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Effects of Tirzepatide in Type 2 Diabetes: Individual Variation and Relationship to Cardiometabolic Outcomes

Journal of the American College of Cardiology 2025年第19期85卷 1858-1872页

作者： Aminorroaya, Arya Oikonomou, Evangelos K. Biswas, Dhruva Jastreboff, Ania M. Khera, Rohan Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven CT United States Cardiovascular Data Science (CarDS) Lab Yale School of Medicine New Haven CT United States Section of Endocrinology and Metabolism Department of Medicine Yale School of Medicine New Haven CT United States Section of Pediatric Endocrinology Department of Pediatrics Yale School of Medicine New Haven CT United States Yale Obesity Research Center (Y-Weight) Yale School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation (CORE) Yale New Haven Hospital New Haven CT United States Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Section of Health Informatics Department of Biostatistics Yale School of Public Health New Haven CT United States

Background: Tirzepatide—a dual GIP/GLP-1 receptor agonist—exerts pleiotropic effects on cardiometabolic health. Objectives: The authors sought to investigate the efficacy of tirzepatide in improving different cardiometabolic risk factors across individuals and subpopulations. Methods: Using an independent, global data-sharing and analytics platform, we performed an individual participant data meta-analysis by pooling data from 7 Phase 3 randomized clinical trials that compared tirzepatide with placebo or standard antihyperglycemic agents in individuals with type 2 diabetes. The study outcomes were the presence of a range of cardiometabolic abnormalities, representing components of metabolic syndrome (MetS) (elevated waist circumference, triglycerides, blood pressure, and fasting blood glucose, and decreased high-density lipoprotein cholesterol), as well as elevated body mass index and MetS (≥3 cardiometabolic abnormalities). Outcomes were modeled using mixed-effects models, with inverse probability weighting to account for study design differences. Results: We included 7,805 participants with a weighted median age of 59 years (Q1-Q3: 51-66 years) and 43.2% women. Over a weighted median treatment duration of 41.0 weeks, tirzepatide reduced the odds of all cardiometabolic abnormalities, ranging from 34% reduction for the odds of decreased high-density lipoprotein cholesterol (OR: 0.66 [95% CI: 0.52-0.84]) to 96% reduction in the odds of elevated body mass index (OR: 0.04 [95% CI:0.02-0.08]), and 72% reduction for the odds of MetS (OR: 0.28 [95% CI: 0.24-0.33]). Tirzepatide's superior efficacy in resolving MetS was consistent across demographic and clinical subpopulations, with higher efficacy in age <65 years vs ≥65 years, and in individuals without vs with baseline use of sodium-glucose cotransporter 2 inhibitors (P for interaction = 0.008 and 0.009, respectively). Conclusions: This pooled analysis suggests that tirzepatide may improve cardiometabolic abnormalities a

关键词： diabetes mellitus type 2 heart disease risk factors meta-analysis randomized controlled trial tirzepatide

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Gastroenterology 2025年第5期168卷 1040-1041页

作者： Dennis L. Shung Section of Digestive Diseases Department of Medicine Yale School of Medicine New Haven Connecticut Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven Connecticut

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Psoriasis: Perspectives from Irwin M. Braverman, MD

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Clinics in Dermatology 2025年第2期43卷 166-169页

作者： Cohen, Jeffrey M. Bunick, Christopher G. Department of Dermatology Yale School of Medicine New Haven CT United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Program in Translational Biomedicine Yale University New Haven CT United States

Dr Irwin M. Braverman is recognized as an expert on many skin diseases, including psoriasis, an immune-mediated disorder that presents with scaly erythematous plaques. The most common clinical subtype is plaque psoriasis, with scalp, inverse, guttate, pustular, and nail psoriasis representing additional subtypes. Psoriasis has been associated with several comorbidities, particularly psoriatic arthritis, which affects up to 30% of those with psoriasis. Psoriasis can have an enormous impact on patients, and Dr Braverman reflects that “it is hard to think of another disease that can be so destructive or impactful.” Over the course of his career, Dr Braverman treated many patients with psoriasis and observed several significant therapeutic advances, including the widespread use of psoralens with UV-A phototherapy and the introduction of methotrexate. He also contributed to research on the pathophysiology of psoriasis, studying the hallmark dilated capillary loops. Dr Braverman ultimately believes that “taking care of a patient with psoriasis goes into the making of a dermatologist.” © 2024 Elsevier Inc.

关键词： Goeckerman regimen blood vessels electron microscopy methotrexate phototherapy psoriasis

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TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment 29th

TX-Phase: Secure Phasing of Private Genomes in a Trusted ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Dokmai, Natnatee Zhu, Kaiyuan Sahinalp, S. Cenk Cho, Hyunghoon Department of Biomedical Informatics and Data Science Yale School of Medicine New HavenCT United States Department of Computer Science and Engineering UC San Diego La JollaCA United States Cancer Data Science Laboratory National Cancer Institute National Institutes of Health BethesdaMD United States Department of Computer Science Yale University New HavenCT United States

ISBN: (纸本)9783031902512

Genotype imputation servers enable researchers with limited resources to extract valuable insights from their data with enhanced accuracy and ease. However, the utility of these services is limited for those with sensitive study cohorts or those in restrictive regulatory environments due to data privacy concerns. Although privacy-preserving analysis tools have been developed to broaden access to these servers, none of the existing methods support haplotype phasing, a critical component of the imputation workflow. The complexity of phasing algorithms poses a significant challenge in maintaining practical performance under privacy constraints. Here, we introduce TX-Phase, a secure haplotype phasing method based on the framework of Trusted Execution Environments (TEEs). TX-Phase allows users’ private genomic data to be phased while ensuring data confidentiality and integrity of the computation. We introduce novel data-oblivious algorithmic techniques based on compressed reference panels and dynamic fixed-point arithmetic that comprehensively mitigate side-channel leakages in TEEs to provide robust protection of users’ genomic data throughout the analysis. Our experiments on a range of datasets from the UK Biobank and Haplotype Reference Consortium demonstrate the state-of-the-art phasing accuracy and practical runtimes of TX-Phase. Our work enables secure phasing of private genomes, opening access to large reference genomic datasets for a broader scientific community. Our open-source software is available at https://***/hcholab/txphase. The full manuscript is available at https://***/10.1101/2024.09.16.613301. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensitive data

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gdGSE: An algorithm to evaluate pathway enrichment by discretizing gene expression values

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Computational and Structural Biotechnology Journal 2025年 27卷 1772-1783页

作者： Luo, Jiangti Lu, Qiqi He, Mengjiao Zhang, Xiaobo Yang, Xiang Wang, Xiaosheng Biomedical Informatics Research Lab School of Basic Medicine and Clinical Pharmacy China Pharmaceutical University Nanjing211198 China Intelligent Pharmacy Interdisciplinary Research Center China Pharmaceutical University Nanjing211198 China Big Data Research Institute China Pharmaceutical University Nanjing211198 China Institute of Innovative Drug Discovery and Development China Pharmaceutical University Nanjing211198 China School of Science China Pharmaceutical University Nanjing211198 China Jiangsu Key Laboratory of Carcinogenesis and Intervention School of Basic Medicine and Clinical Pharmacy China Pharmaceutical University Nanjing210009 China Department of Oncology JunXie Hospital Nanjing University of Chinese Medicine Nanjing210002 China

We proposed gdGSE, a novel computational framework for gene set enrichment analysis. Unlike conventional methods that rely on continuous gene expression values, gdGSE employs discretized gene expression profiles to assess pathway activity. This approach effectively mitigates discrepancies caused by data distributions. This algorithm consists of two steps: (1) applying statistical thresholds binarizing gene expression matrix, and (2) converting the binarized gene expression matrix into a gene set enrichment matrix. Our results demonstrated that gdGSE could robustly extract biological insights from a diverse array of simulated and real bulk or single-cell gene expression datasets. Notably, gene set enrichment scores by gdGSE exhibited enhanced utility in downstream applications: (1) precise quantification of cancer stemness with significant prognostic relevance;(2) enhanced clustering performance in stratifying tumor subtypes with distinct prognoses;and (3) more accurate identification of cell types. Remarkably, the pathway activity scores by gdGSE showed > 90 % concordance with experimentally validated drug mechanisms in patients-derived xenografts and estrogen receptor-positive breast cancer cell lines. Our algorithm proposes that discretizing gene expression values provides an alternative method for evaluating pathway enrichment, applicable to both bulk and single-cell data analysis. © 2025 The Authors

关键词： Transcription

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Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care

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Journal of the Society for Cardiovascular Angiography and Interventions 2025年第3期4卷 102562-102562页

作者： Aminorroaya, Arya Biswas, Dhruva Pedroso, Aline F. Khera, Rohan Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven Connecticut United States Cardiovascular Data Science (CarDS) Lab Yale School of Medicine New Haven Connecticut United States Center for Outcomes Research and Evaluation (CORE) Yale New Haven Hospital New Haven Connecticut United States Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven Connecticut United States Section of Health Informatics Department of Biostatistics Yale School of Public Health New Haven Connecticut United States

Artificial intelligence (AI) serves as a powerful tool that can revolutionize how personalized, patient-focused care is provided within interventional cardiology. Specifically, AI can augment clinical care across the spectrum for acute coronary syndrome, coronary artery disease, and valvular heart disease, with applications in coronary and structural heart interventions. This has been enabled by the potential of AI to harness various types of health data. We review how AI-driven technologies can advance diagnosis, preprocedural planning, intraprocedural guidance, and prognostication in interventional cardiology. AI automates clinical tasks, increases efficiency, improves reliability and accuracy, and individualizes clinical care, establishing its potential to transform care. Furthermore, AI-enabled, community-based screening programs are yet to be implemented to leverage the full potential of AI to improve patient outcomes. However, to transform clinical practice, AI tools require robust and transparent development processes, consistent performance across various settings and populations, positive impact on clinical and care quality outcomes, and seamless integration into clinical workflows. Once these are established, AI can reshape interventional cardiology, improving precision, efficiency, and patient outcomes. © 2025 The Author(s)

关键词： artificial intelligence interventional cardiology percutaneous coronary intervention structural heart intervention

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Time-dependent reduction of somatic variants in lichen striatus as evidence for the clearance of mutated skin cells

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Journal of the American Academy of Dermatology 2025年第6期92卷 1377-1379页

作者： Lee, Byunghyuk Lim, Dong Min Shin, Kihyuk Lee, Hyun Jung Lee, Jungsoo Kim, Hoon-Soo Kim, Byung-Soo Kim, Moon-Bum Kim, Kihun Kim, Won Kyu Kang, Junho Ko, Hyun-Chang Kim, Yun Hak Department of Dermatology College of Medicine Pusan National University Yangsan South Korea Interdisciplinary Program of Genomic Data Science Pusan National University Yangsan South Korea Department of Dermatology Pusan National University Yangsan Hospital Yangsan South Korea Research Institute for Convergence of Biomedical Science and Technology Pusan National University Yangsan Hospital Yangsan South Korea Department of Pathology School of Medicine Pusan National University Yangsan South Korea Department of Biomedical Informatics School of Medicine Pusan National University Yangsan South Korea Department of Anatomy School of Medicine Pusan National University Yangsan South Korea Natural Product Research Center Korea Institute of Science and Technology Gangneung South Korea Department of Research Keimyung University Dongsan Medical Center Daegu South Korea

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Assessing the Impact of Post-COVID Clinics on 6-Month Health Care Utilization for Patients With Long COVID: A Single-Center Experience

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Mayo Clinic Proceedings: Innovations, Quality and Outcomes 2025年第3期9卷 100603-100603页

作者： Gong, Kirby D. Afshar, Ali S. Brown, Fred Alavi, Reza Ganesh, Ravindra Kharrazi, Hadi ArtiMed Inc. Baltimore MD Division of General Internal Medicine Mayo Clinic Rochester MN Division of Biomedical Informatics and Data Science Johns Hopkins School of Medicine Baltimore MD

Objective: To assess the impact of post-COVID clinics by examining the association between their early usage and downstream health care utilization. Patients and Methods: In a case-control study spanning data from March 11, 2020 to June 1, 2023, patients with Long COVID were identified from a major health system using diagnosis codes. The Fast, Large-Scale Almost Matching Exactly algorithm was used to match patients who presented early to post-COVID clinics with patients with Long COVID who did not attend such clinics. Matching was performed on demographic characteristics, acute COVID severity, comorbidities, diagnosis date, and vaccination, to reduce confounders for the comparison of the health care utilization and mortality between cohorts. Results: When exactly matching on all 46 features, the algorithm yielded 2814 matched patients, of whom 692 (24.6%;66.6% females;mean [SD] age, 48.8 [14.5] years) were seen in post-COVID clinics within the first 6 months and 2122 (75.4%;64.1% females;mean [SD] age, 49.7 [15.2] years) who were not. The average treatment effect (95% CI) of early post-COVID clinic usage was −0.60 (−0.83 to −0.39) on inpatient visits, −0.19 (−0.26 to −0.11) on emergency department visits, 7.62 (6.96-8.56) on outpatient visits, −$3467 (−$6267 to −$754) on estimated costs, and −0.006 (−0.010 to −0.003) on mortality. Conclusion: Early usage of post-COVID clinics by patients with Long COVID is associated with not only fewer downstream inpatient stays, emergency department visits, estimated costs, and reduced mortality within the first 6 months but also greater outpatient utilization. Results suggest early post-COVID clinic involvement shifts care to outpatient settings, potentially reducing costs and mortality. © 2025 The Authors

关键词： COVID coronavirus disease COVID-19 COronaVIrus Disease 2019 ED emergency department EHR electronic health record ICD International Classification of Diseases SARS-CoV-2 severe acute respiratory syndrome coronavirus 2

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rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms

arXiv

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arXiv 2025年

作者： Sufriyana, Herdiantri Su, Emily Chia-Yu Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University 155 Section 2 Linong Street Beitou District Taipei112304 Taiwan

Background: Current nomogram can only be created for regression algorithm. Providing nomogram for any machine learning (ML) algorithms may accelerate model deployment in clinical settings or improve model availability. We developed an R package and web application to construct nomogram with model explainability of any ML algorithms. Methods: We formulated a function to transform an ML prediction model into a nomogram, requiring datasets with: (1) all possible combinations of predictor values;(2) the corresponding outputs of the model;and (3) the corresponding explainability values for each predictor (optional). Web application was also created. Results: Our R package could create 5 types of nomograms for categorical predictors and binary outcome without probability (1), categorical predictors and binary outcome with probability (2) or continuous outcome (3), and categorical with single numerical predictors and binary outcome with probability (4) or continuous outcome (5). Respectively, the first and remaining types optimally allowed maximum 15 and 5 predictors with maximum 3,200 combinations. Web application is provided with such limits. The explainability values were possible for types 2 to 5. Conclusions: Our R package and web application could construct nomogram with model explainability of any ML algorithms using a fair number of predictors. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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Physical activity exercise behavior in individuals with a history of stroke: a retrospective study

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Discover medicine 2025年第1期2卷 1-7页

作者： Arora, Shiv Ayyathan, Smriti Sudhir Burzynski, Emily Phillips, Vidith Department of Neurology Sardar Patel Medical College Bikaner India Department of Neurology Brihanmumbai Municipal Corporation (BMC) Mumbai India Department of Neurology Medical University of Warsaw Warsaw Poland Department of Internal Medicine Division of Biomedical Informatics and Data Science School of Medicine Johns Hopkins University Baltimore USA

Stroke remains a significant public health challenge in the United States, impacting the physical capabilities and quality of life of affected individuals. This study assesses the prevalence of self-reported physical activity among stroke patients in relation to demographic, socioeconomic, and healthcare access factors. To examine the levels of physical activity among individuals with a history of stroke and identify the variables influencing these levels. Utilizing the Behavioral Risk Factor Surveillance System (BRFSS) database for 2021, this retrospective study analyzed responses from participants identified with a stroke history. data were assessed using the BRFSS Web Enabled Analysis Tool, focusing on self-reported physical activity and including variables such as age, gender, race, education level, employment status, income, and timing of last routine checkup. Of 17,158 respondents reporting a stroke history, 58.5% engaged in physical activity. Physical activity rates were higher among younger individuals, males, and those with higher socioeconomic status. Physical activity in past one month is significantly associated with a lower prevalence of stroke in age groups 25–44 years, 45–64 years, and 65 + years. Younger individuals (18–24 years) do not show a significant association between physical activity and stroke incidence. While a considerable percentage of stroke patients reported engaging in physical activity, disparities exist influenced by age, gender, and socioeconomic factors. These findings highlight the need for targeted interventions to promote physical activities among stroke survivors, particularly those in lower socioeconomic groups or older age brackets.

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