1. Metabolomic analysis of plasma samples from obese African American women identified lipid markers that can predict type 2 diabetes (T2D) status and polymorphisms in the UCP3 gene. 2. Certain monoacylglycerols, N-acylethanolamides, and 18-carbon epoxides were increased in T2D and correlated with fasting glucose levels, while 5-lipoxygenase activity was decreased. 3. The effects of T2D on lipid markers were modified by UCP3 genotype, with linoleate di- and tri-hydroxyls and steroylethanolamide increased in the UCP3 g/a genotype compared to the g/

1. Bioactive lipid markers of Type 2 Diabetes and UCP3 gene polymorphism in obese
African-American women
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
Abstract Results Conclusions
Non-esterified free fatty acids (NEFA), endocannabinoids
Only 10% of T2D-dependent metabolic OPLS-DA Models of Entire Population Regression analyses of the OPLS-DA Increased
(eCBs) and oxylipins (OxL) are hypothesized to influence in T2D Type 2 Diabetes Effects
changes are conserved between UCP3 Discriminate T2D but not UCP3 Latent Variable Discriminating T2D
and/or reflect insulin resistance (IR) and lipid/carbohydrate
Genotypes sub-populations
fuel selection. We further hypothesize that the quantitative 2.45
1
g/a UCP3
metabolomic investigation of these lipid classes will provide 2.35
0.8
g/g UCP3
log(Fasting Glucose)
metabolic signatures of organismal fat oxidation and insulin 2.5 2.25
0.6
sensitivity. To test this hypothesis we conducted a study to
2.15
2
All 2
R = 0.508
p<1e-7 2.05
evaluate the ability of these metabolites to report clinical
Loadings 1
1.5 0.4
1.95
diabetes and changes in mitochondrial function in a cohort of 1 1.85
BMI- and age-matched obese African-American women 33 0.5 1.75
0.2
-5 -4 -3 -2 -1 0 1 2 3
containing both diabetic (n=43) and non-diabetic (n=12) 0
23
0 t[1]
6
t[2]
subjects equally distributed between the wildtype (g/g) and -6 -5 -4 -3 -2 -1 0 1 2 3
4 -0.5 Fig 3A: OPLS-DA Latent Variable and the -0.2
missense (g/a) UCP3 genotypes. This study aims to answer 28 7 -1 log of the fasting glucose are linearly MAGs NAEs C18- MUFA/Sat. 5-LOX
epoxides
the following: 4 g/g T2D -1.5 correlated.
-0.4
g/a T2D
1. Can bioactive lipids be used to predict the T2D -2
Summarized effects of T2D:
52
g/g non-T2D
phenotype and UCP3 gene polymorphism? g/g UCP3 g/a UCP3 -2.5 47
g/a non-T2D
2. Do circulating lipid markers of UCP3 polymorphism Significance determined from two-tailed Student’s t-tests (p<0.05 , q=0.1) Test Set -3 42
1. Metabolites correlated to BMI change e.g.:
provide insight into UCP3 function? evaluated on log transformed metabolite concentrations. t[1] 37
  monoacylglycerols
BM I
2
R = 0.45
3. Does UCP3 impact the metabolic signature of T2D? p<0.02 32
Fig 1: A Venn Diagram of plasma Fig 2: Scores plot of OPLS-DA models built 27   N-acylethanolamides
Using an array of megavariate statistics we identified metabolites significantly altered in T2D with from a subset of metabolites selected for 22
previously unknown relationships between NEFA, eCBs and UCP3 polymorph interactions. A 62% optimal sub-population discrimination. 17 2. Metabolites marking glucose control increase
18 carbon epoxides (C18-epoxides). These relationships increase in 4 MUFA species occurred in all Physical and clinical parameter driven models
-5 -4 -3 -2 -1
t[1]
0 1 2 3
  18 carbon epoxides
only emerge in the T2D state. Changes in these species are T2Ds regardless of UCP3 genotype. (i.e. fasting glucose, BMI, triglycerides) perform Fig 3B: OPLS-DA Latent Variable and   MUFA and  Sats
shown to be dependent on UCP3 function, suggesting worse than metabolite driven models: 11% v. 7% BMI of non-T2D but not to T2D are
evidence for UCP3’s role in modifying the observed T2D misclassification). linearly correlated. 3.  5-lipoxygenase activity, potentially linked to
phenotype. insulin secretion.
OPLS-DA Models of T2D Effects Among UCP3 Sub-populations UCP3 effects are more evident in non-T2D compared to T2D
6 3
g/g non-T2D 6
g/a non-T2D g/g non-T2D 5
g/g T2D
A g/g T2D B A B
Background g/a non-T2D Increased in
5
g/a T2D 2 4 g/a T2D
Test Set
4
4
Test Set
1
Test Set
3
Test Set g/a UCP3 UCP3 Effects
3 2
2 1
0
Biosynthetic Sources of NEFAs, eCBs and OxLs 2
0 -3 -2 -1 0 1 2 3 1
non-T2D
to[1]
-4 -3 -2 -1 0 1 2
to[1]
to[1]
to[1]
1 -1 0
0.5
0
-2 -3 -2 -1
-1
0 1 2 3 4
T2D
-5 -4 -3 -2 -1 0 1 2 3 -2
-1 -4 -2
-3
0
Loadings1
-2 -3
-6
-3 -4
-4
-8
-4 -5 -5 -0.5 5-LOX LA-polyols n-3 OXL SEA
t[1] t[1]
t[1] t[1]
-1
Fig 4: Scores plots of T2D v non-T2D OPLS-DA models for (A) g/g Fig 6: Scores plots of g/g v g/a UCP3 OPLS-DA models for (A) non-
UCP3 and (B) g/a UCP3 subjects. Sub-group discrimination is greatly T2D and (B) T2D subjects. Models can predict UCP3 genotype better -1.5
improved relative to whole population analysis (see Fig 2). in non-T2D compared to T2D subjects.
Increased
g/a UCP3 VIP Increased in VIP Summarized effects of G304A UCP3:
in T2D g/a UCP3
g/g UCP3 non-T2D
Bioactive lipids involvement in T2D development 1.  linoleate di- and tri- hydroxyls may be related to
1
1 T2D increased oxidative stress.
0.8
0.6
4 3 1 6 2 7 5
2.  5-LOX metabolites in non-T2D may signify
0.5
0.4
4 3 1 6 2 7 5
decreased stimulation of insulin secretion
0.2
0 3.  steroylethanolamide (SEA) in T2D, an
Loadings 1
Loadings 1
0 endogenous PPARα agonist.
-0.2
n-3 OXL
5-LOX
oxidation
MUFA/Sat.
MAGs
NAEs
Epoxides
LA-polyols
SEA
-0.5 Auto-
Increased in
C18-
MUFA/Sat.
PUFA
5-LOX
NA-AA
MAGs
NAEs
epoxides
Epoxides
n-3 OXL
oxidation
LA-polyols
VLC-
-0.4
Auto-
C18-
C20-
g/a UCP3 T2D x UCP3 Effects
-0.6 0.4
-1 non-T2D
-0.8 0.3
T2D
-1 Param eter Groups
4 3 1 6 2 7 5 0.2
4 3 1 6 2 7 5 -1.5 Param eter Groups
0.1
Methods Fig 5: Bar graph of loadings of T2D v non-T2D OPLS-DA models for Fig 7: Bar graph of loadings of g/g v g/a UCP3 OPLS-DA models
1
Loadings
UCP3 sub-populations. The relative importance of metabolites for T2D for the T2D sub-populations. Relative increases in LA-polyols are 0
Metabolomic Analysis discrimination is influenced by UCP3 polymorphism. associated with g/a UCP3 regardless of T2D status. -0.1
• Fasting plasma MAGs NAEs C18-Epoxides MUFA/Sat. Auto-oxidation
• Selected Reaction Monitoring mode LC-ESI-MS/MS (OxLs and eCBs) -0.2
• Selected Ion Monitoring mode GC-EI-MS (NEFA) Multivariate Linear Parameter Connectivities are Shifted by T2D Status
-0.3
Statistical Analysis Software Varied
Decreased Increased in T2D
• R (http://www.R-project.org)
Fig 8: Undirected graph
A B
• SIMCA-P+ 12.0 (http://www.umetrics.com/simca) -0.4 in T2D
MAGs eCBs and OxL representing multi-
Abbreviations
NEFA (non-esterified free fatty acids); eCBs (endocannabinoids); OxL (oxylipins); T2D (type dimensionally scaled Evidence of UCP3 and T2D interaction:
2 diabetic); UCP3 (uncoupling protein 3); PUFA (polyunsaturated fatty acid); Sat correlations among
(saturated fatty acid); MAG (monoglycerol); NAE (N-acylethanolamide); FA-AA (fatty BMI 1. Compared to g/g carriers, T2D-associated metabolites
acyl amino acid); c18 (18 carbon); c20 (20 carbon); VIP (variable’s importance on parameters in (A) non-
projection); PPAR (peroxisome proliferator-activated receptor); n-3 (omega-3)
T2D (B) T2D subjects.
are:
Data Interpretation Strategy NAEs Graphed edges (i.e. lines   in g/a non-T2D subjects
BMI between nodes) indicate
Univariate Tests
(t-tests with FDR)
Megavariate Analysis Empirical Metabolic Networks positive (orange) and   in g/a T2D subjects
MAGs negative (blue) bivariate
fasting glucose correlations (α=0.05). Interpretative Summary
PCA OPLS-DA Correlation Analysis
and HbA1c C18- Increases in node
epoxides proximity indicates  Targeted lipidomic analysis including NEFA, eCBs,
increases in multivariate and OxLs successfully discriminate both T2D and
NEFA correlation strength.
Feature Selection
Multi-Dimensional
Scaling UCP3 dependent phenotypes.
Ellipses indicate the 95%
confidence interval of  Circulating markers of UCP3 activity suggest
fasting glucose lipid classes grouped by
Model Comparison Model Validation
Significance Testing
NEFA impacts on insulin signaling and oxidative stress
and HbA1c biochemical and
structural similarities.
responses.
Biological Interpretation Network Visualization
Non-T2D Parameter Connectivity T2D Parameter Connectivity  UCP3 dysfunction reduces markers of T2D
severity in clinical T2D subjects.
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).