Metabolomic analysis of serum alpha-tocopherol among men in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study

Abstract

Background/objectives: The role of vitamin E in chronic disease risk remains incompletely understood, particularly in an un-supplemented state, and evidence is sparse regarding the biological actions and pathways involved in its influence on health outcomes. Identifying vitamin-E-associated metabolites through agnostic metabolomics analyses can contribute to elucidating the specific associations and disease etiology. This study aims to investigate the association between circulating metabolites and serum α-tocopherol concentration in an un-supplemented state.

Subjects/methods: Metabolomic analysis of 4,294 male participants was conducted based on pre-supplementation fasting serum in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study. The associations between 1,791 known metabolites measured by ultra-high-performance LC-MS/GC-MS and HPLC-determined α-tocopherol concentration were estimated using multivariable linear regression. Differences in metabolite levels per unit difference in α-tocopherol concentration were calculated as standardized β-coefficients and standard errors.

Results: A total of 252 metabolites were associated with serum α-tocopherol at the Bonferroni-corrected p value (p < 2.79 × 10^-5). Most of these metabolites were of lipid and amino acid origin, with the respective subclasses of dicarboxylic fatty acids, and valine, leucine, and isoleucine metabolism, being highly represented. Among lipids, the strongest signals were observed for linoleoyl-arachidonoyl-glycerol (18:2/20:4)[2](β = 0.149; p = 8.65 × 10^-146) and sphingomyelin (D18:2/18:1) (β = 0.035; p = 1.36 × 10^-30). For amino acids, the strongest signals were aminoadipic acid (β = 0.021; p = 5.01 × 10^-13) and l-leucine (β = 0.007; p = 1.05 × 10^-12).

Conclusions: The large number of metabolites, particularly lipid and amino acid compounds associated with serum α-tocopherol provide leads regarding potential mechanisms through which vitamin E influences human health, including its role in cardiovascular disease and cancer.

Figure 1.

Gaussian graphical models of metabolites among associated chemical super-pathways and sub-pathways most related to serum alpha-tocopherol concentration in the study. Metabolites are drawn as hexagons, and the pairs with an absolute value of conditional correlation ≥0.2 are connected by a line. The colors represent the association directions of conditional correlations, with pink indicating positive conditional correlations, and blue indicating negative conditional correlations. Magnitudes of the conditional correlations are represented by line width (i.e., wider lines for stronger correlations). Metabolites with an “●” indicates an inverse association with serum α-tocopherol and “✶” indicates that the association met the Bonferroni correction threshold p<2.79 × 10⁻⁵. Enzyme on the pathway between metabolites are indicated by “*”.