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American Cancer Society Study 900,000 adults Calle et al., NEJM, 348:1625-1638, 2003. Overall Relative Cancer Risk (BMI >40 vs 18.5 to 24.9): M: 1.52 (1.13 – 2.05): F:1.62 (1.40 – 1.87) - PowerPoint PPT Presentation

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Overall Relative Cancer Risk (BMI >40 vs 18.5 to 24.9):M: 1.52 (1.13 2.05): F:1.62 (1.40 1.87)

Increased risk of colorectal, pancreatic, liver, esophagus, kidney, multiple myeloma, non-Hodgkins lymphoma, gallbladder, prostate, breast, cervical, ovarian, uterine

Current patterns of overweight and obesity in the United States could account for 14% of all deaths from cancer in men and 20% of those in women

American Cancer Society Study900,000 adults Calle et al., NEJM, 348:1625-1638, 2003BMI of 26 vs 21Coronary Heart Disease:2x increase Hypertension:2-3x increaseType II Diabetes:8x increase

Weight Change of 15 kgCoronary Heart Disease:2x increase Hypertension:2-3x increaseType II Diabetes:6x increase

Guidelines for Healthy WeightNurses Health Study: Willett et al., NEJM, 341:427-434, 1999Caloric Restriction (CR)CR is an experimental paradigm in which the dietary/caloric intake of a group of animals is reduced relative to that eaten by ad libitum fed controlsCaloric restriction is the most potent, most robust, and most reproducible known means of reducing morbidity and mortality in mammalsSurvival Data, 1987 Cohort, Casein Diet

1400120010008006004002000020406080100DaysSurvival RateALDRDR Reduces Morbidity:Breast CancerDelays tumor onset (initiation and promotion)Slows progressionCan modulate oncogene penetrancev-Ha-ras tumors decreased 67% (Fernandes et al., PNAS 92:6494-6498, 1995)Can prevent carcinogen-induced tumors7,12-demethyl-benz(a)anthracene (60% AL, 0% DR)Kritchevsky et al. Cancer Res 44, 3174-3177, 1984Even with high fat diet, tumor yield, size, burden down 93-98%Klurfied et al. Cancer Res 47, 2759-2762, 1987CR Beneficial EffectsLower oxidative stressBetter redox balance Improved glucose metabolismIncreased insulin sensitivityReduced blood glucoseReduced diabetes riskReduced inflammationHow do we study complex biological/clinical problems?

How do we address such questions in humans, where our ability to manipulate and analyze the system is limited?High Throughputand/or Data Density StudiesGenomics/SNPsmRNA expression arraysProteomicsSmall metabolitesMetabolomics: The omics face of biochemistryMeasurement of changes in populations of low molecular weight metabolites under a given set of conditions Fiehn

GENOMEENVIRONMENTTRANSCRIPTOMEPROTEOME

METABOLOME

HEALTHSTATE

DISEASE STATEGENOMEENVIRONMENTTRANSCRIPTOMEPROTEOME

METABOLOME

HEALTHSTATE

DISEASE STATE0.020.040.060.080.0100.00.000.200.400.600.80Retention time (minutes)Response (A)1

AL8AL7AL5AL1AL4AL3AL2AL6DR8DR6DR5DR7DR1DR4DR2DR30.00.20.40.60.81.0PredictedObserved Values vs. Predicted Values

Mechanistic InsightDrug DevelopmentToxicologyClassificationPredictionFunctional genomicsSub-threshold studiesOthersSample CollectionSample AnalysisDatabase CurationBioinformatics

Actual 2 SD2 SD3 SDComputational Modeling of Metabolic SerotypesModeling Metabolic InteractionsObjectively Defining Class IdentityFollowing Biochemical Pathways%F11/14/201213Experimental DesignModel: F344 x BN F1 RatOverall Design:AL/DR, male/female, 5 different agesDifferent extents and duration of diets Total experiment ~36 groups, 82 cohorts. HPLC separations with coulometric array detectionMultilayer statistical and data analysisIn the end, we have a model What we measure -- biochemicallyMetabolites small molecules

Pathways (eg, purine catabolites)

Interactive pathways (eg, amino acid metabolism)

Compound classes (eg, lipids)

Conceptually linked systemseg antioxidants, redox damage products

What we measure -- conceptuallyBiochemical constituentsExcretion productsPrecursor product Balances (eg, redox systems)collection depotsFluxSnapshot view of biochemistryIntegrated signal from genome and environmentShort and long term statusTemporal image Sub-threshold changes (eg (toxicology, nutrition)

Metabolomics Some AdvantagesSensitivity silent phenotypes/sub-threshold effectsDiscovery Knowledge base (ie, metabolic pathways)

Limited repertoire simplifies possibilities(2500 non-lipid endogenous metabolites??)

Metabolome integrates signalNature and Nurture -- genome and environmentMeasurement of system status/defects

Metabolome has the fastest response time

Metabolomics Some DisadvantagesToo Sensitive?cohort effects, site effects, time effectssample handlingindividual metabolites responsive to multiple factorsgenes, environment, health status, locationexperiment design must account for all factorscontrolled or fuzzy, multiple sourcesPractical Set-up costsPossible need for multiple platforms (NMR, MS, HPLC)early industry dominance lots of propriety dataincompatible data standards

Metabolomics Technology

=

Metabolomics PlatformBiologyAnalyticalChemistryData Analysis0.020.040.060.080.0100.00.000.200.400.600.80Retention time (minutes)Response (A)1

AL8AL7AL5AL1AL4AL3AL2AL6DR8DR6DR5DR7DR1DR4DR2DR30.00.20.40.60.81.0PredictedObserved Values vs. Predicted Values

Mechanistic InsightDrug DevelopmentToxicologyClassificationPredictionFunctional genomicsSub-threshold studiesOthersSample CollectionSample AnalysisDatabase CurationBioinformatics

Actual 2 SD2 SD3 SDComputational Modeling of Metabolic SerotypesModeling Metabolic InteractionsObjectively Defining Class IdentityFollowing Biochemical PathwaysAnalytical%F11/14/201220Experimental DesignModel: F344 x BN F1 RatOverall Design:AL/DR, male/female, 5 different agesDifferent extents and duration of diets Total experiment ~36 groups, 82 cohorts. HPLC separations with coulometric array detectionMultilayer statistical and data analysisIn the end, we have a model 0.020.040.060.080.0100.00.000.200.400.600.80Retention time (minutes)Response (A)1

AL8AL7AL5AL1AL4AL3AL2AL6DR8DR6DR5DR7DR1DR4DR2DR30.00.20.40.60.81.0PredictedObserved Values vs. Predicted Values

Mechanistic InsightDrug DevelopmentToxicologyClassificationPredictionFunctional genomicsSub-threshold studiesOthersSample CollectionSample AnalysisDatabase CurationBioinformatics

Actual 2 SD2 SD3 SDComputational Modeling of Metabolic SerotypesModeling Metabolic InteractionsObjectively Defining Class IdentityFollowing Biochemical PathwaysDataAnalysis%F11/14/201221Experimental DesignModel: F344 x BN F1 RatOverall Design:AL/DR, male/female, 5 different agesDifferent extents and duration of diets Total experiment ~36 groups, 82 cohorts. HPLC separations with coulometric array detectionMultilayer statistical and data analysisIn the end, we have a model 0.020.040.060.080.0100.00.000.200.400.600.80Retention time (minutes)Response (A)1

AL8AL7AL5AL1AL4AL3AL2AL6DR8DR6DR5DR7DR1DR4DR2DR30.00.20.40.60.81.0PredictedObserved Values vs. Predicted Values

Mechanistic InsightDrug DevelopmentToxicologyClassificationPredictionFunctional genomicsSub-threshold studiesOthersSample CollectionSample AnalysisDatabase CurationBioinformatics

Actual 2 SD2 SD3 SDComputational Modeling of Metabolic SerotypesModeling Metabolic InteractionsObjectively Defining Class IdentityFollowing Biochemical PathwaysBiology%F11/14/201222Experimental DesignModel: F344 x BN F1 RatOverall Design:AL/DR, male/female, 5 different agesDifferent extents and duration of diets Total experiment ~36 groups, 82 cohorts. HPLC separations with coulometric array detectionMultilayer statistical and data analysisIn the end, we have a model Caloric intakeas a case studyHigh points onlyIgnoring details, other studies, etcSurvival Data, 1987 Cohort, Casein Diet

1400120010008006004002000020406080100DaysSurvival RateALDRHypothesis:Long-term, low-calorie diets induce changes in metabolism that persist throughout the lifespanPredictionsCR alters the sera metabolome

There exists a CR Serotype

Part of CR serotype reflects beneficial physiological status --- ie, serotype defines health without reference to disease

GoalsInsights into the mechanism of CRRecognize CR in other organisms (e.g., non-human primates)3) Biochemically determine the effective, long-term caloric intake of an individual (e.g., for epidemiological studies)Identify predictive markers of disease (e.g., to intervene/prevent/focus resources;focus on diseases where intervention is possible)Model: F344 x BN F1 RatOverall Design:AL/CR, male/female, 5 different agesDifferent extents and duration of diets Total experiment ~36 groups, 82 cohorts. Approach:HPLC separations with coulometric array detection(LC/LC-MS for plasma proteomics)Multilayer statistical and data analysis

Experimental DesignAnalytical Stability

Biologic Variability

In Rats: Biological variability 5 fold greater than analytical variability Analytical variability does not influence biological variabilityAnalytical vs Biological VariationPrimary Data Analysis

Multivariate analyses are relatively noise-resistantMinimize loss of informative metabolitesReduce false negatives (Type II errors)Increase false positives (Type I errors)Does Serotype Encode Sufficient Information to Identify Diet Group?Data Exploration and Classification AnalysisHierarchical Cluster Analysis (HCA)Identifies natural groups in data

Principal Component Analysis (PCA)Finds linear combinations of original variables that account for maximal variationT-tests, p

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