Clinical Phenotype Research Articles

Background: Truncating variants (TVs) in the filamin C gene ( FLNC ) have been associated with familial cardiomyopathies. However, phenotypes of individuals carrying the same variant exhibit variable penetrance in disease onset, rate of progression, and severity of arrhythmias and cardiomyopathy. Research Question: Can patient-specific, stem-cell-derived cardiomyocytes (iCMs) recapitulate differences in FLNC clinical phenotypes. Methods: A proband with severe dilated cardiomyopathy and a TV in FLNC (c.1844dup, p.Ile616Aspfs*4) was identified in a family where the FLNC variant and cardiac disease co-segregated with variable severity. To investigate differences in clinical penetrance and phenotype, we generated patient-specific induced pluripotent stem cell (iPSC) from the severely affected proband and the father with mild disease. iPSCs were differentiated into iCMs and were studied using multi-electrode array (MEA), immunofluorescence analyses (IFA), calcium indicators, and RNAseq for differential gene expression analyses (DGEA). Results: Compared to control iCMs, an increase in beat period irregularity and arrhythmogenicity was observed in both FLNC -iCMs. Proband iCMs showed more prominent changes in calcium dynamics, decreased field potential duration, and shortening of the action potential. Sarcomeric structure as seen in IFA of α-actinin and filamin C was more disorganization in the proband iCMs. Gene expression analysis revealed changes in pathways related to cardiac muscle contraction, electrophysiology, cell communication, and cardiac conduction of varying degrees corresponding to clinical severity of the patient derived iCMs. Confirmatory analysis using RNAseq was performed using CRISPR-knock-in of the FLNC TV into an isogenic iPSC line. Conclusions: Our results provide evidence that patient-specific iPSCs can provide mechanistic insights that reflect phenotypic differences between patients with the same cardiomyopathic FLNC pathogenic variant. DGEA identified common and unique pathway perturbations that may lead to specific therapeutic approaches.

Background: Variants at the APOH genomic locus have been associated with lipid traits, Lp(a), coagulation traits, coronary artery disease, and fatty liver disease. The mechanisms by which the ApoH protein influences these cardiometabolic phenotypes are not understood. An APOH missense variant C325G (rs1801689) has a minor allele frequency of 2.8% and is predicted to impact protein structure, making it a potential genetic tool for understanding ApoH biology. Hypothesis: We hypothesize that the association of APOH C325G with clinical phenotypes and circulating proteins and metabolites will provide new insights into the relationship between APOH and cardiometabolic traits. Methods: We accessed internal data from the Penn Medicine BioBank (PMBB) and publicly available data from the UK Biobank, the Million Veteran Program, and the All of Us cohort for these analyses. In addition, siRNA was used to knock down APOH in HuH-7 hepatocytes. Results: PheWAS of APOH C325G in the PMBB identified association with a number of cardiometabolic conditions, several of which were replicated in the other cohorts. Analyses of lab data indicated that APOH C325G was associated with lower triglyceride (TG) levels and higher LDL-C, apoB, and Lp(a) levels (p < 0.01). Analysis of NMR-derived plasma metabolites in UKB showed that APOH C325G is associated with highly significant reductions in the larger VLDL particle subclasses, increases in the small VLDL particle subclasses, and increases in all LDL subclasses. Analysis of UKB proteomic data revealed that APOH C325G is associated with significantly reduced plasma levels of the ApoH protein and changes in other circulating proteins that are related to lipid metabolism and cardiometabolic traits (p < 5 x 10 -8 ). Mendelian randomization analysis suggested that genetically determined lower ApoH plasma levels causally decrease plasma TG levels (p < 5 x 10 -8 ). Finally, siRNA silencing of APOH in human Huh7 cells led to reduced TG secretion (two-sample t-test, p < 0.05). Conclusions: The APOH missense variant C325G is associated with reduced TGs, large VLDLs, and increased LDL, apoB, Lp(a), and cardiometabolic conditions. It may serve as a useful genetic tool to understand the biology of APOH . Silencing of APOH in hepatocytes reduces TG production. Recall-by-genotype deep phenotyping of APOH C325G carriers in the PMBB is underway.

Introduction: Heart failure with preserved ejection fraction (HFpEF) is a geriatric syndrome characterized by a high burden of frailty, sarcopenia, and physical dysfunction. Given this clinical phenotype, patients with HFpEF would be anticipated to demonstrate elevated rates of incontinence; however, the prevalence and burden of bladder symptoms in this population and their associations with quality of life and physical function remain poorly characterized. Methods: This single-center study included patients enrolled in a prospective HFpEF registry at UT Southwestern between 8/2023-5/2025. Participants completed the Bladder Condition Assessment Tool (BCAT), a 4-item questionnaire that ranges from 0 to 20, with higher scores indicating more severe symptoms of incontinence. Outcomes included health-related quality of life (Kansas City Cardiomyopathy Questionnaire [KCCQ-12]), physical function (Short Physical Performance Battery [SPPB]), 6-minute walk distance (6MWD), and frailty (Fried Frailty Scale). Participants were stratified by data-derived BCAT tertiles representing minimal (tertile 1), intermediate (tertile 2), and severe (tertile 3) symptoms of incontinence. Linear regression assessed unadjusted associations between BCAT scores and outcomes, with additional adjustment for age, sex, and BMI. Results: 193 participants (age 70±12 years; 77% female; 29% Black) were included. The median (IQR) BCAT scores for the minimal, intermediate, and severe symptom groups were 3 (2-4), 7 (6-9), and 13 (11-15), respectively. Across symptom severity groups, there were no significant differences in age, sex, race, or comorbidities, though BMI was higher with greater symptom severity (p=0.042). In unadjusted analysis, severe (vs minimal/intermediate) symptoms were associated with worse 6MWD (β: -70 m; p=0.002), KCCQ-12 scores (β: -11; p=0.011), frailty (β: 0.52; p=0.005), and SPPB (β: -1.3; p=0.015). After adjustment, severe (vs minimal/intermediate) symptoms remained significantly associated with worse 6MWD (aβ: -50 m; p=0.022), KCCQ-12 scores (aβ: -9.0; p=0.036), and frailty (aβ: 0.46; p=0.016), with a trend towards worse SPPB Score (aβ: -0.92; p=0.075). Discussion: Worse symptoms of incontinence were associated with poorer physical function, decreased quality of life, and greater frailty. Given the substantial burden of symptoms in this population, clinicians should assess for incontinence as part of comprehensive HFpEF care.

Introduction: PH increases RV afterload, often leading to RV systolic and diastolic failure. The metabolic mechanisms underlying RV dysfunction remain poorly understood and are largely unexplored across non-PAH forms of PH. Aims: We aimed to identify which metabolites and metabolic pathways associated with imaging measures of RV systolic function. Methods: We analyzed data from the multi-center PVDOMICS cohort, which includes echo, CMR, invasive hemodynamics, clinical phenotyping, and untargeted plasma metabolomics (via Metabolon). RV systolic function metrics selected as the outcome measures for this study were fractional area change (echo), global longitudinal strain (echo), and RVEF (CMR). Associations between non-xenobiotic, named metabolites and RV function measures were assessed using linear regression adjusted for age, sex, BMI, and PH group, with significance defined by FDR <0.05. Metabolite set enrichment analysis (MSEA) was performed to identify significantly enriched pathways (FDR <0.20). To complement MSEA, we conducted mean-aggregation analysis: we averaged metabolite concentrations within each pathway and tested associations with RV function using the same covariate adjustments (FDR <0.05). Results: We identified 979 participants with plasma metabolomics and at least one available outcome measure (317 Group 1 PH; 106 Group 2 PH; 140 Group 3 PH; 51 Group 4 PH; 26 Group 5 PH; 81 Healthy Controls; 258 Disease Comparators). Linear regression identified 170 metabolites from the 647 named metabolites significantly associated with all 3 RV metrics (Top 10 by average ranked p-value displayed in Image 1 ). MSEA revealed seven enriched pathways across all RV metrics: Androgenic Steroids, Pregnenolone Steroids, Progestin Steroids, Fatty Acid Metabolism (Acyl Choline), Histidine metabolism, Vitamin A metabolism, Gamma-glutamyl Amino Acid ( Image 2) . Mean aggregation analysis showed that higher concentrations of Androgenic/Pregnenolone Steroids and Vitamin A metabolites strongly associated with improved RV function, while higher levels of Fatty Acid Metabolism (Acyl Carnitine) and Acetylated Peptides associated with worse function ( Image 3 ). Conclusions: Using data from the multi-institutional PVDOMICS cohort, we provide a blueprint of metabolites and metabolic pathways associated with RV systolic function across the spectrum of PH. Steroid hormones as well as Vitamin A and fatty acid metabolites emerged as central to RV systolic function.

Background: Preeclampsia is a heterogeneous disorder, with emerging evidence indicating the presence of multiple phenotypes. Identifying distinct clinical phenotypes may facilitate precise therapies and improve the clinical outcomes. This study aims to identify and validate preeclampsia phenotypes using machine learning and evaluate their associations with adverse pregnancy outcomes. Hypothesis: Machine learning-based clustering methods applied to routinely-collected clinical variables can identify distinct preeclampsia phenotypes, each associated with unique clinical profiles and differential risks of adverse pregnancy outcomes. Methods: In the derivation cohort (n=2,386), phenotypes were derived using k-means clustering applied to 26 routinely-collected clinical variables. A machine learning classifier incorporating key biomarkers was developed and externally validated to assign phenotypes within the validation cohort (n=1,570). Biological markers, clinical outcomes (primary outcome: composite of small for gestational age [SGA], preterm delivery, stillbirth, and neonatal death), and heterogenous impacts of delivery timing in term preeclampsia were analyzed across phenotypes. Results: Four distinct phenotypes were identified in the derivation cohort. Phenotype A exhibited hypocoagulation, while Phenotype B displayed relative thrombocytopenia. Phenotype C demonstrated hypercoagulation, and Phenotype D presented with hepatic and renal dysfunction, elevated potassium, and coagulation abnormalities. These findings were replicated in the validation cohort. Compared with Phenotype A, Phenotype D had the highest risk for the primary outcome (relative risk [RR] 2.83, 95% CI 2.48–3.23, P < 0.001), followed by Phenotypes C (RR 1.72, 95% CI 1.49–1.99) and B (RR 1.28, 95% CI 1.08–1.50). In term preeclampsia, delivery at 37 weeks increased adverse outcome risks relative to after 40 weeks in Phenotypes A, B, and D; Phenotype C exhibited elevated risks from 37–39 weeks. Conclusion: Four clinical phenotypes of preeclampsia were identified by using routinely-collected health data, each characterized by unique maternal feature profiles and associated with varying fetal outcomes. These phenotypes reflect diverse underlying pathophysiological processes, and may inform individualized decisions regarding delivery timing. Machine learning-based phenotyping represents a promising strategy to advance precision obstetrics and improve the understanding and management of preeclampsia.

Background: Disease-causative variants in LMNA -encoded lamin A/C cause the laminopathies, a heterogeneous group of diseases variably resulting in arrhythmogenic/dilated cardiomyopathy (ACM/DCM), lipodystrophy, muscular dystrophy, and progeria. The artificial intelligence (AI)-derived electrocardiographic age gap (AI-EAG), determined by the discrepancy between a patient’s artificial AI-enabled electrocardiogram (ECG) predicted biological age versus their chronological age, is accelerated in laminopathy. Thus, we sought to determine if the AI-EAG serves as a prognostic marker in patients with cardiac laminopathy. Methods: Retrospective analysis of 1,049 genotype-positive patients with genetic ACM/DCM was used to identify those with pathogenic/likely pathogenic (P/LP) variants in LMNA . After the exclusion of those who lacked a 12-lead ECG while in sinus rhythm, a previously trained AI-ECG age algorithm was used to determine the AI-EAG by subtracting the patient’s chronological age from the AI-ECG derived biological age. The AI-EAG was then correlated with a combined outcome of major ventricular arrhythmia [sudden cardiac arrest, sustained ventricular tachycardia, and appropriate implantable cardioverter-defibrillator shocks], heart transplantation, and cardiovascular death. Results: Overall, 147/1,049 (14%) patients with genetically-mediated ACM/DCM had a P/LP variant in LMNA . Of these, 80/147 (54%) LMNA variant-positive patients (52% female, mean chronological age 36 ± 15 years) had ECGs suitable for AI-EAG analysis. Most (52/80; 65%) had an AI-EAG >10 years with a mean AI-EAG of 17 ± 13 years. Of note, the AI-EAG was greater in those with a clinical cardiac phenotype (20 ± 14 vs. 13 ± 10 years; p = 0.011). As a continuous variable, an increased AI-EAG was associated with increased risk of the composite outcome at a median follow-up of 20 months (HR 1.036; 95% CI 1.004–1.068; p = 0.026) and AI-EAG > 10 and > 20 years were both associated with elevated risk (HR 4.272; 95% CI 1.388–13.147; p = 0.011 and HR 3.732; 95% CI 1.505–9.255; p = 0.004, respectively). Conclusion: In cardiac laminopathy, an increased AI-EAG was common and correlated with adverse cardiovascular outcomes. Non-invasive ascertainment of electrocardiographic aging by AI may provide a novel prognostic marker in cardiac laminopathy. Future studies are needed to validate this finding and further define the role of the AI-EAG in the risk-stratification of patients with cardiac laminopathy.

Introduction: Pathogenic variants (PVs) in the LMNA gene are associated with a range of diseases called laminopathies. While PVs in LMNA have diverse phenotypes, some of the most consequential are are observed in the heart. Cardiomyopathies associated with LMNA PVs may vary in terms of severity of mechaincal and electrical disturbance as well as age of onset and progression. Methods: We examined the expression profiles of IPSC-derived cardiomyocytes (iCMs) and cardiac fibroblasts (iCFs) derived from patients with a several different LMNA PVs and variable clinical presentations. These allow us to explore the expression profiles of key cells involved in these diseases with te clinincally relevant gentic cause. The RNA seq data analyzed consists of three replicates each for three control lines and five patient-lines. We investigated differential gene expression for four clinical phenotypes of interest: atrial fibrillation, cardiac conductive disorder, striated muscle disease and ventricular arrythmia compared to control cell profiles in which these are absent using DESeq2. Results: Each phenotype showed between 1300 and 2100 differentially expressed genes. Our exploration currently suggests a shared core of expression corresponding with the presence of exacerbated cardiomyopathy phenotypes. In iCMs 538 genes or 50% of differentially expressed upregulated genes were shared while in the downregulated pool 51% were common between phenotypes. This phenomenon was lower in iCFs with 43% of upregulation and 41% of downregulation being shared. These cores are distinct to each of the two cell types with little to no overlap in significantly up or downregulated genes. Gene Set Enrichment Gene ontology analysis on the shared cardiomyocyte cores revealed upregulation of genes in the system development and multicellular organismal process pathways. Enrichment gene ontology of the upregulated core for cardiomyocytes showed 102 enriched pathways with the top result of chromosome segregation. Conclusion: We conclude that while these phenotypes have distinct outcomes and presentation, they share a core expression profile that could be indicative of overall severity. Having identified this shared core of expression will allow us to refine our search into potential genes and pathways to further target with drugs or gene therapy.

Background: We used Social Network Analysis (SNA) to phenotypic and clinical characterization of tissue Doppler (TD)signals representing different phases of the cardiac cycle in heart failure with preserved ejection fraction (HFpEF). Methods: Retrospectively, 275 HFpEF patients (64±18 years, 54% females, EF: 57±7%, followed for median 2.8 years for heart failure hospitalization and mortality) were studied. TD signals were measured [Positive and negative pre systolic (PREp and PREn) and ejection systolic (S'), negative and positive post systolic velocities (POEn and POEp) and early diastolic (e’) velocities]. PREp/n, POEn/p, PREp/S’ and POEn/e’ were calculated to represent relationships between the biphasic PRE and POE and different parts of systole and diastole. SNA was done using Python and google Colab. The above parameters were treated as the edges and patients as the nodes. Cosine similarity and Louvain clustering were used for SNA.. Results: SNA yielded 6 clusters (edges: 2654, density 0.1, average degree: 19.3, average clustering coefficient: 0.42, assortativity: .165, path length: 2.77, figure 1, table 1). Cluster 1 (40 patients) and 2 (16 patients) had preserved TD signals relationships. Cluster 3 (54 patients) exaggerated PRE and POE with mild S’ depression, cluster 4 (44 patients) had depressed PRE and POE despite preserved S’ and e’. Cluster 5 (53 patients) and 6 (68 patients) showed all signals depressed. Clusters were not different for age, sex, risk factors or co-morbidities. NYHA class was worst in clusters 4 and 5, while clusters 1 and 5 had the worst diastolic profiles, and cluster 5 had intermediate diastolic profiles. Survival analyses suggested clusters 0,1, and 2 as low risk, cluster 3 was intermediate, and clusters 4 and 5 as high risk. Moreover, clusters were subdivided into smaller clusters that differentiate TD relationships further; in addition to specific clinical and echocardiographic features (Cluster 3 differentiates based on E/A ratio, Cluster 5 differentiates based on E/e’ and symptoms, Cluster 6 differentiates based on LAVi). Conclusions: In HFpEF patients PRE, ejection, POE, and early diastolic TD signals share complex clinically meaningful relationships that are difficult to express using traditional statistical techniques. Clustering using SNA can help computationally and visually stratify such relationships. Exploring such relationships in larger studies can unlock phenotypic characterization and management strategies.

Background: Single ventricle disease (SVD) is the most severe form of congenital heart disease. Despite surgical advances improving survival, heart failure (HF) remains a key contributor to early morbidity and mortality, especially in the first months of life. Defining genetic drivers of HF in SVD could enable early risk stratification and guide precision therapies to improve outcomes. Hypothesis: Ultra-rare variants in genes associated with dilated cardiomyopathy (DCM) increase HF risk in neonates with SVD. Approach: Neonates ≤21 days old with SVD were prospectively enrolled and followed at Duke (n=40). An additional 69 individuals (0.2–65y) from Duke and UNC formed an ambispective cohort. Chromosomal abnormalities were excluded. Ultra-rare (MAF<e⁻⁴) variants in DCM genes were manually classified as likely pathogenic/pathogenic (LP/P) or of uncertain significance (VUS) per ACMG criteria. Multimodal deep clinical phenotyping was performed. HF was defined as severe (VAD, transplant, or death) or medically managed (MM; EF ≤40% and/or HF therapy escalation). Findings were validated in a blinded analysis of an independent SVD cohort from Nationwide Children’s Hospital (NCH; n=36) and compared to the AllofUs population. Results: In the prospective cohort, 8 (20%) neonates developed severe HF, 11 (28%) developed MM HF, and 21 (52%) remained HF free, with mean follow-up of 2 years. Hosting a DCM-associated LP/P variant was linked to an 11-fold increased risk of severe HF ( P =0.0009), while VUSs increased MM HF risk 6-fold ( P =0.01). Most HF occurred within the first month. Findings were independently validated in the NCH cohort, where LP/P variants reduced freedom from severe HF ( P =0.0007). Associations were attenuated in the ambispective cohort, suggesting survivor bias and underscoring the importance of early detection and risk stratification. Compared with the AllofUs cohort, the prospective cohort had a higher prevalence of LP/P variants ( P <0.0001) but similar VUS burden, supporting a model in which LP/P variants drive primary disease risk, while low-penetrant variants may modify susceptibility in the context of SVD. Conclusion: This study provides the first prospective evidence linking DCM-associated variants to significant risk for early-onset HF in SVD. These findings, independently validated in external cohorts, underscore the potential for genetic screening to inform early risk stratification and family counseling in this high-risk population.

Background: Andersen-Tawil Syndrome (ATS) is an autosomal dominant disorder marked by dysmorphic features, periodic paralysis, and ventricular arrhythmias. While the overall risk of cardiac arrest in ATS patients is low, mutations in the C-terminal region of the KCNJ2 gene are linked to a higher risk of arrhythmias. This study aims to evaluate the significance of mutations in this region compared to others. Methods: We conducted a meta-analysis by reviewing literature on KCNJ2-positive ATS cases and cohort studies from 1994 to 2025. Cases were included if they had a cardiac condition, categorized by the location of the mutation. Numerical variables were presented as mean and standard deviation (SD), and Student’s t-test was used for analysis. Categorical variables were evaluated with chi-square or Fisher’s exact test. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for binary comparisons, with a significance level set at p < 0.05. Results: A total of 221 patients with KCNJ2-positive ATS were analyzed, including 85 females. Those with C-terminal mutations had significantly higher odds of presenting with the ATS triad (p < 0.001) and periodic paralysis (p < 0.001). Dysmorphic features such as hypertelorism, micrognathia, and low-set ears were more common in this group. C-terminal mutations were also linked to increased QTc prolongation (p = 0.02) and higher frequencies of non-sustained ventricular tachycardia (nsVT) and sustained ventricular tachycardia (sVT) (52% vs. 50.0% and 12% vs. 9%, respectively). Ventricular fibrillation (VF) affected 18% of the C-terminal group (8% in other locations, p = 0.03). Non-fatal cardiac arrest (NFCA) was slightly more common in the C-terminal group (9% vs. 8%). Sudden cardiac death (SCD) occurred in 7% of patients in the C-terminal group, while none experienced it in the other location group (p = 0.01). Conclusion: Mutations located at the C-terminal of the KCNJ2 gene in ATS are associated with a more severe clinical phenotype, periodic paralysis, QTc prolongation, dysmorphic features, and life-threatening arrhythmias, including ventricular fibrillation and sudden cardiac death.

Background: Coronary spastic angina (CSA) is a functional coronary disorder characterized by transient epicardial constriction in the absence of significant atherosclerosis. However, its clinical phenotypes remain poorly defined, and the contributions of metabolic and inflammatory factors to CSA susceptibility and vascular dysfunction are not fully understood. Hypothesis: We hypothesized that distinct CSA phenotypes can be identified through multidimensional profiling of metabolic and inflammatory stress, and that these phenotypes are associated with differences in endothelial function and spasm risk. Methods: We analyzed data from the FUJI-SPASM study, a prospective single-center registry of 1,125 patients undergoing intracoronary acetylcholine testing. Among them, 568 patients without obstructive coronary artery disease who presented ischemic symptoms were included. Unsupervised k-means clustering was applied to 19 standardized clinical and biochemical variables, including lipid and apolipoprotein profiles, glycemic indices (HOMA-IR, HbA1c), inflammatory markers (CRP, fibrinogen), and demographic factors. Principal component analysis (PCA) was then performed to extract latent axes of biological variation. Results: Clustering identified two phenotypes. Cluster 0 (metabolic phenotype) showed elevated triglycerides, non-HDL-C, apoB, apoC2, apoC3, FFA, and HOMA-IR. Cluster 1 (inflammatory phenotype) was characterized by higher CRP, fibrinogen, older age, and higher smoking. The frequency of CSA did not significantly differ between clusters (46% vs. 39%, p = 0.10). PCA revealed two principal components: PC1, primarily loaded with atherogenic lipids and insulin resistance indices, and PC2, characterized by positive loadings for CRP and fibrinogen and inverse loadings for HDL-related apolipoproteins—representing metabolic and inflammatory burdens, respectively. In a subanalysis of 159 patients with flow-mediated dilation (FMD) data, those with high PC1 and PC2 scores had significantly lower FMD values (p < 0.001) and higher CSA positivity (p < 0.05), suggesting cumulative vascular impact of metabolic and inflammatory stress. Conclusion: This study identified two clinically relevant CSA phenotypes defined by metabolic and inflammatory profiles. PCA further demonstrated that a dual burden of these biological stresses is closely linked to endothelial dysfunction and increased CSA risk. These findings support a phenotype-based, multidimensional approach to CSA evaluation.

Background: Heart failure has traditionally been classified as systolic vs diastolic, however acute heart failure (AHF) hospitalizations, often has various outcomes seen in bedside clinical medicine, driven by comorbidities, hemodynamic states, and initial presentation. Research: Question Can unsupervised machine learning (ML) identify distinct clinical phenotypes among adults hospitalized with acute heart failure and guide management strategies? Methods: We analyzed non-elective adult hospitalizations with AHF from the 2022 National Inpatient Sample (NIS). Five machine learning models were employed for prediction: Logistic Regression, Naive Bayes, Random Forest, XGBoost, and an artificial neural network (ANN) model. Unsupervised clustering was performed using K-means after principal component analysis (PCA) (k=4, Fig 1). Cluster visualization was enhanced via t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) on a 10,000-patient subsample (Fig 2). Clusters were profiled by demographic and clinical characteristics.All analysis was conducted using Python 3.11. Results: Four distinct patient clusters were identified amongst heart failure admissions. Cluster 0 (47.1%) comprised younger healthier patients (mean age 28.8 years) and lowest in-hospital mortality (1%) representing AHF cases with non-chronic triggers. Cluster 1(2.9%) consisted of older individuals, with coronary artery disease and interventions, such as CABG, PCI (mean age 74.1 years) and moderate mortality (3%) confirming an ischemic heart disease phenotype . Cluster 2 (35.3%) exhibited a cardiopulmonary/ metabolic profile with moderate mortality (3%, mean age 66.1 years) indicating a common heart failure phenotype with systemic involvement . Cluster 3 (14.6%) was dominated by arrhythmia and older population (mean age 74.5 years) and had the highest in-hospital mortality(6%) suggesting a high-risk arrhythmia-dominant phenotype (p <0.001 for all).(Image 3) Conclusions: Unsupervised machine learning identified four unique and clinically relevant phenotypes among adults hospitalized with AHF, differentiated by comorbidity burden and in-hospital mortality. Our study highlights the potential of ML driven phenotyping to inform risk stratification, and clinical decisions in acute heart failure management.

Background: Dysregulated aldosterone is a modulator for the development and progression of cardio-renal-metabolic disease. Aim: This study aimed to investigate the prognostic value of aldosterone plasma concentration in relation to incident cardiovascular disease (CVD) and mortality in a population-based cohort. Methods: Participants from the population-based Gutenberg Health Study, a prospective cohort in Western Germany, underwent deep clinical phenotyping including venous blood sampling under standardized conditions. Quantification of baseline aldosterone concentration was performed using a chemiluminescent immunoassay (DiaSorin, Dietzenbach, Germany). Incident CVD was defined as the composite endpoint of incident coronary artery disease, atrial fibrillation, stroke, myocardial infarction and cardiac death. Mortality was assessed through regular verification of vital status via registration offices and death certificates from the mortality registry. Aldosterone concentrations were stratified by quartiles. Cox regression adjusted for age and sex was used to assess the association between aldosterone concentration and clinical outcomes. Based on an observed nonlinear U-shaped relationship, the lowest (Q1) and highest (Q4) quartiles were each compared against the middle quartiles (Q2-Q3). Results: The analysis sample included n=14,756 individuals with aldosterone measurements with a mean±SD age of 55.0±11.1 years, of whom 49.3% were women. The median (interquartile range) plasma concentration of aldosterone was 7.51 ng/dL (5.62/10.10). Follow-up time for all-cause death was 15 years (n events=1,753), for cardiac death 10 years (n events=716) and for incident CVD 5 years (n events=759). Compared to the middle quartiles (Q2-Q3) of aldosterone, individuals in the lowest quartile (Q1) had a higher risk of all-cause death (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.03; 1.30, p=0.012), while the associations with cardiac death (HR 1.21, 95% CI 0.87; 1.68, p=0.26) and incident CVD (HR 0.96, 95% CI 0.81; 1.15, p=0.67) were not statistically significant. In contrast, those in the highest quartile (Q4) showed an increased risk of all-cause death (HR 1.46, 95% CI 1.31; 1.64, p<0.0001), cardiac death (HR 1.65, 95% CI 1.22; 2.24, p=0.0012), and incident CVD (HR 1.29, 95% CI 1.09; 1.53, p=0.004). Conclusion: In this large population-based cohort, dysregulated plasma aldosterone was associated with an increased risk of mortality, cardiac death, and incident CVD.

Background: Type 2 Long QT Syndrome (LQT 2 ), caused by KCNH 2 mutations, is associated with an increased risk of sudden cardiac death (SCD). Clinical severity varies considerably among carriers. We investigated a family harboring the KCNH 2 p.Y427H variant, comprising a symptomatic proband (nine cardiac events), an asymptomatic carrier mother, and a mutation-negative father. Hypothesis: We hypothesized that patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) can recapitulate the observed clinical phenotype discordance in vitro. Methods: Whole-exome sequencing identified the variant. Peripheral blood mononuclear cells (PBMCs) from all three individuals were reprogrammed into iPSCs and differentiated into cardiomyocytes. Electrophysiological assessment included patch-clamp recordings of I Kr and I Ca,L , measurements of action potential duration APD 90 and APD 50 under pacing, and evaluation of early afterdepolarizations (EADs) and triggered activities (TAs) under spontaneous conditions. Nifedipine, an L-type calcium channel blocker, was used to evaluate arrhythmia suppression. Results: I Kr was markedly reduced in the proband-derived iPSC-CMs (0.26 [0.19–0.29] pA/pF) and mother-derived iPSC-CMs (0.29 [0.27–0.30]) compared to the father-derived iPSC-CMs (1.65 [1.23–2.11], P = 0.001). APD 90 was significantly prolonged in the proband-derived iPSC-CMs (824 [691–893] ms) versus the mother-derived iPSC-CMs (559 [491–574] ms, P = 0.010) and father-derived iPSC-CMs (397 [331–403] ms, P < 0.001). Proband-derived iPSC-CMs exhibited a higher incidence of spontaneous EADs and TAs. Notably, I Ca,L was upregulated in the proband-derived iPSC-CMs (–18.4 [–19.6 to –13.0] pA/pF) compared to the mother-derived iPSC-CMs (–7.23 [–9.5 to –5.9], P = 0.002) and father-derived iPSC-CMs (–7.49 [–8.66 to –5.91], P = 0.001). Nifedipine significantly suppressed EADs and TAs in the proband-derived iPSC-CMs. Conclusion: The KCNH 2 p.Y427H variant induces I Kr loss-of-function. Divergent calcium current responses among family members may reflect a compensatory mechanism modulating phenotypic severity. Patient-specific iPSC-CMs reproduce this variability, providing a platform for mechanistic insights, personalized risk assessment, and therapy in LQT 2 .

Introduction: In-hospital cardiac arrest (IHCA) affects roughly 300,000 patients each year in the United States. Compared to out-of-hospital cardiac arrest (OHCA), IHCA remains understudied. OHCA categorization is well established, allowing for efficient and granular quality improvement and clinical research to improve OHCA outcomes. Similar approaches have not been applied to phenotype IHCA despite the abundance of available data from Electronic Medical Records (EMR). We hypothesized that IHCA data collected in the EMR can be used to categorize patients into clinically relevant phenotypes and consequently improve understanding of prognostic risk factors. Methods: We performed a retrospective cohort study of patients admitted to the Hospital of the University of Pennsylvania who suffered IHCA. Trained study team members extracted detailed information from the EMR, including the patient’s pre-admission status, hospital course, and the events in the 24 hours leading up to IHCA. The study team reviewed the pre-arrest trajectory of each case to identify phenotypes of pre-arrest deterioration (phenotypes are not mutually exclusive). Clinical criteria for each trajectory were also developed using expert input of study team members and available scientific literature. These phenotypes were iteratively developed, refining or adding phenotype definitions as new patterns emerged. A multidisciplinary panel of cardiac arrest experts reviewed each case until ten cases were classified without the need to modify or add a clinical phenotype. Additional IHCA cases were then categorized using these novel phenotypes. Results: Our cardiac arrest expert panel identified definitions and criteria for nine novel pre-arrest categories using the initial 100 cases (Figure 1). After review of 40 additional IHCA cases, they assigned all patients into at least one pre-arrest category. The most common pre-arrest trajectories were unstable tachyarrhythmia (41/202, 20%), unstable bradycardia (36/202, 18%), and respiratory failure (39/202, 19%). Conclusion: Our findings demonstrate that IHCA patients can be categorized into clinically relevant phenotypes using EMR-derived criteria. These phenotypes provide insight into pre-arrest trajectory and may help identify prognostic risk factors. By leveraging this categorization, we plan to investigate how these phenotypes are associated with patient outcome and whether they can inform resuscitation strategies or enhance quality improvement efforts.

Background: Combined Pulmonary Fibrosis and Emphysema (CPFE) is a distinct syndrome characterized by upper-lobe emphysema and lower-lobe fibrosis, predominantly in older male smokers. Despite often preserved spirometric volumes, patients exhibit severely reduced diffusing capacity and high susceptibility to complications, including pulmonary hypertension (PH), acute exacerbations, and lung cancer, contributing to poor prognosis. Purpose: This review aims to synthesize current evidence on CPFE, focusing on clinical phenotype, functional impairment, differential diagnosis, complications, and emerging management strategies, highlighting distinctions from idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD). Methods: A narrative review of observational cohorts, retrospective series, and clinical studies examining CPFE patients was performed. Data on demographics, smoking history, symptomatology, pulmonary function, radiology, comorbidities, complications, and treatment approaches were extracted and integrated. Results: CPFE affects mainly males aged 65–70, with >90% reporting > 40 pack–years smoking history. Dyspnea is the cardinal symptom (>95%), often disproportionate to preserved FVC and TLC, accompanied by chronic cough in 30–70%. Exercise-induced desaturation is frequent, correlating with PH, observed in 47–90% of patients. Pulmonary function tests reveal preserved volumes, normal or near-normal FEV1/FVC, and severely reduced DLCO (35–45%), distinguishing CPFE from COPD and IPF. HRCT confirms the combined emphysematous and fibrotic pattern, critical for differential diagnosis. Acute exacerbations occur in 20–28% of cases, lung cancer in 22–46% (mostly squamous cell), and long-term oxygen therapy is required in >70%. Five-year survival is 35–55%, lower than emphysema alone and comparable or worse than IPF. Management focuses on smoking cessation, antifibrotics, oxygen therapy, and complication-specific treatments, and selected patients may undergo lung transplantation. Conclusions: CPFE is a clinically and functionally unique entity with a high burden of pulmonary and systemic complications. Accurate recognition using HRCT and DLCO, along with early intervention and tailored management, is essential to improve patient outcomes and guide prognostic stratification.

Background/Objectives: Lithium remains the first choice for long-term prophylaxis of mood episodes in bipolar disorder (BD), but only 30% of patients will respond, and there is no reliable method by which to predict treatment response. Ketamine is a rapid antidepressant therapy which ostensibly yields greater results in patients with clinical phenotypes that are classically associated with lithium non-response. This inspired a scoping review to map the overlapping and divergent clinical and mechanistic evidence for acute ketamine response and long-term prophylactic lithium therapy in BD. Methods: We conducted a scoping review of clinical and preclinical studies that examine convergent and divergent predictors and mechanisms of acute response to ketamine and long-term response to lithium. Results: Data from 19 preclinical studies show mechanistic convergence of ketamine and lithium on the GSK-3β/mTOR pathways, and enhancement of synaptic plasticity. Furthermore, lithium appears to consistently limit ketamine-related oxidative stress and hyperlocomotion. However, data from the 23 clinical studies suggest divergence of predictors of response to ketamine and lithium in BD, with ketamine response associated with metabolic risk factors, anxiety/mixed features, and non-melancholic presentations, which are generally predictors of poorer prophylactic lithium response. No study directly tested ketamine response as a predictor of prophylactic lithium response. An important limitation is that clinical studies of ketamine are enriched for lithium-refractory populations and have often included mixed unipolar and bipolar cases. Conclusions: Overall, existing data support mechanistic overlap but clinical divergence between ketamine and lithium responders, though this is confounded by sampling bias. We must therefore undertake longitudinal studies of prophylactic lithium therapy among patients with BD who received ketamine for acute antidepressant treatment in order to investigate if responsiveness to ketamine predicts response to lithium, and establish control over BD earlier in the course of illness.

USH2A mutations are the leading cause of autosomal recessive retinitis pigmentosa (RP), a progressive blinding disease marked by photoreceptor degeneration. Animal models fail to recapitulate the features of USH2A RP seen in humans, and its earliest pathogenic events remain unknown. Here, we established a human model of USH2A RP using retinal organoids derived from patient induced pluripotent stem cells and CRISPR-Cas9-engineered isogenic-USH2A-/- induced pluripotent stem cells. We assessed organoids for cellular, molecular, and morphological defects using serial live imaging and whole organoid and fixed section analyses. Both patient-derived and isogenic-USH2A-/- organoids showed preferential rod photoreceptor loss followed by widespread degeneration, consistent with the clinical phenotype. Additionally, isogenic-USH2A-/- organoids showed early defects in proliferation and structure. Our findings suggest that molecular changes precede overt photoreceptor loss in USH2A RP, and pathogenesis may begin before clinical symptoms emerge. By defining early and late disease features, we provide new insight on the developmental origins of USH2A RP to guide therapeutic strategies.

BackgroundTo provide new insights into the pathological mechanisms of epilepsy associated with variants in the calcium channel voltage-dependent L-type alpha1C subunit gene (CACNA1C, NM_001129837) and to expand the phenotype of CACNA1C-associated neurological disorders: familial mesial temporal lobe epilepsy (FMTLE).MethodsWe conducted a comprehensive analysis of clinical data from a family affected by FMTLE and carried out genetic screening of CACNA1C variants through whole-exome sequencing combined with Sanger sequencing for validation. The clinical characteristics of FMTLE were systematically reviewed, and the pathogenic potential of the identified variant was assessed following the guidelines established by the American College of Medical Genetics and Genomics (ACMG). To explore the underlying pathogenic mechanisms, we utilized bioinformatics tools alongside molecular dynamics simulation methods.ResultsA novel CACNA1C variant (c.5480G > A, p.R1827Q) was identified in a large family with FMTLE. Unlike previous reports, the clinical phenotype of this genotype differs from previous reports, being mild, with focal to bilateral tonic–clonic seizures being more common. Bioinformatics analysis and molecular dynamics simulations indicated that this variant induces local structural changes in the protein.ConclusionsThe findings of this study provide new insights into the complex molecular mechanisms underlying CACNA1C variants and their correlations with patient phenotypes. This research is the first to identify CACNA1C as a potentially new pathogenic gene in FMTLE.Supplementary InformationThe online version contains supplementary material available at 10.1186/s42494-025-00231-5.

Hereditary vitreoretinopathies (HVRs) are genetically based disorders affecting the vitreous and retina, potentially leading to severe vision loss. This review focuses on two major subtypes of HVRs: Stickler syndrome (SS) and VCAN-related vitreoretinopathies (primarily Wagner syndrome and erosive vitreoretinopathy [ERVR]). Stickler syndrome is a multisystemic collagenopathy typically caused by mutations in collagen genes such as COL2A1 and COL11A1. It is characterized by distinctive ocular findings (notable vitreous anomalies, high myopia, and an increased risk of retinal detachment), along with craniofacial, auditory, and skeletal manifestations. VCAN-related vitreoretinopathies result from mutations in the VCAN gene, which encodes the extracellular matrix proteoglycan versican. These conditions are typically characterized by specific ocular features, such as an ‘optically empty vitreous’ (as seen in Wagner syndrome), and generally lack prominent systemic manifestations. This review discusses the molecular genetic basis, detailed clinical phenotypes, and key elements for differential diagnosis of both disease groups, along with current ophthalmologic management strategies. Furthermore, genetic diagnostic methods used in these complex disorders, their diagnostic yields, genotype-phenotype correlations, diagnostic challenges, and future research directions are explored. Early and accurate diagnosis—especially when supported by molecular genetic testing—is vital for appropriate patient monitoring, prophylactic treatment planning to prevent complications such as retinal detachment, and genetic counseling for affected family members. A deeper understanding of these disorders will be achievable through ongoing research in ocular genetics and multidisciplinary approaches.