搜索结果：Clinical Medicine Insights. Cardiology

The number of individuals engaging in sports continues to rise, and identifying those with cardiac substrates associated with increased risk of exercise-related adverse events is crucial. Athlete evaluation requires a refined diagnostic strategy to distinguish physiological cardiac remodelling from pathology. This joint European Association of Preventive Cardiology/European Association of Cardiovascular Imaging consensus provides a multimodality approach for advanced cardiovascular imaging in sports cardiology. Cardiovascular magnetic resonance, cardiac computed tomography, and nuclear imaging each offer complementary insights into cardiac structure, function, coronary anatomy, tissue characterization, perfusion, and inflammation. When integrated with clinical data and first-line tests, they improve diagnostic precision and risk stratification in scenarios frequently encountered in athletes, including ventricular arrhythmias, cardiomyopathies, congenital coronary anomalies, inflammatory myocardial disease, and coronary artery disease. Standardized protocols tailored to age, training, and clinical indication are essential to ensure reliability and avoid misinterpreting physiological adaptation as disease. The consensus emphasizes responsible reporting, considering performance and legal implications of diagnoses, and recommends second-line imaging when justified. Functional imaging, for ischaemia or inflammation, is central in guiding return-to-play decisions. Persistent evidence gaps include limited normative datasets across athletic subgroups and uncertain significance of subtle tissue abnormalities. Overall, this consensus supports harmonized, safe, and judicious multimodality imaging to protect athletes while preventing unnecessary sport restriction.

Alterations in lipid metabolism are increasingly recognized as important contributors to breast cancer biology. Lipids regulate membrane structure, intracellular signaling, and energy homeostasis, and cancer cells undergo lipid metabolic reprogramming to support tumor growth and progression. This narrative review summarizes current evidence on the relationship between circulating lipid profiles and breast cancer risk, tumor characteristics, and outcomes, with a focus on total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), as well as the effects of endocrine therapies on lipid metabolism. Epidemiological studies show heterogeneous and lipid-specific associations that vary by menopausal status, tumor subtype, and study design. Triglycerides and LDL-C are most consistently linked to aggressive tumor features and disease progression, whereas total cholesterol appears to reflect broader metabolic and hormonal disturbances rather than a direct causal role. HDL-C shows inconsistent associations, likely due to functional heterogeneity not captured by circulating HDL-C levels alone. Mechanistic studies support a role for lipid metabolism in promoting tumor growth, invasion, angiogenesis, and therapy resistance. Endocrine therapies further modify lipid profiles, with tamoxifen generally reducing total cholesterol and LDL-C while increasing triglycerides, and aromatase inhibitors showing largely neutral effects in recent population-based studies. Overall, circulating lipid fractions are not suitable as standalone biomarkers but may provide clinically relevant information when interpreted in the context of tumor biology, metabolic health, and long-term survivorship care.

Using AI algorithms can exacerbate health disparities if care or resources are allocated away from underserved populations. We evaluated an algorithm for its potential to worsen health disparities across different clinical use cases. This was a retrospective study of patients with heart failure (HF) at an academic health system using an algorithm that predicts pharmacy fill nonadherence to evidence-based HF medications. We compared prediction performance metrics (accuracy, false positive rate, false negative rate), using rate-ratios (RRs), between subgroups with and without known HF care disparities: below vs above median neighborhood-level socioeconomic status (nSES) and Black vs White race. Results were then applied to 3 hypothetical clinical use cases. Among 34 697 patients (13% Black, 10% Hispanic, 65% White), algorithm accuracy was similar across nSES and racial subgroups. The algorithm assigned more false positives for medication nonadherence among low vs high nSES (RR [95%CI] 1.50 [1.44-1.56]) and Black vs White (2.05 [1.92-2.19]) subgroups. The algorithm also assigned fewer false negatives (0.63 [0.59-0.67]) to Black vs White subgroups. When applied to 3 hypothetical use cases, worsening of existing disparities was pertinent for clinical applications where false positives could be particularly harmful (e.g, if predictions of nonadherence prompted lower treatment priority). Although accuracy was similar across demographic groups, differences in false positive and false negative rates revealed that the same prediction may worsen disparities in some use cases, but not others. Evaluation of predictions in the context of clinical use is essential to avoid unintentionally worsening inequities.

Valvular heart diseases (VHDs), including mitral regurgitation (MR), aortic stenosis (AS), mitral stenosis (MS), and mitral valve prolapse (MVP) represent a significant global health burden particularly among older adults. Digital auscultation platforms can transform traditional cardiac assessments that are primarily subjective and clinician-driven into a data-driven diagnostic tool enabling advanced signal processing, feature extraction and machine learning (ML)-based interpretation. This creates new opportunities for early, objective, and precise disease screening, diagnosis, longitudinal monitoring, and personalized clinical decision-making. In this study, we present an explainable ML framework for early detection and precise classification of VHDs using digital auscultation data. Acoustic features extracted from digital auscultation data are used to build ML models on a public dataset followed by validation using clinical hospital data. Shapley Additive Explanations (SHAP) helps create more understandable models for early detection by pinpointing unique acoustic characteristics of VHD, which enhances the interpretability and accuracy of ML models. The SHAP tree explainer is utilized to improve interpretability and guide feature selection by identifying unique, consistent, and overlapping features relevant to VHDs, providing physiological insights and enhancing model transparency. Among the five models assessed, XGBoost with SHAP stood out as the most reliable, delivering high interpretability and 85 % accuracy on the clinical dataset, achieving condition-specific accuracies with minimal variability across different practitioners. By combining predictive performance with explainability, the proposed framework shows high promise in objective screening, early diagnosis, and informed clinical decision-making for VHDs.

The advent of novel Human Epidermal growth factor Receptor 2 (HER2)-targeted therapies and tyrosine kinase inhibitors (TKIs) has significantly improved outcomes in HER2-positive malignancies, particularly breast cancer. However, these agents carry a growing burden of cardiovascular adverse events, representing a critical concern in modern oncology. This narrative review explores the evolving landscape of cardiovascular toxicity associated with these therapeutic classes, integrating mechanistic insights with real-world clinical data. HER2-targeting monoclonal antibodies and antibody-drug conjugates exert off-target effects on cardiomyocytes via HER2 pathway inhibition, leading to reversible or irreversible myocardial dysfunction. In parallel, small-molecule TKIs, especially those targeting multiple kinases, have been associated with hypertension, arrhythmia, QT prolongation, and heart failure, through mechanisms such as mitochondrial dysfunction, endothelial damage, and disruption of cardioprotective signaling. We summarize clinical evidence elucidating the molecular basis of these toxicities and critically review clinical trials and post-marketing data highlighting their incidence and management. The review emphasizes the heterogeneity of cardiotoxicity profiles across different agents, underscoring the need for individualized cardiovascular risk stratification and monitoring. Finally, we address the emerging role of cardio-oncology in bridging oncologic efficacy with cardiac safety, advocating for multidisciplinary approaches, biomarker-guided surveillance, and standardized definitions of cardiotoxicity. As precision oncology advances, a parallel refinement in cardiotoxicity prediction and prevention is imperative to optimize patient outcomes.

Vericiguat is currently indicated for patients with heart failure with reduced ejection fraction (HFrEF) following recent clinical worsening, based on evidence demonstrating a reduction in cardiovascular death or heart failure hospitalization in a high-risk population. While this positioning is clinically justified, it may underestimate the broader pathophysiological context in which soluble guanylate cyclase (sGC) stimulation may be relevant, particularly in phases of persistent biological activation following apparent clinical stabilization. In routine practice, acute coronary syndromes (ACS), acute heart failure (AHF), and chronic HFrEF are approached as distinct clinical entities. However, these conditions often represent sequential manifestations of a continuous disease trajectory driven by persistent endothelial dysfunction, impaired nitric oxide-sGC-cyclic guanosine monophosphate (NO-sGC-cGMP) signaling, and residual vascular risk. In this perspective, we revisit the mechanistic and clinical rationale for vericiguat and propose a reframing of its therapeutic role. Its greatest utility may lie in patients with recently worsening HFrEF who remain biologically vulnerable after stabilization. Extension of this concept to post-ACS populations remains hypothesis-generating and is not supported by direct clinical evidence. This "post-stabilization vulnerable state" represents a clinically recognizable yet insufficiently targeted phase, characterized by ongoing biological activation despite apparent clinical improvement. Adopting a continuum-based view of cardiovascular disease may improve alignment between pathophysiology and treatment, refine patient selection, and inform future trial design focused on this early post-event window. Importantly, this perspective is hypothesis-generating and reflects an effort to align emerging mechanistic insights with clinical trajectory, rather than to extend current indications beyond the available evidence base.

The COVID-19 pandemic has severely impacted cardiology, with myocardial injury and new-onset cardiac dysfunction observed even without respiratory symptoms. The Aim of the study aimed to evaluate angiographic and clinical characteristics of COVID-19-positive patients who were presented with acute coronary syndrome. This retrospective case-control study involved 80 COVID-19-positive patients with acute coronary syndrome who underwent angiography, compared to matched COVID-19-negative controls. Conducted at King Saud Medical City, Riyadh, between June 2021 and July 2022. It included angiographic, echocardiographic and laboratory evaluations. 80 (1.6%) of 5134 COVID-19 patients underwent coronary angiography for coronary artery disease over 14 months. The COVID-19-positive and control groups were primarily male (78.75% vs. 75%) and had similar mean ages (57.1±10.86 vs. 55.93±10 years). The control group had higher rates of diabetes (81.3% vs. 66.3%) and hypertension (85% vs. 57.5%), while the COVID-19 group had higher smoking rates, STEMI (50% vs. 26.3%), and elevated D-dimer, C reactive protein (CRP), and cardiac troponins. COVID-19 patients had more ventricular thrombus (15% vs. 3.75%), RV dilation (36.25% vs. 11.25%), and pulmonary hypertension (50% vs. 26.3%). COVID-19 patients had more single-vessel disease (35% vs. 17.5%), and controls had more three-vessel disease (47.5% vs 22.5%). The COVID-19 group also used more thrombus aspiration (17.5% vs. 5%) and glycoprotein IIb/IIIa inhibitors (37.5% vs. 6.33%). The findings of this study show that COVID-19 increases cardiovascular injury. Even if the cause is unknown, cardiac injury must be detected quickly. ACS, including STEMI, can kill if not diagnosed and treated immediately. All COVID-19-positive patients with chest pain should be treated with a high index of suspicion due to the risk of coronary thrombosis due to hypercoagulability.

Marijuana, or cannabis, is the most commonly used illicit substance in the United States, with prevalence nearly doubling over the past decade. Accumulating evidence implicates cannabis use as a potentially modifiable risk factor for acute myocardial infarction, particularly among younger adults without traditional cardiovascular risk factors. Marijuana precipitates acute myocardial infarction through multiple converging mechanisms: increased myocardial oxygen demand via sympathetic activation, impaired oxygen delivery through carboxyhemoglobin elevation, coronary vasospasm, endothelial dysfunction, and a prothrombotic state characterized by enhanced platelet activation. Genetic variability in cannabinoid receptor expression and CYP2C9-mediated tetrahydrocannabinol metabolism further modulates individual susceptibility. Among patients with established coronary artery disease, population-based data suggest elevated cardiovascular risk with frequent use, though prospective cohort data remain conflicting. In post-percutaneous coronary intervention patients on dual antiplatelet therapy, cannabidiol inhibition of CYP2C19 may impair clopidogrel bioactivation, warranting consideration of alternative P2Y12 inhibitors. These findings highlight the importance of cannabis use screening in clinical practice and the need for prospective studies to guide evidence-based management.

Early feasibility studies (EFS) are critical to high-risk medical devices development, providing initial insights into safety, performance, and usability. However, the lack of a harmonized European Union regulatory framework creates significant barriers to their consistent and efficient conduct. This study examined current EFS practices in European university hospitals to identify key regulatory and operational challenges and inform future harmonization efforts. A qualitative, multisite study was conducted involving 6 European university hospitals in November 2024 and July 2025. Experts involved in EFS or comparable early-phase clinical investigations participated in semistructured interviews. Participants were eligible if they were currently involved in, or had prior experience with, early-phase medical device or methodologically comparable clinical research; no role-based exclusion criteria were applied. A standardized questionnaire covering 6 thematic areas including a weighted scoring system to assess site selection criteria was developed to guide the interviews, and data collection and analysis followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) 32-item checklist. Site-level findings were analyzed through qualitative and descriptive content analysis validated by a multidisciplinary review team. Quantitative data from the site‑selection exercise were summarized using nonparametric descriptive statistics. As this was an organizational and process‑focused qualitative study, no clinical outcomes or adverse events were assessed. Twenty-one participants representing clinical, regulatory, operational, and ethics roles were included. Investigator expertise and engagement emerged as the most influential determinants of EFS success and site selection, followed by institutional readiness and operational infrastructure. Participants consistently reported challenges related to regulatory fragmentation, variable interactions with ethics committees and competent authorities, contract and budget negotiation processes, and uneven clinical research organization experience in early-phase device research. Six overarching themes were identified: investigator capacity and engagement; clinical site readiness and feasibility; regulatory environment and governance; operational infrastructure and clinical research organization capacity; financial and organizational constraints; and structural and ecosystem-level factors. Several challenges paralleled those reported in the United States despite different regulatory contexts. The findings indicate that EFS implementation in Europe is constrained by interdependent regulatory, operational, and organizational barriers linked to the absence of a harmonized European Union EFS framework. Although the limited number of sites and qualitative design restrict generalizability, the results provide empirically grounded insight into current European EFS practices. These findings support the need for coordinated guidance, standardized processes, and structured stakeholder interaction to improve the feasibility, consistency, and efficiency of early-phase medical device investigations in Europe.

Background: Socio-cognitive deficits constitute a core and persistent feature of adolescent-onset schizophrenia, significantly impairing functional outcomes. However, the interplay between genetic metabolic markers such as CYP2D6 and specific socio-cognitive phenotypes remains poorly understood. Methods: This cross-sectional study included 73 adolescents with schizophrenia and 58 matched healthy controls. Theory of Mind (ToM) was evaluated using the Reading the Mind in the Eyes Test (RMET), while empathy was assessed with the Cambridge Empathy Quotient. Symptom severity was measured via the Positive and Negative Syndrome Scale (PANSS). CYP2D6 polymorphisms were genotyped using RT-PCR, classifying participants as Normal or Reduced (Intermediate) metabolizers. Hierarchical multiple regression analyses were performed, controlling for sex, IQ, and psychosocial factors. Results: Patients demonstrated significantly lower RMET and empathy scores compared to controls. Reduced CYP2D6 metabolizers exhibited poorer ToM performance and more severe negative symptoms. The final RMET model accounted for 88.8% of variance (p < 0.001), with CYP2D6 status emerging as a significant independent predictor (&#x3b2; = 0.178, p = 0.005), alongside IQ and negative symptoms. In contrast, the empathy model explained 49.0% of variance, with CYP2D6 effects fully mediated by negative symptom severity. Conclusion: Adolescents with reduced CYP2D6 metabolic activity exhibit greater negative symptom burden and impaired social-cognitive functioning. Our findings reveal a double dissociation: ToM functions as a stable, biologically anchored trait, while empathy serves as a state-dependent construct primarily driven by the negative syndrome. These insights advocate for the integration of pharmacogenetic stratification in the treatment of early-onset schizophrenia.

Insulin resistance and metabolic dysregulation are increasingly recognized as contributors to myocardial remodeling and phenotypic heterogeneity in hypertrophic cardiomyopathy (HCM). While the triglyceride-glucose (TyG) index offers a simple surrogate marker of insulin resistance, its relationship with left ventricular outflow tract (LVOT) obstruction in HCM patients remains underexplored. This study aimed to investigate the association between the TyG index and LVOT obstruction in a retrospective observational cohort of 124 adult HCM patients.&#xa0;LVOT obstruction was defined as a resting or provoked gradient of &#x2265;30 mmHg. Clinical, laboratory, and echocardiographic data were systematically evaluated. The TyG index was computed from fasting triglyceride and glucose levels. Multivariable logistic regression analysis, with the TyG index scaled per 0.1-unit increment, was used to assess independent associations. Receiver operating characteristic (ROC) curve analysis determined discriminatory performance.&#xa0;Patients with obstructive HCM exhibited significantly higher TyG index values compared to those without obstruction (9.07 &#xb1; 0.25 vs. 8.76 &#xb1; 0.21, p<0.001). The TyG index maintained an independent association with LVOT obstruction after adjusting for relevant clinical and echocardiographic covariates (per 0.1-unit increase: OR 1.84, 95% CI 1.40-2.42, p<0.001). Furthermore, the TyG index demonstrated moderate correlations with both resting (r=0.519, p<0.001) and provoked (r=0.557, p<001) LVOT gradients. ROC analysis indicated good discriminatory performance (AUC: 0.826, 95% CI: 0.752-0.901), with a cut-off value of 8.75 yielding 87.9% sensitivity and 47.0% specificity.&#xa0;In conclusion, the TyG index was independently associated with LVOT obstruction in HCM patients, with higher values observed in those with the obstructive phenotype. This suggests a potential link between metabolic status and disease manifestation in HCM. Given its cost-effectiveness and accessibility, the TyG index may offer valuable complementary clinical insights. However, this association requires cautious interpretation, and prospective studies are essential to establish its clinical and prognostic utility.

Heart failure with preserved ejection fraction (HFpEF) is an increasingly prevalent clinical syndrome with limited effective therapies, representing a major unmet need in cardiovascular medicine. A comorbidity-driven systemic proinflammatory state, together with coronary microvascular endothelial inflammation, has emerged as a central paradigm in HFpEF pathogenesis. Accumulating evidence over the past decade has highlighted the extensive crosstalk between inflammation, metabolic dysregulation, and aging-related processes. In this review, we summarize key advances defining the roles of systemic and microvascular inflammation, metabolic abnormalities, and cellular senescence, and integrate these findings into an interconnected inflammation-metabolism-aging axis in HFpEF. We further discuss the current clinical and emerging preclinical therapeutic strategies targeting these pathways. By linking mechanistic insights with translational perspectives, this review provides a conceptual framework to guide precise therapeutic development for this complex and heterogeneous syndrome.

Female sex has been associated with poor prognosis in hypertrophic cardiomyopathy (HCM), but the factors contributing to this disparity remain insufficiently defined. We aimed to clarify sex differences in clinical characteristics and factors associated with cardiovascular death in Japan. This multicenter, retrospective observational study of HCM was conducted between January 1, 2006, and December 31, 2018 (REVEAL-HCM study [Multicenter Registry to Evaluate Risk Factors for Disease Progression, Sudden Cardiac Death and Adverse Clinical Outcomes in Japanese Patients With Hypertrophic Cardiomyopathy]; UMIN000046932). Patients aged &#x2265;16&#x2009;years with HCM were enrolled. Baseline characteristics and clinical outcomes including cardiovascular death were assessed. Univariable and multivariable Cox proportional hazards models were used to identify factors associated with cardiovascular death. Of 3247 patients (median age, 67&#x2009;years; 43% women), women were older and more symptomatic at presentation (52% New York Heart Association class II-IV versus 35% in men). Cardiovascular death was more common in women (hazard ratio [HR], 1.37 [95% CI, 1.02-1.82]; P=0.03). In multivariable analysis, older age (per 1-year increase; HR, 1.04 [95% CI, 1.02-1.06]), advanced New York Heart Association class (HR, 1.99 [95% CI, 1.40-2.82]), history of atrial fibrillation (HR, 1.55 [95% CI, 1.10-2.18]), greater maximal wall thickness indexed to body surface area (per 1-mm/m2 increase; HR, 1.12 [95% CI, 1.06-1.20]), and apical HCM (HR, 0.53 [95% CI, 0.32-0.88]) were independently associated with cardiovascular death, attenuating the HR for female sex. Sex differences in clinical characteristics and outcomes were observed. The excess cardiovascular death in women with HCM was largely explained by older age, more advanced symptoms, greater indexed wall thickness, and sex-specific differences in both the prevalence and prognostic impact of apical HCM.

Identifying serum biomarkers that accurately reflect the progression of coronary artery disease (CAD) remains a major challenge. Integrative proteomic and metabolomic profiling can provide novel insights into disease pathogenesis and improve clinical prediction. We conducted a four-phase study. In the discovery phase, serum from 40 patients (controls, stable CAD, and acute coronary syndrome (ACS)) was analyzed using data-independent acquisition (DIA) proteomics and liquid chromatography-tandem mass spectrometry (LC-MS)/gas chromatography-mass spectrometry (GC-MS) metabolomics to identify differentially expressed proteins (DEPs) and metabolites. In the verification phase, selected DEPs were validated by parallel reaction monitoring (PRM) in an independent 40-patient cohort. In the derivation phase, six validated proteins were measured by ELISA in 207 angina patients to assess their association with coronary obstruction (&#x2265;&#x2009;50% stenosis). In the validation phase, a support vector machine (SVM) model incorporating clinical risk factors and these biomarkers was developed in the derivation cohort and tested in an independent 97-patient cohort. Model performance was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis. Coronary obstruction is defined as &#x2265;&#x2009;50% luminal diameter stenosis in at least one major coronary artery on angiography. Proteomic analysis identified 97 DEPs, and metabolomic profiling revealed 322 DEMs (including 289 from LC-MS and 33 from GC-MS analyses). Seven proteins showed consistent changes in both DIA and PRM validation. Among these, thrombospondin-1 (TSP-1) was significantly upregulated in stable CAD compared with controls, while serum amyloid A1 (SAA1) was markedly elevated in ACS compared with stable CAD. In an independent angina cohort (n&#x2009;=&#x2009;207), serum levels of TSP-1 and SAA1 were significantly higher in patients with coronary obstruction. Multivariate logistic regression adjusted for conventional cardiovascular risk factors (including age, sex, homocysteine, and other clinical variables) demonstrated that TSP-1 remained independently associated with coronary artery occlusion (OR&#x2009;=&#x2009;1.424, 95% CI 1.057-1.918, P&#x2009;=&#x2009;0.020). A support vector machine (SVM) model incorporating conventional clinical risk factors was constructed, and the addition of TSP-1 and SAA1 significantly improved diagnostic performance for CAD severity (AUC&#x2009;=&#x2009;0.919 in derivation, 0.992 in validation). In conclusion, our dual-omics approach identified novel biomarkers and pathways in CAD progression. The SVM-based prediction model offers a promising non-invasive tool for early CAD detection, potentially reducing unnecessary invasive procedures.

Coronary computed tomography angiography (CCTA) is widely used for noninvasive evaluation of coronary artery disease (CAD) and is highly sensitive for detecting anatomic coronary stenosis with a high negative predictive value. However, CCTA is limited in its ability to determine the physiological significance of lesions, resulting in reduced specificity and disagreement with invasive coronary angiography in a substantial proportion of cases. Fractional Flow Reserve derived from CCTA (FFR-CT) was developed to address this anatomic-physiologic discordance by providing noninvasive, lesion-specific functional assessment of ischemia. This narrative review summarizes the current state of FFR-CT technology, its diagnostic performance relative to CCTA alone and invasive FFR, and its evolving role in contemporary CAD evaluation. Across prospective trials and meta-analyses, FFR-CT consistently improves diagnostic accuracy for ischemia-producing lesions, driven primarily by gains in specificity, with favorable agreement to invasive FFR at clinically relevant thresholds. Advances in computational modeling and machine learning have substantially reduced processing times, improving feasibility and workflow integration. Clinical studies demonstrate that incorporation of FFR-CT following CCTA improves selection for invasive coronary angiography, reduces unnecessary diagnostic catheterization, and provides prognostic information beyond anatomic disease burden alone. Important limitations remain, including dependence on CCTA image quality, reduced reliability in heavily calcified or complex coronary anatomy, and uncertainty near ischemic thresholds, necessitating careful interpretation within the clinical context. When applied selectively after high-quality CCTA, FFR-CT offers a robust noninvasive surrogate for invasive coronary physiology and supports a more targeted, physiology-guided diagnostic pathway for patients with suspected CAD.

Low enrollment and retention in clinical research disproportionately impact Black, Hispanic or Latinx, women, and rural populations, undermining generalizability and perpetuating health disparities. However, few studies have compared mechanisms driving underrepresentation across populations. Freelisting is a qualitative methodology that elicits lists of terms, explores perspectives about domains, and identifies common themes within groups with shared characteristics; however, it has not been systematically applied to understand research participation across underrepresented populations. To explore perspectives on clinical research participation across underrepresented populations using freelisting methodology to ultimately inform culturally-responsive recruitment strategies. We conducted a web-based freelisting survey among adults who identified as Black, Hispanic or Latinx, women, and/or resided in rural communities between May and September 2023 across the Philadelphia, Atlanta, and Washington, DC metro areas. Participants listed words or phrases that came to mind in response to three prompts about research and participation. Using Anthropac software, we calculated salience indices to assess the relative importance of terms within and across the underrepresented groups. Terms were categorized by sentiment (positive, neutral, negative) and examined by demographic group and prior research experience. Of 101 participants (56% Black, 23% Hispanic or Latinx, 80% women, 46% rural), several salient terms were shared, including 'study,' 'knowledge,' 'search,' and 'scary.' Sentiment regarding being approached for research was generally positive. In contrast, sentiment about becoming a participant varied, with more negative terms among those never previously invited to join research. 'Research misconduct' emerged as uniquely salient among Black participants. Individuals with prior research experience conveyed more positive sentiments overall. Underrepresented populations hold positive and negative views about clinical research, with more negative perceptions among those never previously approached. These findings suggest that proactive outreach to individuals who have never previously been approached, combined with efforts to address persistent negative perceptions such as fear, may be among the most impactful strategies for improving research representativeness. Future work is needed to understand the contextual information surrounding the sentiments we found, and to elucidate the mechanisms underlying these sentiments, ultimately enabling the development of more effective, culturally-responsive recruitment and retention strategies across diverse groups.

A substantial proportion of cardiovascular (CV) events occurs in individuals without previously diagnosed CV disease, underscoring the need for improved primary prevention strategies. Traditional risk scores provide probabilistic estimates but fail to directly identify the presence and heterogeneity of subclinical atherosclerosis. This review summarizes current evidence on advanced multimodality imaging approaches for identifying high-risk individuals without prior CV events. Evidence from cohort studies, randomized trials, and meta-analyses was examined to evaluate the role of coronary artery calcium (CAC) scoring, coronary computed tomography angiography (CCTA), perivascular fat attenuation index (FAI), and vascular ultrasound in risk stratification. CAC scoring remains the most validated and widely recommended tool, offering robust prognostic value and significant risk reclassification, particularly in intermediate-risk individuals. CCTA provides additional insights into plaque burden and high-risk phenotypes, while FAI enables noninvasive assessment of coronary inflammation, improving risk prediction beyond anatomical measures. Vascular ultrasound offers a radiation-free, accessible method for detecting systemic plaque burden and refining risk estimation. Overall, multimodality imaging enhances the identification of subclinical disease and supports more individualized, disease-based risk assessment. Future research should clarify cost effectiveness, optimize patient selection, and determine whether imaging-guided strategies improve long-term clinical outcomes.

Previous studies showed that the Pooled Cohort Equations substantially overestimate atherosclerotic cardiovascular disease (ASCVD) risk in older adults, and the recently published PREVENT equation offers better performance. Identifying markers that can further refine risk estimates is essential for personalized prevention in this age group. This study evaluates the clinical utility of 6 markers for reclassifying intermediate-risk older adults (10-year PREVENT-estimated ASCVD risk, 7.5% to 20%), with a focus on de-escalation. This post hoc analysis evaluated intermediate-risk older adults aged &#x2265;70 years using data from ASPREE (Aspirin in Reducing Events in the Elderly; URL: https://www.clinicaltrials.gov; Unique identifier: NCT01038583). ASPREE was a randomized trial of low-dose aspirin versus placebo in healthy older adults who were free of prior cardiovascular events at enrollment. Six markers were evaluated: the lowest quartile (Q1) of NT-proBNP (N-terminal pro-B-type natriuretic peptide), hs-TnI (high-sensitivity troponin I), and hs-CRP (high-sensitivity C-reactive protein), polygenic risk for lipoprotein(a) and polygenic risk for coronary artery disease, and absence of family history of ASCVD. Incident ASCVD events were adjudicated by expert panels. Statistical performance of each marker was assessed using diagnostic likelihood ratios, change in Harrell C statistic (&#x394;C), and net reclassification index relative to a baseline model incorporating variables included in the PREVENT equation. The study included 7764 participants (48% female; median age, 74 years [interquartile range, 72-77]), with a median follow-up of 10.3 years. During follow-up, 725 participants (9.3%) experienced ASCVD events. Q1 polygenic risk for coronary artery disease was the most powerful marker for down-grading risk, providing 37% risk reduction (diagnostic likelihood ratio: 0.627) and improving discrimination (&#x394;C: +1.44%; P=0.004) when compared with the baseline model, and correctly reclassifying 25.9% of nonevents downward (net reclassification index: 0.101). Q1 hs-TnI showed second-best performance (diagnostic likelihood ratio: 0.727; &#x394;C: +0.71%, net reclassification index: 0.075). Adding polygenic risk for coronary artery disease to the PREVENT equations may further personalize risk estimates and support clinical decision-making for older adults at intermediate risk for ASCVD events.

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a major global health challenge, characterised by a heterogeneous clinical spectrum. While metabolomic studies have identified disruptions in amino acid, lipid, nucleotide, and energy metabolism during COVID-19, these investigations often lack fine-grained clinical stratification. In this study, we performed untargeted metabolomic profiling of plasma from 25 participants, including five healthy controls and twenty COVID-19 patients classified into four severity groups (COV1-COV4) based on pulmonary involvement and the need for respiratory support. Using ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS), 541 metabolites were detected and analysed across all samples. Principal component analysis revealed a progressive metabolic divergence corresponding to disease severity. Monocarboxylic acid dysregulation was predominant in early to moderate cases (COV1-COV3), whereas severe disease (COV4) demonstrated a shift toward pyrimidine metabolism enrichment, consistent with heightened nucleotide turnover driven by viral replication and immune cell proliferation. Phenylalanine metabolism emerged as a consistently enriched pathway in COV1-COV3, suggesting aromatic amino acid perturbations as early markers of metabolic stress and immune activation. In contrast, pyrimidine pathway activation in COV4 could reflect profound systemic metabolic reprogramming associated with critical illness. These findings provide novel insights into COVID-19 pathophysiology, highlighting stage-specific metabolic signatures and potential biomarkers for disease monitoring. Our results support the concept of metabolomics-guided precision medicine, offering a rationale for targeted therapeutic interventions based on disease stage and metabolic phenotype.