Integrative Personal Omics Profiles during Periods of Weight Gain and Loss

Abstract

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Integrative omics for health and disease

“…A recent study monitoring various omics profiles across 23 individuals identified inflammatory signatures during weight gain, and found that certain metabolic pathways did not return to baseline after subsequent weight loss 52 . This analysis highlights the extent of similarities in longitudinal omics profiles across individuals, as well as individual-specific signatures at steady state and under experimental perturbations.…”

iNetModels 2.0: an interactive visualization and database of multi-omics data

“…The data included in the MOBN are obtained from three independent studies with large-scale personalized longitudinal multi-omics data: (1) the Swedish SciLifeLab SCAPIS Wellness Profiling project which collected multi-omics data from 98 different subjects during 6 visits in 2 years (Bergstrom, et al, 2015), (2) the P100 Wellness study (Price, et al, 2017) where 108 subjects are followed for 9 month and profiled with different omics data (accessible phs001363.v1.p1from dbGaP) and (3) the iPOP diet perturbation study which employed multi-omics data to describe the systematic dynamics in human with weight gain and weight loss during 3 visits within 1 year (Piening, et al, 2018). All of these studies provided information of the anthropometrics, clinical chemistry, plasma proteome, plasma metabolome and gut microbiome for each individual involved in the study, and thus, the consensus of these multiomics data is included in MOBN.…”

<b><i>Lifestyle Genomics</i></b>: Addressing the Multifactorial Nature of Personalized Health

“…Moreover, 'OMIC technologies can be used for the discovery of new therapeutic targets and biomarkers to either predict disease risk or monitor response to lifestyle interventions [43,44]. For example, the integrative Personal Omics Profile (iPOP) study was a longitudinal study conducted in ∼100 individuals that monitored genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from each individual during a 14-month period [45]. The results of the iPOP study revealed extensive molecular changes during different health states [45].…”

“…For example, the integrative Personal Omics Profile (iPOP) study was a longitudinal study conducted in ∼100 individuals that monitored genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from each individual during a 14-month period [45]. The results of the iPOP study revealed extensive molecular changes during different health states [45]. Similarly, an integrative 'OMIC analysis used transcriptomics, peptidomics, and fatty acid profiling to demonstrate that obese subcutaneous adipose tissue has a distinct fatty acid signature compared to lean subcutaneous adipose tissue [46].…”