Dr. Christopher D. Anderson is a Neurocritical Care and Stroke Neurologist at Massachusetts General Hospital (MGH), whose career goal is to develop an independent research program as a computational genetic biologist capable of using advanced bioinformatic and statistical methods to obtain maximal scientific yield from large-scale genetic and genomic studies, with the aim of returning meaningful and actionable results that improve understanding of cerebral small vessel disease (CSVD) and identify novel targets for therapeutic development. As a first step toward this goal, he plans to use data from genome-wide studies to examine genetic variants within biological networks associated with lipid levels to uncover the mechanisms by which lipids influence CSVD. This proposal addresses a key area of controversy, as current data suggest conflicting roles for lipid levels depending on the particular form of cerebrovascular disease under study. Cerebral small vessel disease, which underlies intracerebral hemorrhage (ICH), radiographic white matter disease, and cerebral microbleeds, may be worsened by reduced lipid levels, while overall ischemic stroke risk appears to benefit from this strategy. Dr. Anderson has established an early career track record in the analysis of common genetic variant data to identify new associations in ischemic stroke, ICH, and Alzheimer Disease, and his preliminary data demonstrate the feasibility of biologically-informed genetic analysis in cerebrovascular disease. His career plan leverages the extensive resources and exceptional environments of Massachusetts General Hospital and the Broad Institute, under the mentorship of Dr. Jonathan Rosand and co-mentorship of Drs. Sekar Kathiresan, Mark Daly, and Ona Wu. In this Career Development Award, Dr. Anderson proposes to: 1) discover the impact of common genetic variants known to affect lipid levels on the risk of ICH and severity of neuroimaging manifestations of CSVD, 2) determine the impact of rare genetic variants in gene networks with a role in lipid levels on risk of ICH and severity of these same neuroimaging measures, and 3) use advanced bioinformatics tools to construct novel gene networks associated with lipid levels, and test these networks for association with ICH and neuroimaging measures to uncover new biological targets. The proposed study will employ genetic and informatic tools to demonstrate the direction and magnitude of effect that lipids exert on CSVD. These analyses offer the promise of yielding novel targets for drug development, and will further our understanding of the potential risks of lipid modifying therapy. Dr. Anderson has assembled a team with expertise in complex disease genetics, lipid epidemiology, neuroimaging, and advanced bioinformatics techniques that will ensure that this proposal maximally leverages the data generated. This Award will provide Dr. Anderson with the skills to evolve into an independent clinician-scientist with a computational research program that can nimbly analyze large genetic datasets to derive results that are highly relevant to the prevention and treatment of cerebrovascular disease in his clinical patient population.

Public Health Relevance

While advances in medical management have led to improvements in prevention and treatment, intracerebral hemorrhage occurs in over 70,000 people in the U.S. each year, half of whom die of the disease. Related manifestations of cerebral small vessel disease include age-related cognitive impairment, gait disorders, and late-life depression. Dr. Anderson's proposed career development plan and research strategy hold great promise in improving our understanding of the precise role cholesterol plays in the onset and progression of intracerebral hemorrhage and cerebral small vessel disease, which is a crucial step towards the development of more effective treatment strategies for treatment and prevention of these disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Mentored Patient-Oriented Research Career Development Award (K23)
Project #
5K23NS086873-05
Application #
9457499
Study Section
National Institute of Neurological Disorders and Stroke Initial Review Group (NSD)
Program Officer
Koenig, James I
Project Start
2014-04-01
Project End
2019-03-31
Budget Start
2018-04-01
Budget End
2019-03-31
Support Year
5
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
Marini, Sandro; Devan, William J; Radmanesh, Farid et al. (2018) 17p12 Influences Hematoma Volume and Outcome in Spontaneous Intracerebral Hemorrhage. Stroke 49:1618-1625
Morotti, Andrea; Dowlatshahi, Dar; Boulouis, Gregoire et al. (2018) Predicting Intracerebral Hemorrhage Expansion With Noncontrast Computed Tomography: The BAT Score. Stroke 49:1163-1169
Akhter, Murtaza; Morotti, Andrea; Cohen, Abigail Sara et al. (2018) Timing of INR reversal using fresh-frozen plasma in warfarin-associated intracerebral hemorrhage. Intern Emerg Med 13:557-565
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Sawyer, Russell P; Sekar, Padmini; Osborne, Jennifer et al. (2018) Racial/ethnic variation of APOE alleles for lobar intracerebral hemorrhage. Neurology 91:e410-e420
Marini, Sandro; Morotti, Andrea; Lena, Umme K et al. (2018) Men Experience Higher Risk of Pneumonia and Death After Intracerebral Hemorrhage. Neurocrit Care 28:77-82
Morotti, Andrea; Boulouis, Gregoire; Charidimou, Andreas et al. (2018) Integration of Computed Tomographic Angiography Spot Sign and Noncontrast Computed Tomographic Hypodensities to Predict Hematoma Expansion. Stroke 49:2067-2073
Marini, Sandro; Lena, Umme K; Crawford, Katherine M et al. (2018) Comparison of Genetic and Self-Identified Ancestry in Modeling Intracerebral Hemorrhage Risk. Front Neurol 9:514
Bapat, Aneesh; Anderson, Christopher D; Ellinor, Patrick T et al. (2018) Genomic basis of atrial fibrillation. Heart 104:201-206
Udler, Miriam S; Kim, Jaegil; von Grotthuss, Marcin et al. (2018) Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med 15:e1002654

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