Literature DB >> 11781753

Food intake patterns and body mass index in observational studies.

P Togo1, M Osler, T I Sørensen, B L Heitmann.   

Abstract

OBJECTIVE: To review studies of patterns of food intake, as assessed by diet index, factor analysis or cluster analysis, and their associations with body mass index or obesity (BMI/Ob).
DESIGN: Systematic literature review MEDLINE search with crosscheck of references. STUDIES: Thirty observational studies relating food intake patterns to anthropometric information were identified and reviewed. Food intake patterns were defined using a diet index, factor or cluster analysis in 12, nine and nine studies, respectively. Measures of body mass were made concurrently with the diet assessment in all studies, and only in a few cases were the primary outcomes related to BMI/Ob.
RESULTS: The food intake patterns identified could, in most factor or cluster analysis studies, be categorised as: (a) meat, fatty, sweet or energy dense foods; (b) vegetables, fruit, whole grain and low-fat foods; or (c) by high alcohol consumption. The diet indexes were designed to capture a high diversity and/or food combinations matching the recommendations. The relationships with BMI/Ob were inconsistent-ten studies found that intake patterns, which we categorised as fatty, sweet or energy dense were positively associated with BMI/Ob, while similar patterns in four other studies were negatively associated with BMI. The significant associations between diet index score and BMI/Ob were consistently negative, while the associations between factor scores or cluster membership and BMI/Ob were less clear in terms of food intake pattern. Men and women had similar food intake patterns, but food intake patterns were less often positively associated with BMI/Ob in women. In 11 studies, there were no significant associations between food intake pattern and BMI/Ob.
CONCLUSION: This review showed that no consistent associations could be identified between BMI or Ob and food intake patterns, derived from diet index, factor analysis or cluster analysis. However, the heterogeneity of food intake patterns identified by such analyses and the lack of gold standards for the application of these techniques hampers consistent analysis of a relation between food intake patterns and health.

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Year:  2001        PMID: 11781753     DOI: 10.1038/sj.ijo.0801819

Source DB:  PubMed          Journal:  Int J Obes Relat Metab Disord


  63 in total

1.  Clustering eating habits: frequent consumption of different dietary patterns among the Italian general population in the association with obesity, physical activity, sociocultural characteristics and psychological factors.

Authors:  Francesca Denoth; Marco Scalese; Valeria Siciliano; Laura Di Renzo; Antonino De Lorenzo; Sabrina Molinaro
Journal:  Eat Weight Disord       Date:  2015-10-08       Impact factor: 4.652

2.  Higher Intake of Fruit, but Not Vegetables or Fiber, at Baseline Is Associated with Lower Risk of Becoming Overweight or Obese in Middle-Aged and Older Women of Normal BMI at Baseline.

Authors:  Susanne Rautiainen; Lu Wang; I-Min Lee; JoAnn E Manson; Julie E Buring; Howard D Sesso
Journal:  J Nutr       Date:  2015-02-18       Impact factor: 4.798

3.  Associations between dietary patterns, socio-demographic factors and anthropometric measurements in adult New Zealanders: an analysis of data from the 2008/09 New Zealand Adult Nutrition Survey.

Authors:  K L Beck; B Jones; I Ullah; S A McNaughton; S J Haslett; W Stonehouse
Journal:  Eur J Nutr       Date:  2017-04-04       Impact factor: 5.614

4.  Associations between a posteriori defined dietary patterns and bone mineral density in adolescents.

Authors:  Teresa Monjardino; Raquel Lucas; Elisabete Ramos; Carla Lopes; Rita Gaio; Henrique Barros
Journal:  Eur J Nutr       Date:  2014-05-08       Impact factor: 5.614

5.  Mis-reporting, previous health status and health status of family may seriously bias the association between food patterns and disease.

Authors:  Agneta Hörnell; Anna Winkvist; Göran Hallmans; Lars Weinehall; Ingegerd Johansson
Journal:  Nutr J       Date:  2010-10-30       Impact factor: 3.271

6.  Dietary patterns of adults living in Ouagadougou and their association with overweight.

Authors:  Elodie Becquey; Mathilde Savy; Peggy Danel; Hubert B Dabiré; Sylvestre Tapsoba; Yves Martin-Prével
Journal:  Nutr J       Date:  2010-03-22       Impact factor: 3.271

7.  Hyperphagia: current concepts and future directions proceedings of the 2nd international conference on hyperphagia.

Authors:  Steven B Heymsfield; Nicole M Avena; Leslie Baier; Phillip Brantley; George A Bray; Lisa C Burnett; Merlin G Butler; Daniel J Driscoll; Dieter Egli; Joel Elmquist; Janice L Forster; Anthony P Goldstone; Linda M Gourash; Frank L Greenway; Joan C Han; James G Kane; Rudolph L Leibel; Ruth J F Loos; Ann O Scheimann; Christian L Roth; Randy J Seeley; Val Sheffield; Maïthé Tauber; Christian Vaisse; Liheng Wang; Robert A Waterland; Rachel Wevrick; Jack A Yanovski; Andrew R Zinn
Journal:  Obesity (Silver Spring)       Date:  2014-02       Impact factor: 5.002

8.  Differences in dietary patterns between older and younger obese and overweight outpatients.

Authors:  E M Inelmen; E D Toffanello; G Enzi; G Sergi; A Coin; L Busetto; E Manzato
Journal:  J Nutr Health Aging       Date:  2008-01       Impact factor: 4.075

9.  Food intake patterns associated with incident type 2 diabetes: the Insulin Resistance Atherosclerosis Study.

Authors:  Angela D Liese; Kristina E Weis; Mandy Schulz; Janet A Tooze
Journal:  Diabetes Care       Date:  2008-11-25       Impact factor: 17.152

10.  Five meal patterns are differently associated with nutrient intakes, lifestyle factors and energy misreporting in a sub-sample of the Malmö Diet and Cancer cohort.

Authors:  Isabel Holmbäck; Ulrika Ericson; Bo Gullberg; Elisabet Wirfält
Journal:  Food Nutr Res       Date:  2009-09-09       Impact factor: 3.894

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