| 1. |
- Agogo, George O., et al.
(author)
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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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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:11, s. e113160-
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Journal article (peer-reviewed)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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| 2. |
- Assi, Nada, et al.
(author)
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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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In: Mutagenesis. - : Oxford University Press (OUP). - 0267-8357 .- 1464-3804. ; 30:6, s. 743-753
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Journal article (peer-reviewed)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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| 3. |
- Ferrari, Pietro, et al.
(author)
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Challenges in estimating the validity of dietary acrylamide measurements
- 2013
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In: European Journal of Nutrition. - : Springer Science and Business Media LLC. - 1436-6215 .- 1436-6207. ; 52:5, s. 1503-1512
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Journal article (peer-reviewed)abstract
- Acrylamide is a chemical compound present in tobacco smoke and food, classified as a probable human carcinogen and a known human neurotoxin. Acrylamide is formed in foods, typically carbohydrate-rich and protein-poor plant foods, during high-temperature cooking or other thermal processing. The objectives of this study were to compare dietary estimates of acrylamide from questionnaires (DQ) and 24-h recalls (R) with levels of acrylamide adduct (AA) in haemoglobin. In the European Prospective Investigation into Cancer and Nutrition (EPIC) study, acrylamide exposure was assessed in 510 participants from 9 European countries, randomly selected and stratified by age, sex, with equal numbers of never and current smokers. After adjusting for country, alcohol intake, smoking status, number of cigarettes and energy intake, correlation coefficients between various acrylamide measurements were computed, both at the individual and at the aggregate (centre) level. Individual level correlation coefficient between DQ and R measurements (r (DQ,R)) was 0.17, while r (DQ,AA) and r (R,AA) were 0.08 and 0.06, respectively. In never smokers, r (DQ,R), r (DQ,AA) and r (R,AA) were 0.19, 0.09 and 0.02, respectively. The correlation coefficients between means of DQ, R and AA measurements at the centre level were larger (r > 0.4). These findings suggest that estimates of total acrylamide intake based on self-reported diet correlate weakly with biomarker AA Hb levels. Possible explanations are the lack of AA levels to capture dietary acrylamide due to individual differences in the absorption and metabolism of acrylamide, and/or measurement errors in acrylamide from self-reported dietary assessments, thus limiting the possibility to validate acrylamide DQ measurements.
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| 4. |
- Hoeft, Birgit, et al.
(author)
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Polymorphisms in fatty acid metabolism-related genes are associated with colorectal cancer risk
- 2010
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In: Carcinogenesis. - : Oxford University Press (OUP). - 0143-3334 .- 1460-2180. ; 31:3, s. 466-472
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Journal article (peer-reviewed)abstract
- Colorectal cancer (CRC) is the third most common malignant tumor and the fourth leading cause of cancer death worldwide. The crucial role of fatty acids for a number of important biological processes suggests a more in-depth analysis of inter-individual differences in fatty acid metabolizing genes as contributing factor to colon carcinogenesis. We examined the association between genetic variability in 43 fatty acid metabolism-related genes and colorectal risk in 1225 CRC cases and 2032 controls participating in the European Prospective Investigation into Cancer and Nutrition study. Three hundred and ninety two single-nucleotide polymorphisms were selected using pairwise tagging with an r(2) cutoff of 0.8 and a minor allele frequency of > 5%. Conditional logistic regression models were used to estimate odds ratios and corresponding 95% confidence intervals. Haplotype analysis was performed using a generalized linear model framework. On the genotype level, hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD), phospholipase A2 group VI (PLA2G6) and transient receptor potential vanilloid 3 were associated with higher risk for CRC, whereas prostaglandin E receptor 2 (PTGER2) was associated with lower CRC risk. A significant inverse association (P < 0.006) was found for PTGER2 GGG haplotype, whereas HPGD AGGAG and PLA2G3 CT haplotypes were significantly (P < 0.001 and P = 0.003, respectively) associated with higher risk of CRC. Based on these data, we present for the first time the association of HPGD variants with CRC risk. Our results support the key role of prostanoid signaling in colon carcinogenesis and suggest a relevance of genetic variation in fatty acid metabolism-related genes and CRC risk.
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| 5. |
- Steffen, Annika, et al.
(author)
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Development and Validation of a Risk Score Predicting Substantial Weight Gain over 5 Years in Middle-Aged European Men and Women
- 2013
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:7
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Journal article (peer-reviewed)abstract
- Background: Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population. Methods: We used lifestyle and nutritional data from 53 degrees 758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining >= 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample). Results: Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of >= 200 points were 9% and 96%, respectively. Conclusion: The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.
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