Natalia Palacios, Shilpa N. Bhupathiraju, Rachel S. Kelly, Jong Soo Lee, Jose M. Ordovas, Katherine L. Tucker
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引用次数: 0
Abstract
Introduction
Recent studies have implicated acetyl-l-carnitine as well as other acylcarnitines in depression. To our knowledge, no untargeted metabolomics studies have been conducted among US mainland Puerto Ricans.
Objectives
We conducted untargeted metabolomic profiling on plasma from 736 participants of the Boston Puerto Rican Health Study.
Methods
Using Weighted Gene Co-expression Network Analysis, we identified metabolite modules associated with depressive symptomatology, assessed via the Center for Epidemiologic Studies Depression scale. We identified metabolites contributing to these modules and assessed the relationship between these metabolites and depressive symptomatology.
Results
621 annotated metabolites clustered into eight metabolite modules, of which one, the acylcarnitine module, was significantly inversely associated with depressive symptomatology (β = − 27.7 (95% CI (− 54.5—0.8); p = 0.043). Several metabolite hub features in the acylcarnitine module were significantly associated with depressive symptomatology, after correction for multiple comparisons.
Conclusions
In this untargeted plasma metabolomics study among mainland Puerto Rican older adults, acylcarnitines, as a metabolite module were inversely associated with depressive symptomatology.
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.