Comparison of two methods for identifying dietary patterns associated with obesity in preschool children: the genesis study

Comparison of two methods for identifying dietary patterns associated with obesity in preschool children: the genesis study


Play all audios:

Loading...

ABSTRACT BACKGROUND/OBJECTIVES: The aim of this work was to identify dietary patterns based on reduced rank regression (RRR) and principal component analysis (PCA) and to evaluate the


association of these patterns with the prevalence of childhood obesity. SUBJECTS/METHODS: A sample of 2317 toddlers and preschoolers from Greece (Growth, Exercise and Nutrition


Epidemiological Study In preSchoolers) was used. In total, 12 food groups were used as predictors of RRR and PCA. Nutrients such as total fat, simple carbohydrate and fiber intake were used


as response variables to apply RRR. RESULTS: One factor/pattern was retained from RRR and PCA in order to ensure the comparability of the methods. The pattern derived from PCA was mainly


characterized by consumption of fruits, vegetables, legumes, fish and seafood, grains and oils. This pattern explained 12.5% of the total variation in food groups. On the other hand, the


pattern extracted from RRR was mainly characterized by reduced consumption of fruits, vegetables and legumes, and by increased consumption of sweets and red meat. The pattern derived from


RRR explained 8.2% of the total variation in food groups. Simple and multiple logistic regression revealed that the pattern extracted from RRR is significantly associated with the prevalence


of childhood obesity (OR=1.11, 95% CI: 1.00–1.28 for each unit increase of dietary pattern) as opposed to the pattern derived from PCA. CONCLUSIONS: The preferable technique to derive


dietary patterns related to childhood obesity seems to be RRR compared with PCA. Access through your institution Buy or subscribe This is a preview of subscription content, access via your


institution ACCESS OPTIONS Access through your institution Subscribe to this journal Receive 12 print issues and online access $259.00 per year only $21.58 per issue Learn more Buy this


article * Purchase on SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in


* Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS USE OF A HYBRID METHOD TO DERIVE DIETARY PATTERNS IN 7 YEARS OLDS


WITH EXPLANATORY ABILITY OF BODY MASS INDEX AT AGE 10 Article 05 March 2021 THE ASSOCIATION BETWEEN DIETARY PATTERNS DERIVED BY THREE STATISTICAL METHODS AND TYPE 2 DIABETES RISK: YAHS-TAMYZ


AND SHAHEDIEH COHORT STUDIES Article Open access 09 January 2023 FOOD CONSUMPTION PATTERNS RELATED TO EXCESS WEIGHT AND OBESITY IN SPANISH PRESCHOOLERS Article 04 January 2023 REFERENCES *


Anderson DR, Field DE, Collins PA, Lorch EP, Nathan JG (1985). Estimates of young children's time with television: a methodological comparison of parent reports with time-lapse video


home observation. _Child Dev_ 56, 1345–1357. Article  CAS  Google Scholar  * Aranceta J, Perez-Rodrigo C, Ribas L, Serra-Majem L (2003). Sociodemographic and lifestyle determinants of food


patterns in Spanish children and adolescents: the enKid study. _Eur J Clin Nutr_ 57 (Suppl 1), S40–S44. Article  Google Scholar  * Ballew C, Kuester S, Serdula M, Bowman B, Dietz W (2000).


Nutrient intakes and dietary patterns of young children by dietary fat intakes. _J Pediatr_ 136, 181–187. Article  CAS  Google Scholar  * Chagnon YC, Rankinen T, Snyder EE, Weisnagel SJ,


Perusse L, Bouchard C (2003). The human obesity gene map: the 2002 update. _Obes Res_ 11, 313–367. Article  CAS  Google Scholar  * DiBello JR, Kraft P, McGarvey ST, Goldberg R, Campos H,


Baylin A (2008). Comparison of 3 methods for identifying dietary patterns associated with risk of disease. _Am J Epidemiol_ 168, 1433–1443. Article  Google Scholar  * Dietz WH (2006).


Sugar-sweetened beverages, milk intake, and obesity in children and adolescents. _J Pediatr_ 148, 152–154. Article  Google Scholar  * Division of Public Health Surveillance Informatics.


Centers for Disease Control Prevention (CDC) (2004) EpiInfo-Database and Statistics Software for Public Health Professionals. * Fung TT, McCullough ML, Newby PK, Manson JE, Meigs JB, Rifai N


_et al_. (2005). Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction. _Am J Clin Nutr_ 82, 163–173. Article  CAS  Google Scholar  * Hedley


AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM (2004). Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. _JAMA_ 291, 2847–2850. Article


  CAS  Google Scholar  * Hill JO, Wyatt HR, Reed GW, Peters JC (2003). Obesity and the environment: where do we go from here? _Science_ 299, 853–855. Article  CAS  Google Scholar  * Hoffmann


K, Boeing H, Boffetta P, Nagel G, Orfanos P, Ferrari P _et al_. (2005). Comparison of two statistical approaches to predict all-cause mortality by dietary patterns in German elderly


subjects. _Br J Nutr_ 93, 709–716. Article  CAS  Google Scholar  * Hoffmann K, Schulze MB, Schienkiewitz A, Nothlings U, Boeing H (2004). Application of a new statistical method to derive


dietary patterns in nutritional epidemiology. _Am J Epidemiol_ 159, 935–944. Article  Google Scholar  * Hoffmann K, Zyriax BC, Boeing H, Windler E (2004). A dietary pattern derived to


explain biomarker variation is strongly associated with the risk of coronary artery disease. _Am J Clin Nutr_ 80, 633–640. Article  CAS  Google Scholar  * Hu FB (2002). Dietary pattern


analysis: a new direction in nutritional epidemiology. _Curr Opin Lipidol_ 13, 3–9. Article  CAS  Google Scholar  * Jacques PF, Tucker KL (2001). Are dietary patterns useful for


understanding the role of diet in chronic disease? _Am J Clin Nutr_ 73, 1–2. Article  CAS  Google Scholar  * Kant AK (1996). Indexes of overall diet quality: a review. _J Am Diet Assoc_ 96,


785–791. Article  CAS  Google Scholar  * Kaufman F (2005). Back away from the soda! Nearly half of all children between the ages of 6 and 11 drink sweetened sodas. Are these sugar-laden


beverages partly to blame for the obesity epidemic? _Diabetes Forecast_ 58, 42–45. PubMed  Google Scholar  * Lakkakula AP, Zanovec M, Silverman L, Murphy E, Tuuri G (2008). Black children


with high preferences for fruits and vegetables are at less risk of being at risk of overweight or overweight. _J Am Diet Assoc_ 108, 1912–1915. Article  Google Scholar  * Lim S, Zoellner


JM, Lee JM, Burt BA, Sandretto AM, Sohn W _et al_. (2009). Obesity and sugar-sweetened beverages in African-American preschool children: a longitudinal study. _Obesity (Silver Spring)_ 17,


1268–1268. Article  Google Scholar  * Linardakis M, Sarri K, Pateraki MS, Sbokos M, Kafatos A (2008). Sugar-added beverages consumption among kindergarten children of Crete: effects on


nutritional status and risk of obesity. _BMC Public Health_ 8, 279. Article  Google Scholar  * Lobstein T, Jackson-Leach R (2006). Estimated burden of paediatric obesity and co-morbidities


in Europe. Part 2. Numbers of children with indicators of obesity-related disease. _Int J Pediatr Obes_ 1, 33–41. Article  Google Scholar  * Manios Y (2006). Design and descriptive results


of the Growth, Exercise and Nutrition Epidemiological Study In preSchoolers: the GENESIS study. _BMC Public Health_ 6, 32. Article  Google Scholar  * Manios Y, Magkos F, Christakis G,


Kafatos AG (2005). Twenty-year dynamics in adiposity and blood lipids of Greek children: regional differences in Crete persist. _Acta Paediatr_ 94, 859–865. Article  Google Scholar  *


Nanchahal K, Morris JN, Sullivan LM, Wilson PW (2005). Coronary heart disease risk in men and the epidemic of overweight and obesity. _Int J Obes (Lond)_ 29, 317–323. Article  CAS  Google


Scholar  * National Center for Health Statistics and The National Center for Chronic Disease Prevention Health Promotion (2000). CDC Growth Charts. * Nettleton JA, Steffen LM, Schulze MB,


Jenny NS, Barr RG, Bertoni AG _et al_. (2007). Associations between markers of subclinical atherosclerosis and dietary patterns derived by principal components analysis and reduced rank


regression in the Multi-Ethnic Study of Atherosclerosis (MESA). _Am J Clin Nutr_ 85, 1615–1625. Article  CAS  Google Scholar  * Nicklas TA, Farris RP, Johnson CC, Webber LS, Berenson GS


(1990). Food sources of nutrients: a tool for dietary management and health. The Bogalusa Heart Study. 1973–1983. Tulane Center for Cardiovascular Health: New Orleans, LA. * Nicklas TA,


Webber LS, Srinivasan SR, Berenson GS (1993). Secular trends in dietary intakes and cardiovascular risk factors of 10-y-old children: the Bogalusa Heart Study (1973-1988). _Am J Clin Nutr_


57, 930–937. Article  CAS  Google Scholar  * North K, Emmett P (2000). Multivariate analysis of diet among three-year-old children and associations with socio-demographic characteristics.


The Avon Longitudinal Study of Pregnancy and Childhood (ALSPAC) Study Team.. _Eur J Clin Nutr_ 54, 73–80. Article  CAS  Google Scholar  * Northstone K, Emmett P (2005). Multivariate analysis


of diet in children at four and seven years of age and associations with socio-demographic characteristics. _Eur J Clin Nutr_ 59, 751–760. Article  CAS  Google Scholar  * Schulze MB,


Hoffmann K (2006). Methodological approaches to study dietary patterns in relation to risk of coronary heart disease and stroke. _Br J Nutr_ 95, 860–869. Article  CAS  Google Scholar  *


Schulze MB, Hoffmann K, Kroke A, Boeing H (2003). ‘An approach to construct simplified measures of dietary patterns from exploratory factor analysis. _Br J Nutr_ 89, 409–419. Article  CAS 


Google Scholar  * Slyper AH (2004). The pediatric obesity epidemic: causes and controversies. _J Clin Endocrinol Metab_ 89, 2540–2547. Article  CAS  Google Scholar  * Tucker KL (2007).


Assessment of usual dietary intake in population studies of gene-diet interaction. _Nutr Metab Cardiovasc Dis_ 17, 74–81. Article  CAS  Google Scholar  * Waijers PM, Feskens EJ, Ocke MC


(2007). A critical review of predefined diet quality scores. _Br J Nutr_ 97, 219–231. Article  CAS  Google Scholar  * Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH (1997). Predicting


obesity in young adulthood from childhood and parental obesity. _N Engl J Med_ 337, 869–873. Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS We thank Vivian Detopoulou,


Anastasia Anastasiadou, Christine Kortsalioudaki, Margarita Bartsota, Thodoris Liarigkovinos, Elina Dimitropoulou, Nikoleta Vidra, Theodoros Athanasoulis, Pari Christofidou, Lilia Charila,


Sofia Tzitzirika and Christos Vassilopoulos for their contribution to the completion of the study. The GENESIS study was supported by a Research Grant from Friesland Foods Hellas. AUTHOR


INFORMATION AUTHORS AND AFFILIATIONS * Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Kallithea, Greece Y Manios, G Kourlaba, E Grammatikaki, O Androutsos 


& E Ioannou * First Department of Pediatrics, Athens University, Ag. Sophia Children Hospital, Thivon & Levadias, Athens, Greece E Roma-Giannikou Authors * Y Manios View author


publications You can also search for this author inPubMed Google Scholar * G Kourlaba View author publications You can also search for this author inPubMed Google Scholar * E Grammatikaki


View author publications You can also search for this author inPubMed Google Scholar * O Androutsos View author publications You can also search for this author inPubMed Google Scholar * E


Ioannou View author publications You can also search for this author inPubMed Google Scholar * E Roma-Giannikou View author publications You can also search for this author inPubMed Google


Scholar CORRESPONDING AUTHOR Correspondence to Y Manios. ETHICS DECLARATIONS COMPETING INTERESTS MY works as a part-time scientific consultant for Friesland Foods Hellas. The remaining


authors declare no conflict of interest. The study sponsor had no interference in the study design, data collection or writing of the manuscript. RIGHTS AND PERMISSIONS Reprints and


permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Manios, Y., Kourlaba, G., Grammatikaki, E. _et al._ Comparison of two methods for identifying dietary patterns associated with obesity in


preschool children: the GENESIS study. _Eur J Clin Nutr_ 64, 1407–1414 (2010). https://doi.org/10.1038/ejcn.2010.168 Download citation * Received: 06 February 2010 * Revised: 30 April 2010 *


Accepted: 01 May 2010 * Published: 01 September 2010 * Issue Date: December 2010 * DOI: https://doi.org/10.1038/ejcn.2010.168 SHARE THIS ARTICLE Anyone you share the following link with


will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt


content-sharing initiative KEYWORDS * reduced rank regression * principal component analysis * dietary pattern analysis * young children