Micronutrients intake and cancer: Protective or promoters? A challenge for risk estimation.

Transcription

1 Micronutrients intake and cancer: Protective or promoters? A challenge for risk estimation. Muñoz SE 1,2 ; Roman D 1 ; Roque F 1 ; Navarro A 1 ; Díaz MP 1. 1.Facultad de Ciencias Médicas, Universidad Nacional de Córdoba. Argentina 2. Consejo Nacional de Investigaciones Científicas y Tecnológicas CONICET. XVIII IEA World Congress of Epidemiology Porto Alegre, Brazil 2008

2 Introduction Cancer is a multicausal disease and it has been associated to environmental, biological and lifestyle conditions. Among these factors, diet components are central. Diet pattern in Argentina XVIII Epi

3 the logistic model. π log 1 π = β 0 + β X β X 2 2 The assumptions of not functionally related covariates Difficulties in micronutrients single effects estimation collinearity

4 Objective To estimate the risk of colorectal cancer and urinary tract tumors, associated to micronutrients intake using different modelling strategies. XVIII Epi

5 Methods Study design Two case control studies in Córdoba, Argentina Central Argenitna, capital of 1,280,000 inh.. COLORRECTAL CANCER (ICD 18adenocarcinomas) 296 cases, 597 controls URINARY TRACT TUMORS ( ICD 6468)urothelium) 129 cases, 257 controls XVIII Epi

6 Instruments Food Frequency Questionnaire NAVARRO, A; Osella, A; GUERRA, V; MUÑOZ, S; LANTIERI, M and EYNARD, A. Reproducibility and validation of a foodfrequency questionnaire to asses dietary intakes and food habits in epidemiological cancer studies in Córdoba, Argentina. J. Exp. Clin. Cancer Res. J. Exp. Clin. Cancer Res. 20:365370, food items. Food Atlas Navarro, A; Cristaldo, PE; Andreatta, MM; Muñoz,. Atlas de Alimentos. ISBN: , XVIII Epi

7 Statistical methods Logistic model (LR) Linear predictor : age, sex, bmi, social status, occupation, micronutrient intake Testing model assumptions (GLMM) Link function (Pregibon(1979) Linear predictor and systematic component (Wolfinger & O Connel 1993) Weighted logistic Model (WLM) using robust Mahalanobis distance Monte Carlo resampling process for both models. Maintainig case control 1:2 and n of original sample. XVIII Epi

8 Ridge Regression Normal equations ridge equations r b * = r b * ( r + ki ) b r = r XX XY XX XY Mean square error b r = ( r + ki XX ) 1 E {( ) } r 2 2 { r } ( { r b β = σ b + E b } β ) 2 XVIII Epi

9 RESULTS Colorrectal cancer XVIII Epi

10 CC Table 1:Correlation Matrix of vitamin intake. Vitamins A B1 B2 Niacine B6 C E K A 1.00 B B Niacine B C E K XVIII Epi

11 Table 2: Risk parameters and standard error estimations for vitamin intake and colorectal cancer employing the original data (β) and MonteCarlo resampling (β ), with the logistic weighted (W) and unweighted (NW) models. CC. CC. β vitamin β *Std.E. Std.E β β NW W NW W NW W NW W A B B Niacine B C E K β Logistic regression estimates fromo original XVIII data. Epi β2008. Logistic regression estimates from Monte 11 Carlo samples. *Std E. Standard error. UW: unweighted, W: weighted.

12 Table 3: Risk estimation for colorectal cancer and vitamin intake using the classical model and Ridge Regression Nutrient Classical Model Ridge Regression OR L Ci 95% OR R Ci 95% VIT B1 VIT B2 NIACINE VIT C VIT. K XVIII Epi

13 CC. Variable Categories* Std. Error**. P value Unweighted Weighted Unweighted Weighted Unweighted Weighted Intercept <0001 Age (years) BMI Social Status Vitamin A 1*** 2 3 Vitamin B1 Vitamin B2 < >75 normal overweighted obessity Low Medium Mediumhigh/ high Niacine XVIII Epi

14 RESULTS Urinary tract tumors XVIII Epi

15 Table 4 correlation matrix coefficients estimation among micronutrients intakes Urinary tract VIT A VIT E VIT B6 VIT C P SE ZN VIT A 1 VIT E VIT B * 0.361* 1 VIT C P 0.483* 0.341* 0.837* SE 0.369* * * 1 ZN 0.382* 0.378* 0.666* * 0.708* * Significants, α = XVIII Epi

16 Table 5 Risk estimation for urothelial tumors and micronutrients intake using the classical model and Ridge Regression. Nutrient Classical Model Ridge Regression OR L Ci 95% OR R Ci 95% p value VIT A VIT E VIT B6 VIT C P Se Zn All variabes as continuous. OR L : Odds ratio of all micronutrients adjusted by confounders using logistic regresion. OR XVIII Epi R : Odds ratio of all micronutrients adjusted by confounders using Ridge regresion..

17 Conclusions Micronutrients show different effect in each tumor type, and their risk estimations should be performed considering the multicollinearity in the model. Ridge regression estimation can offer a better approach for this kind of data. XVIII Epi

18 Acknowledegements This work was supported by a binational cooperation programm : SECyT Argentina. Ministero degli Affari Esteri, Italia and SECyTUNC. XVIII Epi

19 Thank you. XVIII Epi