Published in

American Diabetes Association, Diabetes, 11(65), p. 3362-3368, 2016

DOI: 10.2337/db16-0315

Links

Tools

Export citation

Search in Google Scholar

Metabolomic profile of low-copy number carriers at the salivary α-amylase gene suggests a metabolic shift toward lipid-based energy production

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Low serum salivary amylase levels have been associated with a range of metabolic abnormalities, including obesity and insulin resistance. We recently suggested that a low copy number at the AMY1 gene, associated with lower enzyme levels, also increases susceptibility to obesity. To advance our understanding of the effect of AMY1 copy number variation on metabolism, we compared the metabolomic signatures of high– and low–copy number carriers. We analyzed, using mass spectrometry and nuclear magnetic resonance (NMR), the sera of healthy normal-weight women carrying either low–AMY1 copies (LAs: four or fewer copies; n = 50) or high–AMY1 copies (HAs: eight or more copies; n = 50). Best-fitting multivariate models (empirical P < 1 × 10−3) of mass spectrometry and NMR data were concordant in showing differences in lipid metabolism between the two groups. In particular, LA carriers showed lower levels of long- and medium-chain fatty acids, and higher levels of dicarboxylic fatty acids and 2-hydroxybutyrate (a known marker of glucose malabsorption). Taken together, these observations suggest increased metabolic reliance on fatty acids in LA carriers through β- and ω-oxidation and reduced cellular glucose uptake with consequent diversion of acetyl-CoA into ketogenesis. Our observations are in line with previously reported delayed glucose uptake in LA carriers after starch consumption. Further functional studies are needed to extrapolate from our findings to implications for biochemical pathways.