Download - Overall Relative Cancer Risk (BMI >40 vs 18.5 to 24.9):

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-Hodgkin’s 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, 2003

BMI 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, 1999

Caloric 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 controls

Caloric restriction is the most potent, most robust, and most reproducible known means of reducing morbidity

and mortality in mammals

Survival Data, 1987 Cohort, Casein Diet

14001200100080060040020000

20

40

60

80

100

Days

Surv

ival

Rat

e

ALDR

DR Reduces Morbidity:Breast Cancer

• Delays tumor onset (initiation and promotion)

• Slows progression• Can modulate oncogene penetrance

– v-Ha-ras tumors decreased 67% • (Fernandes et al., PNAS 92:6494-6498, 1995)

• Can prevent carcinogen-induced tumors– 7,12-demethyl-benz(a)anthracene (60% AL,

0% DR)• Kritchevsky et al. Cancer Res 44, 3174-3177, 1984

– Even with high fat diet, tumor yield, size, burden down 93-98%

• Klurfied et al. Cancer Res 47, 2759-2762, 1987

CR “Beneficial” Effects• Lower oxidative stress

–“Better” redox balance • “Improved” glucose

metabolism– Increased insulin sensitivity–Reduced blood glucose–Reduced diabetes risk

• Reduced inflammation

How 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 Studies

• Genomics/SNPs• mRNA expression arrays• Proteomics• Small metabolites

Metabolomics: The –omics face of biochemistry

Measurement of changes in populations of low molecular weight metabolites under a

given set of conditions Fiehn

GENOMEE

NV

IRO

NM

EN

T

TRANSCRIPTOME

PROTEOME

METABOLOME

HEALTHSTATE

DISEASE STATE

0.0 20.0 40.0 60.0 80.0 100.0

0.00

0.20

0.40

0.60

0.80

Retention time (minutes)

Res

pons

e (µ

A)

1

AL8AL7AL5AL1AL4AL3AL2AL6DR8DR6DR5DR7DR1DR4DR2DR3 0.00.20.40.60.81.0 Predicted

Observed Values vs. Predicted Values

Mechanistic InsightDrug DevelopmentToxicologyClassificationPredictionFunctional genomicsSub-threshold studiesOthers

Sample Collection Sample Analysis Database Curation

BioinformaticsA

ctua

l 2 SD

2 SD

3 SD

Computational Modeling of Metabolic Serotypes

Modeling Metabolic Interactions

Objectively Defining Class Identity

Following Biochemical Pathways

What we measure -- biochemically

Metabolites – 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 depots”FluxSnapshot view of biochemistryIntegrated signal from genome and environmentShort and long term statusTemporal image Sub-threshold changes (eg (toxicology, nutrition)

Metabolomics – Some Advantages

Sensitivity “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 Disadvantages

Too Sensitive?cohort effects, site effects, time effectssample handlingindividual metabolites responsive to multiple factors

genes, environment, health status, locationexperiment design must account for all factors

controlled 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 Platform

Biology

AnalyticalChemistry

Data Analysis

0.0 20.0 40.0 60.0 80.0 100.0

0.00

0.20

0.40

0.60

0.80


Res

pons

e (µ

A)

1



Mechanistic InsightDrug DevelopmentToxicologyClassificationPredictionFunctional genomicsSub-threshold studiesOthers


BioinformaticsA

ctua

l 2 SD

2 SD

3 SD





Analytical

0.0 20.0 40.0 60.0 80.0 100.0

0.00

0.20

0.40

0.60

0.80


Res

pons

e (µ

A)

1



Mechanistic InsightDrug Development

ToxicologyClassification

PredictionFunctional genomics

Sub-threshold studiesOthers


BioinformaticsA

ctua

l 2 SD

2 SD

3 SD





DataAnalysis

0.0 20.0 40.0 60.0 80.0 100.0

0.00

0.20

0.40

0.60

0.80


Res

pons

e (µ

A)

1



Mechanistic InsightDrug Development

ToxicologyClassification

PredictionFunctional genomics

Sub-threshold studiesOthers


BioinformaticsA

ctua

l 2 SD

2 SD

3 SD





Biology

Caloric intakeas a case study

High points onlyIgnoring details, other studies,

etc

Survival Data, 1987 Cohort, Casein Diet

14001200100080060040020000

20

40

60

80

100

Days

Surv

ival

Rat

e

ALDR

Hypothesis:Long-term, low-calorie diets

induce changes in metabolism that persist throughout the

lifespan

Predictions• CR 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…

Goals1) Insights into the mechanism of CR2) Recognize 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)

4) Identify predictive markers of disease (e.g., to intervene/prevent/focus resources;

focus on diseases where intervention is possible)

Model: F344 x BN F1 Rat

Overall 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 Design

Analytical Stability

Biologic Variability

0.00 0.12 0.24 0.36 0.48 0.600.00

0.12

0.24

0.36

0.48

0.60

0.00 0.15 0.30 0.45 0.60 0.750.00

0.15

0.30

0.45

0.60

0.75

0.00 0.15 0.30 0.45 0.60 0.750.00

0.15

0.30

0.45

0.60

0.75

0.00 0.25 0.50 0.75 1.00 1.25 1.500.00

0.25

0.50

0.75

1.00

1.25

1.50

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.000.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0.00 0.25 0.50 0.75 1.00 1.25 1.500.00

0.25

0.50

0.75

1.00

1.25

1.50

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.000.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0.00 0.25 0.50 0.75 1.00 1.25 1.500.00

0.25

0.50

0.75

1.00

1.25

1.50

MDRCV0.00 0.25 0.50 0.75 1.00 1.25 1.50

ANAL

CV1

0.00

0.25

0.50

0.75

1.00

1.25

1.50

0.00 0.25 0.50 0.75 1.00 1.250.00

0.25

0.50

0.75

1.00

1.25

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.000.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

r ² = 0.01

r ² = 0.31

r ² = 0.00 r ² = 0.01r ² = 0.01

MALCV FALCV

MDRCVMALCVFALCVMALCV

ANALCV1ANALCV2ANALCV1

FDRCV

ANAL

CV1

ANAL

CV1

ANAL

CV1

FDRC

V

FALC

V

FDRC

V

MDR

CV

ANAL

CV2

ANAL

CVT

ANAL

CVT

r ² = 0.75 r ² = 0.55 r ² = 0.36

r ² = 0.31 r ² = 0.25 r ² = 0.09

AvAAvA

AL AL vs vs DRDR

Biological Biological vs vs AnalyticalAnalytical

In Rats: Biological variability 5 fold greater than analytical variability Analytical variability does not influence biological variability

Analytical vs Biological Variation

Primary Data Analysis

• Multivariate analyses are relatively noise-resistant• Minimize loss of informative metabolites

• Reduce false negatives (Type II errors)• Increase false positives (Type I errors)

Does Serotype Encode Sufficient Information to

Identify Diet Group?

Data Exploration and Classification Analysis

• Hierarchical Cluster Analysis (HCA)– Identifies natural groups in data

• Principal Component Analysis (PCA)– Finds linear combinations of original variables

that account for maximal variation

T-tests, p<0.2 ?!

ALALDRDR

ALALDRDR

ALALDRDR

ALALDRDR

ALALDRDR

ALALDRDR

Single Complete Single Complete CentroidCentroid

AutoscaleAutoscale

Range Range ScaleScale

PreprocessingPreprocessingDependentDependent

Categorization Categorization AccuracyAccuracy

100%100%

100%100%

100%100%100%100%100%100%Linkage MethodLinkage Method

DependentDependentCategorization Categorization

AccuracyAccuracy

0.20.20.40.40.60.60.80.81.01.0 0.00.00.20.20.40.40.60.60.80.81.01.0

AL8AL8AL7AL7AL5AL5AL1AL1AL4AL4AL3AL3AL2AL2AL6AL6DR8DR8DR6DR6DR5DR5DR7DR7DR1DR1DR4DR4DR2DR2DR3DR3 0.00.00.20.20.40.40.60.60.80.81.01.0

0.00.00.20.20.40.40.60.60.80.81.01.0 0.00.00.20.20.40.40.60.60.80.81.01.00.00.00.20.20.40.40.60.60.80.81.01.0

AL8AL8AL7AL7AL1AL1AL5AL5AL6AL6AL4AL4AL3AL3AL2AL2DR8DR8DR7DR7DR1DR1DR6DR6DR5DR5DR4DR4DR2DR2DR3DR3





HCA Distinguishes Female AL and DR RatsHCA Distinguishes Female AL and DR Rats

63 variables (confirmed), Non63 variables (confirmed), Non--independent samplesindependent samples

ALALDRDR

ALALDRDR

ALALDRDR

ALALDRDR

ALALDRDR

ALALDRDR

Single Complete Single Complete CentroidCentroid

AutoscaleAutoscale

Range Range ScaleScale

PreprocessingPreprocessingDependentDependent

Categorization Categorization AccuracyAccuracy

100%100%

100%100%

100%100%100%100%100%100%Linkage MethodLinkage Method

DependentDependentCategorization Categorization

AccuracyAccuracy

0.20.20.40.40.60.60.80.81.01.0 0.00.00.20.20.40.40.60.60.80.81.01.0

AL8AL8AL7AL7AL5AL5AL1AL1AL4AL4AL3AL3AL2AL2AL6AL6DR8DR8DR6DR6DR5DR5DR7DR7DR1DR1DR4DR4DR2DR2DR3DR3 0.00.00.20.20.40.40.60.60.80.81.01.0

0.00.00.20.20.40.40.60.60.80.81.01.0 0.00.00.20.20.40.40.60.60.80.81.01.00.00.00.20.20.40.40.60.60.80.81.01.0






HCA Distinguishes Female AL and DR RatsHCA Distinguishes Female AL and DR Rats

63 variables (confirmed), Non63 variables (confirmed), Non--independent samplesindependent samples63 variables (confirmed), Non63 variables (confirmed), Non--independent samplesindependent samples

PCA Distinguishes AL and DR Female RatsPCA Distinguishes AL and DR Female Rats

Autoscale Range ScaleAutoscale Range Scale

Factor1Factor1

Factor2Factor2DRDR ALALFactor3Factor3Factor3Factor3

Factor1Factor1Factor2Factor2

DRDR

ALAL

63 variables (confirmed), Non63 variables (confirmed), Non--independent samplesindependent samples


Autoscale Range ScaleAutoscale Range Scale

Factor1Factor1

Factor2Factor2DRDR ALALFactor3Factor3Factor3Factor3


DRDR

ALAL

HCA Proof of Principle PCA

Biological ModelBiological Model

1400140012001200100010008008006006004004002002000000

2020

4040

6060

8080

100100

DaysDays

Surv

ival

Rat

eSu

rviv

al R

ate

ALALDRDR

Biological ModelBiological Model

1400140012001200100010008008006006004004002002000000

2020

4040

6060

8080

100100

DaysDays

Surv

ival

Rat

eSu

rviv

al R

ate

ALALDRDR

1075 analytically detectable peaks1075 analytically detectable peakssensitivity ~300 sensitivity ~300 pApA = ~10 fmole/125 = ~10 fmole/125 l seral sera

0.0 20.0 40.0 60.0 80.0 100.0

0.00

0.20

0.40

0.60

0.80

Retention time (minutes)Retention time (minutes)

Resp

onse

(µA)

Resp

onse

(µA)

16151413121110987654321

1075 analytically detectable peaks1075 analytically detectable peakssensitivity ~300 sensitivity ~300 pApA = ~10 fmole/125 = ~10 fmole/125 l seral sera

0.0 20.0 40.0 60.0 80.0 100.0

0.00

0.20

0.40

0.60

0.80

Retention time (minutes)Retention time (minutes)

Resp

onse

(µA)

Resp

onse

(µA)

16151413121110987654321

050

100150200250300350

050

100150200250300350

Model Feature Selection

HCA Validation PCA

DRDRALAL

0.00.00.20.20.40.40.60.60.80.81.01.0

94% Accuracy94% Accuracy

CompleteComplete

DR13DR13DR14DR14DR12DR12DR11DR11DR9DR9DR10DR10AL16AL16AL15AL15AL12AL12AL10AL10AL13AL13AL9AL9AL14AL14

AL11DR16DR16DR15DR15

Factor1

Factor3

Factor2

DR

63 variables (confirmed), independent female Cohort #263 variables (confirmed), independent female Cohort #2


AL

Factor1

Factor3

Factor2

DR

63 variables (confirmed), independent female Cohort #263 variables (confirmed), independent female Cohort #2


AL

HCA Simplify Model PCA

ALALDRDR DRDR

ALALDR21DR21AL21AL21AL17AL17AL18AL18AL20AL20AL19AL19AL22AL22DR19DR19DR20DR20DR18DR18DR17DR17

0.00.00.20.20.40.40.60.60.80.81.01.0

36 Variables36 Variables

DR21DR21AL21AL21AL19AL19AL22AL22DR19DR19AL17AL17AL18AL18AL20AL20DR20DR20DR18DR18DR17DR17

0.00.00.20.20.40.40.60.60.80.81.01.0


91% Accuracy91% Accuracy 63% Accuracy63% Accuracy

Removed variables can contribute Removed variables can contribute to ALto AL--DR separationsDR separations

Autoscale, completeAutoscale, complete

63/(37/36) variables (confirmed), Independent female Cohort #363/(37/36) variables (confirmed), Independent female Cohort #3

ALALDRDR DRDR

ALALDR21DR21AL21AL21AL17AL17AL18AL18AL20AL20AL19AL19AL22AL22DR19DR19DR20DR20DR18DR18DR17DR17

0.00.00.20.20.40.40.60.60.80.81.01.0


DR21DR21AL21AL21AL19AL19AL22AL22DR19DR19AL17AL17AL18AL18AL20AL20DR20DR20DR18DR18DR17DR17

0.00.00.20.20.40.40.60.60.80.81.01.0


91% Accuracy91% Accuracy 63% Accuracy63% Accuracy

Removed variables can contribute Removed variables can contribute to ALto AL--DR separationsDR separations

Autoscale, completeAutoscale, complete

63/(37/36) variables (confirmed), Independent female Cohort #363/(37/36) variables (confirmed), Independent female Cohort #3

Factor2Factor2



Factor1Factor1

Factor3Factor3

Factor2Factor2


DRDRDRDR

ALALALAL

PCA Distinguishes AL/DR Using Either PCA Distinguishes AL/DR Using Either DatasetDataset

37 variables (confirmed), Independent female Cohort #337 variables (confirmed), Independent female Cohort #3

Factor2Factor2



Factor1Factor1

Factor3Factor3

Factor2Factor2


DRDRDRDR

ALALALAL

PCA Distinguishes AL/DR Using Either PCA Distinguishes AL/DR Using Either DatasetDataset

37 variables (confirmed), Independent female Cohort #337 variables (confirmed), Independent female Cohort #3

Status

Proof of principle accuracy: HCA (100%)PCA (100%)

Validation Accuracy: HCA (94%)PCA (100%) - subjective rotation

Simplification – HCA (Fails)PCA (100% Accuracy)

Use larger models?Test components vs distance

“Expert Systems/Supervised Analysis”

KNN – k-nearest neighbor analysis– Supervised HCA (HCA is KNN with K=1)– Distance-based metric– Strength is with small (training) datasets

SIMCA – Soft Independent Modeling of Class Analogy– Supervised PCA– Component-based metric– Strength is modeling flexibility (eg, group-specific interactions)

In our DR sera metabolomics data – components greatly outperform distance-based

algorithms

In OUR DR SERA METABOLOMICS data –

components greatly outperform distance-based

algorithms

Profiles are cohort specific

-6-4-2024

-8-6

-4-20246

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t[3]

t[1]

t[2]

male samples modeled with male/female data set

AMALAMDRBMALBMDRCMALCMDR

Cohort Separations

Cohort Effects

-6-4-2024

-8-6

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Cohort SeparationsCohort Separations

-6-4-2024

-8-6

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Cohort SeparationsCohort Separations

-8-6

-4-20

24

68

10

-12

-10

-8

-6

-4

-2

0

24

68

-10-8

-6-4

-20

24

68

10

t[3]

t[1]

t[2]

(a) none

-8-6

-4-20

2

4

6

-10

-8

-6

-4

-2

0

2

46

8

-10-8

-6-4

-20

24

68

10

t[3]

t[1]

t[2]

(b) log

-8-6

-4-20

24

6

8

-10

-8

-6

-4

-2

0

2

46

8

-8-6

-4-2

02

46

810

t[3]

t[1]

t[2]

(c) winsorize (3SD)

-8-6

-4

-2

0

2

4

6

-10

-8

-6

-4

-2

0

2

4

6

8

-12-10

-8-6

-4-2

02

46

810

t[3]

t[1]

t[2]

(f) log + win (2SD)

-8-6

-4-20

2

4

6

8

-10

-8

-6

-4

-2

0

2

4

6

-10-8

-6-4

-20

24

68

10

t[3]

t[1]

t[2]

(e) winsorize (2SD)

-8-6

-4-20

2

4

6

-10

-8

-6

-4

-2

0

2

46

8

-12-10

-8-6

-4-2

02

46

8

t[3]

t[1]

t[2]

(d) log + win (3SD)

AMALAMDRBMALBMDRCMALCMDR (a) no scaling (females)

0.0 0.2 0.4 0.6 0.8 1.0

none

log

winsorize (2SD)

winsorize (3SD)

log + win (2SD)

log + win (3SD)

(e) UV scaling (females)

0.0 0.2 0.4 0.6 0.8 1.0

none

log

winsorize (2SD)

winsorize (3SD)

log + win (2SD)

log + win (3SD)

(c) Pareto scaling (females)

0.0 0.2 0.4 0.6 0.8 1.0

none

log

winsorize (2SD)

winsorize (3SD)

log + win (2SD)

log + win (3SD)

(b) no scaling (males)

0.0 0.2 0.4 0.6 0.8 1.0

none

log

winsorize (2SD)

winsorize (3SD)

log + win (2SD)

log + win (3SD)

(f) UV scaling (males)

0.0 0.2 0.4 0.6 0.8 1.0

none

log

winsorize (2SD)

winsorize (3SD)

log + win (2SD)

log + win (3SD)

(d) Pareto scaling (males)

0.0 0.2 0.4 0.6 0.8 1.0

none

log

winsorize (2SD)

winsorize (3SD)

log + win (2SD)

log + win (3SD)

R2XR2YQ2

(cum)

PLS-DA

PLS-DA

00..000000..110000..220000..330000..440000..550000..660000..770000..880000..990011..0000

Com

p[1]

Com

p[1]

Com

p[2]

Com

p[2]

Com

p[3]

Com

p[3]

--00..2200

00..0000

00..2200

00..4400

00..6600

00..8800

--00..2200

00..0000

00..2200

00..4400

00..6600

00..8800

00..0000 00..1100 00..2200 00..3300 00..4400 00..5500 00..6600 00..7700 00..8800 00..9900 11..000000..0000 00..1100 00..2200 00..3300 00..4400 00..5500 00..6600 00..7700 00..8800 00..9900 11..0000

--66--44--220022446688

--1122 --1100 --88 --66 --44 --22 00 22 44 66 88 1100 1122

t[2]

t[2]

t[1]t[1]

Act

ual A

L pr

obab

ility

Act

ual A

L pr

obab

ility

00..0000

00..2200

00..4400

00..6600

00..8800

11..0000

Predicted AL probabilityPredicted AL probability

p<0.001

13.46713.46721.11721.11770.01770.017

14.97514.97562.94262.942

24.48324.48392.79292.792

65.40865.40893.89293.892

52.85852.858

100.242100.24244.32544.325

40.36740.367

72.34272.342--00..2200

--00..1100

00..0000

00..1100

00..2200

00..3300

--00..1100 00..00 00 00..1100 00 ..2200

w*c

[2]

w*c

[2]

w*c[1]w*c[1]

5.0425.0425.0675.067

5.7585.758

6.4086.408

7.6427.642

8.1838.1839.4429.442

9.459.45

9.8179.817

11.4511.45

11.45811.458

12.60812.608

13.52513.525

14.03314.033

15.47515.475

16.92516.925

16.93316.933

17.47517.475

20.120.1

22.19222.192

22.46722.467

23.523.5

25.49225.49226.39226.39227.14227.142

27.89227.89228.60828.608 28.85828.858

29.31729.317

30.13330.133

30.24230.24231.61731.617

34.78334.783

34.934.934.99234.992

35.535.537.35837.358

39.41739.417

40.29240.29240.440.4

40.82540.825

41.64241.642

43.03343.033

44.22544.225

45.30845.308

45.41745.41746.14246.142

46.57546.575 46.72546.725

47.19247.192

47.44247.442

47.96747.967

48.53348.533

48.80848.808 49.649.6

52.10852.108

54.05854.058

54.28354.283

54.80854.808

55.65855.658

56.11756.117

58.16758.167

61.09261.092

62.49262.492

62.6562.6563.35863.358

67.64267.642

68.57568.57568.668.668.768.7

69.0569.05

73.07573.075

75.48375.483

75.52575.525

76.276.277.44277.442

77.91777.91782.22582.225

84.80884.808

CRCR

ALAL

Actual Degree of Restriction

40 50 60 70 80 90 100 110 120

Predic

ted De

gree R

estrict

ion

40

50

60

70

80

90

100

110

120

r ² = 0.877; r ² = 0.994 (means)

N = 90Each group p<0.05 vs others

w*c[1]

5.042

5.067

5.758

6.408

7.642

8.183

9.4429.45

11.308

11.4511.458

13.467

13.525

14.975

15.475

16.925

16.93317.475

21.117

22.192

23.5

24.483

25.492

26.392

27.14227.892

28.60828.858

29.31730.108

30.133

30.24234.483

34.783

34.934.992

35.5

37.358

39.417

40.292

40.4

41.642

43.033

44.225

44.325

45.308

45.417

46.142

47.19247.442

47.967 48.533

48.808

49.652.108

52.858

54.058

54.283

54.808

55.658

56.117

58.167

61.09262.442

62.492

62.65

62.942

63.358

65.408

67.642

68.57568.6

68.7

70.017

72.933

73.075

75.483

75.525

76.2

77.442

77.867

77.917

81.782.225

84.808

92.792100.242

INTAK

E

LK w*c

[2]Actual Degree of Restriction

40 50 60 70 80 90 100 110 120

Predic

ted De

gree R

estrict

ion

40

50

60

70

80

90

100

110

120

r ² = 0.877; r ² = 0.994 (means)

N = 90Each group p<0.05 vs others

w*c[1]

5.042

5.067

5.758

6.408

7.642

8.183

9.4429.45

11.308

11.4511.458

13.467

13.525

14.975

15.475

16.925

16.93317.475

21.117

22.192

23.5

24.483

25.492

26.392

27.14227.892

28.60828.858

29.31730.108

30.133

30.24234.483

34.783

34.934.992

35.5

37.358

39.417

40.292

40.4

41.642

43.033

44.225

44.325

45.308

45.417

46.142

47.19247.442

47.967 48.533

48.808

49.652.108

52.858

54.058

54.283

54.808

55.658

56.117

58.167

61.09262.442

62.492

62.65

62.942

63.358

65.408

67.642

68.57568.6

68.7

70.017

72.933

73.075

75.483

75.525

76.2

77.442

77.867

77.917

81.782.225

84.808

92.792100.242

INTAK

E

LK w*c

[2]w*c[1]

5.042

5.067

5.758

6.408

7.642

8.183

9.4429.45

11.308

11.4511.458

13.467

13.525

14.975

15.475

16.925

16.93317.475

21.117

22.192

23.5

24.483

25.492

26.392

27.14227.892

28.60828.858

29.31730.108

30.133

30.24234.483

34.783

34.934.992

35.5

37.358

39.417

40.292

40.4

41.642

43.033

44.225

44.325

45.308

45.417

46.142

47.19247.442

47.967 48.533

48.808

49.652.108

52.858

54.058

54.283

54.808

55.658

56.117

58.167

61.09262.442

62.492

62.65

62.942

63.358

65.408

67.642

68.57568.6

68.7

70.017

72.933

73.075

75.483

75.525

76.2

77.442

77.867

77.917

81.782.225

84.808

92.792100.242

INTAK

E

LK w*c

[2]

Markers “Predict” Caloric Intake with High Quantitative

Accuracy-- Proof of Concept --

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

t[2]O

t[1]P

female basic model.M1 (OPLS)t[Comp. 1]/t[Comp. 2]Colored according to value in variable female basic model(Group Y new)

R2X[1] = 0.105822 R2X[2] = 0.162921 Ellipse: Hotelling T2 (0.95)

Series (Variable Group Y new)

1 - 1.51.5 - 2

SIMCA-P 11 - 3/22/2006 2:34:11 PM

O-PLS models built for better testing

Iteratively improve models – focus on

analytical robustness

Then test one model…

-6-4-20246

024681012141618-6-4-202468

AGE Duration Extent

010203040-6-4-20246

Plot 1 egroup vs m17e Plot 2 Regr

O-P

LS P

redi

ctio

n S

core

(A.U

.)

Weeks on CR Diet Percent RestrictionAL [Months of Age] CR

6 12 18 24 30 6 12 18 24 30 r ² = 0.645

Validation: Across Lifespan

-6-4-20246

024681012141618-6-4-202468

AGE Duration Extent

010203040-6-4-20246


O-P

LS P

redi

ctio

n S

core

(A.U

.)


6 12 18 24 30 6 12 18 24 30 r ² = 0.645

Validation: Duration

-6-4-20246

024681012141618-6-4-202468

AGE Duration Extent

010203040-6-4-20246


O-P

LS P

redi

ctio

n S

core

(A.U

.)


6 12 18 24 30 6 12 18 24 30 r ² = 0.645

Validation: Extent

Percent Restriction01020304050-4-20246

r ²0.636

r ² = 0.338Only including 8 week CRNo AL group

Percent Restriction

OP

LS S

core

AU

0 10 20 30 40

OP

LS S

core

AU

-8

-6

-4

-2

0

2

4

6

8Includes 12 and 18 AL and CRr ² = 0.636

Percent Restriction01020304050-4-20246

r ²0.636

r ² = 0.338Only including 8 week CRNo AL group

Percent RestrictionO

PLS

Sco

re A

U0 10 20 30 40

OP

LS S

core

AU

-8

-6

-4

-2

0

2

4

6

8Includes 12 and 18 AL and CRr ² = 0.636

Validation: Extent

Experimental Design

AL vs DRAnalytical

IssuesBiological

Issues

0.00 0.12 0.24 0.36 0.48 0.600.00

0.12

0.24

0.36

0.48

0.60

0.00 0.15 0.30 0.45 0.60 0.750.00

0.15

0.30

0.45

0.60

0.75

0.00 0.15 0.30 0.45 0.60 0.750.00

0.15

0.30

0.45

0.60

0.75

0.00 0.25 0.50 0.75 1.00 1.25 1.500.00

0.25

0.50

0.75

1.00

1.25

1.50

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.000.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0.00 0.25 0.50 0.75 1.00 1.25 1.500.00

0.25

0.50

0.75

1.00

1.25

1.50

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.000.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0.00 0.25 0.50 0.75 1.00 1.25 1.500.00

0.25

0.50

0.75

1.00

1.25

1.50

MDRCV0.00 0.25 0.50 0.75 1.00 1.25 1.50

ANAL

CV1

0.00

0.25

0.50

0.75

1.00

1.25

1.50

0.00 0.25 0.50 0.75 1.00 1.250.00

0.25

0.50

0.75

1.00

1.25

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.000.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

r ² = 0.01

r ² = 0.31

r ² = 0.00 r ² = 0.01r ² = 0.01

MALCV FALCV

MDRCVMALCVFALCVMALCV

ANALCV1ANALCV2ANALCV1

FDRCV

ANAL

CV1

ANAL

CV1

ANAL

CV1

FDRC

V

FALC

V

FDRC

V

MDR

CV

ANAL

CV2

ANAL

CVT

ANAL

CVT

r ² = 0.75 r ² = 0.55 r ² = 0.36

r ² = 0.31 r ² = 0.25 r ² = 0.09

AvAAvA

AL AL vs vs DRDR

Biological Biological vs vs AnalyticalAnalytical

In Rats: Biological variability 5 fold greater than analytical variability Analytical variability does not influence biological variability

Analytical vs Biological Variation

Human Studies: Profile = …

• Recent Food (ie, Fast)• BMI• Food intake• Physiology


• Recent Food (ie, Fast)–Effect size ~ 0.2*StDev

• BMI• Food intake• Physiology

Profile Score (Individual)

-8 -6 -4 -2 0 2 4 6 8

BM

I

15

20

25

30

35

40

45

-8 -6 -4 -2 0 2 4 615

20

25

30

35

40

45

Profile Score (3 point mean)

Profile Score Does Not Reflect BMI

Human Studies: FFQ vs score (individuals)

1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

-8

-6

-4

-2

0

2

4

6

8

10

f(x) = 0.00100965579286458 x − 1.92440997194325R² = 0.0329559398823117

Extreme FFQ Quintiles I• No obvious association with individual

FFQs, but…

• FFQs are known to be weakly predictive of individual caloric intake

• But extreme quintiles on the FFQ should differ – or we would likely never see anything in epidemiology

Extreme FFQ Quintiles II

1200 1400 1600 1800 2000 2200 2400 2600

-0.80-0.60-0.40-0.200.000.200.400.60f(x) = 0.0010794306233 x − 2.0509182856653R² = 0.833550524003422

FFQ Quintiles “predict” score

Individual values in extreme quintiles

differ, p<0.005

Extreme FFQ Quintiles II

1200 1400 1600 1800 2000 2200 2400 2600

-0.80-0.60-0.40-0.200.000.200.400.60f(x) = 0.0010794306233 x − 2.0509182856653R² = 0.833550524003422

-0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.600

50010001500200025003000

f(x) = 772.21315200072 x + 1881.4884123559R² = 0.833550524003422

FFQ Quintiles “predict” scoreScore “predicts” FFQ quintile


• Recent Food (ie, Fast)• BMI• Food intake • Physiology

Testing Physiology

• Hypothesis: Profile score will differ/predict future risk of diseases reduced by CR, eg, breast cancer

• Proposed 1000 cases/paired controls for breast cancer– Nested within Nurses’ Health Study– Samples taken 2-10 years before onset

• Initial funding, 2 years, 750 case control pairs

• Today, 210 case:control pairs (first blind break)

Case-Controls - DesignUsing the raw RAT models, no corrections, no optimization except for those that addess analytical issues in humans All observations that served as both cases and controls (due to conversion) were excluded for this analysis 210 total case/control pairs scored The original model currently the best, so I'll present those numbers...others are close

Case-Controls - Data

These slides not cleared for posting at cohort study level, contact me at [email protected] if you have questions

mailto:[email protected]


• Recent Food (ie, Fast)• BMI• Food intake • Physiology

SummaryCreated and validated a working model of the CR serotype

in both male and female rats

Profiles distinguish diet in blinded studies

Markers pass analytical tests in human plasma

Rat profiles pass key tests in human case/control studies

Other studies ongoing, lipids, macronutrient shifts

The metabolomic markers and profiles identified appear analytically and biologically suitable for studies in defined human populations such as national clinical trials and epidemiological cohorts

Data suggests that the protective phenotype exemplified in the CR rat is broadly conserved across species

Data suggests that we have the “raw material” needed to build marker profiles that can be tailored to provide a continuous ruler for objective measurement of factors such as caloric intake in epidemiological and clinical studies

Data suggests that we have the “raw material” to begin to address personalized disease risk from the nutritional/environmental side

Long-Range Disease Risk Prediction – Algorithmic Information Fusion in a Life and Death Environment

OpportunitiesGenetics

Demographics

Questionnaires

Clinical data

Metabolomics, proteomics, lipidomics = GxE


ComplicationsGenetics…low effect size, low proportion explained, low penetrance, multi-gene effects, sequence vs SNP, Epigenome vs sequence, tissue specificityDemographics…definition and measurement problemsQuestionnaires…people lie, people are biased liars, questions are *hard* and often population specificClinical data… limited, costly, by the time you have it may be too late, thresholds, different data structure/distributionsMetabolomics… proteomics, lipidomics = GxE, very complex interactions, time effects, diet effects


ComplicationsAre we limited to decision level analysis?


ComplicationsAre we limited to decision level analysis?

No… Biology-based fusion works


Step 1What is Success?What is Failure?

How do we balance Success or FailureWhat does it mean to optimize?

How do we assess?

Long-Range Disease Risk Prediction

Algorithmic Information Fusion

in a Life and Death Environment

Acknowledgements• Brigham and Women’s Hospital and Burke/Cornell

– Yevgeniya Shurubor, Honglian Shi, Diane Sheldon, Sophie Guo, Susan (Schiavo) Bird, Rose Gathungu

– Ugo Paolucci, Ruiwen Zhou, Vasant Marur, Neil Russell, Matt Sniatynski

• Outside Collaborators– Walter Willett, Sue Hankinson, Frank Hu, Paul Vouros – Wayne Matson, Karen Vigneau-Callahan, Paul Milbury– Tom Vogl, Frank Hsu, Christina Schweikert

• Funding– NIH (NIA; NCI; NIEHS)– Winifred Masterson Burke Relief Foundation– Brigham and Women’s Hospital, and Dept of

Neurosurgery

Acknowledgments

Bioinformatics

Animal Studies and Mitochondria Research

Analytical Work

Download - Overall Relative Cancer Risk (BMI >40 vs 18.5 to 24.9):

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