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Related Concept Videos

Overview of Fatty Acid Metabolism01:28

Overview of Fatty Acid Metabolism

Lipids also are sources of energy that power cellular processes. Like carbohydrates, lipids are composed of carbon, hydrogen, and oxygen, but these atoms are arranged differently. Most lipids are nonpolar and hydrophobic. Major types include fats and oils, waxes, phospholipids, and steroids.
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Related Experiment Video

Updated: Jun 6, 2026

LC-MS Analysis of Human Platelets as a Platform for Studying Mitochondrial Metabolism
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Published on: April 4, 2016

Exploring Acylcarnitine Metabolism Using Reverse Metabolomics.

Allison J Keys1, Daniel J Wiener1, Sara M Pacini1

  • 1Virginia Tech, Department of Chemistry, Blacksburg, Virginia 24061, United States.

Analytical Chemistry
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Reverse metabolomics effectively identifies acylcarnitines (ACs) in human and mouse samples, revealing their potential as broad biomarkers for disease detection across all life stages.

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Last Updated: Jun 6, 2026

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Area of Science:

  • Metabolomics
  • Biochemistry
  • Biomarker Discovery

Background:

  • Untargeted mass spectrometry (MS) is crucial for human metabolism studies and identifying disease biomarkers.
  • Chemical structure annotation and cross-cohort validation of MS findings present significant challenges.
  • Reverse metabolomics offers a structure-driven solution by searching known spectra within large untargeted LC-MS/MS datasets.

Purpose of the Study:

  • To investigate acylcarnitine (AC) metabolism in humans and other animals using reverse metabolomics.
  • To identify and validate ACs as potential biomarkers for disease detection.

Main Methods:

  • Chemical synthesis of a library containing 76 acylcarnitines.
  • Searching the AC library against public untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) data.
  • Application of machine learning models to AC presence/absence data for health status prediction.

Main Results:

  • Acylcarnitines were predominantly detected in human and mouse samples, with ~90% of synthesized ACs found in both blood and fecal matrices.
  • Positive associations were identified between specific AC structures and disease states, highlighting their biomarker potential.
  • Machine learning models accurately predicted healthy versus unhealthy individuals based on AC profiles, though disease specificity was limited.

Conclusions:

  • Acylcarnitine profiles are robust indicators of metabolic status and hold significant potential as biomarkers for disease detection throughout the lifespan.
  • Further investigation into ACs beyond current clinical screening is warranted due to their diagnostic capabilities.