Albert Higgins-Chen, MD/PhD

The Higgins-Chen lab develops and applies novel aging biomarkers to test the modifiability of aging to prevent or delay diseases such as Alzheimer’s, cardiovascular disease, and cancer. The primarily computational lab utilizes machine learning techniques to estimate the biological age of individuals from high-dimensional omics data. These aging biomarkers are then tested in human clinical trials and mouse intervention studies testing geroscience-based treatments to determine if longitudinal changes predict reductions in long-term morbidity and mortality risk. If so, these biomarkers could serve as early indicators for whether an intervention is truly modifying aging for any given individual. We recently reported that epigenetic clocks, commonly used aging biomarkers based on DNA methylation, suffer from technical noise that make their test-retest reliability inadequate for longitudinal and intervention studies, and developed a new machine learning approach to solve this problem (Higgins-Chen 2022, Nature Aging). Using this technique, we are studying the causes, consequences, and molecular mechanisms of longitudinal changes in aging biomarkers, utilizing data from humans, rodents, and in vitro experiments from collaborators. Current projects include studying how aging biomarkers interact with psychiatric disorders, Alzheimer’s, circadian rhythms, and stress. We are utilizing our findings to inform the development of the next generation of machine learning approaches and aging biomarkers.

Given that psychiatric disorders are major risk factors for age-related disease, a central focus of the lab is to study how aging biomarkers are affected by mental health. This includes current collaborations examining how our novel aging biomarkers are affected by schizophrenia, bipolar disorder, depression, Alzheimer’s, PTSD, and stress. We have found that DNA methylation-based aging biomarkers may be reduced by psychiatric medications, which is consistent with reported benefits in ameliorating age-related mortality risk in humans and model organisms. We are developing biomarkers that can specifically monitor morbidity and mortality risk related to mental health and psychiatric treatments, with the aim using these to select personalized geroscience-based treatments that will prevent or delay age-related disease for individuals with psychiatric conditions.

Members of the lab typically develop new machine learning pipelines and aging biomarkers, then apply them to investigate important questions about the aging process. They often lead collaborations with clinicians, experimental biologists, epidemiologists, social scientists, and/or industry. As aging and mental health interact with nearly all other topics in biology, lab members are encouraged to think “outside the box” and feel free to bring any of their interests or passions to the lab.