Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Seasonal variation in food intake and the interaction effects of sex and age among adults in southern Brazil

European Journal of Clinical Nutrition volume 69, pages 1015–1022 (2015)Cite this article

Subjects

Abstract

Background/objectives:

Because studies have evidenced variations in nutrient intake, further investigation of the interaction between demographic characteristics and the seasons is necessary. We aimed to test the differences in food intake throughout the seasons and the interaction between the seasons and sex and age.

Subjects/methods:

This study included 273 individuals. Food intake was evaluated with 24-hour dietary recalls, and the reported food items were sorted into food groups. We performed the test on the differences in intake of food groups throughout the seasons with repeated measures and on the interaction effect by using the Generalized Estimate Equation.

Results:

Intake of fruits and natural fruit juices and sweetened beverages was lower, whereas that of grains and derivatives was higher in the winter. The intake of leafy vegetables and fish and seafood was lower in the autumn. The consumption of coffee and eggs was higher in the spring. Intake of chocolate powder and sugar, salt and lean poultry was higher in the winter. The variation in consumption of grains and derivatives, eggs, fatty poultry and processed meat over the seasons was more likely to be modified by sex. Age interacted with the seasons for leafy vegetables, beans and lentils, lean beef, lean poultry, low fat milk and light yogurt, vegetable oil and unsalted margarine, chocolate powder and sugar and processed meat.

Conclusions:

This study shows that food intake may change seasonally and that seasonal variation depends on sex and age, which might aggregate a specific co-variation component.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2

Similar content being viewed by others

References

  1. Rossato SL, Olinto MTA, Henn RL, Anjos LA, Bressan AW, Wahrlich V . Seasonal effect on nutrient intake in adults living in Southern Brazil. Cad Saúde Pública 2010; 26: 2177–2187.

    Article  Google Scholar 

  2. Capita R, Alonso-Calleja C . Differences in reported winter and summer dietary intakes in young adults in Spain. Int J Food Sci Nutr 2005; 56: 431–443.

    Article  Google Scholar 

  3. Cai H, Shu XO, Hebert JR, Jin F, Yang G, Liu DK et al. Variation in nutrient intakes among women in Shanghai, China. Eur J Clin Nutr 2004; 58: 1604–1611.

    Article  CAS  Google Scholar 

  4. Bezerra IN, Goldman J, Rhodes DG, Hoy MK, Souza AM, Chester DN et al. Difference in adult food group intake by sex and age groups comparing Brazil and United States nationwide surveys. Nutr J 2014; 13: 74–84.

    Article  Google Scholar 

  5. Malisova O, Bountziouka V, Panagiotakos D, Zampelas A, Kapsokefalou M . Evaluation of seasonality on total water intake, water loss and water balance in the general population in Greece. J Hum Nutr Diet 2013; 26: 90–96.

    Article  Google Scholar 

  6. Fahey MT, Sasaki S, Kobayashi M, Akabane M, Tsugane S . Seasonal misclassification error and magnitude of true between-person variation in dietary nutrient intake: a random coefficient analysis and implications for the Japan Public Health Center (JPHC) Cohort Study. Public Health Nutr 2003; 6: 385–391.

    Article  Google Scholar 

  7. Locke E, Coronado GD, Thompson B, Kuniyuki A . Seasonal variation in fruit and vegetable consumption in rural agricultural community. J Am Diet Assoc 2009; 109: 45–51.

    Article  Google Scholar 

  8. Darvin ME, Patzelt A, Knorr F, Blume-Peytavi U, Sterry W, Landemann J . One-year study on the variation of carotenoid antioxidant substances in living human skim: influence of dietary supplementation and stress factors. J Biomed Opt 2008; 13: 1–9.

    Article  CAS  Google Scholar 

  9. Crawford VLS, McCann M, Stout RW . Changes in seasonal death from myocardial infarction. Q J Med 2003; 96: 45–52.

    Article  CAS  Google Scholar 

  10. Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D et al. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc 2006; 106: 1640–1650.

    Article  Google Scholar 

  11. Rossato SL, Fuchs SC . Handling of random errors and bias in methods of short-term evaluation of diet. Revista de Saude Publica 2014; 48: 845–850.

    Article  Google Scholar 

  12. Shahar DR, Yerushalmi N, Lubin F, Froom P, Shahar A, Kristal-Boneh E . Changes in dietary intake account for seasonal changes in cardiovascular disease risk factors. Eur J Clin Nutr 1999; 53: 395–400.

    Article  CAS  Google Scholar 

  13. Ockene IS, Chiriboga DE, Stanck EJ, Harmatz MG, Nicolosi R, Saperia G et al. Seasonal variation in serum cholesterol levels. Arch Intern Med 2004; 164: 863–870.

    Article  CAS  Google Scholar 

  14. Ishiwaki A, Yokoyama T, Fugii H, Saito K, Nozue M, Yoshita K et al. A statistical approach for estimating the distribution of usual dietary intake to assess nutritional at-risk population based on the new Japanese dietary reference intake (DRIs). J Nutr Sci Vitaminol 2007; 53: 337–344.

    Article  CAS  Google Scholar 

  15. Henn RL, Fuchs SC, Moreira LB, Flavio D . Fuchs. Development and validation of a food frequency questionnaire (FFQ-Porto Alegre) for adolescent, adult and elderly populations from Southern Brazil. Cad Saúde Pública 2010; 26: 2068–2079.

    Article  Google Scholar 

  16. Anjos LA, Wahrlich V, Vasconcellos MTL, Souza DR, Olinto MTA, Waissmann W et al. Development of a food frequency questionnaire in a probabilistic sample of adults from Niterói, Rio de Janeiro, Brazil. Cad Saúde Pública 2010; 26: 2196–2204.

    Article  Google Scholar 

  17. Vitolo MR . Nutrição da gestação ao envelhecimento. Editora Rubio: Rio de Janeiro, Brazil, 2008.

    Google Scholar 

  18. WHO expert consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157–163.

    Article  Google Scholar 

  19. Pinheiro ABV, Lacerda EMA, Benzecry EH, Gomes MCS, Costa VM . Tabela para avaliação de consumo alimentar em medidas caseiras. Editora Atheneu: Rio de Janeiro, Brazil, 2005.

    Google Scholar 

  20. NEPA-Núcleo de Estudos e Pesquisas em Alimentação. Universidade Estadual de Campinas-UNICAMP-[Brazilian Table of Food Composition] / TACO Versão II 2 Ed. 2006 Campinas, SP.

  21. U.S. Department of Agriculture, Agricultural Research Service. 2007. USDA National Nutrient Database for Standard Reference, Release 20. Nutrient Data Laboratory Home Page. Available from http://www.ars.usda.gov/ba/bhnrc/ndl.

  22. US Department of Health and Human Services. National Institute of Health. National Heart, Lung and Blood Institute. Facts about the DASH Eating Plan 2006, On line version in http://www.nhlbi.nih.gov/files/docs/public/heart/hbp_low.pdf (accessed 25 August 2013).

  23. Liang KY, Zeger SL . Longitudinal data analysis using generalized linear models. Biometrika 1986; 73: 13–22.

    Article  Google Scholar 

  24. Hanley JA, Nagassa A, Edwardes MDB, Forrester JE . Statistical analysis of correlated data using Generalized Estimated Equations: an orientation. Am J Epidemiol 2003; 157: 364–375.

    Article  Google Scholar 

  25. Hubbard AE, Ahern J, Fleisher NL, Van der Laan M, Lippman SA, Jewell N et al. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology 2010; 21: 467–474.

    Article  Google Scholar 

  26. Westerterp KR, Plasqui G, Goris AH . Water loss as a function of energy intake, physical activity and season. Brit J Nutr 2005; 93: 199–203.

    Article  CAS  Google Scholar 

  27. Mitchikpe CES, Dossa RAM, Ategbo EAD, Van Raaij JMA, Kok FJ . Seasonal variation in food pattern but not in energy and nutrient intake of rural Beninese school-aged children. Public Health Nutr 2007; 12: 414–422.

    Google Scholar 

  28. Mudal R, Kjeldsen SE, Sandvik L, Erikseen G, Thaulow E, Erikseen J . Seasonal covariation in physical fitness and blood pressure at rest and during exercise in healthy middle-aged men. Blood Press 1997; 6: 269–273.

    Article  Google Scholar 

  29. Granner ML, Sargent RG, Calderon KS, Hussey JR, Watkins KW . Factors of fruit and vegetable intake by race, gender, and age among young adolescents. J Nutr Educ Behav 2004; 36: 173–180.

    Article  Google Scholar 

  30. Crispim SP, Geelen A, Siebelink E, Huybrechts I, Lillegaard IT, Margaritis I et al. Design aspects of 24 h recall assessment may affect the estimates of protein and potassium intake in dietary surveys. Public Health Nutr 2012; 15: 1196–1200.

    Article  Google Scholar 

  31. Aslam HM, Alvi AA, Mughal A, Hag Z, Qureshi WA, Haseeb A et al. Association of socioeconomic classes with diet, stress and hypertension. J Pak Med Assoc 1013; 63: 289–294.

    Google Scholar 

  32. Zhang D, Maalouf NM, Beverley AH, Moe OW, Sakhaee K . Effect of sex and postmenopausal estrogen use on serum phosphorous levels: a cross-sectional study of the National Health and Nutrition Examination Survey (NHANES) 2003-2006. Am J Kidney Dis 2014; 63: 198–205.

    Article  CAS  Google Scholar 

  33. Södergren M, Wang WC, Salmon J, Ball K, Crawford D, McNaughton SA . Predicting healthy lifestyle patterns among retirement age older adults in WELL study: a latent class analysis of sex differences. Maturitas 2014; 77: 41–46.

    Article  Google Scholar 

  34. Freedman SL, Guether PM, Krebs-Smith SM, Dodd KW, Midthune D . A population’s distribution of healthy eating index-2005 component scores can be estimated when more than one 24-hour recalls is available. J Nutr 2010; 140: 1529–1534.

    Article  CAS  Google Scholar 

  35. Tooze JA, Subar AF, Thompson FE, Troiano R, Schtzkin A, Kipnis V . Psychosocial predictors of energy underreporting in a large doubly labeled water study. Am J Clin Nutr 2004; 79: 795–804.

    Article  CAS  Google Scholar 

  36. Sahar DR, Yu B, Houston DK, Kritchevsky SB, Newman AB, Sellmeyer DB et al. Misreporting of energy intake in the elderly using doubly labeled water to measeure total energy expenditure and weight change. J Am Coll Nutr 2010; 29: 14–24.

    Article  Google Scholar 

  37. Ocke MC, Larranaga N, Grioni S, Van Den Berg SW, Ferrrari P, Salvani S et al. Energy intake and sources of energy intake in European Prospective Investigation into cancer and nutrition. Eur J Clin Nutr 2009; 63: S3–S15.

    Article  Google Scholar 

  38. Rothman KJ, Greenland S, Lash T . Modern Epidemiology, 3rd edn. Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2008.

    Google Scholar 

  39. Jose P . Doing Statistical Mediation & Moderation. The Guilford Press: New York City, NY, USA, 1952.

    Google Scholar 

  40. Hayes A . Introduction to mediation, moderation and conditional process analysis: a regression-based approach. Methodology in social science. The Guilford Press, 2013.

    Google Scholar 

  41. Donofry SD, Roecklein KA, Rohan KJ, Wildes JE, Kamarck ML . Prevalence and correlates of binge eating in seasonal affective disorder. Psychiatry Res 2014; S0165-1781: 214–215.

    Google Scholar 

  42. Schätzer M, Rust P, Elmadfa L . Fruit and vegetable intake in Austrian adults: intake frequency, serving sizes, reasons for and barriers to consumption, and potential for increasing consumption. Public Health Nutr 2009; 13: 480–487.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the following: CAPES—Brazilian Federal Agency for Support and Evaluation of Graduated Education; PROCAD 01/2005 no. 0257052, PROSUP no. 097/2007; National Council for Scientific and Technological Development—CNPq; no. 308833/2006, no. 308489/2009-8, no. 311801/2006-4, no. 70/2008 and no. 211521/2013; Studies and Projects Finance Organization—FINEP; Supporting the Program for Centers of Excellence—PRONEX; and HCPA-FIPE no. 00-176.

Author information

Authors and Affiliations

  1. Department of Nutrition, Harvard School of Public Health, Boston, MA, USA

    S L Rossato

  2. Postgraduate Program in Collective Health, University of Vale do Rio dos Sinos, São Leopoldo, Brazil

    M T A Olinto & R L Henn

  3. Postgraduate Studies Program in Cardiology, School of Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    L B Moreira, F D Fuchs & S C Fuchs

  4. Postgraduate Studies Program in Epidemiology, School of Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    L B Moreira, S A Camey & S C Fuchs

  5. Department of Social Nutrition, Nutritional and Functional Assessment Laboratory, Fluminense Federal University, Niterói, Brazil

    L A Anjos & V Wahrlich

  6. Oswaldo Cruz Foundation, Sergio Arouca National School of Public Health, Workers’ Health and Human Ecology Research Center, Rio de Janeiro, Brazil

    W Waissmann

Authors
  1. S L Rossato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M T A Olinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R L Henn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L B Moreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S A Camey
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. L A Anjos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. V Wahrlich
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. W Waissmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. F D Fuchs
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. S C Fuchs
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S L Rossato.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on European Journal of Clinical Nutrition website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rossato, S., Olinto, M., Henn, R. et al. Seasonal variation in food intake and the interaction effects of sex and age among adults in southern Brazil. Eur J Clin Nutr 69, 1015–1022 (2015). https://doi.org/10.1038/ejcn.2015.22

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ejcn.2015.22

This article is cited by

Search

Advanced search

Quick links