Metabolomic changes responsive to diet, excercise and an oral glucose tolerance test (OGTT)

7/29/2019 Metabolomic changes responsive to diet, excercise and an oral glucose tolerance test (OGTT)

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Oral Glucose Tolerance Test (OGTT)

Metabolomics: identification of type 2

diabetes effects on primary metabolism

Dmitry Grapov, et al.


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What is type 2 diabetes mellitus (T2DM)?

Clinically T2DM is:

Can we do a better job by defining the metabolic

signature of T2DM?

type 2

diabetic


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No Data 9.0%

19942000

2009

National trends for T2DM

FY2011 Obama administration proposes around $159.3 billion for the

Iraq and Afghanistan wars (wikipedia)


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Study Design

CohortOverweight women (12-15, obese sedentary, 100 < glucose < 128 mg/dL )

14 week diet* and exercise intervention

Weight management and exercise (4 days/week, 30-40 min)*same diet for all subjects = same background metabolites due to diet!

Sean Adams


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Study Design (cont.)

14 week diet and exercise intervention

OGTT / exercise

intervention

MeasurementsPre- and post-intervention

During Exercise

During oral glucose tolerance test (OGTT)Primary metabolites (n > 300) by GC/TOF

Clinical panel: insulin, glucose, lipids

0, 30, 60, 90, 120 minutes


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Study Goals: identify metabolites that are responsive toexercise, OGTT, and changes in insulin sensitivity

Study factors (X) :1. Dietary intervention leading to weight loss and increased fitness

2. Exercise

3. OGTT4. Insulin sensitivity

Questions:1. how does X affect metabolite baselines and excursion profiles

2. what are the independent metabolic markers for X

3. what is the biological consequence of metabolic changes associated with X

* Analyses I will talk about


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Dimensionality (n x m, 150 x 321)

Data Properties:

2 endpoints

(10) 5 time-points

15 samples x 3210

(48, 150) 321 metabolites x 10

(3210)

Structure

2 endpoints

5 time-points

15 samples 321 metabolitesn

m


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Metabolic Properties of Interest:

Excursions

Baseline and Area Under the Curve (AUC)


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Univariate Statistics:

Intervention associated effects: mixed effects models for changesin baseline and AUC

OGTT or exercise associated effects: one-sample t-Test (AUC 0)or ~2-way ANOVA with repeated measures

Some considerations Normality

Independence

False Discovery Rate

risk =

1-(1-p.value)tests


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Getting those significant differences is a

function of:significance level () and power (1- )

effect size (standardized difference in means)

sample size (n)


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Multivariate Analyses

Examples

1. Principal Components Analysis (PCA)

Unsupervised projection of X based on maximum

variance (exploratory)

2. Partial Least Squares Projection to Latent Structures(PLS)

Supervised projection of X based on maximum

correlation to Y (test of hypothesis)


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Interpreting PCA Results

Variance explained (eigenvalues)

Row (sample) scores and column (variable) loadings


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PCA example (OGTT time course data)

*no scaling or centering

glucose

219021


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How are scores and

loadings related?


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Data scaling is very important!

*autoscaling (unit variance and centered)

glucose

(GC/TOF)

glucose (clinical)

219021


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Intervention adjusted PLS model: Scores

Loadings on thefirst latent

variable (x-axis)

can be used to

interpret the

multivariate

changes in

metabolites

which are

correlated withtime during the

OGTT

time = 0 120 min.


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goodness of the model is all about the

perspective

Determine in-sample (Q2) and out-

of-sample error (RMSEP) andcompare to a random model

permutation tests

training/testing


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Model Training/Testing

Data set selection/splitting is not trivial

Where should

these guys go?


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Use networks to interpret statistical and

multivariate results within a biochemical and

mathematical context

Make aBasic network ingredients

adjacency matrix (what is connected)product/precursor (KEGG rpairs)

chemical similarity (Tanimoto distances)

Dependency (partial correlation)

Vertex and edge attributes(legend)


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Example: OGTT metabolomic networkVertex

Size =importance,

|PLS coefficient|

Color =

direction of

change, sign ofcoeff.

Border = p-

value < 0.05

(ANCOVA)

Edges

Tanimoto

similarity > 70

with time:

pink ()

cyan ()


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pink ()

cyan ()

?

?


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Gaussian Markov Network (intervention)


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Gaussian Markov Network (cont.)


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Future Goals

ExCytR: Excel + Cytoscape + R = Awesome

GUI for generating mapped networks

Devium -

Dynamic Multivariate Data Analysis and Visualization Platform

GUI for multivariate analysis of Omics data

Successor to imDEV

Fork me on GitHub: https://github.com/dgrapov/devium

The stack:

Metabolomic changes responsive to diet, excercise and an oral glucose tolerance test (OGTT)

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