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Albert Higgins-Chen, MD/PhD

Assistant Professor of Psychiatry; NRTP, Yale Department of Psychiatry

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Albert Higgins-Chen, MD/PhD


Arriving at Yale in 2017, I was initially unsure how to best combine my clinical psychiatry interests with my research in the basic biology of aging. During my MD/PhD at the University of Michigan, I did C. elegans work identifying genes regulating longevity downstream of the IGF-1/FoxO pathway. To analyze my RNA-seq data, I taught myself how to code which sparked my interest in bioinformatics. I chose to specialize in psychiatry during my M3 clerkship, post-PhD.

Within the Yale psychiatry department, I found a welcoming environment with both fellow residents and faculty very curious about ideas outside of traditional psychiatry topics, and I was encouraged to pursue my aging interests. Because of research time in PGY-1 and 2 years as well as many mentors helping me identify a research niche, I joined Morgan Levine's lab in the pathology department (I am the only person in the lab with a psychiatric background, most others are bioinformaticians and/or model organism biologists). My work focuses on using machine learning approaches to develop biomarkers of aging based on DNA methylation and proteomic data that can predict one’s risk of age-related morbidity and mortality. I quickly found my aging work synergized with my psychiatry training because I could apply the aging biomarkers to discover how severe mental illness impacts aging, mortality, and cancer risk.

My long-term goal is to integrate the biomarkers into clinical trials of drugs to prevent age-related disease such as cancer, Alzheimer’s, cardiovascular disease, etc. Incidentally, the observation that psychotropic medications impact these aging biomarkers in humans (while also extending healthspan in model organisms) may facilitate biomarker development for clinical trials. I have developed a novel method for generating epigenetic biomarkers that have very high test-retest reliability, which is critical for assessing how the biomarkers change longitudinally in response to interventions.

I am largely self-taught in data science and computational biology. Thus, this year I chose to join the VA Informatics Fellowship which fully funds a PGY-5 salary (in addition to opportunities for moonlighting). This program sets aside protected time to take courses at Yale on statistics and machine learning, and offers the opportunity to work with a variety of VA datasets. Owing to Yale's highly multidisciplinary culture, I am engaged in collaborations applying aging biomarkers in COVID-19, idiopathic pulmonary fibrosis, PTSD, psychotherapy, schizophrenia, bipolar disorder, Alzheimer's disease, alcohol use disorder, HIV, and in vitro settings. I'm also collaborating with the Alzheimer's Disease Research Unit to apply machine learning techniques to neuroimaging data, while also serving as a study physician for Alzheimer's clinical trials.

Overall, I’ve discovered that my psychiatry and aging interests synergize in unexpected ways. In my free time, I enjoy going on long walks and bike rides at various Connecticut parks/trails/beaches, catching/cooking/eating delicious New England seafood, watching superhero movies, and concocting various self-improvement and quantified-self projects.

Education & Training

  • MD/PhD
    University of Michigan, Cellular and Molecular Biology (2017)
  • AB
    Harvard University, Biochemical Sciences (2009)

Departments & Organizations