The ASHG Conference 2023 – Annual Meeting is the largest human genetics and genomics meeting and exposition in the world, bringing together more than 7,000 professionals of all stages in the genetics community as well as members of complementary life sciences applications.
Building on the momentum from 2022, we are thrilled to be returning to the ASHG 2023 Conference as an exhibitor and speaker, discussing the complementary role metabolomics plays alongside genomics workflows.
Booth No. 1622
Multiomic Models of Aging: Predicting Biological Age with Genomics, Proteomics, and Metabolomics to Elucidate the Molecular Mechanisms of Aging.
Date: Thursday, November 2nd, 3:30-4:30 PM
Location: Room 143C
Dr. Jessica Lasky-Su
Dr. Lasky-Su has been a leader in applying metabolomics research to epidemiology, covering a range of chronic diseases over the life course, with a focus on respiratory outcomes (e.g. asthma). Much of her work has focused on “integrative metabolomics” - the integration other omics with using a metabolomic-centric perspective to study complex diseases. With integrative metabolomics as an emerging field, Dr. Lasky’s scholarship has contributed broadly, with peer-reviewed publications that focus on a range of disease outcomes (cancers, respiratory, ocular, infections, metabolic, neurodevelopment/mental health) and exposures (air pollutants, PFAS, nutrition, exercise) that may have an impact on health over the life course. Her investigative success is also demonstrated with >225 peer-reviewed publications. To date, she has been invited to give > 60 national and international talks. The breadth of chronic disease outcomes that Dr. Lasky-Su has studied prompted her to study multiomics and aging over the life course using the Mass General Brigham (MGB-Biobank), where she has generated a large, prospective, curated biobank cohort with multiomic data. Through these efforts Dr. Lasky-Su has created robust biological aging clocks with epigenetics, metabolomics, and proteomics. Dr. Lasky-Su’s leadership is metabolomics well-acknowledged, as the most recent past president of the Metabolomics Society – the largest metabolomics society in the world – and the chairman of the NIH’s Consortium of Metabolomics Studies (COMETS) consortium – the largest international consortium of prospective metabolomics cohorts – over the past four years. She has also spearheaded new efforts, including initiating STROBE-metabolomics to provide reporting guidelines in this area, and the Metabolomic Epidemiology Task Group to define and formalize this emerging field of study. In addition, Dr. Lasky-Su has extensive funding in multiomics; she is the PI/MPI of 4 current NIH R01s in metabolomics, the PI on multiple private grants, and the Consortium PI several R/U NIH grants. Her success is also reflected in the success of her mentees and co-mentees who have received K grants and gone on to have successful research careers, with some achieving a rank as high as Full Professor.
Biological age provides a synthesized measure of an individual’s physiological state and is a critical predictor of morbidity and mortality risk. While other omics have been used to predict biological age, limited research has assessed the relationship between biological aging and metabolomics, which may provide molecular drivers of the aging process. In this study, we developed a robust biological aging phenotype using ~30 clinical labs and electronic medical records on >30.000 people from the Mass General Brigham Biobank. We demonstrated the robustness of BioAge by applying this Lasso/Cox approach at four time points in the associated electronic medical records. BioAge had correlations of >0.98 with the other estimates, demonstrating that our prediction model is highly reproducible when created using different EMR data. We created biological aging predictive models for three omic data types (DNAm, metabolomics, proteomics) using individuals from the MGB-Biobank. For each omic model, the sample was split into training and testing sets and applied elastic net regression to select the omic variants to be retained in the final predictive model. For all three models, the training and testing correlations were greater than 0.90 and 0.84 respectively. We further created a multiomic-informed BioAge predictor by using the metabolomic and proteomic data to further reduce the error in the DNAm model. Using this approach, we created a final multiomic model with an RSME = 2.4 and a training and testing correlation of 0.97 and 0.92 respectively. We identified that the multiomic predicted biological age is associated with a significantly increased risk of adverse health outcomes, including all-cause mortality, cardiovascular disease, and cancer. Our results highlight the potential of multiomics for predicting biological age, establishing personalized anti-aging strategies, and elucidating the molecular mechanisms of aging that may ultimately promote healthy aging and longevity.
Global Metabolomics is a Powerful Tool for Diagnosing Rare Diseases Devoid of Clear Genetic Casualties.
Date: Saturday, November 4th, 2:15-4:15 PM
Location: Board no. PB3390