Daily Cardiology Research Analysis
Three impactful cardiology papers stood out today: a network meta-analysis links psoriasis biologics to differential cardiovascular risk; deep learning echocardiography markedly improves detection of transthyretin cardiac amyloidosis with low bias; and phosphoproteomics implicates DYRK1A as a kinase driver of incomplete reverse remodelling after aortic valve replacement, suggesting a new therapeutic target.
Summary
Three impactful cardiology papers stood out today: a network meta-analysis links psoriasis biologics to differential cardiovascular risk; deep learning echocardiography markedly improves detection of transthyretin cardiac amyloidosis with low bias; and phosphoproteomics implicates DYRK1A as a kinase driver of incomplete reverse remodelling after aortic valve replacement, suggesting a new therapeutic target.
Research Themes
- AI-enabled echocardiographic diagnosis and fairness auditing
- Cardiovascular safety of systemic immunomodulators in psoriasis
- Phosphoproteomics and kinase targets in cardiac reverse remodelling
Selected Articles
1. Cardiovascular and Kidney Outcomes After Systemic Treatment for Plaque Psoriasis: A Systematic Review and Network Meta-analysis.
In 68 RCTs (n=34,414), bimekizumab ranked highest for PASI75 and reduced total cardiovascular events versus placebo, whereas ixekizumab increased MACE despite strong PASI90 efficacy. Renal outcomes were similar across treatments. Findings suggest cardiovascular safety differences among psoriasis biologics.
Impact: This NMA integrates a large RCT evidence base to quantify treatment efficacy alongside cardiovascular safety signals, directly informing therapeutic choices in patients at elevated cardiovascular risk.
Clinical Implications: For psoriasis patients with high cardiovascular risk, bimekizumab may be preferred; ixekizumab should be used cautiously with cardiovascular risk assessment and monitoring. Multidisciplinary cardio-dermatology collaboration and postmarketing surveillance are warranted.
Key Findings
- Across 68 RCTs (n=34,414), bimekizumab improved PASI75 and reduced total cardiovascular events versus placebo (OR 0.06, 95% CI 0–0.80).
- Ixekizumab achieved superior PASI90 but increased MACE versus placebo (OR 3.26, 95% CI 1.26–9.31) and versus bimekizumab.
- Renal outcomes did not differ meaningfully among systemic agents; evidence certainty was high for efficacy and moderate for cardiovascular outcomes.
Methodological Strengths
- Prospective protocol registration (PROSPERO) with duplicate study selection and data extraction
- Network meta-analysis enabling indirect comparisons across 68 RCTs with SUCRA-based ranking and certainty grading
Limitations
- Heterogeneity across trials and low event rates for cardiovascular outcomes
- Most included RCTs were not primarily designed or powered for cardiovascular or renal endpoints; need for real-world data
Future Directions: Leverage real-world registries and postmarketing data to validate cardiovascular safety signals and consider head-to-head trials in high-risk populations; mechanistic studies to elucidate differential CV effects.
INTRODUCTION: Systemic immunomodulatory treatments may affect cardiovascular and renal outcomes in patients with chronic plaque psoriasis. We conducted a network meta-analysis (NMA) to compare these outcomes of systemic treatments for plaque psoriasis. METHODS: Databases were searched from inception through June 1, 2023. We conducted duplicate study selection, data extraction, bias assessment risk, and NMA evidence certainty assessment and analyses. Outcomes included proportion of participants achieving Psoriasis Area and Severity Index (PASI) 75 and/or 90 and those with (1) total cardiovascular events, (2) major adverse cardiovascular events (MACE), (3) other cardiovascular events, and (4) total renal events. RESULTS: We included 68 randomized clinical trials (n = 34,414 patients). Compared with placebo, bimekizumab (odds ratio [OR] 101.12, 95% confidence interval [CI] 34.26-301.46, surface under the cumulative ranking curve [SUCRA] 27, high certainty) was the top treatment demonstrating better PASI 75 and had reduced total cardiovascular events (OR 0.06, 95% CI 0-0.80, SUCRA 89, moderate certainty). Ixekizumab (OR 86.92, 95% CI 39.06-199.66, SUCRA 15, high certainty) showed better PASI 90 rates but was associated with increased MACE over placebo (OR 3.26, 95% CI 1.26-9.31, SUCRA 26, high certainty) and bimekizumab (OR 31.92, 95% CI 2.01, 1123.25), moderate certainty). Renal outcomes were similar among groups. CONCLUSION: Bimekizumab showed better therapeutic efficacy scores and safety profile than other agents. Ixekizumab may increase cardiovascular risk and should be used with caution. Reliable long-term safety data of the treatments analyzed here require assessing non-randomized studies and examining postmarketing reports from regulatory agencies. TRIAL REGISTRATION: PROSPERO (CRD42022381489).
2. Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis.
In a matched heart failure cohort (176 ATTR-CM cases; 3,192 controls), two deep learning echocardiography models (AUC 0.88 and 0.92) outperformed a regression score and a claims-based model and met fairness criteria for equal opportunity in Black patients. Claims-based random forest detection performed poorly (AUC 0.49).
Impact: Demonstrates superior diagnostic performance and fairness of deep learning echo models for ATTR-CM, which could reduce diagnostic delays and improve access to disease-modifying therapy.
Clinical Implications: Integrating validated deep learning echo models into heart failure workflows could flag suspected ATTR-CM for confirmatory scintigraphy/MRI and TTR testing, expediting treatment decisions while maintaining equity.
Key Findings
- Echo-based deep learning models achieved AUC 0.88 (EchoNet-LVH) and 0.92 (EchoGo Amyloidosis), surpassing the Mayo ATTR-CM score (AUC 0.79) and a claims-based random forest model (AUC 0.49).
- Fairness auditing met equal opportunity criteria among patients who identified as Black.
- External validation used a matched heart failure cohort with an enriched 5% prevalence (176 cases; 3,192 controls).
Methodological Strengths
- Head-to-head comparison of four distinct algorithms with external validation and DeLong testing
- Formal fairness auditing using standard metrics to assess potential harms due to model bias
Limitations
- Single integrated health system and enriched case prevalence may limit generalizability and introduce spectrum bias
- Retrospective design; prospective impact on diagnostic timelines and outcomes not assessed
Future Directions: Prospective implementation studies assessing diagnostic yield, time-to-diagnosis, cost-effectiveness, and equity across diverse systems; continuous learning and multi-center validation.
BACKGROUND: Delays in the diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) contribute to the significant morbidity of the condition, especially in the era of disease-modifying therapies. Screening for ATTR-CM with artificial intelligence and other algorithms may improve timely diagnosis, but these algorithms have not been directly compared. OBJECTIVES: The aim of this study was to compare the performance of 4 algorithms for ATTR-CM detection in a heart failure population and assess the risk for harms due to model bias. METHODS: We identified patients in an integrated health system from 2010 to 2022 with ATTR-CM and age- and sex-matched them to controls with heart failure to target 5% prevalence. We compared the performance of a claims-based random forest model (Huda et al model), a regression-based score (Mayo ATTR-CM), and 2 deep learning echo models (EchoNet-LVH and EchoGo Amyloidosis). We evaluated for bias using standard fairness metrics. RESULTS: The analytical cohort included 176 confirmed cases of ATTR-CM and 3,192 control patients with 79.2% self-identified as White and 9.0% as Black. The Huda et al model performed poorly (AUC: 0.49). Both deep learning echo models had a higher AUC when compared to the Mayo ATTR-CM Score (EchoNet-LVH 0.88; EchoGo Amyloidosis 0.92; Mayo ATTR-CM Score 0.79; DeLong P < 0.001 for both). Bias auditing met fairness criteria for equal opportunity among patients who identified as Black. CONCLUSIONS: Deep learning, echo-based models to detect ATTR-CM demonstrated best overall discrimination when compared to 2 other models in external validation with low risk of harms due to racial bias.
3. Myocardial phosphoproteomics unveils a key role of DYRK1A in aortic valve replacement-induced reverse remodelling.
Proteomic and phosphoproteomic profiling of AVR patients revealed inflammatory/complement activation and reduced ATP production in incomplete reverse remodelling, whereas complete remodelling associated with better mitochondrial function. Kinase inference highlighted DYRK1A, whose activity inversely correlated with LV mass regression, nominating a potential therapeutic target.
Impact: Combining myocardial phosphoproteomics with metabolic modelling identifies DYRK1A as a mechanistic driver of incomplete reverse remodelling after AVR, advancing target discovery for post-AVR heart failure risk.
Clinical Implications: While preliminary, DYRK1A inhibition may represent a strategy to augment reverse remodelling after AVR; profiling could stratify patients at risk for incomplete remodelling and guide adjunctive therapies.
Key Findings
- LC-MS/MS identified 83 dysregulated myocardial proteins distinguishing complete (≥15% LV mass regression) vs incomplete (≤5%) reverse remodelling groups.
- Gene ontology and kinetic metabolic modelling pointed to inflammation/complement activation and lower ATP production capacity in incomplete remodelling; complete remodelling showed better mitochondrial function.
- Phosphoproteomic kinase inference implicated DYRK1A; its activity inversely correlated with LV mass regression (r = −0.62), suggesting a therapeutic target.
Methodological Strengths
- Integrated myocardial proteomics/phosphoproteomics with gene ontology and kinase inference
- Kinetic metabolic modelling corroborated bioenergetic deficits in incomplete reverse remodelling
Limitations
- Sample size and exact cohort numbers not specified in the abstract; likely limited clinical generalizability
- Associational findings without interventional validation of DYRK1A modulation; abstract truncated
Future Directions: Validate DYRK1A as a modifiable target in larger human cohorts and preclinical models; test kinase inhibitors to enhance reverse remodelling; integrate multi-omics to refine patient stratification post-AVR.
Aortic valve stenosis (AVS) is a growing healthcare burden. Aortic valve replacement (AVR) remains the only effective treatment to eliminate pressure overload and triggers myocardial reverse remodelling (RR), with regression of hypertrophy, fibrosis and diastolic function normalisation. However, many patients show an incomplete RR, being at higher risk of death. We aimed to uncover pathways and new therapeutic targets for incomplete RR through myocardial (phospho)proteomics. AVS patients were categorised based on left ventricle mass regression (LVM): complete RR (≥ 15%) or incomplete RR (≤ 5%). 83 myocardial proteins were dysregulated through LC-MS/MS. Gene ontology enrichment analysis identified inflammation, complement and immune system activation as priming events of an incomplete RR and a better mitochondrial function underscoring complete RR. Kinetic metabolic modelling corroborated the lower ATP production capacity of incomplete RR patients. To uncover therapeutic targets, kinases were predicted from phosphoproteome data. Casein kinase 2 and DYRK1A were among the most dysregulated kinases in RR. DYRK1A was found to be inversely correlated with LVM regression (r = - 0.62). DYRK1A functional role (passive, maximal tension and Ca