Katherine Phoenix Liao, MD, MPH

Katherine Liao, MD, MPH

Associate Professor of Medicine, Brigham and Women's Hospital
Associate Professor of Biomedical Informatics, Harvard Medical School (Secondary)

Brigham and Women's Hospital Rheumatology - PBB-B3 75 Francis St Boston, MA

Katherine Liao is a clinical investigator and practicing rheumatologist. The mission of her lab her lab is two-fold: (1) is to study rheumatoid arthritis (RA), and the clinical and genetic factors that lead to outcomes such as cardiovascular disease and severe joint damage, and (2) is to apply and develop bioinformatics methods to utilize big data for clinical and translational research studies. Liao’s research focuses on applying methods such as natural language processing to electronic medical record (EMR) data to perform clinical studies in RA and other conditions. Heart disease is the leading cause of death in patients with RA. This high risk has been attributed to inflammation, which is an important risk factor for heart disease in the general population. Determining these links can identify strategies to reduce CV risk in RA, as well as lead to potential targets of treatment in the general population. Liao is the PI of the R01 funded study, Lipids, Inflammation and CV risk in RA (LiiRA). The goal of LiiRA is to investigate how inflammation may modify important traditional cardiovascular risk factors such as cholesterol and blood pressure, and the impact of these modifications on CV risk. She is also a co-investigator on an NIH U01 multi-center RCT, Treatment Against RA and Effect on FDG PET CT (TARGET). TARGET specifically tests the hypothesis that reducing inflammation, reduces vascular inflammation and CV risk in RA. In line with her research interests, Liao is co-Director of the Cardiovascular Rheumatology Clinic at Brigham and Women’s Hospital. Through her work with the Informatics for Integrating Biology and the Bedside (i2b2) project, Liao led the team to develop an EMR research platform for RA studies. This platform integrated clinical and biomarker data (e.g. clinical EMR data, genetics, autoantibody data) allowing for both traditional genetic association studies as well as new approaches for data analyses such as the Phenome Wide Association Study (PheWAS). Using this platform, she collaborates closely with investigators from the fields of biostatistics and bioinformatics to apply novel methods to study focused clinical questions such as CVD in RA. Currently, she is leading a pilot project to port and further develop these methods at VA Boston Healthcare using nationwide VA data with a goal to establish an EMR research platform at the VA.

ARCH: Large-scale Knowledge Graph via Aggregated Narrative Codified Health Records Analysis.
Authors: Gan Z, Zhou D, Rush E, Panickan VA, Ho YL, Ostrouchov G, Xu Z, Shen S, Xiong X, Greco KF, Hong C, Bonzel CL, Wen J, Costa L, Cai T, Begoli E, Xia Z, Gaziano JM, Liao KP, Cho K, Cai T, Lu J.
medRxiv
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Potential pitfalls in the use of real-world data for studying long COVID.
Authors: Zhang HG, Honerlaw JP, Maripuri M, Samayamuthu MJ, Beaulieu-Jones BR, Baig HS, L'Yi S, Ho YL, Morris M, Panickan VA, Wang X, Weber GM, Liao KP, Visweswaran S, Tan BWQ, Yuan W, Gehlenborg N, Muralidhar S, Ramoni RB, Kohane IS, Xia Z, Cho K, Cai T, Brat GA.
Nat Med
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Framework of the Centralized Interactive Phenomics Resource (CIPHER) standard for electronic health data-based phenomics knowledgebase.
Authors: Honerlaw J, Ho YL, Fontin F, Gosian J, Maripuri M, Murray M, Sangar R, Galloway A, Zimolzak AJ, Whitbourne SB, Casas JP, Ramoni RB, Gagnon DR, Cai T, Liao KP, Gaziano JM, Muralidhar S, Cho K.
J Am Med Inform Assoc
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Autoantibodies against citrullinated and native proteins and prediction of rheumatoid arthritis-associated interstitial lung disease: A nested case-control study.
Authors: Kronzer VL, Hayashi K, Yoshida K, Davis JM, McDermott GC, Huang W, Dellaripa PF, Cui J, Feathers V, Gill RR, Hatabu H, Nishino M, Blaustein R, Crowson CS, Robinson WH, Sokolove J, Liao KP, Weinblatt ME, Shadick NA, Doyle TJ, Sparks JA.
Lancet Rheumatol
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Multimodal representation learning for predicting molecule-disease relations.
Authors: Wen J, Zhang X, Rush E, Panickan VA, Li X, Cai T, Zhou D, Ho YL, Costa L, Begoli E, Hong C, Gaziano JM, Cho K, Lu J, Liao KP, Zitnik M, Cai T.
Bioinformatics
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Evidence for Biologic Drug Modifying Anti-Rheumatoid Drugs and Association with Cardiovascular Disease Risk Mitigation in Inflammatory Arthritis.
Authors: Weber B, Liao KP.
Rheum Dis Clin North Am
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Association Between Systemic Vasculitis and Coronary Microvascular Dysfunction in the Absence of Obstructive Coronary Artery Disease.
Authors: Weber B, Wallace ZS, Parks S, Cook C, Huck DM, Garshick M, Brown JM, Divakaran S, Hainer J, Dorbala S, Blankstein R, Liao KP, Aghayev A, Choi HK, Di Carli M.
Circ Cardiovasc Imaging
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Reducing cardiovascular risk with immunomodulators: a randomised active comparator trial among patients with rheumatoid arthritis.
Authors: Solomon DH, Giles JT, Liao KP, Ridker PM, Rist PM, Glynn RJ, Broderick R, Lu F, Murray MT, Vanni K, Santacroce LM, Abohashem S, Robson PM, Fayad Z, Mani V, Tawakol A, Bathon J.
Ann Rheum Dis
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A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality.
Authors: Verma A, Minnier J, Wan ES, Huffman JE, Gao L, Joseph J, Ho YL, Wu WC, Cho K, Gorman BR, Rajeevan N, Pyarajan S, Garcon H, Meigs JB, Sun YV, Reaven PD, McGeary JE, Suzuki A, Gelernter J, Lynch JA, Petersen JM, Zekavat SM, Natarajan P, Dalal S, Jhala DN, Arjomandi M, Gatsby E, Lynch KE, Bonomo RA, Freiberg M, Pathak GA, Zhou JJ, Donskey CJ, Madduri RK, Wells QS, Huang RDL, Polimanti R, Chang KM, Liao KP, Tsao PS, Wilson PWF, Hung AM, O'Donnell CJ, Gaziano JM, Hauger RL, Iyengar SK, Luoh SW.
Am J Respir Crit Care Med
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Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis.
Authors: Ishigaki K, Sakaue S, Terao C, Luo Y, Sonehara K, Yamaguchi K, Amariuta T, Too CL, Laufer VA, Scott IC, Viatte S, Takahashi M, Ohmura K, Murasawa A, Hashimoto M, Ito H, Hammoudeh M, Emadi SA, Masri BK, Halabi H, Badsha H, Uthman IW, Wu X, Lin L, Li T, Plant D, Barton A, Orozco G, Verstappen SMM, Bowes J, MacGregor AJ, Honda S, Koido M, Tomizuka K, Kamatani Y, Tanaka H, Tanaka E, Suzuki A, Maeda Y, Yamamoto K, Miyawaki S, Xie G, Zhang J, Amos CI, Keystone E, Wolbink G, van der Horst-Bruinsma I, Cui J, Liao KP, Carroll RJ, Lee HS, Bang SY, Siminovitch KA, de Vries N, Alfredsson L, Rantapää-Dahlqvist S, Karlson EW, Bae SC, Kimberly RP, Edberg JC, Mariette X, Huizinga T, Dieudé P, Schneider M, Kerick M, Denny JC, Matsuda K, Matsuo K, Mimori T, Matsuda F, Fujio K, Tanaka Y, Kumanogoh A, Traylor M, Lewis CM, Eyre S, Xu H, Saxena R, Arayssi T, Kochi Y, Ikari K, Harigai M, Gregersen PK, Yamamoto K, Louis Bridges S, Padyukov L, Martin J, Klareskog L, Okada Y, Raychaudhuri S.
Nat Genet
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