Abstract IA24: Biomarkers of diet predict disease risk.

Over-nutrition and suboptimal dietary macronutrient choices are arguably the major environmental stressor in individuals living in Western societies. Obesity and poor diet are estimated to cause or contribute to as many as 25% of all cancer. One of the most clear examples of this, dietary or caloric restriction (CR), is the most potent and reproducible known means of increasing longevity and reducing morbidity in mammals. CR is also accepted to reduce cancer risk in animals. As one example, risk of breast cancer is generally decreased by more than 90% in CR rodents, and the CR-mediated effects are usually dominant to those induced by genetic risk factors, carcinogens, or co-carcinogens. The robust observations of reduced morbidity in CR animals is directly analogous to studies in humans that link obesity with poor health outcomes, including increased risk of neoplastic disease. We therefore proposed to test the general concept that biomarkers of diet in rats will predict risk of future disease in humans. Methods: Metabolomics measurements in sera/plasma were conducted HPLC coupled with coulometric detector arrays (N∼600 rats, ∼1700 humans). Classification and predictive power were tested, optimized, and subsequently validated using a series of megavariate data analysis approaches in sequential blinded cohorts. Results: Exploratory studies identified 93 redox-active small molecules from sera (measured by) with potential to distinguish dietary groups in both male and female rats (60 6 month old FBFN1 rats/group, AL/CR/male females in primary set). Partial Least Squares Projection to Latent Structures Discriminant Analysis, a projection method optimized for class separation built models with >95% accuracy in distinguishing groups. Data processing choices of transformation, scaling, and winsorizing (outlier removal) each affected strength of the models, and, in some cases, revealed distinct metabolites to be of importance in building these models, often in gender-specific ways. Diets varying in extent and duration of CR were used to develop models for intermediate caloric intakes, which are more relevant for human studies (total N=∼180 males, 180 females). Markers were adapted for human study, analytically validated at both the instrumentation (N=30; 100% accuracy in blinded splits) and at the sample collection levels (N=34; majority stable under worst case shipping conditions), then biologically validated (N∼200, metabolites and profiles had intra-class correlation coefficients from ∼0.65-0.85). We will present these modeling approaches, the models and their ability to distinguish sera based on caloric intake, as well as data from the initial application of these markers to address risk of breast cancer in case-control studies nested within the Nurses' Health Study. We will address some of the checks and cross-checks used to evaluate these data. Conclusion: Metabolomics profiles offer a potential biochemical approach to validate nutritive status and contribute to epidemiological investigations. Citation Format: Bruce S. Kristal. Biomarkers of diet predict disease risk. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr IA24.