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Systemic alterations in the metabolome of diabetic NOD mice delineate
increased oxidative stress accompanied by reduced inflammation
and hypertriglyceremia
Johannes Fahrmann,
1
* Dmitry Grapov,
1
* Jun Yang,
2
Bruce Hammock,
2
Oliver Fiehn,
1
Graeme I. Bell,
3
and Manami Hara
3
1
National Institutes of Health West Coast Metabolomics Center, University of California Davis, Davis, California;
2
Department of Entomology and Cancer Center, University of California Davis, Davis, California; and
3
Department of
Medicine, The University of Chicago, Chicago, Illinois
Submitted 16 January 2015; accepted in final form 1 April 2015
Fahrmann J, Grapov D, Yang J, Hammock B, Fiehn O, Bell GI,
Hara M. Systemic alterations in the metabolome of diabetic NOD mice
delineate increased oxidative stress accompanied by reduced inflammation
and hypertriglyceremia. Am J Physiol Endocrinol Metab 308: E978 –E989,
2015. First published April 8, 2015; doi:10.1152/ajpendo.00019.2015.—
Nonobese diabetic (NOD) mice are a commonly used model of type
1 diabetes (T1D). However, not all animals will develop overt diabe-
tes despite undergoing similar autoimmune insult. In this study, a
comprehensive metabolomic approach, consisting of gas chromatog-
raphy time-of-flight (GC-TOF) mass spectrometry (MS), ultra-high-
performance liquid chromatography-accurate mass quadruple time-of-
flight (UHPLC-qTOF) MS and targeted UHPLC-tandem mass spec-
trometry-based methodologies, was used to capture metabolic
alterations in the metabolome and lipidome of plasma from NOD
mice progressing or not progressing to T1D. Using this multi-platform
approach, we identified ⬎1,000 circulating lipids and metabolites in
male and female progressor and nonprogressor animals (n⫽71).
Statistical and multivariate analyses were used to identify age- and
sex-independent metabolic markers, which best differentiated meta-
bolic profiles of progressors and nonprogressors. Key T1D-associated
perturbations were related with 1) increases in oxidation products
glucono-␦-lactone and galactonic acid and reductions in cysteine,
methionine and threonic acid, suggesting increased oxidative stress;
2) reductions in circulating polyunsaturated fatty acids and lipid
signaling mediators, most notably arachidonic acid (AA) and AA-
derived eicosanoids, implying impaired states of systemic inflamma-
tion; 3) elevations in circulating triacylglyercides reflective of hyper-
triglyceridemia; and 4) reductions in major structural lipids, most
notably lysophosphatidylcholines and phosphatidylcholines. Taken
together, our results highlight the systemic perturbations that accom-
pany a loss of glycemic control and development of overt T1D.
diabetic mice; metabolomics; inflammation; oxidative stress
TYPE 1 DIABETES (T1D) is an autoimmune disease characterized
by the selective destruction of pancreatic -cells. Currently, it
is considered that the autoimmune insult is the primary driver
of -cell destruction that leads to development of T1D. How-
ever, it is becoming increasingly evident that early metabolic
perturbations are inherently involved in the development and
progression of T1D (39, 40, 53). These metabolic alterations
may potentiate or attenuate -cell loss and dysfunction. Oresic
et al. (40) reported that alterations in branched-chain amino
acids (BCAA) and lipid metabolism preceded the appearance
of autoantibodies in children who later progressed to T1D.
Pflueger et al. (44) found that, independent of age-related
differences, autoantibody-positive children had higher levels of
odd-chain triglycerides and polyunsaturated fatty acid (PUFA)-
containing phospholipids than autoantibody-negative children.
Furthermore, it was found that children who developed auto-
antibodies by age 2 yr had a significantly lower concentration
of circulating methionine than those who developed autoanti-
bodies late in childhood or remained autoantibody negative
(44). Collectively, these studies demonstrate an intrinsic rela-
tionship between metabolic perturbations, autoimmune insult,
and -cell destruction.
Metabolomics is the study of small molecules and biochem-
ical intermediates (metabolites), which are highly relevant to
other regulatory mechanisms (e.g., genomics, transcriptome,
and proteome) and sensitive to environmental stimuli, forming
detailed representations of organismal phenotypes. Over the
past decade, the application of metabolomics has been used to
gain new insights into the pathology of numerous diseases
including type 2 diabetes, develop methods predictive of dis-
ease onset, and reveal new biomarkers associated with diag-
nosis and prognosis (20, 51). Therefore, the application of
metabolomics to study T1D pathophysiology represents a
promising avenue of research to identify candidate biomarkers
related to disease development and progression.
Using a nonobese diabetic (NOD) mouse model, we recently
demonstrated marked heterogeneity in pancreatic -cell loss
regardless of age or sex. Importantly, we found that chronic
hyperglycemia (ⱖ250 mg/dl) and overt T1D manifested only
in mice that lost ⬃70% of their total -cell mass (23). Using an
untargeted metabolomics approach, we performed an initial
evaluation of primary metabolites in plasma of young (⬍25 wk)
and old (ⱖ25 wk) nonprogressors (nondiabetic) vs. progressors
(diabetic) to reveal distinct metabolomic pathways involved in
disease progression (23). In the present study, we largely extended
these investigations to capture alterations in primary metabolism,
complex lipids, and lipid signaling mediators in the plasma of
nonprogressors and progressors with the overall aim(s) of identi-
fying circulating factors linked to disease progression and, poten-
tially, -cell destruction and dysfunction.
METHODS
Metabolomic, Lipidomic, and Oxylipin Analysis
NOD mice (n⫽71) were assessed as diabetic or nondiabetic based
on their fasting (4 h) blood glucose levels at death, which defined 31
* J. Fahrmann and D. Grapov contributed equally to this work.
Address for reprint requests and other correspondence: M. Hara, Dept. of
Medicine, Univ. of Chicago, 5841 South Maryland Ave., MC1027, Chicago,
IL 60637 (e-mail: mhara@midway.uchicago.edu).
Am J Physiol Endocrinol Metab 308: E978–E989, 2015.
First published April 8, 2015; doi:10.1152/ajpendo.00019.2015.
http://www.ajpendo.orgE978
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hyperglycemic (glucose ⱖ250 mg/dl) and 40 normoglycemic ani-
mals. All procedures involving mice were approved by the University
of Chicago Institutional Animal Care and Use Committee. Character-
istics of our defined cohort are described in Table 1.
The MiniX database (47) was used as a laboratory information
management system (LIMS) and for sample randomization prior to
all analytical procedures. Detailed information on sample prepa-
ration and data acquisition for all analytical platforms are reported
in the APPENDIX.
For analysis of primary metabolism, plasma aliquots (30 l), stored
at ⫺80°C, were thawed, extracted, and derivatized, and metabolite
levels were quantified by gas chromatography time-of-flight (GC-
TOF) mass spectrometry as previously described (19). All samples
were analyzed in one batch, throughout which data quality and
instrument performance were monitored using quality control and
reference plasma samples [National Institute of Standards and Tech-
nology (NIST)]. Quality controls (n⫽8), comprising a mixture of
standards and analyzed every 10 samples, were monitored for changes
in the ratio of analyte peak heights and used to ensure equivalent
instrumental conditions (P⬎0.05, t-test comparing observed to
expected ratios of analyte response factors) over the duration of the
sample acquisition (19). Acquired spectra were further processed
using the BinBase database (18, 47). Briefly, output results (30) were
filtered based on multiple parameters to exclude noisy or inconsistent
peaks. Detailed criteria for peak reporting, including mass spectral
matching, spectral purity, signal to noise, and retention time, are
discussed in detail elsewhere (17). Known artifact peaks such as
polysiloxanes or phthalates were excluded from data export in Bin-
Base. Missing values were replaced by investigating the extracted ion
traces of the raw data subtracted by the local background noise. All
entries in BinBase were matched against the Fiehn mass spectral
library of 1,200 authentic metabolite spectra, using retention index
and mass spectrum information or the NIST11 commercial library.
Metabolites were reported if present in at least 50% of the diabetic or
nondiabetic samples. Data reported as quantitative ion peak heights
were normalized by the sum intensity of all annotated metabolites and
used for further statistical analysis.
For analysis of the complex lipids, plasma aliquots (20 l), stored
at ⫺80°C, were thawed and extracted using a modified liquid-liquid
phase extraction approach purposed by Matyash et al. (37) and
analyzed on an Agilent 1290A Infinity Ultra High Performance Liquid
Chromatography system with an Agilent Accurate Mass-6530-QTOF
(UHPLC-QTOF). Data quality and instrument performance were
monitored throughout the data acquisition using quality control (in-
ternal STDS) and reference pooled plasma samples. The data were
processed using MZmine 2.10 software. Complex lipids were identi-
fied by searching against a precursor accurate mass and retention time
library in conjunction with matching tandem mass spectra against the
LipidBlast virtual MS/MS database(29). Metabolites were reported if
positively detected at very high mass spectral confidence in at least
50% of the diabetic or nondiabetic samples. Data, reported as peak
heights for the quantification ion (m/z) at the specific retention time
for each annotated and unknown metabolite, was normalized to the
class-specific internal standard (annotated) or to the internal standard
that had the closest retention time (unknowns).
For analysis of oxylipins, samples were extracted in accordance
with previously described protocols (60) and analyzed by Agilent
1200SL-AB Sciex 4000 QTrap ultrahigh performance liquid chroma-
tography tandem mass spectrometry (UPLC-MS/MS). Optimized
conditions and the MRM transitions, as well as extraction efficiencies,
were reported previously (60). Quality control samples were analyzed
at minimum calibration throughout the analysis. Analyst software v.
1.4.2 was used to quantify peaks according to their corresponding
standard curves. Absolute concentrations of oxylipins are presented as
nanomoles per liter.
Data Analysis
Statistical analyses. Statistical analyses were carried out on age and
sex covariate adjusted metabolite values. A linear model was used to
describe differences in metabolite abundances due to animal age and
sex, the residuals from which were tested for differences between T1D
and control animals by use of a nonparametric Mann-Whitney U-test.
The significance levels (Pvalues) were adjusted for multiple hypoth-
esis testing according to Benjamini and Hochberg (4) at a false
discovery rate (FDR) of 5% (abbreviated pFDR ⬍0.05). Statistical
tests were calculated separately for primary metabolite, lipidomic,
oxylipin, and nonannotated measurements.
Multivariate modeling. Multivariate modeling was conducted using
orthogonal signal correction partial least squares discriminant analysis
(O-PLS-DA) (52) to identify robust predictors of metabolic differ-
ences between T1D and control animals. O-PLS-DA models were
fitted to age and sex adjusted, natural logarithm transformed, and
autoscaled data. The 71 animals were split between two-thirds training
and one-third test data sets while the proportion of T1D and control
animals in the full data was preserved. Training data were used for
model optimization and feature selection. Final model performance
was determined based on predictions for the held-out test set and
Monte Carlo cross-validation results from the training data.
Model optimization and feature selection were carried out indepen-
dently for primary metabolite, lipidomic, and oxylipin training data.
Model latent variable (LV) number and orthogonal LV (OLV) number
were selected using leave-one-out cross-validation. Feature selection
was used to identify the top ⬃10% of all metabolic predictors for T1D
from each biochemical domain. The full variable set was filtered to
retain analytes that dispalyed 1) significant correlation with model
scores (Spearman’s pFDR ⱕ0.05) (58) and 2) model loadings on LV1
in the top 90th quantile in magnitude (41) and Mann-Whitney U-test
Pvalues ⬍0.05.
The top 10% selected features (n⫽44; 12 primary metabolites, 29
complex lipids, and 3 oxylipins) from each biochemical domain were
combined and evaluated using Monte Carlo training and testing
cross-validation and permutation testing. Internal training and testing
were performed by further splitting the training set into two-thirds
pseudo-training and one-third pseudo-test sets, while the proportion of
T1D and control animals was preserved. This split was randomly
repeated 1,000 times and used to estimate the distributions for the
O-PLS-DA model performance statistics: the cross-validated fit to the
training data (Q2), root mean squared error of prediction (RMSEP),
area under the receiver operator characteristic curve (AUC), sensitiv-
ity (true positive rate), and specificity (true negative rate). The
probability of achieving the models’ predictive performance was
estimated through comparison of performance statistics to those of
permuted models (random class labels) (45), which were calculated by
replicating the internal training and testing procedures described
above. Additionally, model performance was compared between the
selected (n⫽44; primary metabolites, 12; lipids, 29; oxylipins, 3) and
excluded (bottom 90%, n⫽351; primary metabolites, 168; lipids,
132; oxylipins, 51) feature sets.
Final model classification performance was validated through pre-
diction of class labels for the originally held-out test set and are
Table 1. Nonobese diabetic mice characteristics
Nondiabetic Diabetic
Female 18 6
Male 23 24
Age, wk 36 (26,40) 38 (26,40)
Weigh, g 27.3 ⫾4.8 19.5 ⫾4.8*
Glucose, mg/dl 94 ⫾34 513 ⫾101*
Values are reported as means ⫾SD (median, minimum,maximum). *Un-
paired two-sample t-test, Pⱕ0.05.
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reported as sensitivity, specificity, and the area under the receiver
operator characteristic curve.
Network analysis. Network analysis was used to investigate statis-
tical and multivariate modeling results within a biochemical context.
A biochemical and chemical similarity network (2) was calculated for
all measured metabolites with KEGG (31) and PubChem CID (5)
identifiers using MetaMapR (22). Enzymatic interactions were deter-
mined based on product-precursor relationships defined in the KEGG
RPAIR database. Molecules not directly participating in biochemical
transformations but sharing many structural properties, based on
PubChem Substructure Fingerprints (7), were connected at a threshold
of Tanimoto similarity ⱖ0.7.
Partial correlation. A partial correlation network was calculated to
analyze empirical dependencies among the top metabolic discrimi-
nants between T1D and control animals. Partial correlations were
calculated between covariate adjusted (age and sex) and natural
logarithm transformed O-PLS-DA selected features (top 10% feature
set). To limit the scope of the partial correlation analysis to the
strongest empirical relationships, q-order partial correlations (8) were
first calculated to identify direct from indirect metabolite associations.
q-Order partial correlations (q⫽1, 11, 22, 32) were calculated using
1,000 replications each, and were used to estimate the average
nonrejection rate () for all pairwise relationships. Analysis of the
relationship between vertex number, edge degree (connections), and 
was used to select ⫽0.4 for edge acceptance. Coefficients of partial
correlation and Pvalues were calculated for all qvalue-identified
connections. Significant metabolite relationships were determined
based on FDR-adjusted (4) partial correlations (pFDR ⱕ0.05).
Network mapping. Network mapping was used in Cytoscape (49) to
encode statistical and multivariate modeling results through network
edge and node attributes.
Hierarchical cluster analysis. Hierarchical cluster analysis was
used to summarize relationships between classes of measured mole-
cules, glucose, and BMI. Relationships were evaluated based on
Spearman correlations between total (sum) of metabolite levels for
various biochemical subdomains, glucose, and BMI. Correlations
were visualized using a heatmap organized on the basis of hierarchal
clustering calculated on Euclidean distances and Wards agglomera-
tion.
RESULTS
Analysis of Plasma Metabolites in NOD Mice
Unbiased GC-TOF- and UHPLC-qTOF-based metabolo-
mics coupled with targeted UPLC-MS/MS analysis of oxylip-
ins was used to compare the metabolome of 41 nondiabetic and
30 diabetic animals (Table 1). A total of 1,041 metabolomic
peaks were detected, of which 395 were structurally annotated
metabolites. For GC-TOF- and UHPLC-qTOF-based metabo-
lomics, measured peak heights are reported in normalized units
using the diagnostic “unique ion” for each compound. Lipid
signaling mediators (oxylipins) are reported as concentrations
(nmol/l).
Comparison of NOD Mice’s Physical and Biochemical
Characteristics
Statistical analyses were used to compare physical charac-
teristics and metabolomic measurements between diabetic and
nondiabetic NOD mice. Diabetic mice displayed significant
elevations in fasting glucose but reduced body weight com-
pared with nondiabetic mice (Table 1).
A nonparametric Mann-Whitney U-test with FDR correction
was used to identify 493 (47%) significantly altered age- and
sex-adjusted metabolic features (P
adj
ⱕ0.05) between diabetic
and nondiabetic mice (see Supplemental Table S1 in SUPPLE-
MENTAL MATERIALS, linked to the online version of this article).
Statistical comparisons of major classes of molecules and
biochemical subdomains identified general T1D-dependent in-
creases in the majority of carbohydrates, aromatics, amino
acids or amides, nucleotides, and triacylglycerides (TGs)
whereas prostacyclins, C18-diols, C18- and C20-ketones, C20-
hydroxy acids, triols, sterols, acylcarnitines (ACs), phosphati-
dylethanolamines (PEs), phosphatidylcholines (PCs), phos-
phatidylinositols (PIs), sphingomyelins (SMs), and lysophos-
phatidylcholines (LPCs) showed a general decrease (Table 2).
Table 2. Significantly altered biochemical domains in T1D vs. control animals
Biochemical Class Control Diabetic FC PValue*
Increase
Amino acid or amide (47)† 1440000 ⫾570000‡ 1730000 ⫾5e ⫹05 1.2 0.02
Aromatic (6) 3070 ⫾1800 4200 ⫾2300 1.37 0.01
Carbohydrate (55) 2690000 ⫾9e ⫹05 4560000 ⫾640000 1.69 ⬍0.00001
Nucleotide (14) 29100 ⫾11000 37700 ⫾17000 1.3 0.03
TG (50) 21500000 ⫾8900000 32600000 ⫾2.3e ⫹07 1.52 0.03
Decrease
AC (4) 134000 ⫾88000 106000 ⫾1e ⫹05 0.79 0.02
C18-diol (4) 124 ⫾80 85 ⫾89 0.69 0.00
C18-ketone (2) 9920 ⫾11000 5480 ⫾3600 0.55 0.001
C20-hydroxy (10) 9480 ⫾5600 4230 ⫾5200 0.45 ⬍0.00001
C20-ketone (3) 3050 ⫾2400 1260 ⫾1600 0.41 ⬍0.00001
LPC (12) 7970000 ⫾2600000 3270000 ⫾1800000 0.41 ⬍0.00001
PC (50) 53500000 ⫾1.1e ⫹07 39300000 ⫾1.6e ⫹07 0.73 ⬍0.00001
PE (10) 210000 ⫾78000 141000 ⫾79000 0.67 0.002
PI (6) 1050000 ⫾290000 478000 ⫾320000 0.46 ⬍0.00001
Prostacyclin (4) 125 ⫾82 49.8 ⫾42 0.4 ⬍0.00001
SM (20) 2380000 ⫾660000 1410000 ⫾600000 0.59 ⬍0.00001
Sterol (9) 1030000 ⫾240000 656000 ⫾280000 0.64 ⬍0.00001
Triol (2) 23 ⫾23 10.7 ⫾9.6 0.47 0.01
FC, fold change in means in diabetic vs. control animals. *Based on Mann-Whitney U-test. †Number of measured molecules. ‡Oxylipins are reported in nM;
all others in peak area heights.
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Multivariate classification modeling using O-PLS-DA cou-
pled with feature selection was used to identify the top meta-
bolic determinants of the T1D plasma phenotype. A subset of
44 metabolites (10%; Supplemental Table S1) was selected as
part of the validated model (see Appendix Table A1) for
discrimination between diabetic and nondiabetic NOD mice.
For primary metabolites and oxylipins, biochemical and
chemical similarity network analysis was conducted to calcu-
late and visualize relationships between precursor and product
metabolite reactant pairs and molecules sharing a high degree
of structural similarity (Fig. 1). A heatmap based on hierarchi-
cal cluster analysis was used to summarize the relationships
between classes of measured molecules, fasting blood glucose,
and body weight (Fig. 2).
A Gaussian graphic model network was calculated to iden-
tify conditionally independent relationships (partial correla-
tion, P
adj
ⱕ0.05) between all significant (P
adj
ⱕ0.05) T1D-
associated metabolic perturbations (Fig. 3).
Diabetic NOD Mice Display Altered Carbohydrate Profiles
Total plasma carbohydrate levels were increased by 169% in
diabetic mice (Table 2) and were generally positively corre-
lated with measured fasting blood glucose and negatively
correlated with body weight (Fig. 2). Moreover, circulating
levels of glucose, gluconic acid lactone, glucuronic acid, idonic
acid, ribose, cellobiose, and 2-deoxytetronic acid, all of which
were elevated in diabetic mice compared with nondiabetic mice,
were selected as being within the top 10% discriminants of the
diabetic phenotype (Supplemental Table S1). Elevations in ribose,
2-deoxytetronic acid and cellobiose were shown to be both bio-
chemically (Fig. 1) and empirically (Fig. 3) related to general
alterations in most carbohydrates. Similarly, we observed T1D-
associated increases in organic acids and metabolites related to
energetics including a 240% increase in 2-hydroxy-2-methylbu-
tanoic acid, which was found to be the single most discriminatory
metabolite of the T1D phenotype (Supplemental Table S1). 2-Hy-
droxy-2-methylbutanoic acid was positively related to the ob-
served increase in indole-3-lactate and ribose and negatively
correlated to PC (38:2), whereas indole-3-lactate was also posi-
tively correlated to the T1D-associated increase in isocitric acid
(Fig. 3). Whereas most carbohydrates were found to be elevated in
diabetic mice, we found significant T1D-dependent decreases
in circulating 1,5-anhydroglucitol, glycerol-3-galactoside,
and threonic acid. Threonic acid was similarly identified as
a top descriptor of T1D and paralleled an observed decrease
in methionine (Fig. 3).
Diabetic NOD Mice Display Alterations in Amino Acid Profiles
Overall, 10 of the 47 measured amino acid or amine metab-
olites (21%) were significantly altered in diabetic compared
with nondiabetic animals (Fig. 1). Of these changes, six were
significantly decreased in diabetic mice, including creatinine,
Fig. 1. Biochemical network displaying metabolic differences between diabetic and nondiabetic NOD mice. Metabolites are connected based on biochemical
relationships (orange, KEGG RPAIRS) or structural similarity (blue, Tanimoto coefficient ⱖ0.7). Metabolite size and color represent the importance (O-PLS-DA
model loadings, LV1) and relative change (gray, P
adj
⬎0.05; green, decrease; red, increase) in diabetic vs. nondiabetic NOD mice. Shapes display metabolites’
molecular classes or biochemical subdomains, and top descriptors of type 1 diabetes (T1D)-associated metabolic perturbations (Supplemental Table S1) are
highlighted with thick black borders.
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5-methoxytryptamine, methionine, cysteine, tyrosine, and tryp-
tophan (Fig. 1). Inversely, four metabolites showed T1D-
associated elevations, including a 180% increase in pan-
thothenic acid and 140% increases in valine, isoleucine, and
5-hydroxyindole-3-acetic acid (Supplemental Table S1). Of the
noted shifts, only methionine was found to be included as a top
predictor of the T1D phenotype. Overall, amino acids were
negatively correlated with body weight but not fasting blood
glucose (Fig. 2).
Diabetic NOD Mice Display Perturbed Lipid Profiles
Cumulatively, alterations in lipid metabolism best delineated
diabetic from nondiabetic mice, encompassing 28 of the top 44
most discriminatory metabolites (64%) between diabetic and
nondiabetic animals (Supplemental Table S1). Jointly, diabetic
animals displayed significant elevations in virtually all mea-
sured triacylglyericides with TG (49:1), TG (50:1), TG (12:0/
18:2/20:5), TG (51:1), TG (54:1), TG (54:2), TG (54:4), TG
(58:3), and TG (58:10) all being ranked in the top 10% of
discriminatory metabolites (Supplemental Table S1) and being
empirically related to each other (Fig. 3). Only TG (50:3) and
TG (56:4) were found to be significantly lower in diabetic than
in nondiabetic mice (Supplemental Table S1). The increase in
TG (54:1) was negatively correlated to LPC (16:1), whereas
TG (58:3) and TG (16:0/18:2/20:5) were both negatively re-
lated to CE (20:4) (Fig. 3). Notably, all measured LPCs were
significantly reduced in diabetic compared with nondiabetic
animals (Supplemental Table S1 and Table 2). The T1D-
associated reduction in LPC was positively related to similar
reductions in several other lipid species, including SM (d40:2)
A, SM (d18:2/23:0), and PI (40:6) (Fig. 3). Intriguingly, LPC
(16:1) was positively associated with methionine, which was
reduced in diabetic mice and positively correlated to the
T1D-associated reduction in LPC (20:4) (Fig. 3). LPC (20:4)
coordinately correlated with LPC (16:0), which served as a
central hub and was positively related to numerous PC and SM
lipid species, including PC (36:4), PC (p-36:3) or PC (o-36:4),
PC (38:5), SM (d18:1), and SM (d18:2/23:0) (Fig. 3). In accord
with the observed T1D-associated decrease in all measured
LPCs, nearly all measured PCs and SMs were similarly re-
duced in diabetic compared with nondiabetic mice (Table 2).
Only SM (d16:1/20:1), PC (35:2), PC (35:2) B, PC (p-36:2) or
PC (o-36:3), and PC (36:5) were found to be elevated in
diabetic mice (Supplemental Table S1). The T1D-associated
reduction in PC (38:5) was also positively associated with
similar reductions in PC (38:2), PC (37:2), and PI (38:4).
Fig. 2. Correlation between body weight,
fasting blood glucose, and major classes of
measured metabolites and lipids. Heatmap is
shown displaying hierarchical clustered Spear-
man correlations between animal characteris-
tics (weight and fasting blood glucose) and
major classes of measured metabolites and
lipids.
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Likewise, the T1D-dependent decrease in PE (38:6) B was
positively related to PE (38:6), which positively correlated
with PI (38:4). Comparable to PCs and SMs, many of the
measured PEs and PIs were decreased in diabetic animals
(Table 2). On a global scale, PCs, SMs, PIs, LPCs, and PEs
were all positively correlated with body weight and negatively
correlated with fasting blood glucose, whereas TGs were
negatively correlated with body weight and positively corre-
lated with fasting blood glucose (Fig. 2).
Plasma free fatty acids were generally lower in diabetic
mice, led by a 60% reduction in palmitoleic acid and 40%
reductions in palmitic acid and arachidonic acid (Supplemental
Table S1). The reductions in circulating free fatty acids were
negatively correlated with fasting blood glucose (Fig. 2).
Diabetic NOD Mice Display Perturbations in Lipid
Signaling Mediators
Overall, 24 of the 54 measured oxylipins (43%) were found
to be significantly altered between diabetic and nondiabetic
animals. Furthermore, all 24 oxylipins were found to be sig-
nificantly decreased in the plasma of diabetic compared with
nondiabetic mice (Fig. 1). Particularly interesting was that
many oxylipins derived from arachidonate metabolism were
significantly reduced in diabetic mice, including LTB
4
, PGE
2
,
PGD
2
, TXB
2
, 8-HETE, 12-HETE, 15-HETE, 12-OxoETE, and
15-OxoETE (Fig. 4). Moreover, global concentrations of cir-
culating 20C-ketones, 20C-hydroxy acids, and prostacyclins
were all positively correlated with body weight and negatively
correlated with fasting blood glucose (Fig. 2 and Supplemental
Table S1). Only 12-oxo-ETE, 9-oxo-ODE, and PGF
2␣
were
identified as being within the top 10% of discriminant metab-
olites (Supplemental Table S1). The T1D-associated decrease
in 12-oxo-ETE paralleled the observed reductions in PC (38:2)
and threonic acid (Fig. 3).
DISCUSSION
In the previous study, using NOD mice, we characterized
progressors (diabetic) and nonprogressors (nondiabetic) to
Fig. 3. Partial correlation network displaying associations between top 10% T1D-dependent metabolic perturbations. All significantly top 10% discriminant
metabolites (n⫽44) of the T1D-phenotype are connected based on partial correlations (P
adj
ⱕ0.5, Supplemental Table S1). Edge width displays the absolute
magnitude and color the direction (orange, positive; blue, negative) of the partial coefficient of correlation. Metabolite size and color represent the importance
(O-PLS-DA model loadings, LV 1) and relative change (green, decrease; red, increase) in diabetic vs. nondiabetic mice. Shapes display metabolites’ molecular
classes or biochemical subdomains (see Fig. 4 legend).
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T1D and performed an initial screening of primary metabolites
in the plasma of these mice with the aims of identifying
circulating factors linked with -cell viability and/or T1D
progression (23). In the current study, we aimed to expand
upon our initial observations by using a multiplatform ap-
proach to recapitulate our initial metabolomic findings in
addition to capturing perturbations in the lipidome and lipid
signaling mediators. Using this unique multiplatform ap-
proach, we detected more than 1,000 lipids and metabolites
(396 annotated) in the plasma of progressors and nonprogres-
sors.
Our analysis of the primary metabolites (carbohydrates,
amino acids, organic acids, nucleotides, free fatty acids) re-
vealed numerous age- and sex-adjusted T1D-dependent altera-
tions that extended to multiple biochemical domains. Importantly,
29 of the 60 annotated metabolites distinguishing progressors
from nonprogressors were also found to be significantly different
in our initial study (23) (see Appendix Table A2). Furthermore, all
29 metabolites maintained the same direction in change providing
validation for both our initial and current study (see Appendix
Table A2).
In comparison with nonprogressors, diabetic mice indicated
a 69% increase in global circulating carbohydrates. Most
notably, diabetic mice displayed significant elevations in cir-
culating glucose and significant reductions in 1,5-anhydroglu-
citol (1,5-AG), a marker of glycemic control (28). The eleva-
tion in glucose and reduction in 1,5-AG is consistent with the
diabetic model and provides assurance in our analytical anal-
ysis.
In concurrence with the increases in circulating carbohy-
drates, carbohydrate oxidation products glucono-␦-lactone
(GDL), an oxidation product of glucose leading to the produc-
tion of hydrogen peroxide (33), and galactonic acid, an oxida-
tion product of galactose, were elevated in diabetic compared
with nondiabetic mice (Fig. 1). The elevation in circulating
GDL and galactonic acid may reflect elevated states of oxida-
tive stress, a well-known hallmark of T1D (36). Consistent
with this notion, we observed significant T1D-dependent re-
ductions in circulating levels of methionine and cysteine (Fig.
1). Methionine and cysteine are both components of the trans-
sulfuration pathway, which leads to the synthesis of glutathi-
one (not measured), an important intracellular antioxidant.
Previous studies have demonstrated that insulin-deprived T1D
subjects have reduced rates of homocysteine-methionine rem-
ethylation and increased rates of transsulfuration compared
with control subjects (1). Accordingly, patients with poor
glycemic control have been shown to have depleted glutathione
pools and reduced erythrocyte free cysteine concentrations
(11). The reduction in circulating methionine and cysteine
may, therefore, reflect an increased flux into the transsulfura-
tion pathway and glutathione synthesis. In the current investi-
gation, the reduction in methionine paralleled the observed
reduction in threonic acid, a breakdown product of the antiox-
idant ascorbic acid (vitamin C; Fig. 4) (54). The T1D-associ-
ated reduction in circulating threonic acid suggests impaired
ascorbic acid metabolism. This is a noteworthy observation
given that ascorbic acid uptake into the tissue of diabetic
animals is often depressed during states of hyperglycemia (10),
rendering cells more susceptible to oxidative stress. Addition-
ally, diabetic mice indicated a significant (raw Pvalue 0.017)
33% reduction in ␣-tocopherol, a lipophilic antioxidant (see
Supplemental Table S2). Collectively, the above-mentioned
metabolic aberrations point toward a heightened state of oxi-
dative stress in mice that progress to T1D. In this sense, it is
Fig. 4. Significantly perturbed eicosanoids (P
adj
⬍0.05) in the KEGG arachidonate metabolism pathway. Pathway enrichment was determined based on FDR
adjusted hypergeometric t-test, P⬍0.05 for KEGG pathways for Mus musculus. Figure displays relative fold changes (blue, decrease; yellow, increase) in means
between diabetic and nondiabetic mice. *Values reported as means ⫾SD unless otherwise noted; †values reported median (minimum, maximum); ‡unpaired
two-sample t-test, Pⱕ0.05.
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interesting to note that cysteine, threonic acid, and methionine
were negatively correlated with fasting blood glucose and
positively correlated with 1,5-AG (see Appendix Table A3).
We therefore postulate that reductions in circulating cysteine,
threonic acid, and methionine serve as candidate biomarkers
for monitoring oxidative stress and predicting T1D develop-
ment and progression.
Although it was evident that there were many T1D-associ-
ated alterations in primary metabolism, one of the more pro-
nounced differences reflective of the progression to T1D was
observed in lipid species and lipid signaling mediators. Con-
sistent with our initial finding, diabetic NOD mice displayed
significant reductions in selected free fatty acids, most notably
arachidonic acid (AA), palmitoleic acid (PA) and oleic acid
(OA) compared with nonprogressors (Fig. 1). Free fatty acids
exhibit complex interactions with -cells, acting as both secre-
tagogues (14) and agents of apoptotic signaling (9, 26, 42, 55).
Moreover, the development of T1D is known to have a strong
inflammatory component (3, 34, 43). Cyclooxygenase
(COX)-2 and 12-lipoxygenase (LOX) derived products of AA,
such as prostaglandin E2 (PGE2) and 12-hydroxyeicosatetra-
enoic acid (12-HETE), have been shown to play critical roles
in cytokine-induced human -cell destruction (9, 26, 42, 55).
Thus, one may anticipate that the reduction in circulating AA
represents a shift in equilibrium toward eicosanoid production.
On the contrary, we found significant T1D-associated reduc-
tions in several AA-derived eicosanoids, including thrombox-
ane A2 (TXA2), leukotriene B4 (LTB4), PGD
2
, PGE2, 11-,12-
and 15-HETE, and 12-oxo-ETE. This suggests a reduced
bioavailability of AA rather than an increased flux into these
lipid signaling pathways (Fig. 4). The reduction in circulating
oxylipins, particularly those derived from AA, also suggests
that mice that have progressed to T1D are under a reduced state
of systemic inflammation. Kriegel et al. (32) previously re-
ported marked infiltration of CD11c⫹CD11b⫹dendritic cells
(DC) into the pancreatic islets of NOD mice. Interestingly,
these DCs exhibited a hypoactive phenotype, failed to induce
proliferation of diabetogenic CD4⫹T-cells in vitro, and were
potent suppressors of diabetes development (32). Additionally,
Bouma et al. (6) demonstrated that NOD mice exhibit impaired
recruitment of leukocytes into sites of inflammation in the
peritoneum and subcutaneously elicited air pouches. It should
be noted that in both studies the cohort of NOD mice were
under 8 wk of age and, as such, cannot be exclusively com-
pared with our diabetic cohort. Despite this limitation, both
studies demonstrate marked modulation of overall immune and
inflammatory status in NOD mice and T1D development.
Moreover, both studies hint toward the fact that reduced
recruitment of inflammatory mediators accompanies T1D de-
velopment. However, it is important to note that we evaluated
systemic alterations of inflammatory mediators as opposed to
pancreas-specific changes; thus, changes in circulation reflect
the contributions all organ types. Despite these findings, AA, in
addition to PA and OA, has been also been shown to attenuate
the deleterious effects of high glucose on human pancreatic
-cell turnover and function (12, 13, 35). Similarly, reductions
in 9,10-DiHODE and 11- and 12-HETE have been observed in
men with hyperlipidemia, a frequent T1D-associated compli-
cation (15, 48). Future studies will be required to confirm our
initial observations and fully elucidate how the observed re-
ductions in circulating lipid mediators and free fatty acids
relate to T1D development and progression.
The coexistence of lipid dysfunction and hyperlipidemia is a
known to be intertwined with T1D pathophysiology. Hypertri-
glyceridemia is strongly linked to poor glycemic control (21,
46). Our analysis of the lipidome revealed significant T1D-
associated increases in virtually all circulating triglycerides
(Supplemental Table S1). Moreover, global abundances of
triglycerides were positively correlated with fasting blood
glucose (Fig. 3), indicative of hypertriglyceridemia. Inverse to
the observed increases in circulating triglycerides, diabetic
NOD mice displayed striking reductions in several lyso-
phophospholipids, phospholipids, and sphingolipids (Supple-
mental Table S1). It is important to note that these major lipid
classes were highly correlated with body weight, which was
found to be significantly lower in our diabetic cohort, and
negatively correlated with fasting blood glucose (Fig. 3). T1D
is characterized by increases in amino acid catabolism (25) and
fatty acid oxidation (24, 27), a consequence of the inability to
utilize glucose as a source of energy. Weight loss is frequently
observed in T1D, which is attributed to several T1D-associated
complications such as muscle atrophy and dehydration (57,
59). Consequently, the observed reduction in major structural
lipids may be a consequence of increased fatty acid -oxida-
tion and reduced bioavailability of fatty acids for phospholipid/
structural lipid synthesis, a notion that is partially supported by
the observed decrease in circulating free fatty acids and weight
loss. Despite the strong association between body weight and
major structural lipids, alterations in lipid metabolism are
involved in the etiology of T1D. Transiently elevated LPC
serum levels have been positively associated with seroconver-
sion to islet autoantibody positivity in children who subse-
quently progress to T1D (40). Furthermore, LPCs have been
shown to improve blood glucose levels in mouse models of
T1D and T2D (61) and act as insulin secretagogues (50).
Sysi-Aho et al. (53) found that female NOD mice that did not
progress to T1D after 8 wk of age despite being insulin
autoantibody (IAA) positive had elevated levels of selected
serum LPC species compared with progressors. Additionally, it
was demonstrated that IAA positivity and LPC concentrations
were able to reasonably discriminate between progressors and
nonprogressors (53). Collectively, the findings by Sysi-Aho et
al. and others suggest a protective effect by LPCs. It is
important to note that the studies by Sysi-Aho and colleagues
focused primarily on metabolic alterations that occur prior and
during seroconversion in young (⬍25 wk) NOD mice. The
cohort of NOD mice used in the current investigation extended
beyond 25 wk and included NOD mice that escaped chronic
hyperglycemia, a group that has been largely excluded in
previous studies. The inclusion of mice that avoided develop-
ing overt T1D was particularly important, since it allowed us to
directly compare between progressors and nonprogressors both
undergoing similar autoimmune insults. In our model system,
LPCs were negatively correlated with fasting blood glucose
(Fig. 3) corroborating the notion that LPCs are associated with
glycemic control and -cell dysfunction/destruction. Interest-
ingly, selected LPCs were positively related to the T1D-
associated reduction in methionine (Fig. 4). Pflueger et al. (44)
previously found that concentrations of methionine were lower
in children who developed autoantibodies by age 2 yr com-
pared with those who developed autoantibodies later in child-
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hood or remained autoantibody negative. Methionine is a vital
component in one-carbon metabolism and serves as the pre-
cursor for s-adenosylmethionine synthesis. s-Adenosyl methi-
onine in turn acts as a methyl donor for the synthesis of PCs
from PEs (38). One-carbon metabolism has been shown to be
perturbed in animal models of T1D (38). The observed reduc-
tion in methionine in diabetic mice may therefore not only be
an indicator of increased flux into the transsulfuration pathway
but may also partially account for the T1D-dependent reduc-
tions in PCs and related LPCs. This is a notable observation as
Oresic et al. (40) demonstrated that children who developed
T1D had reduced serum levels of PCs at birth and were
consistently low in progressors compared with nonprogressors.
Similarly, analysis of umbilical cord serum lipidome in infants
who later developed T1D revealed distinct reductions in major
choline-containing phospholipids, including sphingomyelins
and PCs (39). Our results suggest that the reduction in circu-
lating LPC and PC species can be used as potential biomarkers
for T1D onset and progression and -cell loss.
In conclusion, a comprehensive metabolomics approach was
used to identify 493 (192 annotated) significant age- and
sex-independent metabolic alterations associated with T1D
development and progression. Our findings suggest that pro-
gression to T1D is characterized by increased oxidative stress,
perturbed states of inflammation, and altered lipid metabolism.
More importantly, these findings serve as a basis for the
identification of metabolic states (i.e., altered amino acid pro-
files in conjunction with altered lipid profiles) that can be used
as diagnostic and prognostic indicators of T1D pathophysiol-
ogy and consequently -cell death. In particular, we propose
that reductions in circulating threonic acid, methionine, and
cysteine may serve as stable markers of oxidative stress and
T1D progression, whereas reductions in phosposphatidylcho-
lines and related lysophosphatidylcholines may serve as can-
didate biomarkers of glycemic control, T1D onset, and -cell
destruction.
APPENDIX
Analysis of the Metabolome, Lipidome, and Lipid Signaling
Mediators
For analysis of primary metabolites, 30-l plasma aliquots, which
were extracted with 1 ml of degassed acetonitrile-isopropanol-water
(3:3:2) at ⫺20°C and centrifuged, the supernatant was removed, and
solvents were evaporated to dryness under reduced pressure. To
remove membrane lipids and triglycerides, dried samples were recon-
stituted with acetonitrile-water (1:1), decanted, and taken to dryness
under reduced pressure. Internal standards, C8 –C30 fatty acid methyl
esters (FAMEs), were added to samples and derivatized with me-
thoxyamine hydrochloride in pyridine and subsequently by MSTFA
(Sigma-Aldrich) for trimethylsilylation of acidic protons and analyzed
by GC-TOF-MS. An Agilent 7890A gas chromatograph (Santa Clara,
CA) was used with a 30 m long, 0.25 mm ID Rtx5Sil-MS column with
0.25-m 5% diphenyl film; an additional 10-m integrated guard
column was used (Restek, Bellefonte, PA) (16, 30, 56). A Gerstel
MPS2 automatic liner exchange system (ALEX) was used to elimi-
nate sample cross-contamination during the GC-TOF analysis. A
0.5-l of sample was injected at 50°C (ramped to 250°C) in splitless
mode with a 25-s splitless time. The chromatographic gradient con-
sisted of a constant flow of 1 ml/min, ramping the oven temperature
from 50°C to 330°C over 22 min. Mass spectrometry was done using
a Leco Pegasus IV TOF mass spectrometer, 280°C transfer line
temperature, electron ionization at ⫺70 V, and an ion source temper-
ature of 250°C. Mass spectra were acquired at 1,525 V detector
voltage at m/z 85–500 with 17 spectra/s. Acquired spectra were further
processed using the BinBase database (18, 47). Briefly, output results
(30) were filtered based on multiple parameters to exclude noisy or
inconsistent peaks. All entries in BinBase were matched against the
Fiehn mass spectral library of 1,200 authentic metabolite spectra,
using retention index and mass spectrum information or the NIST11
commercial library.
For analysis of the lipidome, plasma aliquots (20 l), stored at
⫺80°C, were thawed and extracted using a modified liquid-liquid
phase extraction approach purposed by Matyash et al. (37). Briefly,
225 l of chilled methanol containing an internal standard mixture
[PE(17:0/17:0); PG(17:0/17:0); PC(17:0/0:0); C17 sphingosine; C17
ceramide; SM (d18:0/17:0); palmitic acid-d3; PC (12:0/13:0); choles-
terol-d7; TG (17:0/17:1/17:0)-d5; DG (12:0/12:0/0:0); DG (18:1/2:0/
0:0); MG (17:0/0:0/0:0); PE (17:1/0:0); LPC (17:0); LPE (17:1)], and
750 l of chilled MTBE containing the internal standard 22:1 cho-
lesteryl ester was added to 10-l aliquots of sample. Samples were
shaken for 6 min at 4°C using an Orbital Mixing Chilling/Heating
Plate (Torrey Pines Scientific Instruments) followed by the addition of
188 l of room temperature distilled water. Samples were vortexed
and centrifuged, and the upper layer was transferred to a new 1.5-ml
Eppendorf tube. An aliquot was dried to completeness using a Lab-
conco Centrivap. Upon complete dryness, samples were resuspended
in methanol-toluene (90:10) with 50 ng/ml CUDA ([[12-(cyclohexy-
lamino)carbonyl]amino]-dodecanoic acid, Cayman Chemical). Sam-
ples were vortexed, sonicated for 5 min, and centrifuged, whereafter
100 l of the sample was transferred to an amber glass vial (National
Scientific C4000-2W) with a microinsert (Supelco 27400-U). Lipid
extracts were subsequently analyzed on an Agilent 1290A Infinity
Ultra High Performance Liquid Chromatography system with an
Agilent Accurate Mass-6530-QTOF in both positive and negative
modes. The column (65°C) was a Waters Acquity UPLC CSH C18
(100 mm length ⫻2.1 mm ID; 1.7 M particles) coupled with a
Waters Acquity VanGuard CSH C18 1.7 M precolumn. The solvent
system included (A) 60:40 vol/vol acetonitrile-water (LCMS grade)
containing 10 mM ammonium formate and 0.1% formic acid and (B)
90:10 vol/vol isopropanol-acetonitrile containing 10 mM ammonium
formate and 0.1% formic acid. The gradient started from 0 min 15%
(B), 0 –2 min 30% (B), 2–2.5 min 48% (B), 2.5–11 min 82% (B),
11–11.5 min 99% (B), 11.5–12 min 99% (B), 12–12.1 min 15% (B),
and 12.1–15 min 15% (B). The flow rate was 0.6 ml/min and with an
injection volume of 1.67 l for ESI (⫹) and 5 l for ESI (⫺) mode
acquisition. ESI capillary voltage was ⫹3.5 kV and ⫺3.5 kV with
collision energies of 25 and 40 eV for MS/MS collection in positive
and negative acquisition modes, respectively. Data were collected at a
mass range of m/z 60 –1700 Da with a spectral acquisition speed of 2
spectra/s. Method blanks and pooled sterile human plasma samples
were included to serve as additional quality controls. CUDA was used
to monitor instrument performance. Data were processed using
MZmine 2.10. All peak intensities are representative of peak heights.
Annotations were completed by matching experimental accurate mass
MS/MS spectra to MS/MS libraries, including Metlin-MSMS,
NIST12, and LipidBlast (29). Spectral matching was automated using
the MSPepSearch tool and manually curated using The NIST Mass
Spectral Search Program v. 2.0g. Metabolite libraries were created, in
positive and negative ionization modes, containing all confirmed
identified compounds. MZmine’s Custom Database Search tool was
used to assign annotations based on accurate mass and retention time
matching.
For analysis of lipid signaling mediators (oxylipins), samples were
extracted in accordance with previously described protocols (60).
Briefly, plasma samples underwent solid-phase extraction (SPE) on
60-mg Waters Oasis-HLB cartidges (Milford, MA). The elutions from
the SPE cartridges were evaporated to dryness using a Speedvac
(Jouan, St-Herblain, France) and reconstituted in 200 nM CUDA in
methanol and analyzed by UPLC-MS/MS. The LC system used for
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analysis was an Agilent 1200 SL (Palo Alto, CA) equipped with a
2.1 ⫻150 mm Eclipse Plus C18 column with a 1.8 M particle size
(Agilent). The autosample was kept at 4°C. Mobile phase A consisted
of water with 0.1% glacial acetic acid. Mobile phase B consisted of
LCMS-grade acetonitrile-methanol (84:16 vol/vol) with 0.1% glacial
acetic acid. Gradient elution was performed at a flow rate of 250
l/min. Chromatography was optimized to separate all analytes in
21.5 min according to their polarity, with the most polar analytes,
prostaglandins, and leukotrienes eluting first, followed by the hy-
droxyl and epoxy fatty acids. The column was connected to a 4000
QTrap tandem mass spectrometer (Applied Biosystems Instrument,
Foster City, CA) equipped with an electrospray source (Turbo V). The
instrument was operated in negative multiple reaction monitoring
(MRM) mode. The optimized conditions and the MRM transitions, as
well as extraction efficiencies, were reported previously (60). Quality
control samples were analyzed at minimum calibration throughout the
analysis. Analyst 1.4.2 software was used to quantify peaks according
to their standard curve.
Table A1. O-PLS-DA model performance and validation statistics.
Table A2. List of compounds significantly different in both the
initial and follow-up studies.
Supplemental Table S2. List of all identified peaks which were
significantly different in T1D compared to control animals.
Supplemental Table S3. List of all identified peaks which were not
significantly different in T1D compared to control animals.
Table A3. Spearman rank correlations between fasting blood glu-
cose, threonic acid, methionine, cysteine and 1,5-anhydroglucitol.
GRANTS
This research is funded by the National Institutes of Health Grant U24
DK-097154, and is partially supported by NIEHS R01 ES-002710 and P42
ES-004699.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the
author(s).
AUTHOR CONTRIBUTIONS
Author contributions: J.F. and J.Y. performed experiments; J.F., D.G., and
J.Y. analyzed data; J.F., D.G., J.Y., and M.H. interpreted results of experi-
ments; J.F. and D.G. prepared figures; J.F., D.G., and M.H. drafted manuscript;
J.F., D.G., J.Y., B.H., O.F., G.I.B., and M.H. approved final version of
manuscript; J.Y., B.H., O.F., and G.I.B. edited and revised manuscript; O.F.,
G.I.B., and M.H. conception and design of research.
Table A1. O-PLS-DA model performance and validation statistics
Model Q
2
RMSEP AUC Sensitivity Specificity
Training data 0.691 ⫾0.05* 0.341 ⫾0.06* 0.844 ⫾0.09* 0.902 ⫾0.08** 0.786 ⫾0.17*
Test data 0.652 0.277 0.944 1 0.889
Q
2
, cross-validated fit to the training data; RMSEP, root mean squared error of prediction; AUC, area under the receiver operator characteristic curve.
*Performance worse than only 5% of all permuted models. **Performance worse than only 10% of all permuted models.
Table A2. List of compounds significantly different in both the initial and follow-up studies
Initial Study (23) Current Study
Metabolite† FC‡ Direction§ Padj** FC‡ Direction§ Padj**
1,5-Anhydroglucitol 0.4 Down ⬍0.00000 0.13 Down ⬍0.00000
2-Deoxytetronic acid 1.4 Up 0.04390 1.12 Up 0.00384
2-Hydroxy-2-methylbutanoic acid 2.2 Up 0.00061 2.44 Up ⬍0.00000
3,6-Anhydrogalactose 4.3 Up ⬍0.00000 1.82 Up 0.00002
4-Hydroxybutyric acid 2.5 Up ⬍0.00000 1.23 Up 0.02300
5-Hydroxyindole-3-acetic acid 2 Up 0.00002 1.41 Up 0.03900
Adenosine-5-phosphate 0.5 Down 0.00113 0.52 Down 0.00021
Arabinose 1.6 Up 0.01850 1.59 Up 0.00755
Arachidonic acid 0.7 Down 0.00136 0.57 Down 0.00065
Creatinine 0.6 Down 0.01130 0.36 Down 0.00374
Cysteine 0.7 Down 0.03990 0.67 Down 0.03400
Erythritol 1.6 Up 0.01140 1.85 Up 0.00391
Galactinol 2.6 Up 0.00964 1.15 Up 0.04980
Galactonic acid 2.2 Up ⬍0.00000 1.61 Up 0.00531
Glucose 3 Up ⬍0.00000 2.08 Up ⬍0.00000
Glycerol-␣-phosphate 0.6 Down 0.00003 0.52 Down 0.00091
Indole-3-lactate 2.2 Up 0.00114 2.08 Up 0.00379
Isocitric acid 1.4 Up 0.02390 1.56 Up 0.00564
Isoleucine 1.9 Up 0.01870 1.43 Up 0.01250
Isothreonic acid 1.5 Up 0.00584 1.69 Up 0.00415
Lactobionic acid 3.2 Up 0.00044 2.86 Up ⬍0.00000
Maltose 3.1 Up ⬍0.00000 2.50 Up ⬍0.00000
Oleic acid 0.7 Down 0.03470 0.68 Down 0.04830
Pipecolic acid 2 Up 0.00901 1.85 Up 0.03890
Saccharic acid 3.3 Up ⬍0.00000 1.64 Up 0.01020
Sorbitol 2.4 Up 0.00048 2.27 Up 0.00003
Threonic acid 0.7 Down 0.00209 0.36 Down ⬍0.00000
Tyrosine 0.9 Down 0.03990 0.65 Down 0.03700
Valine 2 Up 0.00785 1.43 Up 0.04440
*GC-TOF. †Metabolite name reported as a BinBase identifier. ‡Fold change of means in diabetic vs. nondiabetic animals. §Direction in change relative to
nondiabetic animals. **False discovery rate adjusted Pvalue.
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Table A3. Spearman rank correlations between fasting
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Correlation Matrix
(Spearman)
1
Blood
Glucose
Threonic
Acid Methionine Cysteine
Blood glucose — — — —
Threonic acid ⫺0.696* — — —
Methionine ⫺0.496* 0.732* — —
Cysteine ⫺0.441* 0.636* 0.643* —
1,5-Anhydroglucitol ⫺0.839* 0.753* 0.553* 0.537*
1
Values represent Spearman rank correlation coefficients (R). *Pvalue ⬍0.001.
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