Hadlock Lab Overview

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“Accelerating translational research by integrating clinical data into systems biology at scale”

–Associate Professor, Jennifer Hadlock, MD
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Research Overview

Our interdisciplinary lab is accelerating translational research by integrating clinical data into systems biology at scale. Our goal is to improve the lives of people with immune-mediated inflammatory diseases (IMIDs).

IMIDs are clinically diverse conditions characterized by immune dysregulation, chronic inflammation and potential organ damage. IMIDs include ulcerative colitis, Crohn’s disease, rheumatoid arthritis, psoriasis, psoriatic arthritis, ankylosing spondylitis, multiple sclerosis, systemic lupus erythematosus, Sjögren’s syndrome and others, including numerous rare diseases. The collective estimated prevalence of IMIDs is 5 percent to 7 percent, and IMIDs are frequently underdiagnosed. Individuals may experience years of delay and long-term harm before diagnosis, and often try multiple treatments before finding which ones provide benefit. In addition, medications that work well at first may lose efficacy over time.

To advance understanding of IMID transitions, we are integrating domain-agnostic computational methods, knowledge ontologies, and longitudinal data with genotype, phenotype, and exposures. Applying a systems biology approach, we do not limit research to high-level disease labels, but instead consider genotype and longitudinal patterns in both phenotype (such as multiomics, clinical observations and patient reported outcomes) and exposures (such as immunomodulatory medications, infectious disease and health-related social needs). We also investigate the intersection of IMIDs, aging and common chronic multimorbidities, which occur at a higher rate in the population of people with IMIDs.

Through interdisciplinary collaboration, we aim for earlier IMID prediction, better personalized treatment and prevention of sequelae.

Research Focus

Our research focuses on two areas.

  1. Explainable predictive risk models for informing clinical decisions and surfacing new biomedical insights into mechanisms of disease.
  2. Domain-agnostic methods for accelerating discovery into mechanisms of disease.

Risk modeling

Our initial research has focused on real-world evidence from over 26 million electronic health records (EHRs) to inform decisions about patient care and research priorities. EHRs provide longitudinal data for large cohorts, which enable multivariate modeling across medical conditions, medications, and clinical phenotype observations – even when focusing on specific subpopulations. For example, with the COVID-19 pandemic, we investigated questions about pregnancy, aging, hypertension and anti-hypertensive drugs and developed risk-prediction models for COVID-19 integrating IMIDs, history of immunomodulatory drugs, vaccination status and additional chronic comorbidities.

EHR data is vast (billions of observations), sensitive, and siloed, with significant barriers to even foundational levels of interoperability. In addition, the data is predominantly sparse, lacking in temporal alignment, and subject to significant ascertainment bias that varies over both time and space. We navigate with care to design models that will inform clinical decisions or research, and our assessments go beyond simple performance metrics to focus on reproducibility, explainability and biomedical relevance.

Our lab has established productive collaborations with clinical experts in IMIDs (rheumatology, gastroenterology and neurology), infectious disease, obstetrics, gerontology and nephrology. These experts help us prioritize investigations that are of greatest importance for patient care, and understand where artifacts of current healthcare delivery and EHR documentation may reflect or mask underlying biomedical patterns.

We have developed curated ontology-based, physician-reviewed human/computer-readable phenotype libraries for higher fidelity measures in areas essential for understanding IMIDs. This includes sharable human/computer-readable phenotypes with a focus on hierarchical definitions for IMID conditions, chronic multimorbidities, immunomodulatory medications, and features related to endothelial function and immune status. These phenotype libraries have also been valuable for collaboration with colleagues, investigating antivirals during acute COVID-19 and mortality, and integrating EHR data with prospective studies for deep-immunophenotyping (acute COVID-19 and post-acute sequelae (PASC).

We are currently conducting research on data which combines retrospective real-world evidence with richer prospective data. For example, we are investigating IMIDs in the NIH All of Us Research Hub, which includes data from a diverse cohort: EHRs, genomics, social determinants of health, lifestyle information, physical measurements and wearables.

Domain agnostic methods

Our research includes both hypothesis discovery and traditional pharmacoepidemiology. As such, we evaluate and apply a wide range of existing and emerging methods in data science to improve the accuracy and trustworthiness of models, including multiple machine-learning methods, explainability and fairness algorithms, and both per-patient and geocoded census-tract level measures of social determinants of health. Currently, there are two areas where we are developing novel methods.

One is work with the NIH NCATS Biomedical Data Translator Consortium, bridging the semantic gap between real-world clinical data and knowledge graphs. Our contribution is developing privacy-preserving knowledge graphs from real-world evidence for integration with multiomic knowledge graphs from other sources.

Another is advances for addressing sparse, irregular and multivariate time series, a problem encountered frequently with both real-world evidence and clinical study data. This work includes data transformation, such as temporal synthetic minority oversampling, and alternate approaches to analyzing health trajectories over time.



Piekos, Samantha N., Yeon Mi Hwang, Ryan T. Roper, Tanya Sorensen, Nathan D. Price, Leroy Hood, and Jennifer J. Hadlock. 2023. “Effect of COVID-19 Vaccination and Booster on Maternal-Fetal Outcomes: A Retrospective Cohort Study.” The Lancet. Digital Health 5 (9): e594–606. Cite Download
Wei, Qi, Philip J. Mease, Michael Chiorean, Lulu Iles-Shih, Wanessa F. Matos, Andrew Baumgartner, Sevda Molani, et al. 2023. “Risk Factors for Severe COVID-19 Outcomes: A Study of Immune-Mediated Inflammatory Diseases, Immunomodulatory Medications, and Comorbidities in a Large US Healthcare System.” MedRxiv: The Preprint Server for Health Sciences, 2023.06.26.23291904. Cite Download
Fecho, Karamarie, Chris Bizon, Tursynay Issabekova, Sierra Moxon, Anne E. Thessen, Shervin Abdollahi, Sergio E. Baranzini, et al. 2023. “An Approach for Collaborative Development of a Federated Biomedical Knowledge Graph-Based Question-Answering System: Question-of-the-Month Challenges.” Journal of Clinical and Translational Science 7 (1): e214. Cite Download
Hwang, Yeon-Mi, Samantha Piekos, Tanya Sorensen, Leroy Hood, and Jennifer Hadlock. 2023. “Adoption of a National Prophylactic Anticoagulation Guideline for Hospitalized Pregnant Women with COVID-19: Retrospective Cohort Study.” JMIR Public Health and Surveillance. Cite
Watanabe, Kengo, Tomasz Wilmanski, Christian Diener, John C. Earls, Anat Zimmer, Briana Lincoln, Jennifer J. Hadlock, et al. 2023. “Multiomic Signatures of Body Mass Index Identify Heterogeneous Health Phenotypes and Responses to a Lifestyle Intervention.” Nature Medicine 29 (4): 996–1008. Cite Download
Hwang, Yeon Mi, Ryan T. Roper, Samantha N. Piekos, Daniel A. Enquobahrie, Mary F. Hebert, Alison G. Paquette, Priyanka Baloni, Nathan D. Price, Leroy Hood, and Jennifer J. Hadlock. 2023. “Timing of Selective Serotonin Reuptake Inhibitor Use and Risk for Preterm Birth and Related Adverse Events.” medRxiv. Cite Download
Johnson, James P., Christian Diener, Anne E. Levine, Tomasz Wilmanski, David L. Suskind, Alexandra Ralevski, Jennifer Hadlock, et al. 2023. “Generally-Healthy Individuals with Aberrant Bowel Movement Frequencies Show Enrichment for Microbially-Derived Blood Metabolites Associated with Impaired Kidney Function.” bioRxiv. Cite Download
Kwasniewski, Miroslaw, Urszula Korotko, Karolina Chwialkowska, Magdalena Niemira, Jerzy Jaroszewicz, Barbara Sobala-Szczygiel, Beata Puzanowska, et al. 2023. “Implementation of the Web-Based Calculator Estimating Odds Ratio of Severe COVID-19 for Unvaccinated Individuals in a Country with High Coronavirus-Related Death Toll.” Allergy 78 (1): 311–14. Cite Download
Mease, P. J., Q. Wei, M. Chiorean, L. Iles-Shih, and J. Hadlock. 2022. “Op0247 Risk Factors for Severe Covid-19 Outcomes: A Study of Immune-Mediated Inflammatory Diseases, Therapies and Comorbidities in a Large Us Healthcare System.” Annals of the Rheumatic Diseases 81 (Suppl 1): 161–62. Cite Download
Watanabe, Kengo, Tomasz Wilmanski, Christian Diener, Anat Zimmer, Briana Lincoln, Jennifer J. Hadlock, Jennifer C. Lovejoy, et al. 2022. “Multiomic Investigations of Body Mass Index Reveal Heterogeneous Trajectories in Response to a Lifestyle Intervention.” medRxiv. Cite Download
Goldman, Jason D., Kai Wang, Katharina Röltgen, Sandra C. A. Nielsen, Jared C. Roach, Samia N. Naccache, Fan Yang, et al. 2022. “Reinfection with SARS-CoV-2 and Waning Humoral Immunity: A Case Report.” Vaccines 11 (1): 5. Cite Download
Duvvuri, Venkata R., Andrew Baumgartner, Sevda Molani, Patricia V. Hernandez, Dan Yuan, Ryan T. Roper, Wanessa F. Matos, et al. 2022. “Angiotensin-Converting Enzyme (ACE) Inhibitors May Moderate COVID-19 Hyperinflammatory Response: An Observational Study with Deep Immunophenotyping.” Health Data Science 2022: 0002. Cite Download
Unni, Deepak R., Sierra A. T. Moxon, Michael Bada, Matthew Brush, Richard Bruskiewich, J. Harry Caufield, Paul A. Clemons, et al. 2022. “Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science.” Clinical and Translational Science 15 (8): 1848–55. Cite Download
Piekos, Samantha N., Nathan D. Price, Leroy Hood, and Jennifer J. Hadlock. 2022. “The Impact of Maternal SARS-CoV-2 Infection and COVID-19 Vaccination on Maternal-Fetal Outcomes.” Reproductive Toxicology (Elmsford, N.Y.), S0890-6238(22)00153-8. Cite
Molani, Sevda, Patricia V. Hernandez, Ryan T. Roper, Venkata R. Duvvuri, Andrew M. Baumgartner, Jason D. Goldman, Nilüfer Ertekin-Taner, et al. 2022. “Risk Factors for Severe COVID-19 Differ by Age for Hospitalized Adults.” Scientific Reports 12 (1): 6568. Cite Download
Fecho, Karamarie, Anne E. Thessen, Sergio E. Baranzini, Chris Bizon, Jennifer J. Hadlock, Sui Huang, Ryan T. Roper, et al. 2022. “Progress toward a Universal Biomedical Data Translator.” Clinical and Translational Science. Cite
Molani, Sevda, Andrew M. Baumgartner, Yeon Mi Hwang, Venkata R. Duvvuri, Jason D. Goldman, and Jennifer J. Hadlock. 2022. “Time to Reinfection and Vaccine Breakthrough SARS-CoV-2 Infections: A Retrospective Cohort Study,” 2022.02.07.22270613. Cite Download
Baumgartner, Andrew, Sevda Molani, Qi Wei, and Jennifer Hadlock. 2022. “Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique.” ArXiv:2201.05634 [Physics, q-Bio, Stat]. Cite Download
Piekos, Samantha N., Ryan T. Roper, Yeon Mi Hwang, Tanya Sorensen, Nathan D. Price, Leroy Hood, and Jennifer J. Hadlock. 2022. “The Effect of Maternal SARS-CoV-2 Infection Timing on Birth Outcomes: A Retrospective Multicentre Cohort Study.” The Lancet. Digital Health 4 (2): e95–104. Cite Download
Su, Yapeng, Dan Yuan, Daniel G. Chen, Rachel H. Ng, Kai Wang, Jongchan Choi, Sarah Li, et al. 2022. “Multiple Early Factors Anticipate Post-Acute COVID-19 Sequelae.” Cell 0 (0). Cite Download
Diaz, George A, Alyssa B Christensen, Tobias Pusch, Delaney Goulet, Shu-Ching Chang, Gary L Grunkemeier, Paul A McKelvey, et al. 2021. “Remdesivir and Mortality in Patients With Coronavirus Disease 2019.” Clinical Infectious Diseases, ciab698. Cite Download
Jiang, Yonghou, Fergal Duffy, Jennifer Hadlock, Andrew Raappana, Sheila Styrchak, Ingrid Beck, Fred D. Mast, et al. 2021. “Angiotensin II Receptor I Auto-Antibodies Following SARS-CoV-2 Infection.” PloS One 16 (11): e0259902. Cite Download
Lee, Jihoon W., Yapeng Su, Priyanka Baloni, Daniel Chen, Ana Jimena Pavlovitch-Bedzyk, Dan Yuan, Venkata R. Duvvuri, et al. 2021. “Integrated Analysis of Plasma and Single Immune Cells Uncovers Metabolic Changes in Individuals with COVID-19.” Nature Biotechnology. Cite Download
Lee, Jewel Y., Sevda Molani, Chen Fang, Kathleen Jade, D. Shane O’Mahony, Sergey A. Kornilov, Lindsay T. Mico, and Jennifer J. Hadlock. 2021. “Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study.” JMIR Medical Informatics 9 (7): e29986. Cite Download
Su, Yapeng, Dan Yuan, Daniel G. Chen, Kai Wang, Jongchan Choi, Chengzhen L. Dai, Sunga Hong, et al. 2021. “Heterogeneous Immunological Recovery Trajectories Revealed in Post-Acute COVID-19.” MedRxiv, 2021.03.19.21254004. Cite Download
Dai, Chengzhen L., Sergey A. Kornilov, Ryan T. Roper, Hannah Cohen-Cline, Kathleen Jade, Brett Smith, James R. Heath, et al. 2021. “Characteristics and Factors Associated with COVID-19 Infection, Hospitalization, and Mortality Across Race and Ethnicity.” Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America. Cite Download
Goldman, Jason D., Kai Wang, Katharina Röltgen, Sandra C. A. Nielsen, Jared C. Roach, Samia N. Naccache, Fan Yang, et al. 2020. “Reinfection with SARS-CoV-2 and Failure of Humoral Immunity: A Case Report.” Cite Download
Su, Yapeng, Daniel Chen, Dan Yuan, Christopher Lausted, Jongchan Choi, Chengzhen L. Dai, Valentin Voillet, et al. 2020. “Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.” Cell, S0092867420314446. Cite Download
Fecho, Karamarie, Stanley C. Ahalt, Saravanan Arunachalam, James Champion, Christopher G. Chute, Sarah Davis, Kenneth Gersing, et al. 2019. “Sex, Obesity, Diabetes, and Exposure to Particulate Matter among Patients with Severe Asthma: Scientific Insights from a Comparative Analysis of Open Clinical Data Sources during a Five-Day Hackathon.” Journal of Biomedical Informatics 100: 103325. Cite
Ahalt, Stanley C., Christopher G. Chute, Karamarie Fecho, Gustavo Glusman, Jennifer Hadlock, Casey Overby Taylor, Emily R. Pfaff, et al. 2019. “Clinical Data: Sources and Types, Regulatory Constraints, Applications.” Clinical and Translational Science. Cite Download
Robinson, Max, Jennifer Hadlock, Jiyang Yu, Alireza Khatamian, Aleksandr Y. Aravkin, Eric W. Deutsch, Nathan D. Price, Sui Huang, and Gustavo Glusman. 2018. “Fast and Simple Comparison of Semi-Structured Data, with Emphasis on Electronic Health Records.” BioRxiv, 293183. Cite Download