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

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 < 0.05) enriched pathways were identified that differentiated the LIFE and CON groups. Top pathways significantly (p < 2.63 × 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.

Fig. 1

PCA of metabolomics, proteomics, epigenomics data of LIFE and CON groups

Fig. 2

Supervised DIABLO analysis, a multi-block PLS-DA technique, evaluating the difference between LIFE and CON based on multi-omics profiles. A The factor scores plot. Blue = CON, orange = LIFE B Plot of the variability in each omics layer that is explained by the integrated factor scores. C 3D multi-block PLS-DA plot of metabolomics, proteomics, epigenomics data of LIFE (red dots) and CON (green dots) groups. Ellipsoids represent the confidence region

Fig. 3

2D multi-omics network of significant metabolites (yellow, p < 0.05), proteins (red, p < 0.05), and genes (green) that correspond with the significant methylation sites (p < 0.01) in circular layout

Fig. 4

Visualization of fatty acid degradation generated by Pathview. CPT1 and Acetyl-CoA C-acyltransferase were expressed more in LIFE group than CON group. Trans-Dodec-2-enoyl-CoA, (S)-3-Hydroxy-dodecanoyl-CoA, 3-Oxo-dodecanoyl-CoA were higher in LIFE group than in CON group. Genes in red and green were higher and lower in the LIFE group, respectively. Metabolites in yellow and blue were higher and lower in the LIFE group, respectively

Fig. 5

Visualization of fatty acid elongation generated by Pathview. Acetyl-CoA C-acyltransferase were expressed more in LIFE group than CON group. Trans-Dodec-2-enoyl-CoA, (S)-3-Hydroxy-dodecanoyl-CoA, 3-Oxo-dodecanoyl-CoA were higher in LIFE group than in CON group. Genes in red and green were higher and lower in the LIFE group, respectively. Metabolites in yellow and blue were higher and lower in the LIFE group, respectively

Fig. 6

Visualization of glutathione metabolism generated by Pathview. There were higher levels of GSH and lower levels of glutamate and oxoproline in LIFE group compared to CON group. Genes in red and green were higher and lower in the LIFE group, respectively. Metabolites in yellow and blue were higher and lower in the LIFE group, respectively

Fig. 7

Visualization of Parkinson disease generated by Pathview. There were higher levels of 3,4-dihyfroxyphenylalanine (DOPA), 3,4-dihydroxyphenylacetaldehyde (DOPAL) and lower levels of tyrosine, dopamine in the LIFE group. There were less expressions of CaMKII, Ubc6/7, 19S in LIFE group than in CON group. Genes in red and green were higher and lower in the LIFE group, respectively. Metabolites in yellow and blue were higher and lower in the LIFE group, respectively

Fig. 8

Visualization of Central Carbon Metabolism in Cancer generated by Pathview. There was lower RAS expression, higher LAT1 expression, lower levels of glucose, lactate, glutamine, glutamate, proline, oxoglutarate, alanine, asparagine, and higher level of succinate in the LIFE group compared to CON group. Genes in red and green were higher and lower in the LIFE group, respectively. Metabolites in yellow and blue were higher and lower in the LIFE group, respectively