Some Differences in Nutritional Biomarkers are Detected Between Consumers and Nonconsumers of Organic Foods: Findings from the BioNutriNet Project.

biomarkers carotenoids epidemiological study fatty acids minerals organic food vitamins

Journal

Current developments in nutrition
ISSN: 2475-2991
Titre abrégé: Curr Dev Nutr
Pays: United States
ID NLM: 101717957

Informations de publication

Date de publication:
Mar 2019
Historique:
received: 19 06 2018
revised: 27 09 2018
accepted: 14 11 2018
entrez: 8 3 2019
pubmed: 8 3 2019
medline: 8 3 2019
Statut: epublish

Résumé

Meta-analyses have compared the nutrient content of both organic and nonorganic foods. However, the impacts of such variations on human nutritional biomarkers still need to be assessed. In a nested clinical study from the NutriNet-Santé study, we aimed to compare the nutritional status of "organic" and "nonorganic" food consumers matched on a propensity score. Based on self-reported organic food consumption assessed through a food frequency questionnaire (FFQ), 150 low and 150 high organic food consumers were selected with <10% or >50% of organic food in their diet, respectively (expressed as the proportion of organic food in the whole diet in g/d). Participants were matched using a propensity score derived from socio-demographic, food, and health variables. Fasting plasma samples were analyzed using acknowledged laboratory methods for measurements of iron status, magnesium, copper, cadmium, carotenoids, vitamins A and E, and fatty acids. We found significant differences between low and high organic food consumers with similar dietary patterns, with respect to plasma concentrations of magnesium, fat-soluble micronutrients (α-carotene, β-carotene, lutein, and zeaxanthin), fatty acids (linoleic, palmitoleic, γ-linolenic, and docosapentanoeic acids), and some fatty acid desaturase indexes. No differences between the 2 groups were detected for plasma concentrations of iron, copper, cadmium, lycopene, β-cryptoxanthin, or vitamins A and E. If confirmed by other studies, our data suggest that a high consumption of organic foods, compared with very low consumption, modulates to some extent, the nutritional status of individuals with similar dietary patterns. Further research including prospective cohort studies is needed to evaluate the clinical relevance of such differences.

Sections du résumé

BACKGROUND BACKGROUND
Meta-analyses have compared the nutrient content of both organic and nonorganic foods. However, the impacts of such variations on human nutritional biomarkers still need to be assessed.
OBJECTIVE OBJECTIVE
In a nested clinical study from the NutriNet-Santé study, we aimed to compare the nutritional status of "organic" and "nonorganic" food consumers matched on a propensity score.
METHODS METHODS
Based on self-reported organic food consumption assessed through a food frequency questionnaire (FFQ), 150 low and 150 high organic food consumers were selected with <10% or >50% of organic food in their diet, respectively (expressed as the proportion of organic food in the whole diet in g/d). Participants were matched using a propensity score derived from socio-demographic, food, and health variables. Fasting plasma samples were analyzed using acknowledged laboratory methods for measurements of iron status, magnesium, copper, cadmium, carotenoids, vitamins A and E, and fatty acids.
RESULTS RESULTS
We found significant differences between low and high organic food consumers with similar dietary patterns, with respect to plasma concentrations of magnesium, fat-soluble micronutrients (α-carotene, β-carotene, lutein, and zeaxanthin), fatty acids (linoleic, palmitoleic, γ-linolenic, and docosapentanoeic acids), and some fatty acid desaturase indexes. No differences between the 2 groups were detected for plasma concentrations of iron, copper, cadmium, lycopene, β-cryptoxanthin, or vitamins A and E.
CONCLUSION CONCLUSIONS
If confirmed by other studies, our data suggest that a high consumption of organic foods, compared with very low consumption, modulates to some extent, the nutritional status of individuals with similar dietary patterns. Further research including prospective cohort studies is needed to evaluate the clinical relevance of such differences.

Identifiants

pubmed: 30842992
doi: 10.1093/cdn/nzy090
pii: nzy090
pmc: PMC6397420
doi:

Types de publication

Journal Article

Langues

eng

Pagination

nzy090

Références

Ann N Y Acad Sci. 2002 Jun;967:183-95
pubmed: 12079847
Environ Health Perspect. 2003 Mar;111(3):377-82
pubmed: 12611667
Appetite. 2003 Apr;40(2):109-17
pubmed: 12781160
J Agric Food Chem. 2003 Sep 10;51(19):5671-6
pubmed: 12952417
Clin Chem Lab Med. 2003 Aug;41(8):979-94
pubmed: 12964802
Environ Health Perspect. 2006 Feb;114(2):260-3
pubmed: 16451864
Am J Clin Nutr. 2006 Aug;84(2):442-8
pubmed: 16895896
J Gerontol A Biol Sci Med Sci. 2007 Mar;62(3):308-16
pubmed: 17389729
Nutr Metab Cardiovasc Dis. 2008 Jul;18(6):436-40
pubmed: 18068341
Prog Lipid Res. 2008 May;47(3):172-87
pubmed: 18328267
BMC Public Health. 2010 May 11;10:242
pubmed: 20459807
Am J Clin Nutr. 2011 Jan;93(1):127-42
pubmed: 20980488
J Sci Food Agric. 2012 Nov;92(14):2774-81
pubmed: 22430502
Ann Intern Med. 2012 Sep 4;157(5):348-66
pubmed: 22944875
Food Funct. 2013 Feb 26;4(3):409-19
pubmed: 23192634
J Agric Food Chem. 2013 Feb 6;61(5):1017-29
pubmed: 23323826
PLoS One. 2013 Oct 04;8(10):e76349
pubmed: 24124548
PLoS One. 2013 Oct 18;8(10):e76998
pubmed: 24204721
Environ Res. 2014 Jul;132:105-11
pubmed: 24769399
Br J Nutr. 2014 Sep 14;112(5):794-811
pubmed: 24968103
J Trace Elem Med Biol. 2015 Jan;29:296-302
pubmed: 25193691
Environ Health Perspect. 2015 May;123(5):475-83
pubmed: 25650532
Environ Health Perspect. 2015 Oct;123(10):1086-93
pubmed: 25861095
Biol Trace Elem Res. 2016 Mar;170(1):33-42
pubmed: 26208810
Nutrients. 2015 Oct 21;7(10):8615-32
pubmed: 26506372
Br J Nutr. 2016 Mar 28;115(6):1043-60
pubmed: 26878105
Br J Nutr. 2016 Mar 28;115(6):994-1011
pubmed: 26878675
Am J Clin Nutr. 2016 Aug;104(2):247-58
pubmed: 27413128
Public Health Nutr. 2017 Mar;20(4):638-648
pubmed: 27731291
Br J Nutr. 2017 Jan;117(2):325-334
pubmed: 28166859
Food Chem Toxicol. 2017 Aug;106(Pt A):430-445
pubmed: 28602857
Eur J Nutr. 2018 Oct;57(7):2477-2488
pubmed: 28770334
Sci Rep. 2017 Oct 6;7(1):12763
pubmed: 28986547
Environ Health. 2017 Oct 27;16(1):111
pubmed: 29073935
Crit Rev Food Sci Nutr. 2017 Nov 30;:1-11
pubmed: 29190113
J Expo Sci Environ Epidemiol. 2018 Sep 5;:null
pubmed: 30185942

Auteurs

Julia Baudry (J)

Nutritional Epidemiology Research team (EREN), Paris 13 University, Inserm (U1153), Inra (U1125), Sorbonne Paris City Epidemiology and Statistics Center, Cnam, COMUE Sorbonne-Paris- City, Bobigny, France.

Véronique Ducros (V)

Biochemistry department, Grenoble-Alpes Hospital, Grenoble cedex 9, France.

Nathalie Druesne-Pecollo (N)

Nutritional Epidemiology Research team (EREN), Paris 13 University, Inserm (U1153), Inra (U1125), Sorbonne Paris City Epidemiology and Statistics Center, Cnam, COMUE Sorbonne-Paris- City, Bobigny, France.

Pilar Galan (P)

Nutritional Epidemiology Research team (EREN), Paris 13 University, Inserm (U1153), Inra (U1125), Sorbonne Paris City Epidemiology and Statistics Center, Cnam, COMUE Sorbonne-Paris- City, Bobigny, France.

Serge Hercberg (S)

Nutritional Epidemiology Research team (EREN), Paris 13 University, Inserm (U1153), Inra (U1125), Sorbonne Paris City Epidemiology and Statistics Center, Cnam, COMUE Sorbonne-Paris- City, Bobigny, France.
Public Health Department, Avicenne hospital, AP-HP, Bobigny, France.

Laurent Debrauwer (L)

Toxalim, Université de Toulouse University, INRA, ENVT, INP-Purpan, UPS, Toulouse, France.

Marie Josèphe Amiot (MJ)

MOISA, Université Montpellier University, CIRAD, CIHEAM-IAMM, INRA, Montpellier SupAgro, Montpellier, France.

Denis Lairon (D)

Aix Marseille University, INSERM, INRA, Marseille, France.

Emmanuelle Kesse-Guyot (E)

Nutritional Epidemiology Research team (EREN), Paris 13 University, Inserm (U1153), Inra (U1125), Sorbonne Paris City Epidemiology and Statistics Center, Cnam, COMUE Sorbonne-Paris- City, Bobigny, France.

Classifications MeSH