TY - JOUR
T1 - Nutrient patterns and their food sources in an international study setting
T2 - Report from the EPIC study
AU - Moskal, Aurelie
AU - Pisa, Pedro T.
AU - Ferrari, Pietro
AU - Byrnes, Graham
AU - Freisling, Heinz
AU - Boutron-Ruault, Marie Christine
AU - Cadeau, Claire
AU - Nailler, Laura
AU - Wendt, Andrea
AU - Kühn, Tilman
AU - Boeing, Heiner
AU - Buijsse, Brian
AU - Tjønneland, Anne
AU - Halkjær, Jytte
AU - Dahm, Christina C.
AU - Chiuve, Stephanie E.
AU - Quirós, Jose R.
AU - Buckland, Genevieve
AU - Molina-Montes, Esther
AU - Amiano, Pilar
AU - Huerta Castaño, José M.
AU - Gurrea, Aurelio Barricarte
AU - Khaw, Kay Tee
AU - Lentjes, Marleen A.
AU - Key, Timothy J.
AU - Romaguera, Dora
AU - Vergnaud, Anne Claire
AU - Trichopoulou, Antonia
AU - Bamia, Christina
AU - Orfanos, Philippos
AU - Palli, Domenico
AU - Pala, Valeria
AU - Tumino, Rosario
AU - Sacerdote, Carlotta
AU - De Magistris, Maria Santucci
AU - Bueno-de-Mesquita, H. Bas
AU - Ocké, Marga C.
AU - Beulens, Joline W.J.
AU - Ericson, Ulrika
AU - Drake, Isabel
AU - Nilsson, Lena M.
AU - Winkvist, Anna
AU - Weiderpass, Elisabete
AU - Hjartåker, Anette
AU - Riboli, Elio
AU - Slimani, Nadia
PY - 2014/6/5
Y1 - 2014/6/5
N2 - Background: Compared to food patterns, nutrient patterns have been rarely used particularly at international level. We studied, in the context of a multi-center study with heterogeneous data, the methodological challenges regarding pattern analyses. Methodology/Principal Findings: We identified nutrient patterns from food frequency questionnaires (FFQ) in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study and used 24-hour dietary recall (24-HDR) data to validate and describe the nutrient patterns and their related food sources. Associations between lifestyle factors and the nutrient patterns were also examined. Principal component analysis (PCA) was applied on 23 nutrients derived from country-specific FFQ combining data from all EPIC centers (N= 477,312). Harmonized 24-HDRs available for a representative sample of the EPIC populations (N= 34,436) provided accurate mean group estimates of nutrients and foods by quintiles of pattern scores, presented graphically. An overall PCA combining all data captured a good proportion of the variance explained in each EPIC center. Four nutrient patterns were identified explaining 67% of the total variance: Principle component (PC) 1 was characterized by a high contribution of nutrients from plant food sources and a low contribution of nutrients from animal food sources; PC2 by a high contribution ofmicro-nutrients and proteins; PC3 was characterized by polyunsaturated fatty acids and vitamin D; PC4 was characterized by calcium, proteins, riboflavin, and phosphorus. The nutrientswith high loadings on a particular pattern as derived from country-specific FFQ also showed high deviations in their mean EPIC intakes by quintiles of pattern scores when estimated from 24-HDR. Center and energy intake explained most of the variability in pattern scores. Conclusion/Significance: The use of 24-HDR enabled internal validation and facilitated the interpretation of the nutrient patterns derived from FFQs in term of food sources. These outcomes open research opportunities and perspectives of using nutrient patterns in future studies particularly at international level.
AB - Background: Compared to food patterns, nutrient patterns have been rarely used particularly at international level. We studied, in the context of a multi-center study with heterogeneous data, the methodological challenges regarding pattern analyses. Methodology/Principal Findings: We identified nutrient patterns from food frequency questionnaires (FFQ) in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study and used 24-hour dietary recall (24-HDR) data to validate and describe the nutrient patterns and their related food sources. Associations between lifestyle factors and the nutrient patterns were also examined. Principal component analysis (PCA) was applied on 23 nutrients derived from country-specific FFQ combining data from all EPIC centers (N= 477,312). Harmonized 24-HDRs available for a representative sample of the EPIC populations (N= 34,436) provided accurate mean group estimates of nutrients and foods by quintiles of pattern scores, presented graphically. An overall PCA combining all data captured a good proportion of the variance explained in each EPIC center. Four nutrient patterns were identified explaining 67% of the total variance: Principle component (PC) 1 was characterized by a high contribution of nutrients from plant food sources and a low contribution of nutrients from animal food sources; PC2 by a high contribution ofmicro-nutrients and proteins; PC3 was characterized by polyunsaturated fatty acids and vitamin D; PC4 was characterized by calcium, proteins, riboflavin, and phosphorus. The nutrientswith high loadings on a particular pattern as derived from country-specific FFQ also showed high deviations in their mean EPIC intakes by quintiles of pattern scores when estimated from 24-HDR. Center and energy intake explained most of the variability in pattern scores. Conclusion/Significance: The use of 24-HDR enabled internal validation and facilitated the interpretation of the nutrient patterns derived from FFQs in term of food sources. These outcomes open research opportunities and perspectives of using nutrient patterns in future studies particularly at international level.
UR - http://www.scopus.com/inward/record.url?scp=84902449229&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0098647
DO - 10.1371/journal.pone.0098647
M3 - Article
SN - 1932-6203
VL - 9
JO - PLoS ONE
JF - PLoS ONE
IS - 6
M1 - e98647
ER -
