Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals

Abstract

Biological age (BA), derived from molecular and physiological measurements, has been proposed to better predict mortality and disease than chronological age (CA). In the present study, a computed estimate of BA was investigated longitudinally in 3,558 individuals using deep phenotyping, which encompassed a broad range of biological processes. The Klemera–Doubal algorithm was applied to longitudinal data consisting of genetic, clinical laboratory, metabolomic, and proteomic assays from individuals undergoing a wellness program. BA was elevated relative to CA in the presence of chronic diseases. We observed a significantly lower rate of change than the expected ~1 year/year (to which the estimation algorithm was constrained) in BA for individuals participating in a wellness program. This observation suggests that BA is modifiable and suggests that a lower BA relative to CA may be a sign of healthy aging. Measures of metabolic health, inflammation, and toxin bioaccumulation were strong predictors of BA. BA estimation from deep phenotyping was seen to change in the direction expected for both positive and negative health conditions. We believe BA represents a general and interpretable “metric for wellness” that may aid in monitoring aging over time.

Integration of molecular profiles in a longitudinal wellness profiling cohort

“…Several projects aiming to perform molecular profiling of various cohorts have been previously reported with different scopes, sizes and depths [4][5][6][7][8] . The primary objective of this study has been to analyze the global molecular omics profiles of one hundred individuals and to investigate the longitudinal stability and interconnections of such profiles, aiming for a deep, and comprehensive profiling.…”

“…A challenge for the field of precision medicine is defining a healthy population to estimate the normal range of clinical parameters across population strata 1 . The dawn of many new omics tools for analyzing clinical samples such as genomics, proteomics and metabolomics has opened up new possibilities to study both health and disease with high throughput along with high analytical precision and clinical accuracy 2 , 3 and projects with broad analytical breadth have been initiated including the Pioneer 100 Wellness Project 4 , multi-omics profiling of the blood and gut microbiota to explore weight gain and loss 5 , omics-based biological phenotyping to probe individualized aging 6 , 7 , precision medicine directed integration of whole genome sequencing combined with imaging 8 , and a generation of a genome atlas for the human plasma proteome 9 . In this way, personal omics profiles, composed of the genomics, transcriptomics, proteomics, metabolomics, and fecal microbiota, could be defined and used to monitor drug interventions.…”

An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging

“…Some prior work has attempted to validate the relevance of biological age metrics to health by relating them to measures of health or mortality (e.g. Holly et al, 2013;Earls et al, 2019), and we are investigating a similar approach in ongoing work. We note that biological age may in general serve as a proxy for longevity or healthspan.…”

“…Biological age refers to biological markers of the aging process, and may be accelerated or slowed in some individuals relative to their chronological age. Recent research has proposed computational aging clocks based on various biomarkers including metabolites, blood cell count and other routine lab tests (Earls et al, 2019;Momoshina, Kochetov et al 2018), DNA methylation (Fraga & Esteller, 2007;Horvath & Raj 2018;Bell et al 2019), gene expression in tissue (Momoshina, Volosnikova et al 2018) or blood (Harries et al, 2011;Lin et al, 2019), taxonomic composition of the gut microbiome (Galkin et al, 2020), among others. Aging clocks propose to use a signal derived from these biomarkers as a health-related metric for aging.…”

Longitudinal Saliva Omics Responses to Immune Perturbation: A Case Study

“…Major efforts have concentrated on evaluating disease risk from genomic information 1,2 , including direct to consumer platforms, like 23andMe 3 , as well as pharmacogenomic evaluations 4 . Implementing omics profiling in the clinic will require evaluation of patients over time, and the utility of such profiling has been evaluated in individual monitoring pioneered in the integrative Personal Omics Profiling (iPOP) study 5 , and expanded recently to include profiling using electronic health devices 6 , the Pioneer study 7 (which also incorporated behavioral coaching to improve clinical biomarkers based on participants' individual data), utilizing host-microbiome data in insulin resistant individuals in a study of weight gain 8 and in prediabetics 9 , investigating biological age 10,11 as well as monitoring of astronauts in the recent NASA twin study 12 .…”