Citations

Abstract Background Emerging evidence suggests that premature ventricular contractions (PVCs) may influence atrial electrophysiology and remodeling, potentially leading to higher rates of atrial fibrillation (AF) recurrence. However, existing data is limited to small-scale studies. Therefore, we conducted a retrospective study to evaluate the potential association between PVCs and AF ablation outcomes, which may suggest the need for more aggressive management of ventricular ectopy in this population. Objective This study aims to assess the 5-year outcomes of catheter ablation for AF in patients with and without a diagnosis of PVCs. Methods Using the TriNetX Analytics Research Network, we included patients aged ≥ 18 years who underwent AF catheter between January 1, 2014, and January 1, 2020. Patients were further categorized into two groups based on the presence of a diagnosis of PVCs. Propensity score matching (PSM) was performed using patient demographics, left ventricular ejection fraction (LVEF), cardiac comorbidities, and medications. Study outcomes included all-cause mortality & hospitalization, acute heart failure (HF), and rhythm control interventions during a 5-year follow-up. Results A total of 15,987 patients with PVCs and 40,605 patients without PVCs were identified. After PSM, 15,152 patients from each group were included for further analysis. Compared to control group, patients with PVCs had significantly higher odds of all-cause mortality (odds ratio [OR], 1.138; 95% confidence interval [CI], 1.034–1.253), all-cause hospitalization (OR, 1.300; 95% CI, 1.243–1.360), acute heart failure (HF) (OR, 1.223; 95% CI, 1.162–1.287), and cardiogenic shock (OR, 2.059; 95% CI, 1.751–2.420) following catheter ablation for AF at 5-year follow-up. Additionally, higher odds of AF requiring cardioversion (OR, 1.135; 95% CI, 1.074–1.199) or repeated ablation (OR, 1.164; 95% CI, 1.085–1.249) were observed in the PVCs group. Conclusion Premature ventricular contractions were associated with poorer clinical outcomes and a higher odds of requiring additional rhythm control intervention following catheter ablation for AF. These findings suggest that PVCs may be a marker for worse outcomes after AF ablation.

Background/Objectives: Individual and increasing numbers of comorbidities have been associated with worse outcomes in patients with fibrotic interstitial lung disease (f-ILD). The association and impact of medical comorbidities on patient-reported outcome measures (PROMs) in f-ILD have yet to be reported. Methods: Analysis was conducted using data from a single-center prospective cohort involving 199 patients with f-ILD. All f-ILD diagnoses and severities were screened and enrolled over a three-year study period. Baseline demographics, pulmonary function test (PFT) measures, and survival status were collected. PROMs, including the Chronic Respiratory Questionnaire (CRQ) and the Self-Management Ability Scale (SMAS-30), were assessed at baseline and serially. Thirteen medical comorbidities were evaluated for their prevalence and impact on PROMs and all-cause mortality. Results: Mean age was 69 years, with a female-to-male ratio of 61% vs. 39%. Dyslipidemia (74%) and gastroesophageal reflux disease (GERD) (71%) were the most prevalent comorbidities. Hypertension, diabetes, GERD, pulmonary hypertension (PH), depression, congestive heart failure (CHF), and obstructive sleep apnea (OSA), were independently associated with lower PROM scores along with increasing numbers of concomitant comorbidities. Increasing numbers of comorbidities, as well as specifically diabetes, PH, hypertension, CHF, and OSA, were associated with greater all-cause mortality. Conclusions: Medical comorbidities may independently impact respiratory-related PROMs in patients with f-ILD. These findings highlight the importance of comprehensive comorbidity management in improving quality of life and survival outcomes in patients with f-ILD.

Abstract Background Measurement of cardiac troponin (cTn) using high-sensitivity assays is recommended for the diagnosis of myocardial infarction. We determined sex-specific and uniform 99th percentile upper reference limits (URLs) using the new Elecsys® Troponin T high-sensitivity Gen 6 assay in a global, healthy reference range cohort. Methods Lithium-heparin plasma and serum samples were prospectively collected from apparently healthy individuals aged ≥20 years across 34 global sites in the United States, Europe, China, and Japan. cTnT was measured using the Troponin T high-sensitivity Gen 6 assay on the Cobas® e 801 analyzer. Exclusion criteria were defined according to the 2022 IFCC guidance. Uniform and sex-specific 99th percentile URLs and nonparametric 95% CIs were determined in plasma and serum separately and combined. Results The final study population comprised 4147 participants (52.5% female) with a median (25th–75th percentiles) age of 48.0 (33.0–59.0) years; 45.8%, 47.9%, 5.2%, and 1.1% were White, Asian, Black, and other/unknown, respectively. For sample matrices combined (n = 8294), 81.0% and 99.2% of cTnT values were above the limit of detection in females and males, respectively. Sex-specific 99th percentile URLs (95% CI) were 18 (16–23) ng/L for females and 32 (28–35) ng/L for males; the uniform 99th percentile URL was 27 (24–31) ng/L. URLs were comparable in plasma and serum samples. Conclusions This study determined sex-specific and uniform 99th percentile URLs for the Troponin T high-sensitivity Gen 6 assay that were comparable irrespective of the matrix used in a large, global, healthy reference population.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.

Abstract Background The link between regional tau load and clinical manifestation of Alzheimer's disease (AD) highlights the importance of characterizing spatial tau distribution. In typical (memory‐predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicenters. However, atypical (non‐amnestic‐predominant) AD variants with heterogeneous tau patterns provide a key opportunity to assess the universality of connectivity as a scaffold for tau progression. Method We included tau‐PET data from 320 subjects with atypical AD, characterized by highly heterogeneous tau patterns ( n = 139 posterior cortical atrophy/PCA‐AD; n = 103 logopenic variant primary progressive aphasia/lvPPA‐AD; n = 35 behavioural variant AD/bvAD; n = 43 corticobasal syndrome/CBS‐AD) from 14 sites, with a subset of patients ( n = 78) having longitudinal tau‐PET data. As an independent sample, we further included regional post‐mortem tau stainings from 93 atypical AD patients from two sites ( n = 19 PCA‐AD, n = 32 lvPPA‐AD, n = 23 bvAD, n = 19 CBS‐AD). Gaussian mixture modeling was used to harmonize different tau‐PET tracers by transforming tau‐PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether 1) brain regions with stronger functional connectivity showed greater covariance in cross‐sectional and longitudinal tau‐PET and post‐mortem tau pathology, and 2) functional connectivity of tau‐PET epicenters and tau‐PET accumulation epicenters was associated with cross‐sectional and longitudinal tau patterns. Result Tau‐PET epicenters—defined as the 5% brain regions with the highest tau load—aligned with clinical variants, e.g. a posterior pattern in PCA‐AD (“visual AD”) and left‐hemispheric temporal predominance in lvPPA‐AD (“language AD”) (Figure 1). More strongly functionally connected regions showed correlated concurrent tau‐PET levels, which was confirmed with post‐mortem data (Figure 2). Moreover, the connectivity profile of tau‐PET epicenters and accumulation epicenters corresponded to tau‐PET progression patterns (Figure 3). Conclusion Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicenters, across all AD clinical variants. Since tau proteinopathy is a key driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant‐specific endpoints in clinical trials.

SUMMARY Artificial intelligence (AI)-based methods are increasingly critical in peptide drug discovery, but their applications are limited in a narrow scope such as antimicrobial peptides. For indications where peptide therapy inherently excels, such as metabolism, endocrinology, and tissue regeneration, AI-based pipeline for therapeutic peptide discovery is long awaited but still unmet. Here, we propose a deep learning-based pipeline, Deepeptide, capable of identifying therapeutic oligopeptides for various metabolism-related indications. Leveraging the intrinsical relationship of ‘disease indication — biological processes — molecular functions’, Deepeptide discovers oligopeptides with indication-ameliorating-related molecular functions as lead candidates for indication of interest. Deepeptide was applied in five representative indications of metabolism, endocrinology, and tissue regeneration: angiogenesis, lipid metabolism, osteogenesis, glucose metabolism, and anti-angiogenesis. Overall, 62% of the identified oligopeptide candidates demonstrated significant bioactivity in vitro, with most of them showing comparable potency to the first-line drugs. Notably, the heptapeptide AP7 exhibited angiogenic potency comparable to VEGF in excisional wound splinting mouse model by promoting cell migration rather than proliferation, and hexapeptide TP6 showed significant dual-efficacy against hyperlipidemia and obesity in high-fat diet mice by inhibiting lipid synthesis and regulating gut microbiota. These findings highlight the potential and generalizability of Deepeptide in therapeutic oligopeptide discovery for metabolic diseases.

Abstract Background The incidence of type 2 diabetes mellitus among young adults has been increasing over the past few decades. There are limited data on contemporary national cardiovascular mortality rates in young adults with diabetes mellitus (DM). Methods We queried the Centers for Disease Control and Prevention's Wide‐Ranging Online Data for Epidemiologic Research database for patients aged 15 to 44 years from 1999 to 2019 in the primary analysis. A secondary analysis included data from 2020 to 2022 to examine the effect of the COVID‐19 pandemic. We analysed patients in whom cardiovascular diseases (CVD) were listed as the main cause of death and DM as a contributing cause of death. In calculating age‐adjusted mortality rates (AAMR) per 100 000 individuals, we used CVD mortality in young adults with comorbid diabetes as the numerator and the overall population as the denominator. We determined temporal trends by estimating the average annual percent change (AAPC) using the Joinpoint regression program. Results Among 3 309 079 individuals aged 15–44 years who died, 30 978 deaths were due to CVD with comorbid DM listed as a contributing cause. The overall AAMR increased from 1.08 per 100 000 individuals in 1999 to 1.23 per 100 000 individuals in 2019, with an average APC of 0.75 (95% CI, 0.31–1.20). Compared with 1999, the percentage increase in CVD deaths in 2019 among young adults with comorbid DM was +6.2%, whereas a decrease of 19.9% was observed among those without comorbid DM. Males had a higher AAMR than females (1.60 vs. 0.97). Non‐Hispanic Black individuals and non‐Hispanic American Indian/Alaska Native individuals had the highest AAMRs of 2.99 and 2.89, respectively. The AAMR was higher in rural regions compared to urban areas (1.63 vs. 1.23). The overall AAMR increased markedly from 1.23 in 2019 to 1.60 in 2022, with a similar pattern observed across demographic subgroups. Conclusion Our study reveals a rising trend in CVD mortality in young adults with DM as a contributing cause. Males, non‐Hispanic Black individuals, and individuals from rural regions had higher AAMR than their counterparts. This warrants the development of specific healthcare policies aimed at these at‐risk populations. Lay summary Our study shows that cardiovascular disease (CVD) deaths are increasing in young adults with diabetes mellitus listed as a contributing cause.

Abstract Background Dynamically predicting patients at risk of Alzheimer's disease (AD) is crucial for timely treatment and care in precision medicine. Method We present a novel architecture, a Kolmogorov‐Arnold networks (KANs)‐based joint prediction model of longitudinal and survival data (JM‐KAN). Real‐life data of patients with Mild Cognitive Impairment (MCI) with no prior AD diagnosis from National Alzheimer's Coordinating Center (NACC) dataset was utilized for individualized predictions. We built joint models with landmark times every 1 year up to 4 years after the MCI diagnosis, and predicted survival outcomes one year after each landmark (i.e. 1, 2, 3, and 4 years). Integrated AUC (iAUC), and integrated Brier score (iBS) were measured for existing prediction models and our novel architecture. Result Out of 2711 unique patients with MCI, 821 patients progressed to develop AD. Mean time‐to‐event in years was 3.19 (standard deviation (SD): 2.29). The number of visits prior to AD diagnosis was 4.03 (SD: 2.41). With iAUC of 0.789, iBS of 0.118, JM‐KAN has both the highest iAUC and lowest iBS among joint models, which indicates superiority in discrimination and overall performance. For individualized predictions, the model successfully predicted both longitudinal risk factors and survival probability, as new information came in every year. Conclusion Using neural network‐based Artificial Intelligence (AI) model, we demonstrated that this architecture improves upon prior methods in dynamically updating predictions of AD as new longitudinal data becomes available.

Abstract Background Several ovarian cancer studies have suggested that a body mass index (BMI) of 30 or higher is associated with lower compliance with National Comprehensive Cancer Network–recommended chemotherapy but primarily involved treatment before 2012, when dose capping was recommended for patients with higher body surface areas. Updated analyses in the contemporary treatment era are warranted. Methods In a retrospective cohort of patients with newly diagnosed ovarian cancer receiving curative-intent carboplatin plus paclitaxel in the Yale-Smilow Cancer Network (2012-2022), we evaluated BMI at diagnosis in relation to relative dose intensity (RDI)—the ratio of completed chemotherapy dose intensity to the National Comprehensive Cancer Network–recommended dose intensity—which reflects dose modification both before and during treatment. We also assessed starting RDI (which reflects modifications before treatment) and received RDI (which reflects modifications during treatment). Data on hospitalizations and hematological chemotoxicities were collected. We examined the association between BMI (<25, 25-30, ≥30) and chemotherapy completion, hospitalizations, and toxicities using multivariable linear and logistic regressions. Results Among 327 patients, the average RDI was 79.7%, and 44.3% had an RDI below 85%. Mean (SD) starting and received RDI were 97.9% (9.1%) and 81.8% (25.7%), respectively. Higher BMI was associated with higher RDI (Paggregate = .03) and received RDI (Paggregate = .04). Body mass index was not associated with starting RDI, dose reductions, delays, hospitalizations, or hematological toxicities. Conclusions Among patients with ovarian cancer treated since 2012, the overall RDI was low. Relative dose intensity was higher among patients with a BMI of 25 or higher compared with a BMI below 25. Most dose modifications occurred during treatment and not before initiation. Studies with body composition data and interventions that maximize chemotherapy completion during treatment are warranted.

Abstract The cortical asymmetry index evaluates the cortical thickness asymmetry between hemispheres. We investigated cortical asymmetry index in asymptomatic and symptomatic mutation carriers of autosomal dominant Alzheimer’s disease to explore the brain asymmetry within the Alzheimer’s disease continuum. Sixty baseline T1-weighted MRI scans were obtained from the Clinic Barcelona cohort. Baseline and longitudinal MRI data from 564 participants within the dominantly inherited Alzheimer network observational study were used as an independent, confirmatory cohort. Cerebrospinal fluid and plasma neurofilament light chain levels were included when available. Cortical thickness was calculated using Freesurfer and cortical asymmetry index was calculated via an open-source pipeline. Cross-sectional analyses examined cortical asymmetry index differences based on clinical classification and APOE ε4 status, adjusting for age, sex and estimated years from onset, while correlations were assessed with age, estimated years from onset, mini-mental state examination scores, and neurofilament light. Longitudinal cortical asymmetry index evolution was modelled using generalized additive models in the dominantly inherited Alzheimer network observational study cohort, incorporating age, sex, and the interaction between group and estimated years from onset. The cortical asymmetry index successfully distinguished asymptomatic mutation carrier and symptomatic mutation carriers from healthy controls in the Clinic Barcelona cohort and symptomatic mutation carriers from controls in dominantly inherited Alzheimer network observational study. Higher cortical asymmetry index in mutation carriers (asymptomatic mutation carrier and symptomatic mutation carriers combined) and in symptomatic mutation carriers were associated with higher plasma neurofilament light levels, a closer proximity to symptom onset, and lower mini-mental state examination in the Clinic Barcelona cohort. In the dominantly inherited Alzheimer network observational study cohort, mutation carriers exhibited increased cortical asymmetry index compared to controls and correlated with elevated neurofilament light (plasma and Cerebrospinal fluid), lower mini-mental state examination, and a closer proximity to symptom onset. APOE3/3 carriers showed greater asymmetry than other APOE genotypes and significant cortical asymmetry index differences between asymptomatic mutation carrier and symptomatic mutation carriers. Longitudinally, cortical asymmetry index increased over time significantly in symptomatic mutation carriers. These findings underscore brain asymmetry as a potential biomarker for early Alzheimer’s disease progression in autosomal dominant Alzheimer’s disease, with implications for detection and monitoring tracking disease-related neuroanatomical changes.