular injury mechanisms (COX-2–mitophagy axis)
- AI-enabled noninvasive diagnosis of obstructive coronary disease
Selected Articles
1. Efficacy and safety of inclisiran in adolescents with heterozygous familial hypercholesterolaemia (ORION-16): a two-part, randomised, multicentre clinical trial.
In a two-part, phase 3 randomized trial of 141 adolescents with HeFH on background therapy, inclisiran reduced LDL-C by 27.1% versus 1.4% with placebo at day 330 (between-group difference −28.5%; p<0.0001) and sustained a −33.7% mean reduction at day 720. Safety was favorable, with only mild injection-site reactions and no treatment-related serious adverse events.
Impact: Provides the first randomized, multicenter efficacy and safety data for inclisiran in adolescents with HeFH, addressing a key therapeutic gap in pediatric lipid management with an infrequent dosing regimen.
Clinical Implications: Inclisiran can be considered as an adjunct to maximally tolerated statins (with or without ezetimibe) in adolescents with HeFH requiring additional LDL-C lowering, offering durable reductions with infrequent dosing and a favorable safety profile.
Key Findings
- At day 330, LDL-C decreased by −27.1% with inclisiran vs 1.4% with placebo; between-group difference −28.5% (95% CI −35.8 to −21.3; p<0.0001).
- LDL-C reduction sustained to day 720 with a mean −33.7% change from baseline.
- Safety profile was favorable with mild injection-site reactions and no treatment-related serious adverse events or deaths.
Methodological Strengths
- Randomized, double-blind, multicenter phase 3 design with predefined endpoints
- Two-year follow-up including open-label extension demonstrating sustained effect
Limitations
- Sample size is modest and predominantly White, which may limit generalizability
- Not powered for cardiovascular outcomes; surrogate endpoint (LDL-C) only
Future Directions: Evaluate long-term cardiovascular outcomes, growth and development effects, adherence in real-world pediatric populations, and cost-effectiveness; compare sequencing with PCSK9 mAbs and ezetimibe.
BACKGROUND: Inclisiran, a small interfering RNA targeting hepatic PCSK9, was previously studied in various adult patient populations, but it has not yet been assessed in paediatric patients with heterozygous familial hypercholesterolaemia (HeFH), a genetic disorder characterised by elevated LDL cholesterol. The ORION-16 study aimed to evaluate the efficacy and safety of inclisiran treatment in adolescents with HeFH. METHODS: ORION-16 was a two-part (1-year double-blind, 1-year open-label), randomised, phase 3 trial at 51 sites across 26 countries. In Part 1, adolescents (aged 12 to <18 years) with HeFH and elevated LDL cholesterol on maximally tolerated statin treatment with or without other lipid-lowering therapy were randomly assigned 2:1 (via interactive response technology) to either inclisiran sodium 300 mg subcutaneously or placebo (administered on days 1, 90, and 270); in Part 2, all patients received inclisiran (days 360 [only patients previously assigned to receive placebo], 450, and 630). The primary endpoint was the percentage change in LDL cholesterol from baseline to day 330, assessed in all randomly assigned patients. Safety was assessed in all patients who received at least one dose of study drug. Endpoints in Part 2 were analysed in all patients who entered and received at least one dose of study drug in Part 2. ORION-16 is registered at ClinicalTrials.gov (NCT04652726) and is completed. FINDINGS: Between Feb 17, 2021, and Dec 14, 2022, 141 patients were randomly assigned (93 to inclisiran, 48 to placebo; median age 15·1 years [IQR 13·4-16·8]; 75 [53%] female; 128 [91%] White). In Part 1, the least squares mean percentage change in LDL cholesterol from baseline to day 330 was -27·1% in the inclisiran group and 1·4% in the placebo group, with a between-group difference of -28·5% (95% CI -35·8 to -21·3; p<0·0001). In Part 2, at day 720, a mean percentage change in LDL cholesterol from baseline of -33·7% (SD 24·0) was observed. Inclisiran was well tolerated, with a safety profile comparable to studies in adults. Injection site reactions were more frequent with inclisiran (15 [16%] of 93 patients) than with placebo (three [6%] of 48) in Part 1 (13 [9%] of 139 in Part 2); all were mild and did not lead to study drug discontinuation. There were no treatment-related serious adverse events, and no deaths during the study. INTERPRETATION: In adolescents with HeFH, inclisiran was effective in lowering LDL cholesterol, with sustained efficacy over 2 years, and was well tolerated. These results support inclisiran as a potentially useful addition for the treatment of adolescents with HeFH, providing an infrequent dosing regimen. FUNDING: Novartis Pharma.
2. Proton pump inhibitors accelerate vascular calcification via COX-2-mediated mitophagy inhibition in chronic kidney disease.
In CKD, PPI exposure was associated with markedly higher odds of elevated coronary calcium in clinical data, and PPIs (omeprazole, esomeprazole, lansoprazole) induced aortic calcification in CKD rats via COX-2 upregulation and inhibition of PINK1/Parkin-mediated mitophagy. Enhancing mitophagy with rapamycin or silencing COX-2 mitigated calcification, revealing a COX-2–mitophagy axis.
Impact: Identifies a mechanistic pathway linking a widely used drug class (PPIs) to vascular calcification in a high-risk population (CKD), with translational evidence across human data, animal models, and molecular interventions.
Clinical Implications: In CKD patients, clinicians should reassess PPI indications, prioritize deprescribing when possible, and consider cardiovascular monitoring; the COX-2–mitophagy axis suggests potential mitigation strategies (e.g., minimizing PPI exposure, exploring mitophagy-supporting or COX-2–modulating approaches in trials).
Key Findings
- PPI use in CKD patients was associated with higher odds of elevated coronary artery calcium (adjusted OR 5.365; 95% CI 2.539–11.338; P<0.001).
- In CKD rats, omeprazole induced dose-dependent aortic calcification with VSMC osteogenic switching and inhibited PINK1/Parkin-mediated mitophagy.
- Mitophagy enhancement (rapamycin) or COX-2 silencing mitigated calcification; esomeprazole and lansoprazole showed similar pro-calcific class effects.
Methodological Strengths
- Translational design integrating human clinical association, in vivo CKD models, and mechanistic molecular assays
- Use of multiple PPIs demonstrating a class effect and rescue experiments (rapamycin, COX-2 silencing)
Limitations
- Human data are observational and subject to residual confounding and indication bias
- Rodent CKD models may not fully recapitulate human vascular biology; clinical interventional validation is pending
Future Directions: Prospective human studies to confirm causality, quantify dose–response, and test whether PPI deprescribing or COX-2/mitophagy-targeted therapies reduce calcification progression in CKD.
Prolonged use of proton pump inhibitors (PPIs) is associated with increased cardiovascular risks, including vascular calcification (VC). Patients with chronic kidney disease (CKD) are particularly vulnerable to the adverse vascular effects of PPIs. However, the underlying mechanism remains poorly understood. Clinical data from CKD patients treated with PPIs showed a higher incidence of elevated coronary artery calcium scores (adjusted odds ratio=5.365, 95% CI: 2.539-11.338, P<0.001), indicating a link between PPI use and accelerated vascular damage. In CKD rats, omeprazole treatment dose-dependently induced aortic calcification, accompanied by a phenotypic switch of vascular smooth muscle cells (VSMCs) from a contractile to an osteoblastic state. This pathological process was associated with mitochondrial dysfunction and inhibited PINK1/Parkin-mediated mitophagy, as evidenced by reduced TOMM20, LC3B-II, PINK1, and Parkin protein levels, impaired mitochondrial-lysosomal colocalization (MitoTracker Green/LysoTracker Red staining), and swollen mitochondria with fewer mitophagosomes (transmission electron microscopy). Enhancement of mitophagy by rapamycin effectively mitigated omeprazole-induced VC. RNA sequencing identified cyclooxygenase-2 (COX-2) as a key mediator, with omeprazole significantly upregulating its expression. Silencing COX-2 reversed omeprazole-induced mitophagy inhibition and VSMC calcification. Esomeprazole and lansoprazole recapitulated these pro-calcific effects, indicating a class effect. Collectively, PPIs promote VC in CKD by upregulating COX-2, which directly inhibits PINK1/Parkin-related mitophagy. This study provides a novel COX-2-mitophagy axis in PPI-accelerated vascular injury, highlighting a potential therapeutic target for high-risk patients.
3. Use of Electrocardiograms to Identify Coronary Artery Disease: Cross-Validation of an Artificial Intelligence Model.
In 16,476 patients undergoing angiography, a deep learning model applied to resting digital 12‑lead ECGs predicted angiographically significant CAD with AUC 0.914 in cross-validation and 0.924 in external validation, maintaining high PPV/NPV. This supports ECG-based AI as a scalable, noninvasive triage tool for obstructive CAD.
Impact: Demonstrates externally validated, high-performing AI using ubiquitous ECG data to detect significant CAD, potentially reshaping diagnostic pathways by reducing unnecessary invasive testing.
Clinical Implications: AI analysis of resting ECGs could serve as a front-end triage to prioritize patients for angiography or advanced imaging, potentially lowering costs and exposure to procedural risks while expediting care.
Key Findings
- Cross-validation cohort (n=16,476) achieved AUC 91.4% (95% CI: 89.4%–94.4%), PPV 91.7%, and NPV 72.8% for predicting significant CAD.
- External validation achieved AUC 92.4% (95% CI: 89.7%–95.1%), PPV 82.5%, and NPV 88.1%.
- Significant CAD defined by angiographic stenosis ≥70% in major epicardial vessels or ≥50% in left main was accurately identified from resting ECGs.
Methodological Strengths
- Large development cohort with 10-fold cross-validation and external validation
- Clinically relevant angiographic ground truth with predefined stenosis thresholds
Limitations
- Retrospective design with potential selection spectrum bias; clinical utility not tested prospectively
- Differences in disease prevalence between cohorts may affect PPV/NPV generalizability
Future Directions: Prospective, multicenter impact studies to assess clinical utility, cost-effectiveness, and equity across diverse populations, and integration into clinical workflows for triage and referral.
BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG). OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant CAD in a patient population presenting for coronary angiography. METHODS: From 2019 to 2021, 16,476 patients had a resting 12-lead digital ECG recorded within 90 days prior to coronary angiography. The artificial intelligence model was developed using 10-fold cross-validation methodology. Clinically significant disease was defined as angiographic diameter stenosis ≥70% in the left anterior descending, left circumflex, or right coronary artery or ≥50% in the left main coronary artery. We then applied the model to an external validation set. RESULTS: In the cross-validation cohort, the prevalence of clinically significant CAD was 64.5%; the model achieved a positive predictive value of 91.7% (95% CI: 89.9%-93.4%), negative predictive value of 72.8% (95% CI: 69.6%-76.0%), and area under the curve of 91.4% (95% CI: 89.4%-94.4%) in predicting clinically significant CAD. In external validation, the prevalence of clinically significant CAD was 36.0%; the model achieved a positive predictive value of 82.5% (95% CI: 75.9%-89.2%), negative predictive value of 88.1% (95% CI: 84.0%-92.1%), and area under the curve of 92.4% (95% CI: 89.7%-95.1%) in predicting clinically significant CAD. CONCLUSIONS: This study demonstrated the clinical utility of a deep learning artificial intelligence algorithm to analyze a digital 12-lead ECG to predict the presence of clinically significant CAD as determined by coronary angiography.