Type 2 diabetes is caused by a complex set of interactions between genetic and environmental
factors. Recent work has shown that human type 2 diabetes is a constellation of disorders
associated with polymorphisms in a wide array of genes, with each individual gene
accounting for <1% of disease risk (1). Moreover, type 2 diabetes involves dysfunction
of multiple organ systems, including impaired insulin action in muscle and adipose,
defective control of hepatic glucose production, and insulin deficiency caused by
loss of β-cell mass and function (2). This complexity presents challenges for a full
understanding of the molecular pathways that contribute to the development of this
major disease. Progress in this area may be aided by the recent advent of technologies
for comprehensive metabolic analysis, sometimes termed “metabolomics.” Herein, we
summarize key metabolomics methodologies, including nuclear magnetic resonance (NMR)
and mass spectrometry (MS)-based metabolic profiling technologies, and discuss “nontargeted”
versus “targeted” approaches. Examples of the application of these tools to diabetes
and metabolic disease research at the cellular, animal model, and human disease levels
are summarized, with a particular focus on insights gained from the more quantitative
targeted methodologies. We also provide early examples of integrated analysis of genomic,
transcriptomic, and metabolomic datasets for gaining knowledge about metabolic regulatory
networks and diabetes mechanisms and conclude by discussing prospects for future insights.
In principal, metabolomics can provide certain advantages relative to other “omics”
technologies (genomics, transcriptomics, proteomics) in diabetes research: 1) Estimates
vary, but one current source, the Human Metabolome Database (HMDB)-Canada (3), currently
lists ∼6,500 discrete small molecule metabolites, significantly less than the estimate
of 25,000 genes, 100,000 transcripts, and 1,000,000 proteins. 2) Metabolomics measures
chemical phenotypes that are the net result of genomic, transcriptomic, and proteomic
variability, therefore providing the most integrated profile of biological status.
3) Metabolomics is in theory a precise tool for discerning mechanisms of action and
possible toxicological effects of drug therapies. However, metabolomics is still a
field in its infancy, with significant limitations and potential for misuse of technologies
and overinterpretation of data. Here we seek to provide a critical evaluation of progress
to date in application of metabolomics technologies for the understanding of diabetes
and obesity mechanisms, for subclassification of different forms of diabetes to assist
in tailoring of therapeutic strategies, and for more detailed evaluation of the safety
and efficacy of drugs used to treat the disease.
Overview of current metabolomics technologies.
Genome-wide association studies and mRNA profiling by microarray analysis are relatively
mature technologies that have developed to a point where core laboratories that provide
these services are common in both the academic and private sectors. This is not yet
the case for metabolomics. One reason for this is the complexity inherent in measuring
large numbers of intermediary metabolites with diverse chemical properties in a quantitatively
rigorous and reproducible fashion. Underlying issues include the wide-ranging concentrations
of metabolites in tissues and bodily fluids (ranging from subnanomolar to millimolar),
problems encountered in efficient extraction of metabolites from different biological
matrices (e.g., tissues, blood, urine), and the chemical diversity of the analytes.
Given these variables, it is perhaps not surprising that no single technology exists
for measurement of all of the metabolites in the “metabolome.” Instead, groups that
practice comprehensive metabolic profiling do so with quite diverse sets of instruments
and methods for data analysis, the choice of which is influenced by factors such as
cost, personal experience, and specific research goals. This places a large burden
on the current core labs and their clients to ensure reproducibility of their findings
in the absence of very little standardization of methods across groups. The U.S. National
Institutes of Health has recognized the problem and recently convened a workshop on
development of standardized methods for metabolomics, leading to a summary of recommendations
(4).
The two major instrument platforms for measuring metabolite levels in biological samples
are NMR and MS (5
–13). Raman and infrared spectroscopy (14) and liquid chromatography coupled to ultraviolet
or coulometric electrode array detection (15) have also been used by several labs.
In general, research groups in the field tend to fall into two camps that adopt either
“nontargeted/top-down” approaches or “targeted/bottom-up” methods as their core technologies,
although a few groups, including our own, have both kinds of platforms in their repertoire.
Targeted methods.
For scientists with core interests in biological mechanisms rather than biomarkers,
knowledge about the identity of metabolites being surveyed and their exact concentrations
is essential. This encourages some metabolic profiling laboratories to emphasize targeted
and quantitative MS-based analyses. These methods focus on quantification of discrete
clusters of chemically related metabolites within a “module” using various combinations
of chromatographic separations technologies and MS instruments that are most compatible
with the analyte class.
Most targeted metabolic profiling methods use stable-isotope dilution for accurate
quantification of analytes, involving addition of several stable isotope–labeled standards
to the biological sample prior to the extraction and derivatization steps that may
be necessary for the particular MS approach (Fig. 1
A). Ideally, each unknown analyte will be paired with its labeled cognate (often an
M+3 heavy isotope, e.g., methionine with methyl-D3 methionine, pyruvate with 13C3-pyruvate,
etc.) to control for differences in analyte loss during sample processing and to compensate
for ionization-suppression effects (example in Fig. 2). One limitation of this approach
is the relatively narrow range of stable isotope–labeled standards that are available
from commercial suppliers and their significant cost. Several groups are now engaged
in expanding standard libraries through custom synthesis, but with the reagents available
today, most labs are limited to measurement of ≤300 discrete metabolites by targeted
methods.
FIG. 1.
Schematic summary of targeted and nontargeted metabolomics methods.
A
: When using targeted methods, quantification of specific analytes is facilitated
by addition of stable isotope-labeled standards to the sample prior to the sample
extraction and derivatization steps. This allows reporting of targeted analytes in
true units of measure (e.g., μmol/l).
B
: In contrast, when performing nontargeted analysis, the goal is usually to obtain
a global comparison of a large number of analytes across several classes. This is
achieved by assaying replicate samples from contrasting conditions (e.g., drug-treated
vs. control cells). Samples are processed and analyzed by MS to generate two independent
datasets that are subjected to univariate and multivariate statistical analysis to
identify features that are different between the two conditions, although in the absence
of added standards, the concentration of the analytes is not reported in true units
of measure. In addition, for many of the peaks, the chemical identity of the metabolite
cannot be immediately discerned due to limitations in current spectral and chemical
standard libraries. PCA, principal components analysis.
FIG. 2.
Targeted isotope-dilution analysis of organic acids by GC-MS. When performing targeted
isotope-dilution analysis, a group of heavy isotope standards specific to the assay
module are added immediately following sample homogenization and prior to chromatographic
resolution and MS.
A
: Total ion chromatogram following GC of a rat liver homogenate in which multiple
organic acids are resolved.
B
: Single ion monitoring for pyruvate and its corresponding internal standard. Each
organic acid peak in the chromatogram (
A
) contains the target analyte and a heavy stable isotope as an internal standard,
which are resolved and quantified by mass spectrometry (
B
) as shown for pyruvate, for example.
Although targeted metabolomics platforms tend to use conventional instruments such
as electrospray ionization (ESI)-tandem MS-MS and gas chromatography (GC)-MS, significant
challenges must still be overcome in building rigorous and fully vetted analysis modules
for groups of metabolites. For example, when using GC-MS for measurement of fatty
acids or organic acids, chromatography is performed in the gas phase at a high temperature,
and analytes must be volatile and have sufficient thermal stability to survive the
analysis. To help stabilize the analytes under study, reactive carboxyl, carbonyl,
sulfhydryl, amine, or hydroxyl groups are derivatized by akylation, oximation, acylation,
or silylation (5,11,16,17). These methods, while effective, add complexity beyond
that already introduced by sample extraction to the analytical procedures and can
result in batch-to-batch variation if not properly controlled. Similarly, analysis
of acylcarnitines or amino acids/urea cycle intermediates by ESI-MS-MS requires module-specific
derivatization, specifically treatment with acidic methanol for acylcarnitines and
n-butanol for amino acids/urea cycle intermediates (17
–20). Nevertheless, experience has proven that these methods can be deployed for reliable
and quantitative analysis of metabolite modules, with coefficients of variation in
replicate assays of major analytes of ∼15% or less.
Targeted and nontargeted MS-based metabolic analysis has been applied for years in
the clinical diagnosis of metabolic disease and newborn screening, and today it is
used to detect >40 different genetic diseases of lipid and amino acid metabolism (21,22).
In these applications, less emphasis is placed on absolute quantification of multiple
analytes in a module, since the screen only requires detection of differences in a
single or discrete cluster of analytes with respect to established laboratory norms.
For example, a defect in HMG-CoA lyase results in large and specific increases in
3-hydroxy-isovaleryl-carnitine and 3-methylglutaryl-carnitine species detected by
MS-MS, whereas defects in long-chain 3-hydroxyacyl CoA dehydrogenase (LCHAD) or mitochondrial
trifunctional protein (MTP) are associated with increases in 3-hydroxy-palmitoyl and
3-hydroxy stearoyl-carnitine metabolites (22).
In more recent years, many of the MS-based targeted metabolic profiling techniques
developed for diagnosing inborn errors of metabolism have been adopted, refined, and
supplemented for studies of mechanisms of disease pathogenesis. The approach taken
in our laboratory has been to assemble multiple targeted MS-based assay modules that
in aggregate report on several critical metabolic pathways (17
–20). Using a combination of GC-MS and MS-MS, we are currently able to perform quantitative
analysis of ∼180 metabolites in seven groups, as summarized in Table 1.
TABLE 1
Analyte modules in the Stedman Center laboratory and methods of analysis
Free fatty acids of varying chain length and degrees of saturation (GC-MS)
Total fatty acids (free + esterified) (GC-MS)
Acylcarnitines of varying chain length and degrees of saturation (representing precursors
and products of mitochondrial fatty acid, amino acid, and carbohydrate oxidation)
(MS-MS)
Acyl CoAs of varying chain length and degree of saturation (MS-MS)
Organic acids (TCA cycle intermediates and related metabolites) (GC-MS)
Amino acids, including urea cycle intermediates (MS-MS)
Ceramides and sphingolipids (MS-MS)
Although the total number of analytes measured with these tools is small relative
to estimates of 6,500 total metabolites in the metabolome, they are nevertheless highly
useful for understanding changes in metabolic function under different physiological
and pathophysiological circumstances. Moreover, expansion of the platform to include
a broader range of analytes of interest in disease pathogenesis is possible in the
near term. A particular recent focus of the metabolomics community has been in the
area of “lipidomics,” and methodologic advances are beginning to emerge for profiling
of phospholipids, prostaglandins, and their metabolites (eicosanoids) and sphingolipids
(9
–13). “Shotgun lipidomics,” or the broad survey of neutral lipids such as triacylglyerols
and diacylglyerols, including the profiling of the acyl side chains of these molecules,
is another emergent technology. Early methods focused on separation of neutral lipid
species by thin-layer chromatography followed by capillary GC and detection of lipid
species with a flame-ionization detector (23). More recent studies use a four-step
procedure that includes organic phase extraction (Bligh and Dyer method), intrasource
separation of lipid species based on propensity for ionization, separation of ionized
species by tandem-MS, and processing of data to assign molecular species and determine
relative abundances (24). It should be emphasized, however, that development of new
targeted modules is not a trivial or inexpensive undertaking, since it requires acquisition
or synthesis of stable isotope–labeled internal standards, development of extraction
procedures that are efficient for multiple analytes in a class, tailoring of protocols
specific to the various biological matrices, and demonstration of quantitative reproducibility
of the methods. The advantage gained is that such tools can be applied to the understanding
of metabolic regulatory mechanisms in isolated cells, animal models of disease, and
human disease states.
Nontargeted metabolite profiling.
Nontargeted profiling involves use of NMR, MS, or complementary technologies for measurement
of as many metabolites as possible in a biological specimen simultaneously, regardless
of the chemical class of the metabolites. In contrast to targeted profiling, in which
added internal standards allow quantification of specific metabolites in molar units,
nontargeted metabolomics generally adopts a strategy of comparison of two biological
states and reporting of those analytes that qualitatively differ in the two states
based on statistical analysis (Fig. 1
B). When applied to nontargeted profiling, both NMR and MS have advantages and limitations,
and neither technology can currently be used for surveying all of the metabolites
in a sample in a quantitative fashion.
NMR spectroscopy is theoretically an excellent tool for nontargeted metabolic profiling
of all small molecule metabolites, since the method detects spectral features emanating
from any molecules that contain carbon or hydrogen (5,7). Moreover, analyses can be
conducted directly in bodily fluids, cells, and even in intact tissues without the
need for chemical extraction or derivatization of the analytes. These advantages are
offset by significant technical challenges, including poor sensitivity, effects of
pH and ionic strength, and the difficulty of deconvolution and normalization of spectra
of complex metabolite mixtures in biological matrices like plasma, urine, or tissue
extracts. Thus, although NMR spectra are information rich, lack of sensitivity and
data complexity limit quantitative profiling to ≤100 metabolites in most biological
samples by current methods. In some applications, NMR datasets are analyzed by statistical
tools such as principal components analysis to identify spectral features (often not
identified as specific metabolites) that characterize different biological or disease
states (7,25).
MS has the immediate advantage of much higher sensitivity compared with NMR, and the
most advanced MS platforms such as Fourier transform ion cyclotron (FT-ICR)-MS have
the ability to detect metabolites in the femtomole range (11). Moreover, modern MS
platforms such as those that incorporate time of flight (TOF), orbitrap, and FT-ICR
mass analyzers offer very high mass resolution and mass accuracy. FT-ICR-MS, for example,
is capable of achieving a mass resolution of >100,000 while providing mass accurate
measurements of <1 ppm. By coupling such MS instrumentation with high-resolution chromatographic
technologies (e.g., ultra high–pressure liquid chromatography [UHPLC] and sub-2 μm
particle stationary phases), it has become possible to resolve literally thousands
of individual small molecules. Seemingly, these platforms could circumvent problems
encountered when using NMR methods for nontargeted metabolic profiling of complex
biological matrices.
In practical reality, the variable lability, solubility, recovery, ionization, and
detection of the different analyte species, coupled with the lack of comprehensive
spectral and stable isotope–standard libraries, make these nontargeted MS methods
semi-informed (with regard to analyte identities) and semiquantitative at best. Concerns
about the semiquantitative nature of the methods can be overcome to some extent by
focusing on tightly controlled biological conditions (e.g., cell lines in tissue culture
studied in response to individual drug, nutrient, or gene manipulations) and performance
of multiple replicate experiments. Under these conditions, statistical filtering can
be applied to peak areas for well-resolved spectral features, thereby identifying
analytes that change in a consistent fashion. However, ultimate proof of a significant
change in an analyte still requires targeted analysis against an internal standard.
The next challenge is identification of the chemical entities represented by the individual
peaks. Help comes from high-accuracy mass measurements afforded by current high-end
MS instruments. These mass measurements can be used to query databases such as METLIN,
HMDB, KEGG, Madison Metabolomics Consortium Database (MMCD), and ChemSpider for chemical
formulae whose theoretical masses match the experimentally determined mass, thereby
usually providing a strong starting point for identifying the analyte. In most laboratories,
current capabilities allow provisional or outright identification of ≤30% of analytes
in complex spectra obtained through nontargeted MS methods.
Overall, it seems clear that further development of technologies for nontargeted metabolic
profiling will be required before these tools can be maximally informative in the
whole animal or human settings. In contrast, several examples of application of targeted
profiling for gaining new insights into diabetes, obesity, and other chronic metabolic
diseases have emerged recently, as will be discussed.
Use of targeted profiling and NMR-based metabolic flux analysis for studies of insulin
secretion.
NMR- and MS-based tools have been used to investigate the process of glucose-stimulated
insulin secretion (GSIS) in pancreatic islet β-cells (26
–35). Stimulation of islet β-cells with glucose causes increases in insulin secretion
within seconds to minutes, and this response is mediated by signals that are generated
by β-cell glucose metabolism. A commonly accepted idea is that increases in the rate
of glucose metabolism in β-cells leads to increases in ATP-to-ADP ratio, which causes
inhibition of ATP-sensitive K+ channels (KATP channels), membrane depolarization,
activation of voltage-gated calcium channels, and Ca2+-mediated activation of insulin
granule exocytosis (36,37). However, this model clearly does not provide a complete
description of signals that regulate GSIS, since pharmacologic or molecular inhibition
of KATP channel activity still allows robust regulation of insulin secretion by glucose
(38,39). This has led to recent investigation of alternate metabolic pathways and
their byproducts in control of GSIS (rev. in 35).
Application of 13C NMR-based isotopomer analysis and MS-based profiling of intermediary
metabolites led to the discovery of a critical link between pyruvate carboxylase (PC)-mediated
pyruvate exchange with tricarboxylic acid (TCA) cycle intermediates (“pyruvate cycling”)
and GSIS and demonstration that these pathways are dysregulated in lipid-cultured
and dysfunctional β-cells (26
–35). More recent studies have focused on identification of the specific pyruvate
cycling pathways that may be involved in generation of signals for insulin secretion.
One important pathway appears to involve export of citrate and/or isocitrate from
the mitochondria via the citrate/isocitrate carrier (CIC) and subsequent conversion
of isocitrate to α-ketoglutarate (α-KG) by the cytosolic NAPD-dependent isoform of
isocitrate dehydrogenase (ICDc) (30,31) (Fig. 3).
FIG. 3.
Schematic diagram of a pyruvate/isocitrate cycle implicated in control of GSIS. The
cycle is initiated by anaplerotic conversion of pyruvate to oxaloacetate by PC. This
leads to accumulation of the TCA cycle intermediates citrate and isocitrate and their
export from the mitochondria to the cytosol by the CIC. Citrate is then converted
to isocitrate by cytosolic aconitase, and isocitrate is converted to α-ketoglutarate
by the cytosolic NADP-dependent ICDc. α-Ketoglutarate can then serve either as a direct
signal for insulin secretion, for example, by serving as a substrate for α-ketoglutarate
hydroxylases, or be recycled to pyruvate by one of several mitochondrial or cytosolic
pathways that remain to be defined (dashed lines). Another byproduct of the pyruvate/isocitrate
cycle with potential as an insulin secretagogue is cytosolic NAPDH, possibly acting
through Kv channels or the glutathione/glutaredoxin system. This pathway was elucidated
by integration of flux analysis by 13C NMR, targeted metabolic profiling by GC-MS
and MS-MS, and evaluation of the effects of knockdown of key genes in the pathway,
including CIC and ICDc.
In the foregoing studies, measurement of pyruvate cycling flux was accomplished by
incubation of β-cells in low (3 mmol/l) or high (12 mmol/l) concentrations of U-13C
glucose for several hours, followed by extraction of cells and analysis of glutamate
spectra by NMR. Specific resonances for each of the carbons of glutamate are affected
by the population of mass isotopomers (glutamate with varying mixtures of 13C and
12C at each of the five carbons of the molecule), and this information can be used
to calculate flux through the oxidative (PDH) and anaplerotic (PC) entry points of
the TCA cycle (26,27,40). These methods reveal that the capacity for GSIS in variously
glucose-responsive INS-1–derived cell lines is tightly correlated with PC-catalyzed
pyruvate cycling activity but not PDH-catalyzed glucose oxidation (26
–28).
Metabolic flux analysis by NMR can be integrated with static metabolite profiling
by MS to gain a more complete picture of fuel-sensing pathways in the β-cell. For
example, GC-MS was used to demonstrate a fall in cytosolic citrate levels in response
to siRNA-mediated suppression of CIC, as would be expected if a major pathway for
export of citrate from the mitochondria to the cytosol is blocked (31). Other recent
experiments show that glucose stimulation of β-cells results in increases in several
TCA cycle intermediates in whole-cell extracts (30,41), but a selective release of
only the early TCA cycle intermediates citrate and α-KG into the medium, with no change
in medium levels of the later TCA cycle intermediates malate, fumarate, or succinate
(M. Jensen, H.E. Hohmeier, O.I., and C.B.N., unpublished observations). These results
serve to confirm that glucose causes a significant release of citrate from the mitochondria
and is consistent with the conversion of this pool to α-KG via ICDc. These examples
illustrate two important applications of MS-based metabolic profiling in cellular
research: 1) use in validation of the expected metabolic impact of a specific genetic
engineering or pharmacologic manipulation of cellular systems and 2) integration with
flux analysis to provide a complete picture of changes in metabolic pathways under
varying experimental conditions. Overall, these studies show that metabolic byproducts
of pyruvate/isocitrate cycling may be an important amplifying signal for control of
GSIS. Possible mediators now under investigation include NADPH (35,42), α-KG or its
metabolites (43,44), or GTP generated by the succinyl CoA dehydrogenase reaction (45),
all of which are direct or downstream products of the ICDc reaction.
Targeted MS-based metabolic profiling applied to mechanisms of insulin resistance.
Lipid infusion or the ingestion of a high-fat diet results in insulin resistance and
eventual development of diabetes. A prevailing model for development of diet-induced
insulin resistance holds that mitochondrial fatty acid oxidation is inadequate to
deal with the large load of dietary fat, thus leading to accumulation of lipid-derived
metabolites such as diacylglycerols (DAGs) and ceramides that can activate stress
kinases to interfere with insulin action (46,47) (Fig. 4
A). The evidence in support of such a mechanism in induction of hepatic insulin resistance
is considerable and may translate to humans. For example, a short period of caloric
restriction in obese humans improves hepatic insulin sensitivity in concert with a
reduction in liver fat (48). A possible link between fatty liver and metabolic changes
in peripheral tissues has been uncovered by liquid chromatography-MS analysis of lipids
in nondiabetic women with and without increased liver fat; women with increased liver
fat had elevated levels of esterifed long-chain fatty acids and ceramides in adipose
tissue (49).
FIG. 4.
Mechanistic models of lipid-induced impairment of muscle insulin action and supporting
metabolomics data. Feeding of diets high in fat results in muscle insulin resistance,
and recent studies suggest the operation of two possible mechanisms for this effect
(
A
). A prevailing theory is that increased delivery of fat to muscle saturates the capacity
for mitochondrial β-oxidation, leading to accumulation of bioactive lipid-derived
metabolites such as diacylglycerols and ceramides in the extramitochondrial space
and activation of stress/serine kinases that interfere with insulin action. More recent
studies have shown that fatty acid oxidation is actually increased in muscle in response
to high-fat feeding but with no coordinate increase in TCA cycle activity. This results
in accumulation of incompletely oxidized lipids in the mitochondria and depletion
of TCA cycle intermediates, possibly resulting in mitochondrial stress and interference
with insulin actions. The metabolic changes that underpin this new mechanism were
identified by targeted GC-MS of organic acids and MS-MS analysis of acylcarnitines
in muscle extracts from lean and obese animals, as summarized in
B
(data reprinted from ref. 51 with permission). Note that these mechanisms are not
mutually exclusive and could work in concert to impair muscle insulin action. CPT1,
carnitine palmitoyltransferase 1; ETS, electron transport system; NEFA, nonesterified
fatty acid; TCAI, TCA cycle intermediates.
Also supporting an important role of hepatic steatosis in development of insulin-resistant
states, whole-animal, muscle, and liver insulin resistance in rats fed a high-fat
diet are all ameliorated in response to hepatic overexpression of malonyl CoA decarboxylase
(MCD), a gene that reduces hepatic steatosis by partitioning lipids toward β-oxidation
(18). Interestingly, the improvement in muscle insulin resistance was not correlated
with changes in muscle triglyceride or fatty acyl CoA levels. Instead, metabolic profiling
of 37 acylcarnitine species by MS-MS revealed a decrease in the concentration of lipid-derived
metabolite β-OH-butyrylcarnitine (βHB) in muscle of MCD-overexpressing animals that
was likely due to a change in intramuscular ketone metabolism.
The association of improved insulin action with a decline in a mitochondrial lipid-derived
metabolite (βHB) encouraged further investigation of the mechanism of lipid-induced
muscle insulin resistance with targeted MS-based metabolic profiling tools (2,50,51).
These studies found that chronic exposure of muscle to elevated lipids in vitro, or
in vivo as a consequence of overnutrition, resulted in an increase rather than a decrease
in expression of genes of fatty acid β-oxidation (50,51). Importantly, this lipid-induced
upregulation of the enzymatic machinery for β-oxidation of fatty acids in muscle was
not coordinated with upregulation of downstream metabolic pathways such as the TCA
cycle and electron transport chain. This resulted in incomplete metabolism of fatty
acids in the β-oxidation pathway, as reflected by broad-scale accumulation of mitochondrial
lipid metabolites (acylcarnitines) and a simultaneous decrease in the levels of TCA
cycle intermediates, as revealed by quantitative MS-MS and GC-MS analysis (50,51)
(Fig. 4
B).
That these abnormalities may contribute to mitochondrial stress and development of
insulin resistance is supported by the finding that exercising of obese mice normalizes
the elevated acylcarnitines in muscle and restores insulin sensitivity and glucose
tolerance (50). Also supporting the model, mice with global MCD knockout fed a high-fat
diet have suppressed fatty acid oxidation and reduced acylcarnitine levels, coupled
with improvement of glucose tolerance and insulin resistance (51). Similar improvements
in insulin sensitivity and glucose uptake have been reported in human myocytes in
response to siRNA-mediated suppression of MCD (52). Also consistent with these findings,
transgenic mice with muscle-specific overexpression of peroxisome proliferator–activated
receptor (PPAR)-α, a nuclear receptor that activates β-oxidative genes, developed
both local and systemic glucose intolerance (53). In contrast, one recent study reported
improved rather than impaired insulin sensitivity in response to overexpression of
CPT1 (carnitine palmitoyltransferase 1) in muscle (54). Nevertheless, the weight of
recent evidence is consistent with a model in which lipid-induced insulin resistance
in muscle is explained at least in part by “overload” of mitochondrial lipid oxidation,
accumulation of incompletely oxidized fats, and depletion of TCA intermediates, leading
to a condition of mitochondrial stress that activates signaling pathways (still to
be defined) that interfere with insulin action. These findings do not preclude an
important role for accumulation of DAGs, ceramides, or other lipid-derived metabolites
in muscle of animals with diet-induced obesity, and the two mechanisms could in fact
work together to cause harmful effects.
Other metabolic profiling studies have led to the identification of a specific lipid
metabolite that might serve to enhance insulin action (55). Thus, liquid and GC-based
methods were used for quantitative profiling of ∼400 lipid species in mice lacking
expression of fatty acid binding proteins (FABPs) in adipose tissue (aP2-mal1−/−).
FABP-deficient mice fed a high-fat diet fail to accumulate lipids in adipose tissue
and remain insulin sensitive. Adipose tissue from these animals was enriched in C16:1n7-palmitoleate,
both as the free fatty acid and in multiple esterified species. Infusion of triglycerides
containing exclusively palmitoleate (C16:1) or palmitate (C16:0) into mice for 6 h
resulted in suppression of the entire insulin-signaling pathway in the case of triglyceride-palmitate,
versus a clear enhancement in insulin action in the case of triglyceride-palmitoleate.
Although interesting, this experiment would have been more convincing if it had included
an additional control of another monounsaturated fatty acid such as oleate (C18:1).
Also, the role of palmitoleate in control of insulin action in physiological or pathophysiological
states remains to be defined, given that palmitoleate levels rise in concert with
levels of other fatty acids in obesity (17) and fasting, even though these are states
of clear insulin resistance.
Another study used LC-MS-MS to profile lipids secreted from the small intestine in
response to ingestion of fat (56). This approach showed an increase in N-acylphosphatidylethanolamines
(NAPEs) in the circulation. Systemic infusion of the most abundant NAPE decreased
food intake in rats, an effect that could not be ascribed to taste aversion. The authors
also demonstrated that systemically administered NAPE enters the brain and accumulates
in the hypothalamus. Chronic administration of NAPEs reduces food intake and decreases
body weight, suggesting a possible medicinal application in treatment of obesity.
The studies summarized above are examples of use of targeted metabolic profiling technologies
for development of new and testable models of disease pathogenesis and novel therapeutic
strategies. We chose to highlight these examples because, in each case, the investigators
were not satisfied with simple reporting of a metabolic signature of nutritional manipulation
(information) and moved beyond this point to explore the significance of their findings
in regulation of metabolic fuel homeostasis. Unfortunately, taking such extra steps
to investigate mechanism has been the exception rather than the rule in application
of metabolomics to diabetes research to this point, and this must change if the full
potential of the technology is to be realized.
Nuclear receptors, including the family of PPAR-α, -δ, and -γ, have emerged as important
mediators of insulin sensitivity. 1H-NMR–, LC-MS–, and GC-MS–based methods were used
to compare PPAR-α null and wild-type mice. The authors reported decreases in glucose,
glutamine, and alanine levels and an increase in lactate, suggesting an increase in
utilization of glucose and amino acids, as might be predicted from the known effects
of PPAR-α to promote the opposing pathways of β-oxidation, ketogenesis, and gluconeogenesis
(57). More recently, targeted GC-MS and LC-MS-MS analysis has provided deeper insights
by showing that PPAR-α knockout results in fasting hypoglycemia accompanied by depletion
of TCA cycle intermediates and free carnitine and short-chain acylcarnitines, as well
as accumulation of long-chain acyl CoAs in skeletal muscle (58). Several of these
metabolic abnormalities could be partially ameliorated by carnitine supplementation.
Another study surveyed changes in lipid metabolites in obese and diabetic mice in
response to treatment with rosiglitazone. These experiments revealed that rosiglitazone
induced circulating hypolipidemia, caused substantial alterations in multiple lipid
species in the heart, and caused accumulation of polyunsaturated lipid species in
adipose tissue (23). These findings are of particular interest given the propensity
of thiazolidinedione drugs to cause weight gain and accumulation of adipose tissue
and the recent report of a link between these drugs and increased risk of cardiovascular
disease (59), although the exact relationship between the changes in lipid metabolism
and these drug side effects, if any, remain to be defined. Finally, a very recent
study has investigated the effects of rosiglitazone in normal and diabetic mice with
targeted quantitative measurement of a remarkable 800 metabolites by tandem MS. The
authors report that methylglutarylcarnitine levels are oppositely affected in healthy
and diabetic mice by rosiglitazone and that an enrichment in phosphatidylcholine relative
to lysophosphatidylcholine levels occurs in diabetic versus normal animals (60). However,
no mechanistic follow-up studies were attempted, and the functional significance of
either of these changes therefore remains to be elucidated.
Integration of metabolomics with other “omics” technologies for studies of metabolic
disease mechanisms.
Recent studies in plants (61), animal models of disease (20), and human families with
early-onset cardiovascular disease (62) have demonstrated that metabolite profiles
are heritable and can be integrated with whole-genome association and microarray profiling
datasets to define gene/metabolite networks. In one such study, diabetes-resistant
C57BL/6-ob/ob mice were bred with diabetes-susceptible BTBR-ob/ob mice to create an
F2 cohort in which blood glucose and insulin levels were distributed across a wide
range. Liver samples from F2 mice were subjected to targeted metabolomics and microarray
analyses, and integration of these datasets with whole-genome single nucleotide polymorphism
(SNP) analysis showed clusters of liver metabolites (e.g., a group of amino acids)
mapped to distinct chromosomal regions, suggesting the presence of genes at those
loci that exert metabolic control on a whole class of metabolites (20) (Fig. 5). Using
refined statistical techniques (63), correlations between genetic loci, transcripts,
and metabolites were used to develop a model that predicts that the amino acid glutamine
acts through alanine:glyoxylate aminotransferase (Agxt) and arginase 1 (Arg1) to affect
phosphoenolpyruvate carboxykinase (Pck1) expression (Fig. 6). Consistent with this
predicted network, glutamine addition to primary hepatocytes causes strong upregulation
of Agxt, Arg1, and PEPCK (20). Moreover, glutamine and PEPCK mRNA levels are reduced
with obesity in diabetes-resistant B6-ob/ob mice but increased in liver of diabetes-susceptible
BTBR-ob/ob animals (C. Ferrara, C.B.N., A.D. Attie, unpublished observations).
FIG. 5.
Metabolite levels are heritable and can be mapped to specific chromosomes in mice.
In this particular study, diabetes-resistant B6-
ob/ob
mice were bred with diabetes-susceptible BTBR-
ob/ob
mice to create a cohort of F2 mice in which individual mice had a wide variation in
blood glucose levels. Whole-genome SNP analysis was integrated with microarray and
targeted GC-MS– and MS-MS–based analysis of metabolites of liver extracts from the
F2 animals. This analysis revealed that metabolites can be mapped to specific chromosomes.
Each row represents a SNP marker, and each column represents a metabolite. The LOD
color scale is indicated, showing blue when the B6 allele at that marker results in
an elevated level of the metabolite and red/yellow when the BTBR allele is dominant.
Note the clusters of amino acids that map in common to regions on chromosomes 8 and
9, suggesting the presence of a gene that controls their levels. Figure reproduced
with permission from ref. 20. LOD, logarithm of odds.
FIG. 6.
A novel metabolic regulatory network predicted by integration of genomic, transcriptomic,
and metabolomic profiling. Whole-genome mapping was integrated with transcriptomic
and metabolomic analysis of liver samples in F2 mice from a cross of diabetes-resistant
B6-
ob/ob
mice and diabetes-susceptible BTBR-
ob/ob
mice. Networks of transcripts (grey ovals) and metabolites (rectangle) were identified
by computational analysis (63). The network shown here predicts that the amino acid
glutamate/glutamine (Glx) regulates expression of the key gluconeogenic enzyme phosphoenolpyruvate
carboxykinase (Pck1) via regulation of alanine/glyoxylate aminotransferase (Agxt)
and arginase1 (Arg1). Consistent with this prediction, glutamine addition to mouse
hepatocytes strongly induces expression of Agxt, Arg1, and Pck1 (20). Figure reproduced
with permission from ref. 19.
In another study with some similarities in design, F2 rats from a cross of diabetic
Goto-Kakizaki (GK) and normoglycemic Brown Norway (BN) rats were surveyed by nontargeted
NMR-based metabolic profiling, and this information was integrated with information
about physiologic quantitative trait loci (QTL) in the same cross (64). As with the
MS-based study described above, the authors were able to map spectral features to
specific chromosomal regions. Candidate metabolites for some of the most significant
QTLs were identified, including glucose and benzoate. Subsequent transcriptomic analysis
of the parent strains revealed the absence of transcripts for uridine diphosphate
(UDP)-glucuronosyl-transerase-2b (Ugt2b), an enzyme that metabolizes benzoate and
other xenobiotics in mammals. The absence of Ugt2b expression was subsequently found
to be the result of a chromosomal deletion in the GK strain, demonstrating the ability
of metabolomics to uncover otherwise undetected chromosomal abnormalities.
Targeted metabolomics has also recently been integrated with genotyping for understanding
the biochemical impact of common genetic polymorphisms (65). These authors performed
ESI-MS-MS to measure 363 metabolites in serum of 284 male participants in the KORA
study (a general population study from Germany). Significant associations were observed
between frequent SNPs and changes in specific metabolites. Moreover, polymorphisms
in four specific genes (FADS1, LIPC, SCAD, MCAD) encoding metabolic enzymes were linked
to perturbations in the metabolic pathways in which the enzymes are known to reside.
The authors suggest that the combination of genotyping and metabolic phenotyping may
provide new roadmaps for application of personalized medicine.
These studies represent very early efforts to integrate metabolomics with other forms
of “omics” sciences but nevertheless serve to illustrate the potential power of integrated
approaches for identification of novel metabolic regulatory networks that contribute
to chronic disease such as type 2 diabetes. As targeted metabolic profiling methods
evolve to encompass a larger set of metabolites, computational methods for integrating
this information with other broad-scale profiling datasets must continue to evolve.
Targeted MS-based metabolic profiling for understanding of human metabolic disease
pathogenesis
Human diabetes and insulin resistance.
Targeted MS-based metabolic profiling has been increasingly applied to studies of
human diseases and conditions (17,19,62,66
–73). For example, profiling of obese (median BMI 37 kg/m2) versus lean (median BMI
23 kg/m2) humans revealed a branched-chain amino acid (BCAA)-related metabolite signature
that differentiates the two groups, is suggestive of increased catabolism of BCAA,
and correlates with insulin resistance (17) (Fig. 7). The signature includes several
metabolites that are byproducts of BCAA catabolism, such as glutamate, α-ketoglutarate,
C3 acylcarnitine (propionylcarnitine), and C5 acylcarnitines (α-methylbutyryl and
isovalerylcarnitines). A subsequent cross-sectional study in sedentary hyperlipidemic
subjects of varying BMI (range 25–35 kg/m2) identified several metabolite clusters
(principal components) that explained most of the data variance and found that the
principal component most related to insulin sensitivity (S
i) in subjects tested by the frequently sampled oral glucose tolerance test was again
one comprised of BCAA and related metabolites (67). To test the possible relevance
of this finding for development of obesity-related insulin resistance, rats were fed
one of several diets—high fat (HF), HF with supplemented BCAA (HF/BCAA), or standard
diet. Despite having reduced food intake and weight gain equivalent to the standard
diet group, HF/BCAA rats were equally as insulin resistant as HF rats. Insulin resistance
induced by HF/BCAA feeding was accompanied by chronic activation of mTOR and JNK and
serine phosphorylation of IRS1(ser307). Moreover, HF/BCAA-induced insulin resistance
was reversed by the mTOR inhibitor rapamycin. These findings show that in the context
of a poor dietary pattern of increased consumption of fat, BCAAs make an independent
contribution to development of obesity-associated insulin resistance.
FIG. 7.
A branched-chain amino acid–related metabolite cluster that correlates with insulin
resistance in humans. Targeted metabolic profiling by MS-MS and GC-MS was performed
on obese, insulin-resistant, but nondiabetic humans and a group of lean controls.
Principal components analysis (PCA) revealed that the cluster of statistically related
metabolites (principal component) with the strongest differences between obese and
lean subjects was one comprised of the branched-chain amino acids leucine/isoleucine
and valine, glutamate/glutamine (glx), C3 and C5 acylcarnitines, and the aromatic
amino acids phenylalanine and tyrosine. A plot of each individual's principal component
score against their homeostasis model assesment index score (a measure of insulin
sensitivity) is shown, indicating significant correlation (
r
= 0.58;
P
< 0.0001). Data reprinted with permission from ref. 17.
Targeted profiling has also been applied during an oral glucose tolerance test in
human subjects (68,69). In two small independent groups of normal non–insulin-resistant
individuals (n = 22 and 25), glucose ingestion caused significant changes in 18 metabolites
(other than glucose) among 191 measured, including some that are involved in pathways
not known to be affected by a glucose load (bile acids and purine degradation products)
(68). The metabolites that changed significantly were also reflective of the known
effects of insulin on proteolysis, lipolysis, ketogenesis, and glycolysis. A third
group of mildly glucose intolerant and hyperinsulinemic subjects were also studied
and found to have blunted glucose-induced changes in the metabolic markers of key
anabolic pathways relative to those reported for the normal subjects. Moreover, 6
of the 18 metabolites identified as “glucose responsive” in the studies of normal
subjects were found to correlate significantly with fasting insulin levels, used as
a surrogate of insulin sensitivity. Among these were all three branched-chain amino
acids, lactate, and β-hydroxybutyrate. Another group applied nontargeted UPLC-qTOF-MS
analysis during oral glucose tolerance testing of 16 normal individuals and found
by multivariate statistical analysis that free fatty acids, acylcarnitines, bile acids,
and lysophosphatidylcholines were the most discriminating biomarkers of the glucose
bolus (69). These studies demonstrate how metabolomics can provide a more detailed
picture of metabolic status of normal and pre-diabetic subjects, which with further
development could contribute to more exact subclassification of different forms of
diabetes, leading to more judicious and effective use of drug therapies. However,
in order for this to be fully realized, more effort must be focused on the testing
of mechanistic hypotheses that emanate from initial associations of metabolic signatures
and disease states.
Human cardiovascular disease.
MS-based metabolic profiling has been applied to cardiovascular disease. In one study,
LC-MS analysis was applied to subjects with exercise-inducible ischemia compared with
normal control subjects (70). Blood samples were taken immediately before and immediately
after exercise, and levels of 173 known and several more minor intermediary metabolites
were measured. A number of metabolites were found to be discordant between the two
groups, including lactate, byproducts of AMP metabolism, and metabolites of the citric
acid cycle. Using the six most discordantly regulated metabolites, a metabolic ischemia
risk score was derived. As the number of subjects was quite small in this study, follow-up
studies will be required to confirm or refute these interesting initial findings.
MS–MS– and GC-MS–based metabolomics have also been applied to subjects from multiple
generations within eight multiplex families with familial early-onset cardiovascular
disease (CVD) (62). Even after adjusting for variables such as diabetes, hypertension,
dyslipidemia, BMI, age, and sex, multiple individual metabolites and metabolite clusters
identified by principal components analysis were found to be highly heritable within
families, including groups of amino acids (arginine, glutamate, alanine, ornithine,
valine, leucine/isoleucine), free fatty acids (arachidonic, linoleic), and acylcarnitines.
Interestingly, families in this study showed two distinct metabolite profiles that
tracked with their clinical characteristics, suggesting different genetic backgrounds
and consequent variation in control of key metabolic pathways that converge on CVD.
Current studies are focused on applying the same metabolic profiling tools to nonfamilial
populations of patients at risk for cardiovascular events. Based on our understanding
thus far, targeted metabolomic profiles show promise for predicting CVD and subsequent
events in high-risk families and even in the general population.
Other studies have recently emerged in the realm of application of metabolomics for
the understanding of metabolic lesions in heart failure and myocardial infarction.
In one study, LC-MS–based profiling of ∼200 metabolites was performed on subjects
undergoing planned myocardial infarction via alcohol-mediated septal ablation or on
subjects with spontaneous myocardial infarction or undergoing elective coronary angiography,
as positive and negative control subjects, respectively (71). Five metabolites were
altered in both spontaneous and planned myocardial infarction, and this metabolic
signature may become useful for early detection of myocardial injury with further
validation. Similarly, GC-MS was used for targeted analysis of serum samples of 52
patients with systolic heart failure (ejection fraction of <40% and symptoms of failure)
and 57 control subjects, resulting in identification of pseudouridine and 2-oxoglutarate
(α-ketoglutarate) as two potential biomarkers of the failing heart (72). One limitation
of this study was the extensive differences between groups with regard to use of medications,
including β-blockers, ACE inhibitors, and diuretics, all of which could have influenced
the metabolic profiles. Finally, targeted ESI-MS was used to measure 63 metabolites
in arterial and coronary sinus blood obtained during cardiac surgery, before and after
ischemia/reperfusion (73). This work demonstrates that the preexisting ventricular
state (left ventricular dysfunction [LVD], coronary artery disease, or neither condition)
is associated with clear differences in myocardial fuel uptake, both at baseline and
following ischemia/reperfusion. In particular, LVD was associated with global suppression
of metabolic fuel intake (glucose, fatty acid, and amino acids) and limited myocardial
metabolic reserve and flexibility following ischemia/reperfusion. Moreover, altered
metabolic profiles following ischemia/reperfusion were associated with a postoperative
hemodynamic course. The growing number of metabolomics studies in the area of heart
failure may ultimately facilitate optimal design of perioperative treatment regimens
based on the particular form of CVD and the metabolic status of the heart.
Nontargeted metabolic profiling applied to metabolic disease research.
Given its inherently nonquantitative nature and lack of ability to identify the majority
of analytes in a given profile, is nontargeted metabolic profiling with MS platforms
suitable for studies of disease mechanisms and etiology? Our perspective is that these
tools must be used with caution in whole-animal or human studies and are currently
best suited to in vitro applications where the biology can be more tightly controlled.
Support for this position comes from a recent and careful study of application of
nontargeted UPLC-MS to human serum samples, in which it was concluded that significant
analytical drift can be introduced when using new chromatographic columns or when
attempting to analyze more than 100 samples in a block (74).
An interesting recent example from the cancer research field further illustrates both
the limitations and the promise of nontargeted approaches (75). LC and GC coupled
with MS was used to perform nontargeted profiling on >1,100 individual metabolites
in prostate tumor explants, blood, and urine from biopsy-positive cancer patients
and biopsy-negative control subjects. Analytes were not measured in physical units
(nmol or μmol) but rather in relative units in the cancer patients versus the control
subjects. No meaningful differences were found in metabolite profiles in urine or
blood of cancer subjects compared with control subjects. In contrast, statistically
meaningful increments were found in a small subset of metabolites in tumor explants,
particularly in metastatic tumors relative to benign prostate. Six metabolites were
found to increase with progression from benign prostate to localized cancer to metastatic
cancer, including sarcosine, a glycine metabolite. Importantly, the authors then developed
a targeted stable isotope–dilution method for quantitative measurement of sarcosine
and found it to be elevated by 10- to 20-fold in metastatic tumors compared with benign
prostrate. They also showed that manipulation of enzymes of sarcosine metabolism influenced
prostate cancer invasion. These results show that nontargeted MS methods are able
to detect changes in metabolites within the tissue of origin of the metabolic variability.
However, the changes in sarcosine may only have been by the semiquantitative nontargeted
approach because the changes were very large in magnitude. Nevertheless, this study
is one of the rare but welcome examples of translation of a metabolic profile associated
with disease to actual mechanistic investigation.
There has been limited application of nontargeted MS-based metabolomics to diabetes
research to date. In one study comparing pre-diabetic insulin-resistant to healthy
and insulin-sensitive individuals, a complex set of technologies including LC-MS and
Fourier-transform ion cyclotron resonance (FTICR)-MS coupled with multivariate statistical
analysis was used to identify a single metabolite, 3-hydoxyhippuric acid, as a biomarker
of the insulin-resistant state (76). In another study, GC-MS coupled with multivariate
statistical analysis was used to evaluate the metabolic impact of three diabetes drugs,
rosiglitazone, metformin, and repaglinide, in newly diagnosed type 2 diabetic subjects
(77). Abnormalities in several amino acids and fatty acids were reported in diabetic
compared with healthy subjects, and rosiglitazone was shown to correct more of these
abnormalities than the other two drugs. The significance of these profiles in terms
of molecular mechanisms or disease progression remains to be addressed.
Similar issues and other problems have emerged when using nontargeted 1H NMR for human
studies. A recent study reported that NMR-based metabolic profiles can predict the
presence and severity of coronary artery disease (78). Partial least-squares discriminant
analysis was used to identify peaks in the major lipid regions of the spectra that
appeared to provide separation between the groups. The specific lipid species involved
were not identified by this analysis, although it was suggested that choline-containing
metabolites were particularly diagnostic. However, a subsequent study using very similar
techniques demonstrated that the predictive value of the NMR-based metabolic profiles
was weak when other factors such as sex and use of medical interventions such as statins
were taken into account (79). This second group of authors demonstrated that the 1H
NMR technique could identify male versus female subjects with 100% accuracy but was
much less able to identify statin users or subjects with CVD, despite expectations
of substantial changes in lipid profile in the former group. Based on these findings,
it seems clear that 1H NMR is currently not a substitute for the more invasive procedure
of angiography in the diagnosis and staging of CVD.
An intriguing and more promising recent application of 1H NMR–based metabolic analysis
has been to study the influence of intestinal bacteria (microbiota) on development
of obesity and metabolic diseases (80). Indeed, inoculation of germ-free mice with
microbiota from the cecum of normal mice causes an increase in body fat content and
appearance of insulin resistance within 14 days of transfer (81). 1H NMR–based metabolic
profiling of plasma and urine samples from a mouse strain known to be susceptible
to hepatic steatosis and insulin resistance (129S6) versus a strain with relative
resistance (BALBc) revealed low circulating levels of plasma phosphatidylcholine and
high levels of methylamines in urine in the 129S6 strain (80). The authors propose
that the increased propensity of the 129S6 strain for metabolic disease could be due
to increased metabolism of phosphatidylcholine to methylamines by intestinal bacteria,
resulting in a reduced pool for the assembly of VLDL particles, leading to deposition
of triglycerides in liver. This hypothesis remains to be tested.
1H NMR has also been applied to research on type 1 diabetes (82,83). One study involved
613 patients with type 1 diabetes and used a novel set of statistical methods to identify
a set of metabolites that stratified subgroups in the population according to micro-
and macrovascular complications and mortality (83). Another study used LC-MS for lipid
profiling, and two-dimensional GC-MS for profiling of organic acids, amino acids,
and other small molecule metabolites to implicate gut microbiota in development of
type 1 diabetes (84). These findings were made in a prospective study of Finnish children
who progressed to type 1 diabetes versus control subjects who remained nondiabetic
and autoantibody negative. Children progressing to diabetes had reduced serum levels
of phosphatidylcholine and succinic acid at birth, possibly suggestive of increased
metabolism of choline by intestinal microbes in the mother or the child. Type 1 diabetic
children also had very high levels of glutamate and branched-chain amino acids in
blood appearing prior to emergence of autoantibodies, for example, against GAD and
insulin. The source of these very interesting surges in amino acid levels and their
potential mechanistic significance remain to be established.
Conclusions and future directions.
In the postgenomic era, biologists and translational investigators alike have gained
a new appreciation for metabolic analysis as a critical tool for assessing the physiological
and pathophysiological impact of genetic variation. The current surge in methods development
in the field of metabolomics is built on the foundation of decades of analytical biochemistry
and its use in detecting inborn errors of metabolism. The major difference between
then and now is that the current emphasis is on methods that allow simultaneous measurement
of multiple analytes in a biological sample, whereas earlier work was often focused
on one or a small number of metabolites per assay. Despite significant advances, no
single profiling method currently allows simultaneous analysis of all of the metabolites
in the metabolome. Ultimate achievement of this goal will require continued intensive
development of deeper libraries of chemical standards, instrument platforms with broad
sensitivity range and high mass accuracy, and likely integration of MS and NMR methods
to gain full analyte coverage. These advances must be coupled with continued development
of computational methods for analysis of complex metabolomic datasets and their integration
with equally complex genomic, transcriptomic, and proteomic profiles. Meanwhile, considerable
progress can be made with the currently available “targeted” technologies that allow
profiling of key intermediates of lipid, carbohydrate, purine, pyrimidine, and protein
metabolism. The examples provided herein about scientific insights gained by application
of current tools suggest a broad horizon and provide strong encouragement for further
technology development in this area. However, it may be apparent to the reader that,
to date, only a subset of the studies cited in this article have gone beyond the description
of metabolic “signatures” that characterize different physiological, pathophysiological,
or drug-treated states (information) to actual use of the signatures to pose and then
test new hypotheses (knowledge). The paramount challenge of the next phase of metabolomics
investigation is to better harvest the information from large datasets to create knowledge
about metabolic regulatory mechanisms, perhaps leading to better understanding of
perturbations in chronic diseases and conditions such as type 2 diabetes, obesity,
CVD, and cancer.