Citations

BACKGROUND: It is unknown whether cardiac resynchronization therapy (CRT) improves systemic metabolomic profile by enhancing left ventricular function. The observational cohort study aimed to investigate the effect of CRT on left ventricular ejection fraction (LVEF) and circulating plasma metabolites in patients who had heart failure with reduced LVEF. METHODS: We prospectively screened patients with ischemic cardiomyopathy and nonischemic cardiomyopathy who received CRT with a defibrillator for LVEF ≤35% according to current guidelines. Clinical assessment included echocardiography and device interrogation. Blood samples for metabolomic analysis were collected before CRT and at 6-month follow-up. Plasma was subjected to gas chromatography-mass spectrometry and 1 H nuclear magnetic resonance-based metabolomic analysis. RESULTS: Totally 92 patients were enrolled, with a mean age of 67.3±11.3 years (37.0% female). LVEF was significantly improved from 28.9±7.6% at baseline to 36.0±11.3% at 6 months ( P <0.001) and to 40.1±12.6% at 12 months ( P <0.001). After CRT, 42 metabolite features were significantly decreased compared with the baseline. The ketone bodies, including 3-hydroxybutyrate ( P <0.001) and acetone ( P =0.01), were reduced. A branched-chain amino acid, isoleucine, was also decreased after CRT ( P =0.01). These metabolomic changes were mainly observed in the nonischemic cardiomyopathy group, while the metabolomic profile in the ischemic cardiomyopathy group was characterized by enhanced amino acid oxidation and elevated levels of lactate and pyruvate. The improvement in LVEF positively correlated with the ratio of changes in ketone bodies and isoleucine. CONCLUSIONS: CRT may modulate the systemic plasma metabolomic profile, which correlated with improvement in left ventricular function in patients with severe heart failure with reduced ejection fraction.

The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient’s unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

ABSTRACT Aims Metabolic dysfunction‐associated steatotic liver disease (MASLD) is increasingly linked to cognitive decline, yet the hepatic factors associated with imaging‐derived neurovascular coupling (NVC) remain unclear. This study aimed to investigate whether liver stiffness or liver fat content was more closely associated with resting‐state CBF‐ReHo surrogate measures. Materials and Methods A total of 130 participants including 98 MASLD patients and 32 age‐ and education‐matched healthy controls (HCs) underwent clinical assessment, neuropsychological testing, and multi‐modal MRI. Liver stiffness and fat content were quantified using MR elastography (MRE) and MRI‐proton density fat fraction (PDFF). Imaging‐derived NVC was assessed using global cerebral blood flow (CBF)‐regional homogeneity (ReHo) coupling and voxel‐wise CBF/ReHo ratios, interpreted as resting‐state surrogates rather than direct stimulus‐evoked NVC. Multivariable regression and exploratory mediation analyses were employed. Results Compared to HCs and patients with lower liver stiffness (MASLD_low), those with higher liver stiffness (MASLD_high) showed reduced global CBF‐ReHo coupling and altered CBF/ReHo ratios, primarily localized to the bilateral superior temporal pole/superior temporal gyrus (TPOsup). In multivariable regression, liver stiffness remained independently associated with TPOsup CBF/ReHo ratios ( p < 0.001). Exploratory mediation analysis showed a statistically significant indirect association between hepatocellular injury markers and TPOsup CBF/ReHo ratios involving MRE‐derived liver stiffness (95% CI: 0.0795–0.2790). Conclusions Within this cohort and the observed PDFF range, MRE‐derived liver stiffness was more closely associated with imaging‐derived NVC surrogate measures than liver fat content in MASLD. These findings are hypothesis‐generating and require validation in longitudinal studies.

Abstract Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.

Objective: To evaluate the institutional learning curve of robotic liver resection (RLR), its association with surgical complexity and outcomes in an international multicenter cohort. Summary of Background Data: RLR is increasingly adopted worldwide, but the dynamics of the learning process and its impact on surgical outcomes remain poorly defined. Methods: This international, multicenter, retrospective cohort study included consecutive patients who underwent RLR between 06/2013 and 12/2024 across 22 expert centers. Based on the first 150 cases at each center, three phases were used to define the learning curve: operative time for competency, major complications for proficiency, and textbook outcome for mastery. Risk-adjusted generalized additive models were used to assess the learning process. Benchmark performance was compared to international reference values. Results: Among 2,331 patients, the most common indications were hepatocellular carcinoma (36%), benign lesions (23%) and colorectal liver metastases (21%). Operative time decreased after achieving competency (case #62), from 240 to 220 minutes ( P <0.001). Major morbidity reached a plateau during the proficiency phase (cases #63–138; 9% vs. 11%, P =0.144), while progression to mastery (cases #139–150) was associated with a higher rate of textbook outcome (76% vs. 87%, P =0.021). In contrast, conversion (5.1% vs. 4.9% vs. 2%), bile leak (4.0% vs. 5.3% vs. 3.8%), and 90-day mortality (0.7% vs. 0.9% vs. 0.9%) remained low and stable across all learning phases. Importantly, benchmark criteria for operative time, hospital stay, and textbook outcome were met across all complexity groups, including during the early learning phase. Conclusions: Robotic liver surgery shows progressive outcome improvement with early achievement of benchmark-level results and stable performance across increasing complexity. These findings indicate that, in experienced centers, RLR can be safely implemented and integrated into clinical practice, supporting its key role in modern hepatobiliary surgery.

ABSTRACT Disease Overview Plasmablastic lymphoma (PBL) is a rare CD20‐negative aggressive lymphoma with poor prognosis when treated with standard treatment options. Although PBL is associated with HIV infection and other immunosuppressed states, it can also affect immunocompetent individuals. Diagnosis The diagnosis of PBL requires a high clinical suspicion and pathological confirmation. EBV‐encoded RNA (EBER) expression and MYC gene rearrangements are frequently detected in the malignant cells. The differential diagnosis includes EBV+ diffuse large B‐cell lymphoma (DLBCL), extracavitary primary effusion lymphoma, ALK+ DLBCL, and HHV8+ large B‐cell lymphoma, among others. Risk Stratification Age ≥ 60 years, advanced clinical stage, and high International Prognostic Index scores are associated with worse survival. Management Combination chemotherapy regimens, such as infusional EPOCH, are recommended. The addition of bortezomib or daratumumab might improve outcomes. B‐cell maturation antigen‐targeted therapies have shown early efficacy. The participation of patients with PBL in prospective clinical trials is warranted.

Context: Traditional Chinese medicine (TCM) techniques, grounded in TCM theory, offer flexible and personalized therapeutic strategies. Increasing evidence indicates that gut microbiota dysbiosis is closely associated with the onset and progression of various diseases, highlighting a complex mutual relationship. Investigating the interaction between TCM techniques and gut microbiota may provide both a theoretical and practical foundation for clinical application. However, comprehensive systematic analyses remain limited. Objective: This study aimed to identify core themes and emerging trends in research on the interactions between TCM techniques and gut microbiota. In addition, it sought to explore underlying correlations using data mining approaches to provide insights supporting clinical applications and mechanistic studies. Materials and Methods: A systematic literature review was conducted using the Web of Science Core Collection and China National Knowledge Infrastructure for bibliometric analysis. Co-occurrence analysis was performed using CiteSpace software to identify research characteristics and development trends. Key data were extracted and visualized through an interactive network diagram generated with Cytoscape. Results: Current research predominantly focuses on acupoint application, moxibustion, and TCM enema, all of which exhibit significant effects in promoting beneficial microbiota and suppressing pathogenic strains. The regulatory impact of these TCM techniques on gut microbiota is closely associated with inflammatory response pathways and immunity. Conclusions: TCM techniques are extensively applied in disease management. Among them, moxibustion, acupoint application, TCM enema, and Tuina remain prominent and evolving areas of interest. Further empirical research is warranted to elucidate the biological mechanism underpinning these effects within the guidance of TCM pattern theory.

Abstract Background Normal weight obesity (NWO) has been linked to metabolic syndrome (MetS), cardiovascular disease (CVD) and mortality, but has not been comprehensively studied in the Hispanic population. Purpose Our study aims to examine the CVD risk factors associated with NWO and to compare the development of CVD risk factors between NWO and normal weight non-obese (NWNO) Hispanic adults over 6 years. Methods This is a prospective study using data from the Hispanic Community Health Study (HCHS). Interviews and clinical assessments were collected from 2008–2011 (visit 1) to 2014–2017 (visit 2). NWO was defined as a body mass index (BMI) between 18.5–24.9 kg/m2 and a body fat percentage of ≥25 % in men and ≥35% in women, estimated using the Tanita scale. We identified 1,000 HCHS participants as having a normal weight at visit 1 and not missing data on covariates of interest including hypertension (HTN), hyperlipidemia (HLD), MetS, and insulin resistance (Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) ≥2.5). Participants were categorized into NWO (n=104) or NWNO (n=896) groups. Univariate logistic regression models were used to estimate the odds ratio (OR) and 95% confidence intervals (CI) of the association between health risk factors and NWO. Multivariate logistic regression models were used to estimate the odds of developing CVD risk factors over 6 years, using propensity score matching. Results The prevalence of NWO among our sample of Hispanic adults was 10.4% and the 6-year incidence of NWO at visit 2 was 3.5% (n=31) from NWNO participants at visit 1. Higher BMI (OR=5.19; 95% CI=3.15–9.27, p&lt;0.001) and a higher mean overnight heart rate (OR=1.34; 95% CI=1.01–1.79, p=0.043) were statistically significantly associated with NWO at visit 1. Furthermore, HTN (OR=2.88; 95% CI=1.31–6.71, p=0.011), HLD (OR=2.12; 95% CI=1.21–3.77, p=0.009), and MetS (OR=3.48; 95% CI=1.62–8.09, p=0.002) were statistically significantly associated with NWO at visit 1. Participants with NWO at visit 1 had higher odds of developing insulin resistance (OR=1.79; 95% CI=1.07–3.04, p=0.029) and MetS (OR=2.03; 95% CI=1.15–3.66, p=0.016) at visit 2 compared to NWNO participants at visit 1, after age and sex matching. Conclusion Among Hispanic adults, the prevalence of NWO was associated with a higher BMI, higher mean overnight heart rate, treated HTN, HLD, and MetS. Hispanic adults with NWO were more likely to develop insulin resistance and MetS over 6 years compared to NWNO Hispanic adults. Overall, NWO among Hispanic adults was associated with CVD risk factors which places this population at a higher risk for CVD morbidity and mortality.