Download Metabolomic Profiling Identifies Biomarkers Associated

Metabolomic Profiling Identifies Biomarkers Associated with Dysglycemia
and Incident Type 2 Diabetes in the METSIM Study
www.metabolon.com | 919.572.1711
JEFF COBB1, HENNA CEDERBERG2, KIRK PAPPAN1, MATTHEW MITCHELL1, KLAUS-PETER ADAM1, PHILIP GUNST1, JOHANNA KUUSISTO2, MARKKU LAAKSO2
1
METABOLON, DURHAM N.C., U.S., 2UNIVERSITY OF EASTERN FINLAND AND KUOPIO UNIVERSITY HOSPITAL, DEPARTMENT OF MEDICINE, KUOPIO, FINLAND
n
Type 2 diabetes can be delayed or prevented with lifestyle or
drug interventions in at risk subjects. There remains a need to
better identify these at risk subjects as they would be expected
to benefit the most from early intervention.
The aim of this work was to identify candidate biomarkers of
dysglycemia for possible use in the prediction of incident type
2 diabetes.
METHODS
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The METSIM (METabolic Syndrome In Men) study is an
observational study in 10,000 Finnish men who live near the
city of Kuopio.1 Cases and controls for this work are subsets
from the METSIM study. Cases include 220 subjects who
were not diabetic at baseline but were found to be diabetic
at the 5 year follow up time point. Most cases exhibited
some form of dysglycemia at baseline with 45% being IFG,
7% IGT, and 38% combined IFG/IGT. Controls (n=440) are
age-matched subjects who had normal fasting glucose and
normal glucose tolerance at both baseline and the 5 year
follow up time point.
Biochemical
Extraction
Plasma
Samples
Instrumentation
UHPLC-MS/MS (-ESI)
Biochemical Analysis
Fasting plasma levels of the metabolites a-hydroxybutyrate (AHB),
linoleoylglycerophosphocholine (LGPC), and oleate correlate with
the Mwbm measure of insulin sensitivity from the hyperinsulinemic
euglycemic clamp.3,4
The Quantose algorithm is a multiple linear regression (natural log
transformed) on the fasting plasma levels of insulin, AHB, LGPC,
and oleate used to estimate Ln(Mwbm).
Quantose metabolites were measured quantitatively using a CLIA
validated, isotope dilution assay.
n
Cases
437
218
Age
60 ± 5
60 ± 6
BMI*
26.46 ± 2.5
28.56 ± 4.2
HbA1C - %*
5.62 ± 0.28
5.95 ± 0.26
2h glucose – mM*
5.35 ± 1.1
7.59 ± 2.0
fasting glucose – mM*
5.18 ± 0.25
6.02 ± 0.49
n
ns
Metabolyzer
™
Peak Detection
Peak Integration
Library Search
RT, Mass, MS/MS
QA/QC
GC-MS (+EI)
FFA AUC (OGTT) – mM/min*
19.6 ± 8
26.9 ± 10
Adiponectin - μg/ml*
8.1 ± 4
6.8 ± 3
fasting insulin - mU/l*
6.18 ± 3.68
11.13 ± 6.8
AHB - μg/ml*
3.23 ± 1.26
4.05 ± 1.44
LGPC - μg/ml*
14.76 ± 4.0
11.99 ± 3.1
Oleate - μg/ml*
43.97 ± 16
52.16 ± 9
Quantose Score MQ*
7.21 ± 1.7
5.21 ± 1.6
HOMA-IR*
1.43 ± 0.87
3.00 ± 1.87
Matsuda Index – mg/dl,mU/l*
8.73 ± 4.3
4.81 ± 3.3
InsAUC0-30/GluAUC0-30 – pM/mM ns
31.4 ± 19
31.1 ± 21
Disposition Index*
220 ± 78
109 ± 52
HOMA-IS^
74.6 ± 45
90.0 ± 57
n
Baseline fasting plasma samples from the cases and
controls underwent non-targeted metabolomic profiling.
Plasma samples were extracted with methanol and
analyzed by gas chromatography-mass spectrometry and
S'
ultra high performance liquid chromatography-tandem mass
spectrometry.
Two-way ANOVA with contrasts and decision tree analysis
(Random Forest) was used to identify and stratify metabolites
on their ability to separate cases from controls.
n
n
p value*
2.1E-22
* = p<0.0001, ^ = p<0.001
by Wilcoxon test - cases vs controls;
ns = not significant;
subjects with complete data;
Disposition Index = (Matsuda Index*
InsAUC0-30/GluAUC0-30)
– A conservative Bonferroni cutoff of significance (p <0.05)
was used
– Glucose is the most significant metabolite
l Not a surprise given the glycemic bias of the cases
l Followed by mannose (a glucose surrogate?)
– Several metabolites previously linked to incident type 2
diabetes replicated
l Tyrosine, glycine, AHB, LGPC, branched-chain amino
acids: valine, leucine, isoleucine
mitochondria
2-methyl
butyryl
carnitine
mannose
4.4E-17
leucine
BCAT
3-methyl-2-oxovalerate
3-methyl-2-oxobutyrate
BCKD
4-methyl-2-oxopentanoate
BCKD
2-methylbutyryl-CoA
BCKD
isobutyryl-CoA
isovaleryl
carnitine
isovaleryl-CoA
3-hydroxy
isovalerate
3-hydroxy-isobutyrate
Top 25 metabolite
Top 50 metabolite
in Random Forest
fatty acid
synthesis
acetyl-CoA
r-values
glucose - glu
mannose-man
alpha-ketoglutarate-akg
4-methyl-2-oxopentanoate-4mp
3-methyl-2-oxovalerate-3mv
LGPC
AHB
alpha-ketobutyrate-akb
glycine-gly
palmitate (16:0)-pal
creatine-cre
palmitoleate (16:1n7)-pol
tyrosine-tyr
oleate-ole
2-methylbutyroylcarnitine (C5)-2mbc
3-hydroxyisobutyrate-3hi
isovalerylcarnitine (C5)-ivc
arachidonate (20:4n6)-ara
dihomolinolenate (20:3n3 or 3n6)-dhl
glutamate-E
valine-val
tryptophan-trp
leucine-leu
stearate (18:0)-ste
3-(4-hydroxyphenyl)lactate-3hpl
pyruvate
1.0E-04
8.3E-13
1.33
urate
1.4E-04
1.08
4-methyl-2-oxopentanoate
2.5E-11
1.13
propionylcarnitine
1.4E-04
1.13
3-methyl-2-oxovalerate
5.1E-11
1.13
isoleucine
1.8E-04
1.07
1-linoleoylglycerophosphocholine -LGPC
1.0E-10
0.81
margarate
2.4E-04
1.1
4.3E-10
1.25
7.7E-04
1.1
5.1E-10
1.32
adrenate
1.3E-03
1.09
glycine
5.4E-10
0.88
7- -hydroxy-3-oxo-4-cholestenoate
2.0E-03
1.13
palmitate
1.2E-09
1.13
stearidonate
2.2E-03
1.24
creatine
3.9E-09
1.24
2-aminobutyrate
2.3E-03
1.11
palmitoleate
7.5E-09
1.26
linoleate
3.1E-03
1.06
tyrosine
1.9E-08
1.11
docosapentaenoate
3.3E-03
1.18
oleate
4.2E-08
1.19
4.3E-03
1.12
2-methylbutyroylcarnitine
1.3E-07
1.15
xanthine
4.4E-03
1.33
3-hydroxyisobutyrate
1.7E-07
1.18
betaine
4.6E-03
0.95
isovalerylcarnitine
3.2E-07
1.19
3-phenylpropionate
4.8E-03
0.75
arachidonate
1.1E-06
1.13
quinate
8.4E-03
0.79
dihomo-linolenate
1.7E-06
1.14
linolenate
1.4E-02
1.07
glutamate
2.9E-06
1.13
pantothenate
1.9E-02
1.15
valine
5.4E-06
1.08
docosapentaenoate
1.90E-02
1.18
tryptophan
1.1E-05
1.05
3-methyl-2-oxobutyrate
2.8E-02
1.07
leucine
1.3E-05
1.07
serine
3.2E-02
0.96
tyrosine
stearate
1.3E-05
1.09
4-androsten-3 -17 -diol-disulfate
4.8E-02
1.59
3-(4-hydroxyphenyl)lactate
2.3E-05
1.16
N-acetylglycine
-glutamylphenylalanine
-hydroxyisovalerate
Plasma Glucose
>0.4
energy
mannose
-ketoglutarate
LGPC
0.39
LGPC
0.28
mannose
0.30
palmitate (16:0)
0.34
isovalerylcarnitine
0.24
dihomolinolenate
0.31
oleate
0.31
stearate (18:0)
0.31
-0.26
0.25
LGPC
tyrosine
0.25
isoleucine
LGPC
mannose
-glutamyltyrosine
leucine
Random
Forest
Random Forest classification
of case and
control subjects at baseline gave a
accuracy
of almost
77%
controls)
using both
and
• predictive
Random Forest
classification
of case
and(cases
controlvs
subjects
at baseline
gave anamed
predictive
accuracy
of almost
77% (cases vs controls) using both named and unnamed compounds
unnamed
compounds
-0.23
0.20
Insulin
0.37
-0.35
n
tyrosine
-glutamyltyrosine
0.48
isoleucine
-0.32
0.38
valine
-0.30
leucine
-0.30
valine
0.38
0.29
isoleucine
0.38
0.28
3-methyl-2-oxovalerate 0.37
HOMA IR
-0.44
0.43
tyrosine
-glutamyltyrosine
-0.34
-glutamylleucine
0.48
3-methyl-2-oxovalerate
0.39
4-methyl-2-oxopentanoate -0.44
alanine
-0.38
valine
0.38
3-hydroxyisobutyrate
-0.41
valine
-0.38
isoleucine
0.37
alpha-ketobutyrate
-0.41
Disposition Index*
(Matsuda* InsAUC0-30/GluAUCo-30)
InsAUC0-30 /GluAUC0-30
HOMA-
tyrosine
tyrosine
0.40
mannose
isoleucine
0.35
LGPC
valine
0.34
AHB
-0.26
0.31
alpha-ketoglutarate
-0.25
3-hydroxyisobutyrate
-0.25
valine
0.21
propionylcarnitine
0.21
-glutamyltyrosine
N-acetylglycine
-0.31
-0.31
trp
leu
ste
3hpl
1.00
0.08
0.10
0.13
0.31
0.18
0.32
0.14
0.14
1.00
0.68
0.31
0.06
-0.07
0.06
0.44
0.05
1.00
0.18
0.10
0.03
0.10
0.55
0.16
1.00
0.09
0.06
0.09
0.09
0.03
1.00
0.35
0.74
0.08
0.29
1.00
0.39
-0.13
0.24
1.00
0.05
0.30
1.00
0.11
1.00
0.2–0.4
n
n
- (0.2–0.4)
Trp
FFAs
AHB
4mp/3mv
n
Branched-chain amino acids inversely correlate with adiponectin levels
n
Branched-chain amino
acids
catabolites
inversely
correlatecorrelate
with Quantose
Branched-chain
amino
acids
catabolites
inversely
with score
Quantose score
3hpl
Akb
n
n
3hi
Gly
Val/Leu
2mbc
lvc
Cre
Man=mannose, Trp=tryptophan, Tyr=tyrosine, Val=valine
n
n
n
n
Nucleotide
Peptide
unnamed
-missing-
A number of metabolites linked to incident type 2 diabetes
previously were replicated in this work
– Tyrosine, glycine, AHB, LGPC, branched-chain amino
acids: valine, leucine, isoleucine
A key finding is that many catabolites of the branched-chain
amino acids are also linked to dysglycemia and incident type
2 diabetes
Metabolites which correlate > 0.4 (r-value) are linked by a dark line
n
All of the fatty acids correlate highly with each other; also Val with Leu, 4mp
with 3mv
AHB is highly correlated to other metabolites linking FFAs with branched-chain
amino acid catabolites
LGPC, glycine and creatine are not strongly correlated with other metabolites
Creation of quantitative assays for the metabolites of interest
Use these new assays in a representative cohort not used to
identify the candidate biomarkers to test their suitability:
– To predict incident type 2 diabetes alone
– For use in models with other metabolites and metabolic
parameters for:
l The prediction of type 2 diabetes
l Monitoring interventions in diabetes and
prediabetes
REFERENCES
n
n
n
Lipid
Cases destined to progress to type 2 diabetes within 5
years show substantial differences at baseline from normal
controls in all 3 key classes of fuel molecules:
– Glucose (increased fasting glucose, 2 h glucose, & A1C)
– Free fatty acids (increased, individually and collectively)
– Amino acids (increased generally, except glycine
and serine)
– There are derangements in glucose, fatty acid, and amino acid metabolism prior to the development of type 2 diabetes
Insulin resistance is an early event as shown by HOMA-IR,
Matsuda Index and Quantose score
NEXT STEPS
Metabolites which correlate > 0.4 (r value) are linked by a dark line
MetaboliteAll
Key:
2mbc=2-methylbutyroylcarnitine,
3hpl=3-(4-hydroxyphenyl)lactate,
3hi=
of the
fatty acids correlate highly with
each other; also Val with Leu, 4mp
with 3mv
3-hydroxyisobutyrate,
3mv=3-methyl-2-oxovalerate,
4mp=4-methyl-2-oxopentanoate,
AHB=alphaAHB is highly
correlated to other metabolites
linking FFAs with branched-chain
amino
acid catabolites
hydroxybutyrate,
Akb=alpha-ketobutyrate, Akg=alpha-ketoglutarate, Cre=creatine, FFA=free fatty acids,
LGPC, glycine and creatine are not strongly correlated with other metabolites
Glu=glutamate, Gly=glycine, Ivc=isovalerylcarnitine, Leu=leucine, LGPC=linoleoylglycerophosphocholine,
Branched-chain amino acids inversely correlate with adiponectin levels
Mannose
and
Quantose
metabolites
AHBcorrelate
& LGPCwith
correlate
with the
Mannose and
thethe
Quantose
metabolites
AHB & LGPC
the Disposition
Index
a measure
of -cell function
Disposition
Index
'
– A measure of b-cell function
n
branched chain amino acids & catabolites
Tyrosine correlates with insulin, BMI, Matsuda Index and HOMA-IR
Tyrosine correlates with insulin, BMI, Matsuda Index and HOMA-IR
n
Tyr
0.30
n
Carbohydrate
Glu
-0.45
-0.41
0.26
val
LGPC
Quantose Score
mannose
isoleucine
Man
Akg
3-methyl-2-oxovalerate 0.38
-0.26
E
3-methyl-2-oxovalerate -0.24
-0.39
N-acetylglycine
dhl
Biomarkers
associated
a disease
correlated
poorly
other
Biomarkers associated
withwith
a disease
whichwhich
correlated
poorly with
each with
othereach
(’orthogonal
biomarkers’)
represent different
‘information’
about ‘information’
the disease andabout
may work
(‘orthogonalmay
biomarkers’)
may represent
different
the well
together in risk assessment models for the disease
disease and may work well together in risk assessment models for the disease
Glucose
-0.26
isoleucine
0.27
ara
top 25 METABOLITES:
INTERCORRELATIONS
DIAGRAM
Top 25 Metabolites: Intercorrelations Diagram
Adiponectin
0.31
Matsuda Index
RANDOM FOREST
Energy
glucose
BMI
*cases vs controls
Color by SUPER_PATHWAY
Amino acid
0.46
3-hydroxypyruvate
tyrosine
2 h glucose
HbA1c
ivc
CONCLUSIONS
r-value color scheme
PARAMETERS, IR AND b-CELL INDICES–r-values
1.19
glu man akg 4mp 3mv LGPC AHB akb
gly
pal
cre
pol
tyr
ole 2mbc 3hi
1.00
.69
1.00
0.35 0.37 1.00
0.20 0.17 0.31 1.00
0.16 0.15 0.32 0.88 1.00
-0.12 -0.30 -0.20 -0.18 -0.14 1.00
0.16 0.23 0.25 0.53 0.42 -0.25 1.00
0.35 0.39 0.34 0.43 0.33 -0.22 0.68 1.00
0.12 0.02 -0.06 -0.14 -0.11 0.25 -0.27 -0.17 1.00
0.10 0.14 0.25 0.35 0.30 -0.19 0.52 0.35 -0.19 1.00
0.16 0.20 0.22 0.15 0.11 -0.23 0.24 0.25 -0.13 0.07 1.00
0.10 0.19 0.25 0.28 0.20 -0.19 0.49 0.34 -0.16 0.82 0.09 1.00
0.14 0.16 0.25 0.22 0.28 -0.17 -0.02 0.01 -0.10 0.05 0.01 0.10 1.00
0.10 0.15 0.22 0.27 0.21 -0.17 0.61 0.40 -0.19 0.81 0.05 0.80 -0.04 1.00
0.10 0.14 0.19 0.30 0.37 -0.12 0.19 0.14 -0.19 0.13 0.03 0.05 0.29 0.05 1.00
0.38 0.35 0.32 0.47 0.41 -0.14 0.53 0.51 -0.09 0.29 0.15 0.21 0.20 0.25 0.38 1.00
0.18 0.19 0.23 0.31 0.28 -0.15 0.22 0.27 -0.19 0.15 0.26 0.16 0.21 0.12 0.47 0.34
0.11 0.07 0.16 0.20 0.15 -0.19 0.28 0.23 -0.15 0.46 0.18 0.38 0.02 0.28 0.03 0.18
0.11 0.05 0.16 0.27 0.22 -0.16 0.31 0.23 -0.19 0.57 0.08 0.47 0.08 0.41 0.02 0.22
0.12 0.19 0.40 0.10 0.11 -0.16 0.06 0.24 -0.18 0.11 0.19 0.10 0.15 0.07 0.06 0.06
0.13 0.14 0.18 0.58 0.61 -0.19 0.17 0.11 -0.11 0.08 0.14 -0.05 0.35 -0.07 0.40 0.39
0.05 -0.04 0.16 0.27 0.32 -0.03 -0.07 -0.06 0.01 -0.09 0.06 -0.09 0.56 -0.22 0.17 0.15
0.12 0.12 0.16 0.60 0.60 -0.16 0.17 0.09 -0.05 0.08 0.12 -0.02 0.40 -0.05 0.40 0.28
0.09 0.09 0.20 0.34 0.32 -0.11 0.52 0.34 -0.15 0.83 0.07 0.61 -0.05 0.68 0.13 0.32
0.12 0.13 0.23 0.35 0.38 -0.11 0.22 0.12 -0.03 0.16 -0.13 0.17 0.49 0.13 0.30 0.30
TCA cycle
succinyl-CoA
BCAT = branched-chain aminotransferase
BCKD = branched-chain -keto acid dehydrogenase
TOP 25 NAMED METABOLITES INTERCORRELATIONS,
r-values Top 25 Named Metabolites Intercorrelations
BCAT
3-methylcrotonyl-CoA
p value*
4.3E-05
-ketobutyrate
mean ± SD;
695 metabolites were measured semi-quantitatively
– 401 named metabolites with a known structure
– 294 unnamed metabolites with a mass spectrometric
signature but no defined structure at this point in time
49 named metabolites were significantly different for cases vs controls
cytosol
Metabolite (26-49)
-hydroxyisocaproate
hydroxybutyrate - AHB
key metabolite findings
BCAT
relative
level*
1.17
1.17
-ketoglutarate
n
n
Metabolite (1-25)
glucose
relative
level*
1.13
valine
-hydroxy
isocaproate
Metabolite Correlations with Metabolic Parameters
METABOLITE CORRELATIONS
WITH
METABOLIC
IR and - Cell Indices - r values
Cases vs Controls (Bonferroni adjusted p values)
Controls
isoleucine
gluconeogenesis
Top 49 Named Metabolites
METSIM BASELINE ANTHROPOMETRIC AND
METABOLIC
PARAMETERS
METSIM Baseline Anthropometric and Metabolic Parameters
Variable
BCAA Oxidation Pathways
BCAA Oxidation
pathways
– Glycine and LGPC being the most significant exceptions
top 49 named metabolites
RESULTS
Metabolon
Platform
Technology
Metabolon
platform
technology
UHPLC-MS/MS (+ESI)
The Quantose score MQ is a measure of insulin sensitivity.2
– Higher scores=greater sensitivity
n
Random Forest Confusion Matrix
Stancáková A, Javorský M, Kuulasmaa T, Haffner S, Kuusisto J, Laakso M. Changes in
Insulin Sensitivity and Insulin Release in Relation to Glycemia and Glucose Tolerance in
6,414 Finnish Men. Diabetes. 58(5):1212.
Cobb J, Gall W, Adam K-P, Nakhle P, Button E, Hathorn J, Lawton K, Milburn M, Perichon
R, Mitchell M, Natali A, Ferrannini E. A novel fasting blood test for insulin resistance and
prediabetes. Journal of Diabetes Science and Technology. 2013; 7(1):100.
Ferrannini E, Natali A, Camastra S, Nannipieri M, Mari A, Adam K-P, Milburn MV,
Kastenmüller G, Adamski J, Tuomi T, Lyssenko V, Groop L, Gall WE. Early metabolic
markers of the development of dysglycemia and type 2 diabetes and their physiological
significance. Diabetes. 2013; 62(5):1730.
Milburn M, Lawton K. Application of metabolomics to diagnosis of insulin resistance.
Annu. Rev. Med. 2013. 64:291.
Predicted Group
Actual
Group
n
n
– 9 branched-chain amino acid catabolites:
l 4-methyl-2-oxopentanoate, 3-methyl-2-oxovalerate,
2-methylbutyroylcarnitine, 3-hydroxyisobutyrate,
isovalerylcarnitine, a-hydroxyisocaproate,
propionylcarnitine, b-hydroxyisovalerate,
3-methyl-2-oxobutyrate
– 13 free fatty acids:
l Many kinds: saturated, MUFA, PUFA, long chain, very
long chain
– The Krebs cycle intermediate a-ketoglutarate
– The purine xanthine
Most metabolites are elevated in cases
increasing importance to group separation
AIM
Quantose
Controls
Controls
348
Cases
56
Cases
92
164
Class Error
20.9%
25.5%
ACKNOWLEDGMENTS
n
Mean Decrease Accuracy
1
2
3
4
5
Support: DEXLIFE (EU FP7 program, Grant agreement no: 279228)