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1. Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data

Author

Ghaffarzadeh-Esfahani, Mohammadreza, Ghaffarzadeh-Esfahani, Mahdi, Salahi-Niri, Arian, Toreyhi, Hossein, Atf, Zahra, Mohsenzadeh-Kermani, Amirali, Sarikhani, Mahshad, Tajabadi, Zohreh, Shojaeian, Fatemeh, Bagheri, Mohammad Hassan, Feyzi, Aydin, Tarighatpayma, Mohammadamin, Gazmeh, Narges, Heydari, Fateme, Afshar, Hossein, Allahgholipour, Amirreza, Alimardani, Farid, Salehi, Ameneh, Asadimanesh, Naghmeh, Khalafi, Mohammad Amin, Shabanipour, Hadis, Moradi, Ali, Zadeh, Sajjad Hossein, Yazdani, Omid, Esbati, Romina, Maleki, Moozhan, Nasr, Danial Samiei, Soheili, Amirali, Majlesi, Hossein, Shahsavan, Saba, Soheilipour, Alireza, Goudarzi, Nooshin, Taherifard, Erfan, Hatamabadi, Hamidreza, Samaan, Jamil S, Savage, Thomas, Sakhuja, Ankit, Soroush, Ali, Nadkarni, Girish, Darazam, Ilad Alavi, Pourhoseingholi, Mohamad Amin, and Safavi-Naini, Seyed Amir Ahmad

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, 92C50, 68T50, J.3
Abstract

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

Published
2024

2. Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models

Author

Safavi-Naini, Seyed Amir Ahmad, Ali, Shuhaib, Shahab, Omer, Shahhoseini, Zahra, Savage, Thomas, Rafiee, Sara, Samaan, Jamil S, Shabeeb, Reem Al, Ladak, Farah, Yang, Jamie O, Echavarria, Juan, Babar, Sumbal, Shaukat, Aasma, Margolis, Samuel, Tatonetti, Nicholas P, Nadkarni, Girish, Kurdi, Bara El, and Soroush, Ali

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, 92C50, 68T50, J.3
Abstract

Background and Aims: This study evaluates the medical reasoning performance of large language models (LLMs) and vision language models (VLMs) in gastroenterology. Methods: We used 300 gastroenterology board exam-style multiple-choice questions, 138 of which contain images to systematically assess the impact of model configurations and parameters and prompt engineering strategies utilizing GPT-3.5. Next, we assessed the performance of proprietary and open-source LLMs (versions), including GPT (3.5, 4, 4o, 4omini), Claude (3, 3.5), Gemini (1.0), Mistral, Llama (2, 3, 3.1), Mixtral, and Phi (3), across different interfaces (web and API), computing environments (cloud and local), and model precisions (with and without quantization). Finally, we assessed accuracy using a semiautomated pipeline. Results: Among the proprietary models, GPT-4o (73.7%) and Claude3.5-Sonnet (74.0%) achieved the highest accuracy, outperforming the top open-source models: Llama3.1-405b (64%), Llama3.1-70b (58.3%), and Mixtral-8x7b (54.3%). Among the quantized open-source models, the 6-bit quantized Phi3-14b (48.7%) performed best. The scores of the quantized models were comparable to those of the full-precision models Llama2-7b, Llama2--13b, and Gemma2-9b. Notably, VLM performance on image-containing questions did not improve when the images were provided and worsened when LLM-generated captions were provided. In contrast, a 10% increase in accuracy was observed when images were accompanied by human-crafted image descriptions. Conclusion: In conclusion, while LLMs exhibit robust zero-shot performance in medical reasoning, the integration of visual data remains a challenge for VLMs. Effective deployment involves carefully determining optimal model configurations, encouraging users to consider either the high performance of proprietary models or the flexible adaptability of open-source models., Comment: Manuscript Pages: 34, Figures: 7, Tables: 2, Supplementary File Pages: 35, Data Transparency Statement: Code is available at: https://github.com/Sdamirsa/LLM-VLM-in-Gastroenterology . Study data from American College of Gastroenterology (ACG) are restricted and available upon request with ACG permission. Correction: updated abstract considering Llama3.1 results

Published
2024

3. Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model

Author

Patra, Braja Gopal, Lepow, Lauren A., Kumar, Praneet Kasi Reddy Jagadeesh, Vekaria, Veer, Sharma, Mohit Manoj, Adekkanattu, Prakash, Fennessy, Brian, Hynes, Gavin, Landi, Isotta, Sanchez-Ruiz, Jorge A., Ryu, Euijung, Biernacka, Joanna M., Nadkarni, Girish N., Talati, Ardesheer, Weissman, Myrna, Olfson, Mark, Mann, J. John, Charney, Alexander W., and Pathak, Jyotishman

Subjects
Computer Science - Computation and Language
Abstract

Background: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented as narrative clinical notes rather than structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of data extraction. Data and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n=300) and Weill Cornell Medicine (WCM, n=225) were annotated and established a gold standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (e.g., social network, instrumental support, and loneliness). Results: For extracting SS/SI, the RBS obtained higher macro-averaged f-scores than the LLM at both MSHS (0.89 vs. 0.65) and WCM (0.85 vs. 0.82). For extracting subcategories, the RBS also outperformed the LLM at both MSHS (0.90 vs. 0.62) and WCM (0.82 vs. 0.81). Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. Intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS were designed and refined to follow the same specific rules as the gold standard annotations. Conversely, the LLM were more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages and are made available open-source for future testing., Comment: 2 figures, 3 tables

Published
2024
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4. A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation

Author

Golden, Eddye A, Zweig, Micol, Danieletto, Matteo, Landell, Kyle, Nadkarni, Girish, Bottinger, Erwin, Katz, Lindsay, Somarriba, Ricardo, Sharma, Vansh, Katz, Craig L, Marin, Deborah B, DePierro, Jonathan, and Charney, Dennis S

Subjects
Medicine
Abstract

BackgroundThe COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with HCWs representing a particularly vulnerable group. The scope of the COVID-19 pandemic necessitates the development of a scalable mental health platform to provide services to large numbers of at-risk or affected individuals. The Mount Sinai Health System in New York City was at the epicenter of the pandemic in the United States. ObjectiveThe Center for Stress, Resilience, and Personal Growth (CSRPG) was created to address the current and anticipated psychological impact of the pandemic on the HCWs in the health system. The mission of the Center is to support the resilience and mental health of employees through educational offerings, outreach, and clinical care. Our aim was to build a mobile app to support the newly founded Center in its mission. MethodsWe built the app as a standalone digital platform that hosts a suite of tools that users can interact with on a daily basis. With consideration for the Center’s aims, we determined the overall vision, initiatives, and goals for the Wellness Hub app, followed by specific milestone tasks and deliverables for development. We defined the app’s primary features based on the mental health assessment and needs of HCWs. Feature definition was informed by the results of a resilience survey widely distributed to Mount Sinai HCWs and by the resources offered at CSRPG, including workshop content. ResultsWe launched our app over the course of two phases, the first phase being a “soft” launch and the second being a broader launch to all of Mount Sinai. Of the 231 HCWs who downloaded the app, 173 (74.9%) completed our baseline assessment of all mental health screeners in the app. Results from the baseline assessment show that more than half of the users demonstrate a need for support in at least one psychological area. As of 3 months after the Phase 2 launch, approximately 55% of users re-entered the app after their first opening to explore additional features, with an average of 4 app openings per person. ConclusionsTo address the mental health needs of HCWs during the COVID-19 pandemic, the Wellness Hub app was built and deployed throughout the Mount Sinai Health System. To our knowledge, this is the first resilience app of its kind. The Wellness Hub app is a promising proof of concept, with room to grow, for those who wish to build a secure mobile health app to support their employees, communities, or others in managing and improving mental and physical well-being. It is a novel tool offering mental health support broadly.

Published
2021
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5. Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

Author

Hirten, Robert P, Danieletto, Matteo, Tomalin, Lewis, Choi, Katie Hyewon, Zweig, Micol, Golden, Eddye, Kaur, Sparshdeep, Helmus, Drew, Biello, Anthony, Pyzik, Renata, Charney, Alexander, Miotto, Riccardo, Glicksberg, Benjamin S, Levin, Matthew, Nabeel, Ismail, Aberg, Judith, Reich, David, Charney, Dennis, Bottinger, Erwin P, Keefer, Laurie, Suarez-Farinas, Mayte, Nadkarni, Girish N, and Fayad, Zahi A

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. ObjectiveWe performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. MethodsHealth care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. ResultsUsing a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). ConclusionsLongitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Published
2021
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6. Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Author

Vaid, Akhil, Jaladanki, Suraj K, Xu, Jie, Teng, Shelly, Kumar, Arvind, Lee, Samuel, Somani, Sulaiman, Paranjpe, Ishan, De Freitas, Jessica K, Wanyan, Tingyi, Johnson, Kipp W, Bicak, Mesude, Klang, Eyal, Kwon, Young Joon, Costa, Anthony, Zhao, Shan, Miotto, Riccardo, Charney, Alexander W, Böttinger, Erwin, Fayad, Zahi A, Nadkarni, Girish N, Wang, Fei, and Glicksberg, Benjamin S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundMachine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. ObjectiveWe aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. MethodsPatient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. ResultsThe LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. ConclusionsThe federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Published
2021
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8. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Author

Vaid, Akhil, Somani, Sulaiman, Russak, Adam J, De Freitas, Jessica K, Chaudhry, Fayzan F, Paranjpe, Ishan, Johnson, Kipp W, Lee, Samuel J, Miotto, Riccardo, Richter, Felix, Zhao, Shan, Beckmann, Noam D, Naik, Nidhi, Kia, Arash, Timsina, Prem, Lala, Anuradha, Paranjpe, Manish, Golden, Eddye, Danieletto, Matteo, Singh, Manbir, Meyer, Dara, O'Reilly, Paul F, Huckins, Laura, Kovatch, Patricia, Finkelstein, Joseph, Freeman, Robert M., Argulian, Edgar, Kasarskis, Andrew, Percha, Bethany, Aberg, Judith A, Bagiella, Emilia, Horowitz, Carol R, Murphy, Barbara, Nestler, Eric J, Schadt, Eric E, Cho, Judy H, Cordon-Cardo, Carlos, Fuster, Valentin, Charney, Dennis S, Reich, David L, Bottinger, Erwin P, Levin, Matthew A, Narula, Jagat, Fayad, Zahi A, Just, Allan C, Charney, Alexander W, Nadkarni, Girish N, and Glicksberg, Benjamin S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundCOVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. ObjectiveThe aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. MethodsWe used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. ResultsUpon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. ConclusionsWe externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Published
2020
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9. Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models

Author

Vaid, Akhil, Lampert, Joshua, Lee, Juhee, Sawant, Ashwin, Apakama, Donald, Sakhuja, Ankit, Soroush, Ali, Bick, Sarah, Abbott, Ethan, Gomez, Hernando, Hadley, Michael, Lee, Denise, Landi, Isotta, Duong, Son Q, Bussola, Nicole, Nabeel, Ismail, Muehlstedt, Silke, Freeman, Robert, Kovatch, Patricia, Carr, Brendan, Wang, Fei, Glicksberg, Benjamin, Argulian, Edgar, Lerakis, Stamatios, Khera, Rohan, Reich, David L., Kraft, Monica, Charney, Alexander, and Nadkarni, Girish

Subjects
Computer Science - Artificial Intelligence
Abstract

Generative Large Language Models (LLMs) hold significant promise in healthcare, demonstrating capabilities such as passing medical licensing exams and providing clinical knowledge. However, their current use as information retrieval tools is limited by challenges like data staleness, resource demands, and occasional generation of incorrect information. This study assessed the potential of LLMs to function as autonomous agents in a simulated tertiary care medical center, using real-world clinical cases across multiple specialties. Both proprietary and open-source LLMs were evaluated, with Retrieval Augmented Generation (RAG) enhancing contextual relevance. Proprietary models, particularly GPT-4, generally outperformed open-source models, showing improved guideline adherence and more accurate responses with RAG. The manual evaluation by expert clinicians was crucial in validating models' outputs, underscoring the importance of human oversight in LLM operation. Further, the study emphasizes Natural Language Programming (NLP) as the appropriate paradigm for modifying model behavior, allowing for precise adjustments through tailored prompts and real-world interactions. This approach highlights the potential of LLMs to significantly enhance and supplement clinical decision-making, while also emphasizing the value of continuous expert involvement and the flexibility of NLP to ensure their reliability and effectiveness in healthcare settings., Comment: Figures: 5, Tables: 3

Published
2024

11. Neuropsychiatric complications of coronavirus disease 2019: Mount Sinai Health System cohort study

Author

Gururangan, Kapil, Peschansky, Veronica J., Van Hyfte, Grace, Agarwal, Parul, Blank, Leah J., Mathew, Brian, Goldstein, Jonathan, Kwon, Churl-Su, McCarthy, Louise, Cohen, Ariella, Chan, Andy Ho Wing, Deng, Pojen, Dhamoon, Mandip, Gutzwiller, Eveline, Hao, Qing, He, Celestine, Klenofsky, Britany, Lemus, Hernan Nicolas, Marcuse, Lara, Navis, Allison, Heredia Nunez, Wilson D., Luckey, Mallory N., Schorr, Emily M., Singh, Anuradha, Tantillo, Gabriela B., Ufongene, Claire, Young, James J., Balchandani, Priti, Festa, Joanne R., Naasan, Georges, Charney, Alexander W., Nadkarni, Girish N., and Jetté, Nathalie

Published
2024
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12. An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets

Author

Gulamali, Faris F., Sawant, Ashwin S., Liharska, Lora, Horowitz, Carol R., Chan, Lili, Kovatch, Patricia H., Hofer, Ira, Singh, Karandeep, Richardson, Lynne D., Mensah, Emmanuel, Charney, Alexander W, Reich, David L., Hu, Jianying, and Nadkarni, Girish N.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The adoption of diagnosis and prognostic algorithms in healthcare has led to concerns about the perpetuation of bias against disadvantaged groups of individuals. Deep learning methods to detect and mitigate bias have revolved around modifying models, optimization strategies, and threshold calibration with varying levels of success. Here, we generate a data-centric, model-agnostic, task-agnostic approach to evaluate dataset bias by investigating the relationship between how easily different groups are learned at small sample sizes (AEquity). We then apply a systematic analysis of AEq values across subpopulations to identify and mitigate manifestations of racial bias in two known cases in healthcare - Chest X-rays diagnosis with deep convolutional neural networks and healthcare utilization prediction with multivariate logistic regression. AEq is a novel and broadly applicable metric that can be applied to advance equity by diagnosing and remediating bias in healthcare datasets.

Published
2023

13. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

Author

Hughes, Odessica, Bentley, Amy R, Breeze, Charles E, Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M, Gilliland, Thomas, Meyer, Mariah C, Du, Jiawen, Raffield, Laura M, Kramer, Holly, Morton, Robert W, Gouveia, Mateus H, Atkinson, Elizabeth G, Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D, Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G, Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O, Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C, Kooperberg, Charles, Krieger, Jose E, Lange, Ethan M, Lanktree, Matthew B, Lash, James P, Lotufo, Paulo A, Loos, Ruth JF, My, Vy Thi Ha, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J, Rich, Stephen S, Rodriquez, Erik J, Tarazona-Santos, Eduardo, Taylor, Kent D, Umans, Jason G, Wen, Jia, Young, Bessie A, Yu, Zhi, Zhang, Ying, Chen, Yii-Der Ida, Rundek, Tanja, Rotter, Jerome I, Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C, Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A, Carson, April P, Lange, Leslie A, Li, Yun, Perez-Stable, Eliseo J, Do, Ron, Charchar, Fadi J, Tomaszewski, Maciej, Mychaleckyj, Josyf C, Rotimi, Charles, Morris, Andrew P, and Franceschini, Nora

Subjects
Biological Sciences, Genetics, Kidney Disease, Prevention, Human Genome, Renal and urogenital, Good Health and Well Being, Humans, Genome-Wide Association Study, Renal Insufficiency, Chronic, Glomerular Filtration Rate, Multifactorial Inheritance, Kidney, admixed populations, chronic kidney disease, eGFR, expression quantitative trait locus, fine-mapping, genome-wide association study, kidney function, multi-ancestry, polygenic scores
Abstract

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p

Published
2024

15. Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

Author

Lucarelli, Nicholas, Ginley, Brandon, Zee, Jarcy, Mimar, Sayat, Paul, Anindya S, Jain, Sanjay, Han, Seung Seok, Rodrigues, Luis, Ozrazgat-Baslanti, Tezcan, Wong, Michelle L, Nadkarni, Girish, Clapp, William L, Jen, Kuang-Yu, and Sarder, Pinaki

Subjects
Epidemiology, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Kidney Disease, Renal and urogenital, Humans, Creatinine, Deep Learning, Kidney, Glomerular Filtration Rate, Demography, cell and transport physiology, cell biology and structure, molecular biology, Clinical sciences
Abstract

KEY POINTS: The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Kidney features vary with age and sex; and glomeruli size may intricately link to creatinine, defying prior notions. BACKGROUND: Reference histomorphometric data of healthy human kidneys are largely lacking because of laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning (DL), computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and eGFR in a multinational set of reference kidney tissue sections. METHODS: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid–Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the association of histomorphometric parameters with age, sex, SCr, and eGFR. RESULTS: Our DL model achieved high segmentation performance for all test compartments. The size and density of glomeruli, tubules, and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Glomerular size was significantly correlated with SCr and eGFR. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of increasing age. CONCLUSIONS: Using DL, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. DL tools can increase the efficiency and rigor of histomorphometric analysis.

Published
2023

16. Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension

Author

Gulamali, Faris, Jayaraman, Pushkala, Sawant, Ashwin S., Desman, Jacob, Fox, Benjamin, Chang, Annette, Soong, Brian Y., Arivazagan, Naveen, Reynolds, Alexandra S., Duong, Son Q., Vaid, Akhil, Kovatch, Patricia, Freeman, Robert, Hofer, Ira S., Sakhuja, Ankit, Dangayach, Neha S., Reich, David S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2024
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18. Author Correction: Divergent landscapes of A-to-I editing in postmortem and living human brain

Author

Rodriguez de los Santos, Miguel, Kopell, Brian H., Buxbaum Grice, Ariela, Ganesh, Gauri, Yang, Andy, Amini, Pardis, Liharska, Lora E., Vornholt, Eric, Fullard, John F., Dong, Pengfei, Park, Eric, Zipkowitz, Sarah, Kaji, Deepak A., Thompson, Ryan C., Liu, Donjing, Park, You Jeong, Cheng, Esther, Ziafat, Kimia, Moya, Emily, Fennessy, Brian, Wilkins, Lillian, Silk, Hannah, Linares, Lisa M., Sullivan, Brendan, Cohen, Vanessa, Kota, Prashant, Feng, Claudia, Johnson, Jessica S., Rieder, Marysia-Kolbe, Scarpa, Joseph, Nadkarni, Girish N., Wang, Minghui, Zhang, Bin, Sklar, Pamela, Beckmann, Noam D., Schadt, Eric E., Roussos, Panos, Charney, Alexander W., and Breen, Michael S.

Published
2024
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19. Divergent landscapes of A-to-I editing in postmortem and living human brain

Author

Rodriguez de los Santos, Miguel, Kopell, Brian H., Buxbaum Grice, Ariela, Ganesh, Gauri, Yang, Andy, Amini, Pardis, Liharska, Lora E., Vornholt, Eric, Fullard, John F., Dong, Pengfei, Park, Eric, Zipkowitz, Sarah, Kaji, Deepak A., Thompson, Ryan C., Liu, Donjing, Park, You Jeong, Cheng, Esther, Ziafat, Kimia, Moya, Emily, Fennessy, Brian, Wilkins, Lillian, Silk, Hannah, Linares, Lisa M., Sullivan, Brendan, Cohen, Vanessa, Kota, Prashant, Feng, Claudia, Johnson, Jessica S., Rieder, Marysia-Kolbe, Scarpa, Joseph, Nadkarni, Girish N., Wang, Minghui, Zhang, Bin, Sklar, Pamela, Beckmann, Noam D., Schadt, Eric E., Roussos, Panos, Charney, Alexander W., and Breen, Michael S.

Published
2024
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25. Genome-wide association study of thoracic aortic aneurysm and dissection in the Million Veteran Program

Author

Klarin, Derek, Devineni, Poornima, Sendamarai, Anoop K, Angueira, Anthony R, Graham, Sarah E, Shen, Ying H, Levin, Michael G, Pirruccello, James P, Surakka, Ida, Karnam, Purushotham R, Roychowdhury, Tanmoy, Li, Yanming, Wang, Minxian, Aragam, Krishna G, Paruchuri, Kaavya, Zuber, Verena, Shakt, Gabrielle E, Tsao, Noah L, Judy, Renae L, Vy, Ha My T, Verma, Shefali S, Rader, Daniel J, Do, Ron, Bavaria, Joseph E, Nadkarni, Girish N, Ritchie, Marylyn D, Burgess, Stephen, Guo, Dong-chuan, Ellinor, Patrick T, LeMaire, Scott A, Milewicz, Dianna M, Willer, Cristen J, Natarajan, Pradeep, Tsao, Philip S, Pyarajan, Saiju, and Damrauer, Scott M

Subjects
Cardiovascular, Genetics, Human Genome, Prevention, Aetiology, 2.1 Biological and endogenous factors, Humans, Genome-Wide Association Study, Veterans, Pedigree, Aortic Aneurysm, Thoracic, Aortic Dissection, VA Million Veteran Program, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.

Published
2023

26. HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes

Author

Vaid, Akhil, Jiang, Joy, Sawant, Ashwin, Lerakis, Stamatios, Argulian, Edgar, Ahuja, Yuri, Lampert, Joshua, Charney, Alexander, Greenspan, Hayit, Glicksberg, Benjamin, Narula, Jagat, and Nadkarni, Girish

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We show that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. Finally, we also show that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Thus, we present the first vision-based waveform transformer that can be used to develop specialized models for ECG analysis especially at low sample sizes.

Published
2022

27. Identifying high-impact variants and genes in exomes of Ashkenazi Jewish inflammatory bowel disease patients.

Author

Wu, Yiming, Gettler, Kyle, Kars, Meltem, Giri, Mamta, Li, Dalin, Bayrak, Cigdem, Zhang, Peng, Jain, Aayushee, Maffucci, Patrick, Sabic, Ksenija, Van Vleck, Tielman, Nadkarni, Girish, Denson, Lee, Ostrer, Harry, Levine, Adam, Schiff, Elena, Segal, Anthony, Kugathasan, Subra, Stenson, Peter, Cooper, David, Philip Schumm, L, Snapper, Scott, Daly, Mark, Haritunians, Talin, Duerr, Richard, Silverberg, Mark, Rioux, John, Brant, Steven, McGovern, Dermot, Cho, Judy, and Itan, Yuval

Subjects
Adult, Humans, Jews, Exome, Inflammatory Bowel Diseases, Risk Assessment, Genetic Predisposition to Disease
Abstract

Inflammatory bowel disease (IBD) is a group of chronic digestive tract inflammatory conditions whose genetic etiology is still poorly understood. The incidence of IBD is particularly high among Ashkenazi Jews. Here, we identify 8 novel and plausible IBD-causing genes from the exomes of 4453 genetically identified Ashkenazi Jewish IBD cases (1734) and controls (2719). Various biological pathway analyses are performed, along with bulk and single-cell RNA sequencing, to demonstrate the likely physiological relatedness of the novel genes to IBD. Importantly, we demonstrate that the rare and high impact genetic architecture of Ashkenazi Jewish adult IBD displays significant overlap with very early onset-IBD genetics. Moreover, by performing biobank phenome-wide analyses, we find that IBD genes have pleiotropic effects that involve other immune responses. Finally, we show that polygenic risk score analyses based on genome-wide high impact variants have high power to predict IBD susceptibility.

Published
2023

28. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Author

Kashani, Kianoush B., Awdishu, Linda, Bagshaw, Sean M., Barreto, Erin F., Claure-Del Granado, Rolando, Evans, Barbara J., Forni, Lui G., Ghosh, Erina, Goldstein, Stuart L., Kane-Gill, Sandra L., Koola, Jejo, Koyner, Jay L., Liu, Mei, Murugan, Raghavan, Nadkarni, Girish N., Neyra, Javier A., Ninan, Jacob, Ostermann, Marlies, Pannu, Neesh, Rashidi, Parisa, Ronco, Claudio, Rosner, Mitchell H., Selby, Nicholas M., Shickel, Benjamin, Singh, Karandeep, Soranno, Danielle E., Sutherland, Scott M., Bihorac, Azra, and Mehta, Ravindra L.

Published
2023
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29. Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

Author

Matsushita, Kunihiro, Kaptoge, Stephen, Hageman, Steven, Sang, Yingying, Ballew, Shoshana, Grams, Morgan, Surapaneni, Aditya, Sun, Luanluan, Arnlov, Johan, Bozic, Milica, Brenner, Hermann, Brunskill, Nigel, Chang, Alex, Chinnadurai, Rajkumar, Cirillo, Massimo, Correa, Adolfo, Ebert, Natalie, Eckardt, Kai-Uwe, Gansevoort, Ron, Gutierrez, Orlando, Hadaegh, Farzad, He, Jiang, Hwang, Shih-Jen, Jafar, Tazeen, Jassal, Simerjot, Kayama, Takamasa, Kovesdy, Csaba, Landman, Gijs, Levey, Andrew, Lloyd-Jones, Donald, Major, Rupert, Miura, Katsuyuki, Muntner, Paul, Nadkarni, Girish, Nowak, Christoph, Ohkubo, Takayoshi, Pena, Michelle, Polkinghorne, Kevan, Sairenchi, Toshimi, Schaeffner, Elke, Schneider, Markus, Shalev, Varda, Shlipak, Michael, Solbu, Marit, Stempniewicz, Nikita, Tollitt, James, Valdivielso, José, van der Leeuw, Joep, Wang, Angela, Wen, Chi-Pang, Woodward, Mark, Yamagishi, Kazumasa, Yatsuya, Hiroshi, Zhang, Luxia, Dorresteijn, Jannick, Di Angelantonio, Emanuele, Visseren, Frank, Pennells, Lisa, and Coresh, Josef

Subjects
Cardiovascular disease, Chronic kidney disease, Meta-analysis, Risk prediction, Humans, Aged, Aged, 80 and over, Cardiovascular Diseases, Risk Factors, Creatinine, Renal Insufficiency, Chronic, Albuminuria, Glomerular Filtration Rate, Heart Disease Risk Factors
Abstract

AIMS: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an Add-on to incorporate CKD measures into these algorithms, using a validated approach. METHODS: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Published
2023

31. Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension

Author

Kelly, Tanika N, Sun, Xiao, He, Karen Y, Brown, Michael R, Taliun, Sarah A Gagliano, Hellwege, Jacklyn N, Irvin, Marguerite R, Mi, Xuenan, Brody, Jennifer A, Franceschini, Nora, Guo, Xiuqing, Hwang, Shih-Jen, de Vries, Paul S, Gao, Yan, Moscati, Arden, Nadkarni, Girish N, Yanek, Lisa R, Elfassy, Tali, Smith, Jennifer A, Chung, Ren-Hua, Beitelshees, Amber L, Patki, Amit, Aslibekyan, Stella, Blobner, Brandon M, Peralta, Juan M, Assimes, Themistocles L, Palmas, Walter R, Liu, Chunyu, Bress, Adam P, Huang, Zhijie, Becker, Lewis C, Hwa, Chii-Min, O’Connell, Jeffrey R, Carlson, Jenna C, Warren, Helen R, Das, Sayantan, Giri, Ayush, Martin, Lisa W, Johnson, W Craig, Fox, Ervin R, Bottinger, Erwin P, Razavi, Alexander C, Vaidya, Dhananjay, Chuang, Lee-Ming, Chang, Yen-Pei C, Naseri, Take, Jain, Deepti, Kang, Hyun Min, Hung, Adriana M, Srinivasasainagendra, Vinodh, Snively, Beverly M, Gu, Dongfeng, Montasser, May E, Reupena, Muagututi A Sefuiva, Heavner, Benjamin D, LeFaive, Jonathon, Hixson, James E, Rice, Kenneth M, Wang, Fei Fei, Nielsen, Jonas B, Huang, Jianfeng, Khan, Alyna T, Zhou, Wei, Nierenberg, Jovia L, Laurie, Cathy C, Armstrong, Nicole D, Shi, Mengyao, Pan, Yang, Stilp, Adrienne M, Emery, Leslie, Wong, Quenna, Hawley, Nicola L, Minster, Ryan L, Curran, Joanne E, Munroe, Patricia B, Weeks, Daniel E, North, Kari E, Tracy, Russell P, Kenny, Eimear E, Shimbo, Daichi, Chakravarti, Aravinda, Rich, Stephen S, Reiner, Alex P, Blangero, John, Redline, Susan, Mitchell, Braxton D, Rao, Dabeeru C, Chen, Yii-Der Ida, Kardia, Sharon LR, Kaplan, Robert C, Mathias, Rasika A, He, Jiang, Psaty, Bruce M, Fornage, Myriam, Loos, Ruth JF, Correa, Adolfo, Boerwinkle, Eric, Rotter, Jerome I, Kooperberg, Charles, and Edwards, Todd L

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Good Health and Well Being, Blood Pressure, Genome-Wide Association Study, Genomics, Humans, Hypertension, Polymorphism, Single Nucleotide, Precision Medicine, allele, blood pressure, genome, hypertension, whole genome sequencing, Samoan Obesity, Lifestyle, and Genetic Adaptations Study (OLaGA) Group, ‡ NHLBI Trans-Omics for Precision Medicine TOPMed) Consortium, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundThe availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.MethodsWe conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.ResultsTwo blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P

Published
2022

32. Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD

Author

Lee, Arthur M., Xu, Yunwen, Hu, Jian, Xiao, Rui, Hooper, Stephen R., Hartung, Erum A., Coresh, Josef, Rhee, Eugene P., Vasan, Ramachandran S., Kimmel, Paul L., Warady, Bradley A., Furth, Susan L., Denburg, Michelle R., Abraham, Alison, Anderson, Amanda, Ballard, Shawn, Bonventre, Joseph, Clish, Clary, Collins, Heather, Coca, Steven, Coresh, Josef, Deo, Rajat, Denburg, Michelle, Dubin, Ruth, Feldman, Harold I., Ferket, Bart S., Foster, Meredith, Furth, Susan, Ganz, Peter, Gossett, Daniel, Grams, Morgan, Greenberg, Jason, Gutiérrez, Orlando M., Hostetter, Tom, Inker, Lesley A., Ix, Joachim, Kimmel, Paul L., Klein, Jon, Levey, Andrew S., Massaro, Joseph, McMahon, Gearoid, Mifflin, Theodore, Nadkarni, Girish N., Parikh, Chirag, Ramachandran, Vasan S., Rebholz, Casey, Rhee, Eugene, Rovin, Brad, Sarnak, Mark, Sabbisetti, Venkata, Schelling, Jeffrey, Seegmiller, Jesse, Shlipak, Michael G., Shou, Haochang, Tin, Adriene, Waikar, Sushrut, Warady, Bradley, Whitehead, Krista, and Xie, Dawei

Published
2024
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34. Artificial Intelligence in Cardiovascular Care—Part 2: Applications: JACC Review Topic of the Week

Author

Jain, Sneha S., Elias, Pierre, Poterucha, Timothy, Randazzo, Michael, Lopez Jimenez, Francisco, Khera, Rohan, Perez, Marco, Ouyang, David, Pirruccello, James, Salerno, Michael, Einstein, Andrew J., Avram, Robert, Tison, Geoffrey H., Nadkarni, Girish, Natarajan, Vivek, Pierson, Emma, Beecy, Ashley, Kumaraiah, Deepa, Haggerty, Chris, Avari Silva, Jennifer N., and Maddox, Thomas M.

Published
2024
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35. Artificial Intelligence for Cardiovascular Care—Part 1: Advances: JACC Review Topic of the Week

Author

Elias, Pierre, Jain, Sneha S., Poterucha, Timothy, Randazzo, Michael, Lopez Jimenez, Francisco, Khera, Rohan, Perez, Marco, Ouyang, David, Pirruccello, James, Salerno, Michael, Einstein, Andrew J., Avram, Robert, Tison, Geoffrey H., Nadkarni, Girish, Natarajan, Vivek, Pierson, Emma, Beecy, Ashley, Kumaraiah, Deepa, Haggerty, Chris, Avari Silva, Jennifer N., and Maddox, Thomas M.

Published
2024
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39. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection

Author

Paranjpe, Ishan, Jayaraman, Pushkala, Su, Chen-Yang, Zhou, Sirui, Chen, Steven, Thompson, Ryan, Del Valle, Diane Marie, Kenigsberg, Ephraim, Zhao, Shan, Jaladanki, Suraj, Chaudhary, Kumardeep, Ascolillo, Steven, Vaid, Akhil, Gonzalez-Kozlova, Edgar, Kauffman, Justin, Kumar, Arvind, Paranjpe, Manish, Hagan, Ross O., Kamat, Samir, Gulamali, Faris F., Xie, Hui, Harris, Joceyln, Patel, Manishkumar, Argueta, Kimberly, Batchelor, Craig, Nie, Kai, Dellepiane, Sergio, Scott, Leisha, Levin, Matthew A., He, John Cijiang, Suarez-Farinas, Mayte, Coca, Steven G., Chan, Lili, Azeloglu, Evren U., Schadt, Eric, Beckmann, Noam, Gnjatic, Sacha, Merad, Miram, Kim-Schulze, Seunghee, Richards, Brent, Glicksberg, Benjamin S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2023
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41. Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

Author

Pacheco, Jennifer A., Rasmussen, Luke V., Wiley, Jr., Ken, Person, Thomas Nate, Cronkite, David J., Sohn, Sunghwan, Murphy, Shawn, Gundelach, Justin H., Gainer, Vivian, Castro, Victor M., Liu, Cong, Mentch, Frank, Lingren, Todd, Sundaresan, Agnes S., Eickelberg, Garrett, Willis, Valerie, Furmanchuk, Al’ona, Patel, Roshan, Carrell, David S., Deng, Yu, Walton, Nephi, Satterfield, Benjamin A., Kullo, Iftikhar J., Dikilitas, Ozan, Smith, Joshua C., Peterson, Josh F., Shang, Ning, Kiryluk, Krzysztof, Ni, Yizhao, Li, Yikuan, Nadkarni, Girish N., Rosenthal, Elisabeth A., Walunas, Theresa L., Williams, Marc S., Karlson, Elizabeth W., Linder, Jodell E., Luo, Yuan, Weng, Chunhua, and Wei, WeiQi

Published
2023
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43. Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts

Author

DiCorpo, Daniel, LeClair, Jessica, Cole, Joanne B, Sarnowski, Chloé, Ahmadizar, Fariba, Bielak, Lawrence F, Blokstra, Anneke, Bottinger, Erwin P, Chaker, Layal, Chen, Yii-Der I, Chen, Ye, de Vries, Paul S, Faquih, Tariq, Ghanbari, Mohsen, Gudmundsdottir, Valborg, Guo, Xiuqing, Hasbani, Natalie R, Ibi, Dorina, Ikram, M Arfan, Kavousi, Maryam, Leonard, Hampton L, Leong, Aaron, Mercader, Josep M, Morrison, Alanna C, Nadkarni, Girish N, Nalls, Mike A, Noordam, Raymond, Preuss, Michael, Smith, Jennifer A, Trompet, Stella, Vissink, Petra, Yao, Jie, Zhao, Wei, Boerwinkle, Eric, Goodarzi, Mark O, Gudnason, Vilmundur, Jukema, J Wouter, Kardia, Sharon LR, Loos, Ruth JF, Liu, Ching-Ti, Manning, Alisa K, Mook-Kanamori, Dennis, Pankow, James S, Picavet, H Susan J, Sattar, Naveed, Simonsick, Eleanor M, Verschuren, WM Monique, van Dijk, Ko Willems, Florez, Jose C, Rotter, Jerome I, Meigs, James B, Dupuis, Josée, and Udler, Miriam S

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Genetics, Obesity, Heart Disease - Coronary Heart Disease, Liver Disease, Heart Disease, Cardiovascular, Digestive Diseases, Diabetes, Nutrition, Prevention, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Alleles, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Genetic Loci, Humans, Pharmaceutical Preparations, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveType 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed.Research design and methodsHere we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD).ResultsDespite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway.ConclusionsOur findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Published
2022

44. Polygenic prediction of preeclampsia and gestational hypertension

Author

Honigberg, Michael C., Truong, Buu, Khan, Raiyan R., Xiao, Brenda, Bhatta, Laxmi, Vy, Ha My T., Guerrero, Rafael F., Schuermans, Art, Selvaraj, Margaret Sunitha, Patel, Aniruddh P., Koyama, Satoshi, Cho, So Mi Jemma, Vellarikkal, Shamsudheen Karuthedath, Trinder, Mark, Urbut, Sarah M., Gray, Kathryn J., Brumpton, Ben M., Patil, Snehal, Zöllner, Sebastian, Antopia, Mariah C., Saxena, Richa, Nadkarni, Girish N., Do, Ron, Yan, Qi, Pe’er, Itsik, Verma, Shefali Setia, Gupta, Rajat M., Haas, David M., Martin, Hilary C., van Heel, David A., Laisk, Triin, and Natarajan, Pradeep

Published
2023
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46. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J. F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A, Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S, Pleiness, Jacob, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D. C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent D., Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T, Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Lindstrom, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rabecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, Yanek, Lisa R., Carrasquilla, German D., Marioni, Riccardo E., Kleber, Marcus E., Trégouët, David-Alexandre, Yao, Jie, Li-Gao, Ruifang, Joshi, Peter K., Trompet, Stella, Martinez-Perez, Angel, Ghanbari, Mohsen, Howard, Tom E., Reiner, Alex P., Arvanitis, Marios, Ryan, Kathleen A., Bartz, Traci M., Rudan, Igor, Faraday, Nauder, Linneberg, Allan, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.

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2024
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48. Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project

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Lake, Blue, Zhang, Kun, Lecker, Stewart, Morales, Alexander, Bogen, Steve, Amodu, Afolarin A., Beck, Laurence, Henderson, Joel, Ilori, Titlayo, Maikhor, Shana, Onul, Ingrid, Schmidt, Insa, Verma, Ashish, Waikar, Sushrut, Yadati, Pranav, Yu, Guanghao, Colona, Mia R., McMahon, Gearoid, Hacohen, Nir, Greka, Anna, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Bush, William, Chen, Yijiang, Crawford, Dana, Madabhushi, Anant, Viswanathan, Vidya S., Bush, Lakeshia, Cooperman, Leslie, Gadegbeku, Crystal, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’Malley, Charles, O’Toole, John, Palmer, Ellen, Poggio, Emilio, Spates-Harden, Kassandra, Sedor, John, Sendrey, Dianna, Taliercio, Jonathan, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Berroue, Cecilia, Bomback, Andrew, Canetta, Pietro A., D’Agati, Vivette, Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Sabatello, Maya, Shang, Ning, de Pinho Gonçalves, Joana, Lardenoije, Roy, Migas, Lukasz, Van de Plas, Raf, Rennke, Helmut, Azeloglu, Evren, Campbell, Kirk, Coca, Steven, He, Cijang, He, John, Iyengar, Srinivas Ravi, Lefferts, Seanee, Nadkarni, Girish, Patel, Marissa, Tokita, Joji, Ward, Stephen, Xiong, Yuguang, Verdoes, Abraham, Sabo, Angela, Barwinska, Daria, Gisch, Debora Lidia, Williams, James, Kelly, Katherine, Dunn, Kenneth, Asghari, Mahla, Eadon, Michael, Ferkowicz, Michael, Dagher, Pierre, Ferreira, Ricardo Melo, Winfree, Seth, Bledsoe, Sharon, Wofford, Stephanie, El-Achkar, Tarek, Sutton, Timothy, Bowen, William, Cheng, Ying-Hua, Slade, Austen, Record, Elizabeth, Cheng, Yinghua, Borner, Katy, Herr, Bruce, Jain, Yashvardhan, Quardokus, Ellen, Atta, Mohamed, Bernard, Lauren, Menez, Steven, Parikh, Chirag, Corona Villalobos, Celia Pamela, Wang, Ashley, Wen, Yumeng, Xu, Alan, Chen, Sarah, Donohoe, Isabel, Johansen, Camille, Rosas, Sylvia, Sun, Jennifer, Ardayfio, Joseph, Bebiak, Jack, Campbell, Taneisha, Fox, Monica, Knight, Richard, Koewler, Robert, Pinkeney, Roy, Saul, John, Shpigel, Anna, Prasad, Pottumarthi, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher, Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George, Mao, Weiguang, Sealfon, Rachel, Troyanskaya, Olga, Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Anjani, Kavya, Laszik, Zoltan G., Mukatash, Tariq, Nolan, Garry, Beyda, David, Bracamonte, Erika, Brosius, Frank, Campos, Baltazar, Marquez, Nicole, Mendoza, Katherine, Scott, Raymond, Thajudeen, Bijin, Tsosie, Rebecca, Woodhead, Gregory, Saunders, Milda, Alloway, Rita R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Bjornstad, Petter, Hsieh, Elena, Kendrick, Jessica, Pyle, Laura, Thurman, Joshua, Vinovskis, Carissa, Wrobel, Julia, Lucarelli, Nicholas, Sarder, Pinaki, Bui, James, Carmona-Powell; Ron Gaba, Eunice, Kelly, Tanika, Lash, James, Meza, Natalie, Redmond, Devona, Renteria, Amada, Ricardo, Ana, Setty, Suman, Srivastava, Anand, Alakwaa, Fadhl, Ascani, Heather, Balis, Ul, Bitzer, Markus, Blanc, Victoria, Bonevich, Nikki, Conser, Ninive, Demeke, Dawit, Dull, Rachel, Eddy, Sean, Frey, Renee, Hartman, John, He, Yongqun Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura, McCown, Phillip, Menon, Rajasree, Nair, Viji, Otto, Edgar, Reamy, Rebecca, Rose, Michael, Schaub, Jennifer, Steck, Becky, Wright, Zachary, Coleman, Alyson, Henderson-Brown; Jerica Berge, Dorisann, Caramori, Maria Luiza, Adeyi, Oyedele, Nachman, Patrick, Safadi, Sami, Flanagan, Siobhan, Ma, Sisi, Klett, Susan, Wolf, Susan, Harindhanavudhi, Tasma, Rao, Via, Bream, Peter, Froment, Anne, Kelley, Sara, Mottl, Amy, Chaudhury; Evan Zeitler, Prabir Roy, Bender, Filitsa, Elder, Michele, Gilliam, Matthew, Hall, Daniel E., Kellum, John A., Murugan, Raghavan, Palevsky, Paul, Rosengart, Matthew, Tan, Roderick, Tublin, Mitchell, Winters, James, Bansal, Shweta, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Cai, Qi, Hendricks, Allen, Hedayati, Susan, Kermani, Asra, Lee, Simon C., Ma, Shihong, Miller, Richard Tyler, Moe, Orson W., Park, Harold, Patel, Jiten, Pillai, Anil, Sambandam, Kamalanathan, Torrealba, Jose, Toto, Robert D., Vazquez, Miguel, Wang, Nancy, Wen, Natasha, Zhang, Dianbo, Alpers, Charles, Berglund, Ashley, Berry, Brooke, Blank, Kristina, Brown, Keith, Carson, Jonas, Daniel, Stephen, de Boer, Ian H., Dighe, Ashveena L., Dowd, Frederick, Grewenow, Stephanie M., Himmelfarb, Jonathan, Hoofnagle, Andrew, Jefferson, Nichole, Larson, Brandon, Limonte, Christine, McClelland, Robyn, Mooney, Sean, Nam, Yunbi, Park, Christopher, Phuong, Jimmy, Rezaei, Kasra, Roberts, Glenda, Sarkisova, Natalya, Shankland, Stuart, Snyder, Jaime, Stutzke, Christy, Tuttle, Katherine, Wangperawong, Artit, Wilcox, Adam, Williams, Kayleen, Young, Bessie, Allen, Jamie, Caprioli, Richard M., de Caestecker, Mark, Djambazova, Katerina, Dufresne, Martin, Farrow, Melissa, Fogo, Agnes, Sharman, Kavya, Spraggins, Jeffrey, Basta, Jeannine, Conlon, Kristine, Diettman, Sabine M., Gaut, Joseph, Kaushal, Madhurima, Jain, Sanjay, Knoten, Amanda, Minor, Brittany, Nwanne, Gerald, Vijayan, Anitha, Zhang, Bo, Arora, Tanima, Cantley, Lloyd, Victoria Castro, Angela M., Kakade, Vijayakumar, Moeckel, Gilbert, Moledina, Dennis, Shaw, Melissa, Wilson, Francis P., El-Achkar, Tarek M., and Eadon, Michael T.

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2024
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