Aims: Lipoprotein(a) (Lp(a)) levels are known to be mainly genetically determined. However, only scarce data are available on the intra-individual variability of Lp(a) levels across time. Methods and results: We included adult patients (≥18 years old) who had baseline and follow-up Lp(a) measurements (between 1997 and 2024) with a minimum of 1 year apart. Patients were categorized into three groups as follows: normal (<30 mg/dL), borderline (30 to 50 mg/dL), and high Lp(a) (≥50 mg/dL). Multivariable logistic regression was conducted to assess the predictors of the intra-individual changes in Lp(a) ≥ 10 mg/dL. A total of 11 669 individuals (median age: 54 years, 60% males) were included in our analysis, with median time between measurements of 4.5 years [interquartile range (IQR): 2.2, 10.6]. The median Lp(a) was 16 mg/dL (IQR: 7, 52) at baseline, compared with 15 mg/dL (IQR: 7, 52) at follow-up. At follow-up, 96.4% of individuals with normal Lp(a) and 89.9% with high Lp(a) remained in their categories, while 51.2% with borderline Lp(a) changed their category. Of the included population, 24.9% had an intra-individual Lp(a) change ≥ 10 mg/dL. Female sex (P < 0.001), history of ASCVD (P = 0.003), statin therapy (P = 0.003), and elevated LDL cholesterol (LDL-C) levels ≥ 100 mg/dL (P < 0.001) were significantly associated with higher odds of intra-individual Lp(a) changes ≥ 10 mg/dL. Conclusion: Lipoprotein(a) levels were generally stable over time; however, patients with borderline levels may require more than one Lp(a) measurement, especially if they are females, have a history of ASCVD, have elevated LDL-C levels, or are on statin therapy.

Aims: Lipoprotein(a) [Lp(a)] has demonstrated its association with atherosclerosis and myocardial infarction. However, its role in the development of in-stent restenosis (ISR) after percutaneous coronary intervention (PCI) is not clearly established. The aim of this study is to investigate the association between Lp(a) and ISR. Methods and results: A retrospective study of adult patients who underwent successful PCI between January 2006 and December 2017 at the three Mayo Clinic sites and had a preprocedural Lp(a) measurement was conducted. Patients were divided into two groups according to the serum Lp(a) concentration [high Lp(a) ≥ 50 mg/dL and low Lp(a) < 50 mg/dL]. Univariable and multivariable analyses were performed to compare risk of ISR between patients with high Lp(a) vs. those with low Lp(a). A total of 1209 patients were included, with mean age 65.9 ± 11.7 years and 71.8% were male. Median follow-up after baseline PCI was 8.8 [interquartile range (IQR) 7.4] years. Restenosis was observed in 162 (13.4%) patients. Median serum levels of Lp(a) were significantly higher in patients affected by ISR vs. non-affected cases: 27 (IQR 73.8) vs. 20 (IQR 57.5) mg/dL, P = 0.008. The rate of ISR was significantly higher among patients with high Lp(a) vs. patients with low Lp(a) values (17.0% vs. 11.6%, P = 0.010). High Lp(a) values were independently associated with ISR events (hazard ratio 1.67, 95% confidence interval 1.18–2.37, P = 0.004), and this association was more prominent after the first year following the PCI. Conclusion: Lipoprotein(a) is an independent predictor for long-term ISR and should be considered in the evaluation of patients undergoing PCI.

Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92). Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy. It follows an autosomal dominant inheritance pattern in most cases, with incomplete penetrance and heterogeneity. It is familial in 60% of cases and most of these are caused by pathogenic variants in the core sarcomeric genes (MYH7, MYBPC3, TNNT2, TNNI3, MYL2, MYL3, TPM1, ACTC1). Genetic testing using targeted disease-specific panels that utilize next-generation sequencing (NGS) and include sarcomeric genes with the strongest evidence of association and syndrome-associated genes is highly recommended for every HCM patient to confirm the diagnosis, identify the molecular etiology, and guide screening and management. The yield of genetic testing for a disease-causing variant is 30% in sporadic cases and up to 60% in familial cases and in younger patients with typical asymmetrical septal hypertrophy. Genetic testing remains challenging in the interpretation of results and classification of variants. Therefore, in 2015 the American College of Medical Genetics and Genomics (ACMG) established guidelines to classify and interpret the variants with an emphasis on the necessity of periodic reassessment of variant classification as genetic knowledge rapidly expands. The current guidelines recommend focused cascade genetic testing regardless of age in phenotype-negative first-degree relatives if a variant with decisive evidence of pathogenicity has been identified in the proband. Genetic test results in family members guide longitudinal clinical surveillance. At present, there is emerging evidence for genetic test application in risk stratification and management but its implementation into clinical practice needs further study. Promising fields such as gene therapy and implementation of artificial intelligence in the diagnosis of HCM are emerging and paving the way for more effective screening and management, but many challenges and obstacles need to be overcome before establishing the practical implications of these new methods.

Athlete’s heart (AH) represents the heart’s remarkable ability to adapt structurally and functionally to prolonged and intensive athletic training. Characterized by increased left ventricular (LV) wall thickness, enlarged cardiac chambers, and augmented cardiac mass, AH typically maintains or enhances systolic and diastolic functions. Despite the positive health implications, these adaptations can obscure the difference between benign physiological changes and early manifestations of cardiac pathologies such as dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic cardiomyopathy (ACM). This article reviews the imaging characteristics of AH across various modalities, emphasizing echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography as primary tools for evaluating cardiac function and distinguishing physiological adaptations from pathological conditions. The findings highlight the need for precise diagnostic criteria and advanced imaging techniques to ensure accurate differentiation, preventing misdiagnosis and its associated risks, such as sudden cardiac death (SCD). Understanding these adaptations and employing the appropriate imaging methods are crucial for athletes’ effective management and health optimization.