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- Agogo, George O., et al.
(författare)
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A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data
- 2016
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Background: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data. Methods: We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study. Results: Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations. Conclusions: The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders.
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| 2. |
- Agogo, George O., et al.
(författare)
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Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design : EPIC Case Study
- 2014
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:11, s. e113160-
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Tidskriftsartikel (refereegranskat)abstract
- In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted twopart regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.
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| 3. |
- Agudo, Antonio, et al.
(författare)
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Impact of Cigarette Smoking on Cancer Risk in the European Prospective Investigation into Cancer and Nutrition Study
- 2012
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Ingår i: Journal of Clinical Oncology. - 0732-183X .- 1527-7755. ; 30:36, s. 4550-4557
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Our aim was to assess the impact of cigarette smoking on the risk of the tumors classified by the International Agency for Research on Cancer as causally associated with smoking, referred to as tobacco-related cancers (TRC). Methods The study population included 441,211 participants (133,018 men and 308,193 women) from the European Prospective Investigation Into Cancer and Nutrition. We investigated 14,563 participants who developed a TRC during an average follow-up of 11 years. The impact of smoking cigarettes on cancer risk was assessed by the population attributable fraction (AF(p)), calculated using the adjusted hazard ratios and 95% CI for current and former smokers, plus either the prevalence of smoking among cancer cases or estimates from surveys in representative samples of the population in each country. Results The proportion of all TRC attributable to cigarette smoking was 34.9% (95% CI, 32.5 to 37.4) using the smoking prevalence among cases and 36.2% (95% CI, 33.7 to 38.6) using the smoking prevalence from the population. The AF(p) were above 80% for cancers of the lung and larynx, between 20% and 50% for most respiratory and digestive cancers and tumors from the lower urinary tract, and below 20% for the remaining TRC. Conclusion Using data on cancer incidence for 2008 and our AF(p) estimates, about 270,000 new cancer diagnoses per year can be considered attributable to cigarette smoking in the eight European countries with available data for both men and women (Italy, Spain, United Kingdom, the Netherlands, Greece, Germany, Sweden, Denmark).
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| 5. |
- Agudo, Antonio, et al.
(författare)
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Polymorphisms in metabolic genes related to tobacco smoke and the risk of gastric cancer in the European prospective investigation into cancer and nutrition
- 2006
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Ingår i: Cancer Epidemiology Biomarkers & Prevention. - 1538-7755. ; 15:12, s. 2427-2434
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Tidskriftsartikel (refereegranskat)abstract
- Metabolizing enzymes, which often display genetic polymorphisms, are involved in the activation of compounds present in tobacco smoke that may be relevant to gastric carcinogenesis. We report the results of a study looking at the association between risk of gastric adenocarcinoma and polymorphisms in genes CYP1A1, CYP1A2, EPHX1, and GSTT1. A nested case-control study was carried out within the European Prospective Investigation into Cancer and Nutrition, developed in 10 European countries. The study includes 243 newly diagnosed cases of histologically confirmed gastric adenocarcinoma and 946 controls matched by center, age, sex, and date of blood collection. Genotypes were determined in nuclear DNA from WBCs. We found an increased risk of gastric cancer for homozygotes for C (histidine) variant in Y113H of EPHX1 (odds ratio, 1.91; 95% confidence interval, 1.19-3.07) compared with subjects with TC/TT. There was also a significant increased risk for smokers carrying at least one variant allele A in Ex7+129C > A (m4) of CYP1A1 and never smokers with null GSTT1 and allele A in the locus -3859G > A of CYP1A2. Most of these genes are involved in the activation and detoxification of polycyclic aromatic hydrocarbons, suggesting a potential role of these compounds in gastric carcinogenesis.
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| 9. |
- Assi, Nada, et al.
(författare)
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A statistical framework to model the meeting-in-the-middle principle using metabolomic data : application to hepatocellular carcinoma in the EPIC study
- 2015
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Ingår i: Mutagenesis. - : Oxford University Press (OUP). - 0267-8357 .- 1464-3804. ; 30:6, s. 743-753
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Tidskriftsartikel (refereegranskat)abstract
- Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum H-1 nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the 'predictors', including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the 'responses'). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.
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| 10. |
- Assi, Nada, et al.
(författare)
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Are Metabolic Signatures Mediating the Relationship between Lifestyle Factors and Hepatocellular Carcinoma Risk? Results from a Nested Case–Control Study in EPIC
- 2018
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - 1055-9965 .- 1538-7755. ; 27:5, s. 531-540
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Tidskriftsartikel (refereegranskat)abstract
- Background: The "meeting-in-the-middle" (MITM) is a principle to identify exposure biomarkers that are also predictors of disease. The MITM statistical framework was applied in a nested case-control study of hepatocellular carcinoma (HCC) within European Prospective Investigation into Cancer and Nutrition (EPIC), where healthy lifestyle index (HLI) variables were related to targeted serum metabolites.Methods: Lifestyle and targeted metabolomic data were available from 147 incident HCC cases and 147 matched controls. Partial least squares analysis related 7 lifestyle variables from a modified HLI to a set of 132 serum-measured metabolites and a liver function score. Mediation analysis evaluated whether metabolic profiles mediated the relationship between each lifestyle exposure and HCC risk.Results: Exposure-related metabolic signatures were identified. Particularly, the body mass index (BMI)-associated metabolic component was positively related to glutamic acid, tyrosine, PC aaC38:3, and liver function score and negatively to lysoPC aC17:0 and aC18:2. The lifetime alcohol-specific signature had negative loadings on sphingomyelins (SM C16:1, C18:1, SM(OH) C14:1, C16:1 and C22:2). Both exposures were associated with increased HCC with total effects (TE) = 1.23 (95% confidence interval = 0.93-1.62) and 1.40 (1.14-1.72), respectively, for BMI and alcohol consumption. Both metabolic signatures mediated the association between BMI and lifetime alcohol consumption and HCC with natural indirect effects, respectively, equal to 1.56 (1.24-1.96) and 1.09 (1.03-1.15), accounting for a proportion mediated of 100% and 24%.Conclusions: In a refined MITM framework, relevant metabolic signatures were identified as mediators in the relationship between lifestyle exposures and HCC risk.Impact: The understanding of the biological basis for the relationship between modifiable exposures and cancer would pave avenues for clinical and public health interventions on metabolic mediators.
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