PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 40886004

Multi-omics signature of healthy versus unhealthy lifestyles reveals associations with diseases.

Grace Fu·Blake R Rushing·Lee Graves·David C Nieman·Matteo Pellegrini·Matthew Soldano·Michael J Thompson·Camila A Sakaguchi·Wimal Pathmasiri·Susan J Sumner
View original source ↗Export metadata ↓

Abstract

This multi-omics cross-sectional study investigated differences in metabolomics, proteomics, and epigenomics profiles between two groups of adults matched for age but differing in lifestyle factors such as body composition, diet, and physical activity patterns. Data from prior studies were utilized for a comprehensive integrative analysis. The study included 52 participants in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). Using multi-omics integration software (OmicsNet and Pathview), 96 significantly (p&#x2009;<&#x2009;0.05) enriched pathways were identified that differentiated the LIFE and CON groups. Top pathways significantly (p&#x2009;<&#x2009;2.63&#x2009;&#xd7;&#x2009;10-5) influenced by group status included fatty acid degradation, fatty acid elongation, glutathione metabolism, Parkinson disease, and central carbon metabolism in cancer. This study identified a distinct metabolic signature comprised of metabolites, proteins, and gene methylation sites associated with a healthy lifestyle. These findings provide unique, but complementary, results to previous single-omics analyses using metabolomics and proteomics procedures which showed that the LIFE group exhibited lower plasma bile acid levels, higher levels of beneficial fatty acids, reduced innate immune activation, enhanced lipoprotein metabolism, and increased HDL remodeling. The current multi-omics analysis builds on these previous results by providing a more holistic view of how metabolites, proteins, and methylation sites associated with a healthy lifestyle, providing a larger, more comprehensive list of altered pathways. Additionally, the integrated analysis revealed connections between lifestyle factors and conditions such as cancer and insulin resistance beyond what identified in the single-omics approaches, highlighting the broader metabolic impact of lifestyle on health. Overall, the signatures identified by this multi-omics approach provide a basis for developing more translational biomarkers, such as those that defined the cancer and insulin resistance pathways that can be used to assess one's state of health and provide guidance on behavior modifications that should be taken to lower disease risk.

Explore related subjects

HumansMaleFemaleMetabolomicsLife StyleMiddle AgedAdultProteomicsCross-Sectional StudiesHealthy LifestyleEpigenomicsMetabolomeMultiomicsJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Grace Fu, Blake R Rushing, Lee Graves, David C Nieman, Matteo Pellegrini, Matthew Soldano, Michael J Thompson, Camila A Sakaguchi, Wimal Pathmasiri, Susan J Sumner. 2025-08-30. Multi-omics signature of healthy versus unhealthy lifestyles reveals associations with diseases.. https://doi.org/10.1186/s40246-025-00817-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗
PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗
PubMed2027

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗