Molecular profiles for mortality risk and longevity: a multiomics approach - PROJECT SUMMARY/ABSTRACT The US life expectancy experienced a generally upward trend over the past few decades; however, in recent years, it has seen a decline. This decline cannot be solely attributed to the excess mortality caused by COVID- 19 but also to an increased death rate from other leading causes (e.g., heart disease, cancer, and stroke). The biological mechanisms that underlie aging process and mortality in humans are multifactorial and remain poorly understood. Although multiple genetic variants have been linked to lifespan in model organisms, many of these genes do not exhibit significant variation in human populations. The heritability of human lifespan also appears to be relatively low. One possible explanation for lack of significant loci and low heritability is the complexity of survival as a phenotype, which involves multiple biological processes, environmental influences, and chance. The stochastic component of survival may dilute the genetic influence on the time-to-death phenotype. Consequently, a substantial mechanistic gap exists between genotype and mortality. The proposed K99/R00 project aims to utilize plasma metabolomic and proteomics profile to bridge this gap and comprehensively investigate the relationship between genes, proteins, metabolites, and mortality risk. To achieve this goal, Dr. Fenglei Wang will incorporate data from multiple sources, including the Nurses’ Health Study (NSH), NHSII, Health Professionals Follow-up Study (HPFS), Hispanic Community Health Study/Study of Latinos (HCHS/ SOL), VITamin D Omega3 TriAL (VITAL), UK Biobank (UKB), and eQTLGen. In Aim 1 (K99), Dr. Wang will determine genetic factors that influence a plasma metabolomic signature, previously developed by him, which has the potential to predict all-cause mortality. He will also evaluate the causal relationship between the metabolomic signature and four diseases that are major causes of death. In Aim 2 (R00), Dr. Wang will construct a plasma proteomic signature capable of predicting all-cause mortality and identify genetic factors influencing the proteomic signature. Then he will compare the influential genetic factors identified for the proteomic signature to those for the metabolomic signature. In Aim 3 (R00), Dr. Wang will conduct plasma proteomic profiling in a nested case-control study to examine the relationship between longitudinal changes in plasma metabolomic and proteomic profiles and healthy longevity. Findings from this project may improve our understanding of the molecular profiles associated with the aging process and mortality, and inform potential interventions for improving health outcomes and extending human lifespan. Dr. Wang has assembled a strong mentoring team to provide expertise in aging research and training in genetics, proteomics, and multi-omics integration. The new skills will complement his current expertise in nutritional epidemiology and metabolomic research. His outlined training plan will provide the necessary knowledge and skills for Dr. Wang to advance towards his career goal of becoming an independent researcher who specializes in the application of multi-omics approach to study nutrition and healthy aging.
