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European Journal of Nutrition

, Volume 51, Issue 6, pp 755–764 | Cite as

Applying multilevel model to the relationship of dietary patterns and colorectal cancer: an ongoing case–control study in Córdoba, Argentina

  • Sonia Alejandra Pou
  • María del Pilar DíazEmail author
  • Alberto Rubén Osella
Original Contribution

Abstract

Purposes

Scientific literature has consistently shown the effects of certain diets on health but regional variations of dietary habits, and their relationship colorectal cancer (CRC) has been poorly studied in Argentina. Our aims were to identify dietary patterns and estimate their effect on CRC occurrence and to quantify the association between family history of CRC and CRC occurrence by applying multilevel models to estimate and interpret measures of variation.

Methods

Principal components factor analysis was performed to identify dietary patterns that were then used in a multilevel logistic regression applied to an ongoing case–control data about dietary exposure and CRC occurrence taking into account familiar clustering.

Results

Three dietary patterns were identified: “Southern Cone pattern” (red meat, wine, and starchy vegetables), “High-sugar drinks pattern”, and “Prudent pattern”. The study considered 41 cases and 95 controls. There was a significant promoting effects on CRC of “Southern Cone” (OR 1.5, 95%CI 1.0–2.2) and “High-sugar drinks” (OR 3.8, 95%CI 2.0–7.1) patterns, whereas “Prudent pattern” (OR 0.3, 95%CI 0.2–0.4) showed a significant protective effect at third tertile level. BMI, use of NSAIDs, and to have medical insurance showed significant effects. Variance of the random effect of family history of CRC was highly significant.

Conclusions

This novel approach for Argentina showed that Southern Cone and High-sugar drinks patterns were associated with a higher risk of CRC, whereas the Prudent pattern showed a protective effect. There was a significant clustering effect of family history of CRC.

Keywords

Dietary patterns Argentina Meat intake Colorectal cancer Multilevel 

Notes

Acknowledgments

We are especially grateful to the Córdoba Cancer Registry, Ministry of Health of Córdoba (Argentina), for allowing us to use the database and for their assistance with all the data-management steps, also to the physicians and all who participated in this study. We would like to thank the National Scientific and Technical Research Council (CONICET) for the SAP’s fellowship.

This research was partially supported by the Science and Technology National Agency, FONCyT grant PICT 2008-1814, PICT-O 2005-36035, and Science and Technical Secretary of the University of Córdoba (SECyT-UNC) grant 05/H207.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    World Cancer Research Fund, American Institute for Cancer Research (eds) (2007) Food, nutrition, physical activity, and the prevention of cancer: a global perspective. AICR, WashingtonGoogle Scholar
  2. 2.
    Edefonti V, Decarli A, La Vecchia C et al (2008) Nutrient dietary patterns and the risk of breast and ovarian cancers. Int J Cancer 122(3):609–613CrossRefGoogle Scholar
  3. 3.
    Edefonti V, Bravi F, Garavello W et al (2010) Nutrient-based dietary patterns and laryngeal cancer: evidence from an exploratory factor analysis. Cancer Epidemiol Biomarkers Prev 19(1):18–27CrossRefGoogle Scholar
  4. 4.
    Edefonti V, Randi G, La Vecchia C et al (2009) Dietary patterns and breast cancer: a review with focus on methodological issues. Nutr Rev 67(6):297–314CrossRefGoogle Scholar
  5. 5.
    Randi G, Edefonti V, Ferraroni M et al (2010) Dietary patterns and the risk of colorectal cancer and adenomas. Nutr Rev 68(7):389–408CrossRefGoogle Scholar
  6. 6.
    Camp NJ, Slattery ML (2002) Classification tree analysis: a statistical tool to investigate risk factor interactions with an example for colon cancer (United States). Cancer Causes Control 13(9):813–823CrossRefGoogle Scholar
  7. 7.
    Durkheim E (1964) The rules of sociological method, 8th edn. Free Press of Glencoe, New YorkGoogle Scholar
  8. 8.
    Rose GA (1992) The strategy of preventive medicine. Oxford University Press, OxfordGoogle Scholar
  9. 9.
    Snow J (1936) Snow on cholera. A reprint of two papers by John Snoe, MD, together with a biographical memoir by BW Richardson, MD, and an introduction by Wade Hampton Frost. The Commonwealth Fund, New YorkGoogle Scholar
  10. 10.
    Diez-Roux AV (2000) Multilevel analysis in public health research. Annu Rev Public Health 21:171–192CrossRefGoogle Scholar
  11. 11.
    Rabe-Hesketh S, Skrondal A, Pickles J (2001) Generalized multilevel structural equation modelling. Psychometrika 69(2):167–190CrossRefGoogle Scholar
  12. 12.
    Merlo J, Chaix B, Ohlsson H et al (2006) A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health 60(4):290–297CrossRefGoogle Scholar
  13. 13.
    Larsen K, Merlo J (2005) Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. Am J Epidemiol 161(1):81–88CrossRefGoogle Scholar
  14. 14.
    Pou SA, Osella AR, Eynard AR et al (2010) Cancer mortality in Córdoba, Argentina, 1986–2006: an age-period-cohort analysis. Tumori 96(2):202–212Google Scholar
  15. 15.
    Pou SA, Osella AR, Eynard AR et al (2009) Colorectal cancer mortality trends in Córdoba, Argentina. Cancer Epidemiol 33:406–412CrossRefGoogle Scholar
  16. 16.
    Niclis C, Del Pilar Díaz M, La Vecchia C (2010) Breast cancer mortality trends and patterns in Córdoba, Argentina in the period 1986–2006. Eur J Cancer Prev 19(2):94–99CrossRefGoogle Scholar
  17. 17.
    Niclis C, Pou SA, Bengió RH et al (2011) Prostate cancer mortality trends in Argentina 1986–2006: an age-period-cohort and joinpoint analysis. Cad Saude Publica 27(1):123–130CrossRefGoogle Scholar
  18. 18.
    MdelP Díaz, Osella AR, Aballay LR et al (2009) Cancer incidence pattern in Córdoba, Argentina. Eur J Cancer Prev 18(4):259–266CrossRefGoogle Scholar
  19. 19.
    Díaz MP, Corrente JE, Osella AR et al (2010) Modeling spatial distribution of cancer incidence in Cordoba, Argentina. Appl Cancer Res 30(2):245–252Google Scholar
  20. 20.
    Pou SA, Osella AR, Díaz MP (2011) Bladder cancer mortality trends and patterns in Córdoba, Argentina (1986–2006). Cancer Causes Control 22(3):407–415CrossRefGoogle Scholar
  21. 21.
    Francisca FM, Carro Perez ME (2009) Assessment of natural arsenic in groundwater in Cordoba Province, Argentina. Environ Geochem Health 31(6):673–682CrossRefGoogle Scholar
  22. 22.
    World Health Organization (1992) International classification of disease: 10th revision, 2nd edn. World Health Organization, GenevaGoogle Scholar
  23. 23.
    Navarro A, Cristaldo PE, Díaz MP et al (2000) Atlas fotográfico para cuantificar el consumo de alimentos y nutrientes en estudios nutricionales epidemiológicos en Córdoba, Argentina. Rev Fac Cienc Méd Córdoba 57(1):67–74Google Scholar
  24. 24.
    Navarro A, Osella AR, Guerra V et al (2001) Reproducibility and validity of a food-frequency questionnaire in assessing dietary intakes and food habits in epidemiological cancer studies in Argentina. J Exp Clin Cancer Res 20(3):203–208Google Scholar
  25. 25.
    Bertuccio P, Edefonti V, Bravi F et al (2009) Nutrient dietary patterns and gastric cancer risk in Italy. Cancer Epidemiol Biomarkers Prev 18(11):2882–2886CrossRefGoogle Scholar
  26. 26.
    Blakely T, Woodward A (2000) Ecological effects in multi-level studies. J Epidemiol Community Health 54:367–374CrossRefGoogle Scholar
  27. 27.
    Maas CLM, Hox JJ (2005) Sufficient sample sizes for multilevel modeling. Methodology 1(3):86–92Google Scholar
  28. 28.
    Merlo J, Yang M, Chaix B et al (2005) A brief conceptual tutorial on multilevel analysis in social epidemiology: investigating contextual phenomena in different groups of people. J Epidemiol Community Health 59(9):729–736CrossRefGoogle Scholar
  29. 29.
    Willet W (1998) Nutritional epidemiology, 2nd edn. Oxford University Press, OxfordCrossRefGoogle Scholar
  30. 30.
    Jacobs DR, Steffen LM (2003) Nutrients, foods, and dietary patterns as exposures in research: a framework for food synergy. Am J Clin Nutr 78(suppl):508S–513SGoogle Scholar
  31. 31.
    Randall E, Marshall JR, Brasure J et al (1992) Dietary patterns and colon cancer in Western New York. Nutr Cancer 18:265–276CrossRefGoogle Scholar
  32. 32.
    Slattery ML, Boucher KM, Caan BJ et al (1998) Eating patterns and risk of colon cancer. Am J Epidemiol 148:4–16CrossRefGoogle Scholar
  33. 33.
    De Stefani E, Deneo-Pellegrini H, Boffetta P et al (2009) Dietary patterns and risk of cancer: a factor analysis in Uruguay. Int J Cancer 124:1391–1397CrossRefGoogle Scholar
  34. 34.
    Bertuccio P, Edefonti V, Bravi F et al (2009) Nutrien dietary patterns and gastric cancer risk in Italy. Cancer Epidemiol Biomarkers Prev 18(11):2882–2886CrossRefGoogle Scholar
  35. 35.
    Witte JS, Greenland S, Haile RW et al (1994) Hierarchical regression analysis applied to a study of multiple dietary exposures and breast cancer. Epidemiology 5:612–621CrossRefGoogle Scholar
  36. 36.
    De Stefani E, Ronco AL, Deneo-Pellegrini H et al (2010) Dietary patterns and risk of advanced prostate cancer: a principal component analysis in Uruguay. Cancer Causes Control 21:1009–1016CrossRefGoogle Scholar
  37. 37.
    Balder HF, Virtanen M, Brants HA et al (2003) Common and country-specific dietary patterns in four European cohort studies. J Nutr 133:4246–4251Google Scholar
  38. 38.
    Donaldson MS (2004) Nutrition and cancer: a review of the evidence for an anti-cancer diet. Nutr J 3:19CrossRefGoogle Scholar
  39. 39.
    Navarro A, Muñoz SE, Lantieri MJ et al (2004) Meat cooking habits and risk of colorectal cancer in Cordoba, Argentina. Nutrition 20(10):873–877CrossRefGoogle Scholar
  40. 40.
    Food and Agriculture Organization (FAO) Statistics Division (2009) FAO food balance sheets. http://www.fao.org/statistics/faostat/foodsecurity/. Accessed 24 Feb 2009
  41. 41.
    Muñoz SE, Navarro A, Lantieri MJ et al (1998) Alcohol, methylxanthine-containing beverages, and colorectal cancer in Córdoba, Argentina. Eur J Cancer Prev 7:207–213CrossRefGoogle Scholar
  42. 42.
    Castelletto R, Castellsague X, Muñoz N et al (1994) Alcohol, tobacco, diet, mate drinking, and esophageal cancer in Argentina. Cancer Epidemiol Biomarkers Prev 3(7):557–564Google Scholar
  43. 43.
    Munné MI (2005) Alcohol and the economic crisis in Argentina: recent findings. Addiction 100(12):1790–1799CrossRefGoogle Scholar
  44. 44.
    Boffetta P, Hashibe M (2006) Alcohol and cancer. Lancet Oncol 7(2):149–156CrossRefGoogle Scholar
  45. 45.
    Moskal A, Norat T, Ferrari P et al (2007) Alcohol intake and colorectal cancer risk: a dose-response meta-analysis of published cohort studies. Int J Cancer 120(3):664–671CrossRefGoogle Scholar
  46. 46.
    Navarro A, Diaz MP, Muñoz SE et al (2003) Characterization of meat consumption and risk of colorectal cancer in Cordoba, Argentina. Nutrition 19(1):7–10CrossRefGoogle Scholar
  47. 47.
    De Stefani E, Deneo-Pellegrini H, Ronco AL et al (2011) Dietary patterns and risk of colorectal cancer: a factor analysis in Uruguay. Asian Pac J Cancer Prev 12(3):753–759Google Scholar
  48. 48.
    Notarnicola M, Caruso MG, Tutino V et al (2011) Low red blood cell levels of deglycating enzymes in colorectal cancer patients. World J Gastroenterol 17(3):329–333CrossRefGoogle Scholar
  49. 49.
    Misciagna G, De Michele G, Guerra V et al (2004) Serum fructosamine and colorectal adenomas. Eur J Epidemiol 19(5):425–432CrossRefGoogle Scholar
  50. 50.
    Flood A, Rastogi T, Wirfält E et al (2008) Dietary patterns as identified by factor analysis and colorectal cancer among middle-aged Americans. J Clin Nutr 88:176–184Google Scholar
  51. 51.
    Dixon LB, Balder HF, Virtanen MJ et al (2004) Dietary patterns associated with colon and rectal cancer: results from the dietary patterns and cancer (DIETSCAN) project. Am J Clin Nutr 80:1003–1011Google Scholar
  52. 52.
    Magalhães B, Bastos J, Lunet N (2011) Dietary patterns and colorectal cancer: a case-control study from Portugal. Eur J Cancer Prev [Epub ahead of print]Google Scholar
  53. 53.
    Kim MK, Sasaki S, Otani T et al (2005) Dietary patterns and subsequent colorectal cancer risk by subsite: a prospective cohort study. Int J Cancer 115:790–798CrossRefGoogle Scholar
  54. 54.
    Van Duijnhoven FJ, Bueno-De-Mesquita HB, Ferrari P et al (2009) Fruit, vegetables, and colorectal cancer risk: the European prospective Investigation into cancer and nutrition. Am J Clin Nutr 89(5):1441–1452CrossRefGoogle Scholar
  55. 55.
    Adlercreutz H (1990) Western diet and western diseases: some hormonal and biochemical mechanisms and associations. Scand J Clin Lab Invest Suppl 201:3–23CrossRefGoogle Scholar
  56. 56.
    Giovannucci E (2001) An updated review of the epidemiological evidence that cigarette smoking increases risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev 10(7):725–731Google Scholar
  57. 57.
    Gandini S, Botteri E, Iodice S et al (2008) Tobacco smoking and cancer: a meta-analysis. Int J Cancer 122(1):155–164CrossRefGoogle Scholar
  58. 58.
    Rabe-Hesketh S, Skrondal A (2008) Multilevel and longitudinal modeling using stata, 2nd edn. Stata Press, College StationGoogle Scholar
  59. 59.
    Molenberghs G, Verbeke G (2005) Models for discrete longitudinal data. Springer, NYGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Sonia Alejandra Pou
    • 1
  • María del Pilar Díaz
    • 2
    Email author
  • Alberto Rubén Osella
    • 3
  1. 1.National Research Council (CONICET). Biostatistics Unit, School of Nutrition, Faculty of Medical SciencesUniversity of CórdobaCórdobaArgentina
  2. 2.Biostatistics Unit, School of Nutrition, Faculty of Medical SciencesUniversity of CórdobaCórdobaArgentina
  3. 3.Laboratory of Epidemiology and BiostatisticsIRCCS “Saverio de Bellis”Castellana Grotte, BariItaly

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