A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study

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

Development and Validation of a Risk Score Predicting Substantial Weight Gain over 5 Years in Middle-Aged European Men and Women

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

Challenges in estimating the validity of dietary acrylamide measurements

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

A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptor-defined breast cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

Abstract Objective Pattern analysis has emerged as a tool to depict the role of multiple nutrients/foods in relation to health outcomes. The present study aimed at extracting nutrient patterns with respect to breast cancer (BC) aetiology. Design Nutrient patterns were derived with treelet transform (TT) and related to BC risk. TT was applied to twenty-three log-transformed nutrient densities from dietary questionnaires. Hazard ratios (HR) and 95 % confidence intervals computed using Cox proportional hazards models quantified the association between quintiles of nutrient pattern scores and risk of overall BC, and by hormonal receptor and menopausal status. Principal component analysis was applied for comparison. Setting The European Prospective Investigation into Cancer and Nutrition (EPIC). Subjects Women (n 334 850) from the EPIC study. Results The first TT component (TC1) highlighted a pattern rich in nutrients found in animal foods loading on cholesterol, protein, retinol, vitamins B12 and D, while the second TT component (TC2) reflected a diet rich in ?-carotene, riboflavin, thiamin, vitamins C and B6, fibre, Fe, Ca, K, Mg, P and folate. While TC1 was not associated with BC risk, TC2 was inversely associated with BC risk overall (HRQ5 v. Q1=0·89, 95 % CI 0·83, 0·95, P tren