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Lipidomic membrane as a molecular basis for precision nutrition in childhood obesity

Published online by Cambridge University Press:  07 March 2019

S. Arranz ,
I. Jauregibeitia ,
I. Tueros ,
J. Amezaga  and
M. Uriarte
S. Arranz
Affiliation:
AZTI, Food Research Division, Food and Health, Derio, Spain.
I. Jauregibeitia
Affiliation:
AZTI, Food Research Division, Food and Health, Derio, Spain.
I. Tueros
Affiliation:
AZTI, Food Research Division, Food and Health, Derio, Spain.
J. Amezaga
Affiliation:
AZTI, Food Research Division, Food and Health, Derio, Spain.
M. Uriarte
Affiliation:
AZTI, Food Research Division, Food and Health, Derio, Spain.
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Abstract

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Information
Proceedings of the Nutrition Society , Volume 78 , Issue OCE1: Winter Meeting, 4–5 December 2018, Optimal diet and lifestyle strategies for the management of cardio-metabolic risk , 2019 , E7
Copyright
Copyright © The Authors 2019 

Obesity is a metabolic chronic disease of high complexity and multifactorial origin. Diet and life style are the main causes of the disease but the effect of food in the organism is not easy to measure. The use of mature erythrocyte membranes as representative site for all other body tissues, is an established protocol for membrane-based molecular diagnostics (Reference Ferreri and Chatgilialoglu1−Reference Ghezzo, Visconti, Abruzzo, Bolotta and Ferreri3). The aim of this study was to compare the fatty acid (FA) composition of erythrocyte membranes from obese children compared with normal weight controls together with the evaluation of the nutritional and behavioral profile.

A prospective observational study was carried out on a cohort of 161 children (104 normal weight and 57 obese), from 6 to 16 years old, recruited at AZTI and Biocruces Bizkaia Health Research Institute. FA composition of mature erythrocyte membrane phospholipids was determined by gas chromatography-mass spectrometry. Nutritional data and life style were also obtained through validated questionnaires.

Obese children showed a mid-quality diet (7.8 points, measured with KIDMED index) characterized by a lower intake of fish, vegetables and nuts and higher intake of sugars and processed meat compared with optimal quality diet observed in normal weight children. Fatty acid profile of obese children showed an inflammatory profile, evidenced by the excess of omega 6 FA (mainly arachidonic acid), as well as a prevalence of saturated and trans FA (Fig. 1).

Fig. 1. Erythrocyte membrane fatty acids in obese and normal weight children. Data are expressed whit means ± standard error. SFA: saturated FA; MUFA: monounsaturated FA; PUFA: polyunsaturated FA. *p < 0.05 Data was compared with Analysis of Covariance test (ANCOVA) adjusting model by age, gender and food intake.

This study reveals an altered lipid profile of erythrocyte membranes of obese subjects evidencing the effect of diet, lifestyle and metabolism on the disease. A comprehensive membrane lipidomic profile of obesity will allow us to design customized food solutions to improve health status, which may include specific food products and supplements to prevent and control obesity.

Work funded by Basque Government (ELKARTEK 2017, RIS3 2016-2017, and Innovation Fund 2017). In consortium with Nutrition and Obesity Group (UPV/EHU), Biocruces Bizkaia Health Research Institute, Bizkaia Biophysics Foundation and the Basque Center for Applied Mathematics.

References

1.Ferreri, C, Chatgilialoglu, C. (2012) Expert Rev Mol Diagn 12(7):767–8010.1586/erm.12.73Google Scholar
2.del Genio, G, Ferreri, C, Marfella, R, Pournaras, D, le Roux, CW, et al. (2015) J Diabetes Metab 6:58210.4172/2155-6156.1000582Google Scholar
3.Ghezzo, A, Visconti, P, Abruzzo, PM, Bolotta, A, Ferreri, C, et al. (2013) PLoS One 8(6): e6641810.1371/journal.pone.0066418Google Scholar
Figure 0

Fig. 1. Erythrocyte membrane fatty acids in obese and normal weight children. Data are expressed whit means ± standard error. SFA: saturated FA; MUFA: monounsaturated FA; PUFA: polyunsaturated FA. *p < 0.05 Data was compared with Analysis of Covariance test (ANCOVA) adjusting model by age, gender and food intake.