Brian K. Lee, PhD

Assistant Professor

Departments and Research Centers

Epidemiology and Biostatistics
Autism Public Health Research Institute

Research Focus

  • Environmental determinants of autism spectrum disorders
  • E-Epidemiology
  • Machine learning
  • Epidemiology of autism spectrum disorders
  • Environmental determinants of health
  • Epidemiology
  • Aging
  • Biomarkers
  • Child and maternal health
  • Gene-environment interaction
  • Neurological disorders

Education

  • PhD, Epidemiology, Johns Hopkins University
  • MHS, Epidemiology, Johns Hopkins University
  • AB, Biological Anthropology, Harvard College

Awards and Honors

  • 2003, AB Cum laude, Harvard College
  • 2003, Ames Award, Harvard College
  • 2003, Fellowship Award, Harvard University Center for Public Interest Careers

Bio Abstract

Dr. Lee received his PhD and MHS degrees in Epidemiology from The Johns Hopkins University, and graduated Cum laude with an AB in Biological Anthropology from Harvard College. His research interests include the epidemiology of neurological development, maintenance and decline.  Current topics include prenatal environmental exposures and autism risk; neighborhoods and psychosocial "stress" in the cognitive decline of older adults; lead toxicity and white matter health; gene-environment interaction; maternal antibody exposure in utero and fetal outcomes.  In addition to his etiological research, Dr. Lee is interested in epidemiological methods including causal inference methodology, data mining and machine learning algorithms, and automated data extraction techniques for structured and unstructured data.

Recent Research

Study of Health Outcomes in Children with Autism and Their Families: (Co-Investigator) The primary goals of this study are to access an appropriate database and assess the accuracy of ASD diagnosis, obtain and analyze data about trajectories of health outcomes and health care use, as well as to identify risk factors that may be related to the etiology and risk of ASD. Epidemiologic aspects of the project will involve assessment of potential for etiologic research involving prenatal pharmacologic exposures.

Effects of midlife blood pressure, anti-hypertensive medication use, and genetic risk factors on white matter health in aging:(Principal Investigator) The goals of this project are: 1) To describe the relations of blood pressure in midlife with white matter health in aging, 2) To determine whether use of anti-hypertensive medications reduces white matter deterioration, and 3) To determine whether specific genetic factors modify the effects of blood pressure and anti-hypertensive medications on white matter health.

Early life environmental exposures and autism in an existing Swedish birth cohort: Principal investigator
This is a case-control study of the early life environment that utilizes two existing resources with coverage of the entire nation of Sweden – a repository of 35+ years’ worth of neonatal blood samples and the electronic health and population data registers. The question that is addressed is whether early life immune challenges (e.g., maternal infections and autoimmune disease) during pregnancy and the neonatal inflammatory response influence ASD risk.

Selected Publications

Idring S, Rai D, Dal H, Dalman C, Sturm H, Zander E, Lee BK, Serlachius E, Magnusson C. Autism spectrum disorders in the Stockholm Youth Cohort: design, prevalence, and validity. PLoS ONE 7(7): e41280. 

Lee BK, Gardner RM, Dal H, Svensson A, Galanti MR, Rai D, Dalman C, Magnusson C. Brief report: maternal smoking during pregnancy and offspring autism spectrum disorders. Journal of Autism and Developmental Disorders, 2011. 

Lee BK, Glass TA, James BD, Bandeen-Roche K, Schwartz BS. Neighborhood psychosocial environment, apolipoprotein E genotype, and cognitive function in older adults. Archives of General Psychiatry.2011;68(3):314-321.

Lee BK. Epidemiologic Research and Web 2.0-the User-driven Web. Epidemiology 2010. Nov;21(6):760-763.

Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Statistics in Medicine 2010; 29(3):337-46. 

 

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