T2D Parameter Connectivity Non-T2D Parameter Connectivity

Dmitry Grapov1, Sean H. Adams2,3, W. Timothy Garvey4, Kerry H. Lok4,John W. Newman2,3

1Agricultural and Environmental Chemistry, University of California Davis, 2Obesity & Metabolism Research Unit, USDA-ARS Western Human Nutrition Research Center, Davis, CA, 3Nutrition, University of California Davis, Davis, CA, 4Nutrition Sciences, University of Alabama, Birmingham, AL

Results ConclusionsAbstractOPLS-DA Models of Entire Population

Discriminate T2D but not UCP3 sub-populations

Fig 2: Scores plot of OPLS-DA models built from a subset of metabolites selected for optimal sub-population discrimination. Physical and clinical parameter driven models (i.e. fasting glucose, BMI, triglycerides) perform worse than metabolite driven models: 11% v. 7% misclassification).

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g/g T2Dg/a T2Dg/g non-T2Dg/a non-T2DTest Set

Regression analyses of the OPLS-DA Latent Variable Discriminating T2D

Fig 3A: OPLS-DA Latent Variable and the log of the fasting glucose are linearly correlated.

Fig 3B: OPLS-DA Latent Variable and BMI of non-T2D but not to T2D are linearly correlated.

R2 = 0.508p<1e-7

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Type 2 Diabetes Effects

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MAGs NAEs C18-epoxides

MUFA/Sat. 5-LOX

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g/a UCP3g/g UCP3

Increased in T2D

Summarized effects of T2D:

1. Metabolites correlated to BMI change e.g.: monoacylglycerols N-acylethanolamides

2. Metabolites marking glucose control increase 18 carbon epoxides MUFA and Sats

3. 5-lipoxygenase activity, potentially linked to insulin secretion.

Methods

Data Interpretation Strategy

Univariate Tests(t-tests with FDR) Megavariate Analysis

PCA OPLS-DA

Feature Selection

Model ValidationModel Comparison

Empirical Metabolic Networks

Correlation Analysis

Multi-Dimensional Scaling

Significance Testing

Network VisualizationBiological Interpretation

Metabolomic Analysis• Fasting plasma• Selected Reaction Monitoring mode LC-ESI-MS/MS (OxLs and eCBs)• Selected Ion Monitoring mode GC-EI-MS (NEFA)

Statistical Analysis Software• R (http://www.R-project.org)• SIMCA-P+ 12.0 (http://www.umetrics.com/simca)

AbbreviationsNEFA (non-esterified free fatty acids); eCBs (endocannabinoids); OxL (oxylipins); T2D (type

2 diabetic); UCP3 (uncoupling protein 3); PUFA (polyunsaturated fatty acid); Sat(saturated fatty acid); MAG (monoglycerol); NAE (N-acylethanolamide); FA-AA (fatty acyl amino acid); c18 (18 carbon); c20 (20 carbon); VIP (variable’s importance on projection); PPAR (peroxisome proliferator-activated receptor); n-3 (omega-3)

Bioactive lipid markers of Type 2 Diabetes and UCP3 gene polymorphism in obese African-American women

Only 10% of T2D-dependent metabolic changes are conserved between UCP3

Genotypes

Fig 1: A Venn Diagram of plasma metabolites significantly altered in T2D with UCP3 polymorph interactions. A 62%increase in 4 MUFA species occurred in all T2Ds regardless of UCP3 genotype.

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28 7

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g/a UCP3g/g UCP3Significance determined from two-tailed Student’s t-tests (p<0.05 , q=0.1) evaluated on log transformed metabolite concentrations.

Bioactive lipids involvement in T2D development

BackgroundBiosynthetic Sources of NEFAs, eCBs and OxLs

Non-esterified free fatty acids (NEFA), endocannabinoids (eCBs) and oxylipins (OxL) are hypothesized to influence and/or reflect insulin resistance (IR) and lipid/carbohydrate fuel selection. We further hypothesize that the quantitative metabolomic investigation of these lipid classes will provide metabolic signatures of organismal fat oxidation and insulin sensitivity. To test this hypothesis we conducted a study to evaluate the ability of these metabolites to report clinical diabetes and changes in mitochondrial function in a cohort of BMI- and age-matched obese African-American women containing both diabetic (n=43) and non-diabetic (n=12) subjects equally distributed between the wildtype (g/g) and missense (g/a) UCP3 genotypes. This study aims to answer the following:

Using an array of megavariate statistics we identified previously unknown relationships between NEFA, eCBs and 18 carbon epoxides (C18-epoxides). These relationships only emerge in the T2D state. Changes in these species are shown to be dependent on UCP3 function, suggesting evidence for UCP3’s role in modifying the observed T2D phenotype.

1. Can bioactive lipids be used to predict the T2D phenotype and UCP3 gene polymorphism?

2. Do circulating lipid markers of UCP3 polymorphism provide insight into UCP3 function?

3. Does UCP3 impact the metabolic signature of T2D?

OPLS-DA Models of T2D Effects Among UCP3 Sub-populations

Fig 4: Scores plots of T2D v non-T2D OPLS-DA models for (A) g/g UCP3 and (B) g/a UCP3 subjects. Sub-group discrimination is greatly improved relative to whole population analysis (see Fig 2).

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4 3 1 6 2 7 5Parameter Groups

Load

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g/a UCP3g/g UCP3

Increased in T2D

VIP

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Fig 5: Bar graph of loadings of T2D v non-T2D OPLS-DA models for UCP3 sub-populations. The relative importance of metabolites for T2D discrimination is influenced by UCP3 polymorphism.

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UCP3 effects are more evident in non-T2D compared to T2D

Fig 6: Scores plots of g/g v g/a UCP3 OPLS-DA models for (A) non-T2D and (B) T2D subjects. Models can predict UCP3 genotype better in non-T2D compared to T2D subjects.

Fig 7: Bar graph of loadings of g/g v g/a UCP3 OPLS-DA models for the T2D sub-populations. Relative increases in LA-polyols are associated with g/a UCP3 regardless of T2D status.

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Multivariate Linear Parameter Connectivities are Shifted by T2D Status

Fig 8: Undirected graph representing multi-dimensionally scaled correlations among parameters in (A) non-T2D (B) T2D subjects.Graphed edges (i.e. lines between nodes) indicate positive (orange) and negative (blue) bivariate correlations (α=0.05). Increases in node proximity indicates increases in multivariate correlation strength. Ellipses indicate the 95% confidence interval of lipid classes grouped by biochemical and structural similarities.

T2D x UCP3 Effects

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Increased in T2D Varied

This work was supported by USDA-ARS Project 5306-51530-016-00D, NIH-NIDDK R01DK078328-01, T32-GM08799, NIH grants DK-038764, DK-083562, and P01 HL-055782 and the Merit Review program of the Department of Veterans Affairs (W.T.G.). The authors also acknowledge support from the research core facilities of the UAB Center for Clinical and Translational Science (UL1 RR025777), the UAB Nutrition and Obesity Research Center (P30-DK56336), and the UAB Diabetes Research and Training Center (P60 DK079626).

Evidence of UCP3 and T2D interaction:

1. Compared to g/g carriers, T2D-associated metabolites are:

in g/a non-T2D subjects

in g/a T2D subjects

Interpretative SummaryTargeted lipidomic analysis including NEFA, eCBs,

and OxLs successfully discriminate both T2D and UCP3 dependent phenotypes.

Circulating markers of UCP3 activity suggest impacts on insulin signaling and oxidative stress responses.

UCP3 dysfunction reduces markers of T2D severity in clinical T2D subjects.

BMI

eCBs and OxL

NEFA

fasting glucose and HbA1c

MAGs A B

NEFA

NAEs

C18-epoxides

fasting glucose and HbA1c

MAGs

BMI

UCP3 Effects

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5-LOX LA-polyols n-3 OXL SEALoad

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T2D

Increased in g/a UCP3

Summarized effects of G304A UCP3:

1. linoleate di- and tri- hydroxyls may be related to increased oxidative stress.

2. 5-LOX metabolites in non-T2D may signify decreased stimulation of insulin secretion

3. steroylethanolamide (SEA) in T2D, an endogenous PPARα agonist.