type 2 diabetes ( t2d ) is an increasingly widespread disease in both developed and developing countries . by the year 2030 , it is predicted that more than half a billion people worldwide will be affected by this disease . not only does t2d burden patients with numerous associated health complications such as cardiovascular disease , nephropathy , neuropathy , and retinopathy , but t2d is also a substantial strain on health care budgets . diagnosis of t2d is typically determined by fasting blood glucose and the oral glucose tolerance test that examines an individual 's ability to dispose of a glucose load . glycated hemoglobin ( hba1c ) , on the other hand , provides information on glucose management during the months preceding the initial testing . despite these systematic measures , up to 60 percent of t2d cases are never diagnosed . the reasoning behind this misdiagnosis , or lack of diagnosis , is likely due to the insensitivity of these assays at predicting prediabetic and diabetic threshold values . early diagnosis of t2d is extremely important , as early interventions might delay or even prevent full - blown disease [ 58 ] . t2d is rarely a static condition , but rather one that evolves and changes over time during the lifespan of the individual . indeed , clinical risk factors appear to cluster in certain individuals more than others and are often independent of body mass index ( bmi ) [ 12 , 13 ] . it is these high - risk individuals that are most likely to benefit from early and aggressive lifestyle interventions . considerable variation is also evident in the response of individuals to treatment as well as their susceptibility to diabetes - related complications . this variability both in disease progression and treatment response emphasizes the need for additional tools for predicting disease progression and treatment success . the term metabolomics is similar to that of older technologies such as genomics ( dealing with genes ) , transcriptomics ( dealing with gene transcripts ) , and proteomics ( dealing with proteins ) . the metabolome is comprised of small intermediary molecules and products of metabolism , including those associated with energy storage and utilization , precursors to proteins and carbohydrates , regulators of gene expression , and signalling molecules . thus , the metabolome as the entirety of metabolites represents a real - time functional portrait of the cell or the organism . the metabolome is influenced by a plethora of factors , such as diet , lifestyle , medications , gender , and age . in this regard , metabolomics becomes a very powerful tool as it views the effects of pathological factors from vastly different origins in a single measurement . specific methods employed in the study of metabolomics include nuclear magnetic resonance ( nmr ) , gas chromatography mass spectrometry ( gc - ms ) , liquid chromatography mass spectrometry ( lc - ms ) , capillary - electrophoresis mass spectrometry ( ce - ms ) , and high performance liquid chromatography ( hplc ) . nmr measures differences in the magnetic properties of atomic nuclei , mass spectrometry measures differences in the mass and electrical charge of the metabolites , and chromatography distinguishes metabolites by differences in adhesion properties . while hyphenated ms methods have the advantages of high sensitivity , small sample volumes , and relatively low costs , nmr has a greater range of simultaneously detectable molecular species , as well as simple sample preparation and excellent reproducibility . in all platforms , targeted profiling , quantitative values for a preselected subset of metabolites are calculated . for accurate results , internal standards must be used to calibrate sample concentrations by adding reference substances of known concentrations . in mass spectrometry , for example , a stable isotope - labeled variant of the metabolite of interest is spiked into the sample . for nmr , a single concentration reference substance is added ; however obtained values may need to be scaled by metabolite - specific individual calibration factors [ 19 , 20 ] . the results are a quantitative measurement of the abundance of the metabolites in the sample . for metabolic fingerprinting , all peaks of a spectrum , also known as features , are used for statistical analysis . after statistical analysis , the features of potential interest have to be assigned to their respective molecule using online metabolite databases such as the human metabolome database hmdb ( http://www.hmdb.ca/ ) and the biological magnetic resonance database bmrb ( http://www.bmrb.wisc.edu/ ) . as researchers are constantly updating these databases with additional substances fingerprinting has the benefit of yielding a more comprehensive view of the metabolome and may help identify previously unknown disease mechanisms ; however targeted approaches often have a lower variance and , thus , larger statistical power to detect small effects . as each platform ( nmr , gc - ms , and lc - ms ) provides unique information , with very little overlap in the detected metabolites , one way to circumvent this issue is to employ more than one system for a given study . although this approach increases the number of detected metabolites and provides a more comprehensive picture , it is not typically performed due to increased costs and a lack of equipment and expertise . samples most commonly analyzed via metabolomics include plasma , serum , and urine [ 22 , 23 ] , while other sample types such as cerebrospinal fluid   and saliva   are used less frequently . metabolomics studies may also analyze tissue extracts ; whole tissue by means of magic angle spinning ( mas ) nmr   and even living organisms may be analyzed by in vivo nmr . there are no commonly used , standardized protocols for sample collection and storage for metabolomics studies , a fact that may contribute to additional variation in metabolomic profiles . for example , some studies collect samples only from fasting participants , while other studies do not have this as a prerequisite . significant concentration differences between the metabolites of plasma and serum prepared from the same blood sample have been found . although concentration differences have been found , plasma and serum metabolites correlate well with each other , indicating that both sample types allow accurate metabolomics measurements as long as serum and plasma samples are not compared to each other . while plasma showed higher over - time stability , serum allowed the quantification of more metabolites due to higher concentrations of selected metabolites . storage conditions are a crucial factor for metabolomics studies , especially for prospective studies , where samples may be analyzed many years after being collected . studies found no differences between plasma that was frozen immediately versus plasma that was stored at 4c for 8   and 24   hours before freezing , respectively . likewise , no significant differences in metabolite profiles were found when comparing storage at 20c and 80c . plasma samples stored at 80c for 13 to 17 years showed no influence of storage time on the metabolic profile . all these studies indicate that the choice of sample type ( serum or plasma ) and the storage conditions may be a minor issue for metabolomics studies . to examine metabolomic differences in response to t2d and pd in this review , we conducted a pubmed search for the following keywords : ( ( diabetes type 2 ) or ( insulin resistance ) ) and ( metabolomics or metabolomic or metabolite ) and filtered for human studies published within the last 10 years . only original articles were included , and studies that did not analyze serum or plasma were excluded . only articles in english language were examined . these results were further filtered by including only papers dealing with diagnosis , prediction , prognosis , and translation of t2d . most t2d metabolomics studies may be classified as either predictive or diagnostic . in this review , we define diagnosis as the identification of a currently occurring medical condition , while prediction is defined as the identification of subjects who will develop a certain medical condition in the future . predictive studies follow initially healthy study participants for several years in prospective study designs . during follow - up such high demands reduce the amount of available study cohorts , and therefore most researchers have used samples from existing prospective studies including the framingham heart study ( fhs ) [ 34 , 35 ] , the cooperative health research in the region of augsburg ( kora ) study [ 36 , 37 ] , or the european prospective investigation into cancer and nutrition ( epic ) study [ 36 , 37 ] for their research . ideally , results from one study cohort are replicated in another , independent cohort , thus ensuring the validity of the results . such studies have the advantage of testing at a single time point with no lengthy follow - up required . studies with as little as 73 participants have led to reproducible results , while other studies of this type have used sample sizes of up to 7,098 individuals . an overview of the most commonly observed metabolite changes from predictive and diagnostic studies is shown in table 1 . data on presymptopmatic individuals , those classified as having prediabetes ( pd ) or diagnosed as t2d , are reported . pd , which includes impaired fasting glucose , impaired glucose tolerance , and insulin resistance , may be seen as an early form of t2d ; diagnosis of pd often yields similar metabolomic results as t2d ( table 1 ) . in the following paragraphs , specific metabolites found to be predictive of t2d or pd will be discussed along with their physiological relevance . one group of metabolites consistently linked to pd and t2d in metabolomics studies is the branched - chain amino acids ( bcaa ) leucine , isoleucine , and valine . elevated levels of bcaa have been found up to 13.7 years ahead of clinical manifestation [ 35 , 36 , 40 ] , in pd [ 39 , 4347 ] and in overt t2d [ 35 , 37 , 43 , 46 , 49 , 50 ] . this association has led to the hypothesis of a causal role of these amino acids in the development of the disease . however , there is growing evidence that elevated bcaa levels may reflect a state of insulin resistance that is not necessarily specific to t2d as similar signatures have been observed for cardiovascular disease , chronic kidney disease , and ischemic stroke . given this , the observed changes could also be indirect and primarily associated with insulin sensitivity rather than insulin secretion . in support of this hypothesis , bcaa have a plethora of biological functions , modulating protein synthesis and turnover as part of the mammalian target of rapamycin ( mtor ) pathway , facilitating glucose uptake in the liver and skeletal muscles , and also enhancing glycogen synthesis . similarly , changes in aromatic amino acids ( aa ) have also been linked to the development of t2d years ahead of clinical manifestation [ 35 , 36 ] , pd [ 39 , 44 , 46 , 47 ] , and overt t2d [ 35 , 46 , 49 ] . specifically , elevated circulating levels of the aa phenylalanine and tyrosine are linked to t2d . as for bcaa , a causative effect has yet to be fully elucidated , but changes in their circulating concentrations are thought to be indirect markers of insulin sensitivity . several metabolomics studies have also found sugars , including glucose , mannose , fructose , and hexose , to be elevated 37 years before clinical manifestation of t2d [ 36 , 41 ] , in pd [ 37 , 41 , 43 , 46 ] and overt t2d [ 37 , 41 , 43 , 46 , 49 ] . this discovery of glucose , which is one of the most standardized clinical parameters for t2d , amongst the top ranked predictive metabolites underscores its high importance for the risk assessment of t2d . nonetheless , elevated sugar levels years ahead of overt t2d may also be caused by undiagnosed t2d amongst study participants . additionally , metabolomics has shown an increase in -hydroxybutyric acid ( ahba ) up to 9.5 years ahead of t2d presentation [ 41 , 42 ] , in pd [ 41 , 43 , 4648 ] and t2d [ 41 , 43 , 46 ] . it is hypothesized that increased lipid oxidation , oxidative stress , and enhanced glutathione synthesis   might explain the observed differences in ahba . glycine , on the other hand , was found to be reduced 7 years before t2d [ 36 , 37 ] , as well as in pd [ 37 , 38 , 47 ] and in manifest t2d . this reduction in glycine is thought to be the result of increased gluconeogenesis   as well as glutathione consumption driven by increased oxidative stress . also , the abundance of incompletely oxidized fuels that are excreted as urinary acylglycine conjugates could reduce glycine levels . it has also been hypothesized that insulin deficiency leads to enhanced -aminolevulinate synthase 1 ( alas - h ) expression , in turn catalyzing the condensation of glycine and succinyl - coa into 5-aminolevulinic acid , thus reducing glycine levels . several studies have also found changes in the ketones -hydroxybutyrate , acetone , and acetoacetate , which originate from fatty acid oxidation . elevated levels were found 3 years ahead of disease manifestation   as well as in overt t2d [ 41 , 49 ] . for pd , a more complex picture is observed , where both elevated [ 41 , 47 ] and reduced levels were found [ 39 , 45 ] . these contradictory results are not surprising , as the role of fatty acid oxidation in the development of pd remains highly controversial . while some studies suggest that pd develops secondary to diminished fatty acid oxidation by cytosolic lipid accumulation , thereby impairing insulin signaling , other studies suggest pd to be characterized by excessive fatty acid oxidation , metabolic inflexibility , and coincident depletion of organic acid intermediates of the tricarboxylic acid cycle . these contradictory findings from different metabolomics studies highlight the complexity of the progression to t2d and the need for further research in this area . glyoxylate levels were found to be increased 3 years ahead of disease onset , in pd and in overt t2d . authors of this particular study hypothesized a connection to hypertension , as glyoxylate is a substrate of alanine - glyoxylate aminotransferase-2 ( agt2 ) , which regulates hypertension . a suppression of agt2 activity may be associated with the development of hypertension and increased glyoxylate . likewise , 2-aminoadipic acid was reported to be elevated in individuals up to 12 years before t2d it is hypothesized that this metabolite is part of a compensatory mechanism upregulating pancreatic insulin secretion to maintain glucose homeostasis in early insulin resistance . to date , the latter two metabolites , 2-aminoadipicacid and glyoxylate , have only been identified as predictive by a single study , respectively . further research on another t2d cohort is necessary to validate these results . differences and predictive profiles for diacyl - phosphatidylcholine c32:1 , elevated levels were detected years ahead of t2d   and also seen in pd   and in overt t2d . although hyperlipidemia is common in t2d , decreased values were detected when analyzing single lipid species . decreased levels of sphingomyelin c16:1 and acyl - alkyl - phosphatidylcholine c34:3 were found ahead of t2d   and in overt t2d , with the latter being decreased also in pd . a lipid that was found in several predictive studies is lysophosphatidylcholine ( lpc ) c18:2 , which was reduced up to 7 years ahead of t2d [ 3537 ] , in pd   and in incident t2d [ 35 , 37 ] . consistently , linoleoyl - glycerophosphocholine ( lgpc ) was found to be reduced 9.5 years ahead of t2d   and in pd [ 47 , 48 ] . as lgpc is one of the isomers of lpc c18:2 , the association found in lpc c18:2 is likely due to the lgpc fraction contained in the total lpc c18:2 level . lgpc has been shown to increase glucose - dependent insulin secretion in a cell line derived from beta cells , an effect that has been linked to the orphan g - protein - coupled receptor gpr119 . on the other hand , reduced lgpc levels have been linked to reduced cpla2 transcription , which would lead to an increased glucose uptake by adipocytes via a decrease in arachidonic acid . while the aforementioned studies all analyzed plasma or serum , there are a few reports employing other sample types . a study on urine found increased levels of glucose and glycine in overt t2d . in saliva , 1,5-anhydroglucitol has been found to be a marker for short - term glycemic control , something that has already been found in blood levels of this diet - derived metabolite . the value of these metabolomics based analyses in the context of t2d prediction and diagnosis has yet to be determined . the use of prediction measures can give an insight on the usefulness of certain metabolites in disease prediction . one of the most common means to assess prediction power is receiver operating characteristic ( roc ) curves , for which the area under the curve ( auc ) is used as prediction measure . the auc , also called c - statistics , is a value between 0 and 1 , where values close to 0.5 indicate no predictive power ( random classification ) and values close to 1 indicate perfect prediction . the study by floegel et al .   found that metabolite levels alone could predict t2d slightly better than the diabetes risk score ( drs ) based on risk factors such as lifestyle , diet , and anthropometry   with an auc 0.849 for metabolites versus 0.847 for drs . adding metabolite profiles to risk prediction always enhanced the prediction , with an auc of 0.912 for a prediction using drs , fasting glucose , hba1c , and metabolites . similarly , walford et al .   found that metabolite profiles scored slightly higher than a genetic risk score ( grs ) , with aucs of 0.874 versus 0.861 . wang et al .   used a clinical model including age , bmi , and fasting glucose and found a c - statistic of 0.801 , which improved to 0.805 when metabolites were included in the model . when only high - risk subjects were included in the analysis , the c - statistic increased from 0.52 to 0.66 , indicating that high - risk patients benefit more from metabolic prediction than low - risk patients . padberg and coworkers   found an auc of 0.82 when using only metabolites compared to an auc of 0.79 when using only fasting glucose levels to predict t2d . in a second cohort , the value improved from 0.83 to 0.86 , respectively , for the prediction of pd . wang et al .   used a prediction based on age , sex , fasting glucose , and bmi , resulting in a c - statistic of 0.91 , which improved to 0.92 when including 2-aminoadipic acid . the net reclassification improvement ( nri ) , an alternative measure for prediction power   for these models , increased from 0.22 to 0.49 . in the study by wang - sattler and coworkers , the auc of a model containing age , sex , bmi , physical activity , alcohol intake , smoking , systolic bp , and hdl was 0.742 and increased to 0.754 when including metabolites . when including hba1c , fasting glucose , and fasting insulin into the model , the values were 0.818 and 0.828 , respectively . .   found an auc of 0.762 when including familial diabetes , sex , age , and bmi . this value increased to 0.790 when including metabolites . in a second study , the values were 0.766 and 0.783 , respectively . the objective interpretation of these numbers is hampered by the use of different combinations of clinical parameters to estimate t2d risk . while some studies use only fasting glucose , some use complex combinations of classical risk factors . this makes it impossible in some cases to determine whether metabolite signatures really improve t2d prediction when compared to the best available classical risk factors . still , some studies use an extensive number of classical risk factors and obtain improved results when adding metabolite profiles to their prediction . this selective overview shows some of the drawbacks of metabolomics studies : the results often differ between studies and at times can even contradict each other . one reason for this may be that different analytical platforms measure different subsets of the metabolome , often with little overlap between different platforms . other reasons include different characteristics of the observed populations ( age and genetic background ) as well as the use of different data analysis techniques . granting all this , t2d risk prediction years ahead of overt disease has been shown to be feasible using metabolomics . as mentioned , a set of 12 metabolites or metabolite classes has been identified as predictive for t2d ( table 1 ) , a substantial number that could possibly be integrated into standard , bench top laboratory tests for diagnostic purposes . apart from diagnosing or predicting the onset of pd or t2d , another important field for metabolomics research is prognosis , eluding disease course and outcome after its onset . the course and outcome of pd and t2d are to a large proportion driven by common complications including cardiomyopathy , nephropathy , peripheral neuropathy , and retinopathy . unfortunately , in this area , little metabolomics research has been performed on human subjects , even though improved prognosis may enable early interventions , alleviate disease burden , and facilitate cost - effective treatments . it is estimated that 6070 percent of individuals with t2d have some form of cardiac dysfunction or cardiovascular disease . as a result , individuals with t2d are twice as likely as healthy controls to have heart disease or stroke , making cardiovascular disease ( cvd ) the number one complication of diabetes . wu et al .   used metabolomics to investigate specific cardiovascular risk factors including high blood pressure , nonalcoholic fatty liver disease , and coronary heart disease in a group of t2d patients . unfortunately , this study only analyzed metabolic differences between these three risk factors and combinations thereof and did not include a control group without cvd risk factors . although the study could show notable metabolic differences between these diseases and their combinations , the authors state that the value of the results is limited as they were not able to recruit a matching control group without t2d complications . likewise , another study failed to predict coronary artery disease in 190 t2d patients within 4 years of follow - up . for diabetic nephropathy , sharma et al .   compared urine metabolites of diabetes patients with and without chronic kidney disease . although both type 1 and type 2 diabetes patients were enrolled in the study , only t2d cases were used in the screening cohort . it was found that 13 urinary metabolites were significantly reduced in chronic kidney disease patients . interestingly , 12 out of these 13 metabolites were found to be connected to mitochondria , and as such the authors suggested impaired mitochondrial metabolism in diabetic kidney disease . however , reduced urinary levels may also stem from reduced kidney function and thus not impaired mitochondrial function per se . in a rare prospective study , niewczas and colleagues   analyzed the plasma of 40 t2d patients who developed end - stage renal disease within 812 years of follow - up and compared it to a matched control group . uremic solutes and acylcarnitines were significantly increased prior to end - stage renal disease , hinting at early reductions in renal function that did not show up in standard renal function tests based on glomerular filtration rate estimated from serum creatinine levels . a set of aa and their derivatives showed decreased levels ; notably , bcaa and aromatic aa as well as ahba were significantly decreased , suggesting enhanced mitochondrial aa -oxidation . of interest , these metabolites were decreased rather than increased as usually seen for both the prediction and diagnosis of t2d ( table 1 ) . the results of this study have not yet been replicated in an independent cohort , but another study comparing t2d with and without diabetic nephropathy also found reduced levels of bcaa and aromatic aa . finally , a study has been conducted on diabetic retinopathy , which is the leading cause of blindness and visual impairment among working - age persons in developed countries , finding reduced levels of galactitol and ascorbic acid in vitreous humor . however , vitreous humor is a hard - to - collect sample type , which limits the application of this kind of analysis . additionally , the control group in this study was not made up of healthy individuals but of patients with a macular hole . all in all , the study of t2d - associated complications is still underrepresented in field metabolomics research , especially regarding prospective studies on human samples . further investigations similar to that of niewczas et al . , who prospectively analyzed metabolomic changes ahead of end - stage renal disease in t2d patients , have the potential to precipitate early interventions based on individual risk assessments . translation into clinical practice is defined as enabling the use of results from basic metabolomics research to enhance diagnosis , prediction , prognosis , and therapy . in the context of metabolomics , this is achieved by defining a predictive or diagnostic profile for a specific population ( age and sex ) under a defined set of conditions ( fed and fasted ) . basic research metabolomics studies typically measure hundreds of metabolites , an approach that is not feasible or cost - effective for large - scale application . additionally , laboratory equipment used for metabolomics studies is expensive and not readily available at the point of care . for these reasons , only a small subset of relevant metabolites that can be assessed using standard equipment or assays could be incorporated into a clinical routine . although much more work needs to be done , such measures have the potential to be quick , cost - effective , and relatively easy to interpret . identification of a high probability of t2d risk in a presymptomatic patient could inform physicians of the need for early testing , intensive intervention , and continued monitoring . this approach has the potential to be far more effective compared to treating full - blown t2d , where irreversible damage may have already occurred . thus , the translation of metabolomics research into clinical application may lead to informed decision - making in the realm of personalized medicine . to date , work translating metabolomics findings into clinical application has been limited , and most studies have focused on t2d prediction rather than t2d comorbidities . there may also be value in combining metabolomics data with other molecular and clinical datasets . a good example of this integration can be found in walford et al . , who analyzed a combination of 62 genetic and 9 metabolic biomarkers for the purpose of predicting t2d risk . results showed that biomarkers such as the bcaa isoleucine , the aromatic aa phenylalanine and tyrosine , triacylglycerides , phosphatidylcholines , and lysophosphatidylcholines were found to be predictive up to 13.4 years before the onset of disease . when metabolomic , genotypic , and clinical data were analyzed together there was a significant rise in predictive performance , suggesting that these datasets can be used to compliment or even validate each other . of note , the authors of this study state that they may not have used the most ideal combination of biomarkers . investigators varvel and colleagues   have also made significant advancements in translating metabolomics research into clinical application . specifically , they assessed a panel of 19 blood - based biomarkers , including metabolites such as glucose and ahba , proteins , and antibodies . based on these values , they analyzed t2d risk in 1,687 patients presenting for risk assessment . patients belonged to an obese high - risk cohort , with 48 percent of patients meeting the criteria for metabolic syndrome and one - third having been previously diagnosed with t2d and/or hypertension . screening by classical clinical parameters classified 929 patients ( 55.1% ) as normoglycemic . when classifying using biomarker analysis , 766 out of these normoglycemic patients ( 82.5% ) were classified as  at - risk . this indicates that a large amount of high - risk patients is missed when relying solely on classical clinical parameters . the classification results were presented to the patient and their physician and included treatment considerations , with the final treatment decision made exclusively by the treating physician . during care , guided by biomarker testing , a significant amount of patients initially classified as although these results are promising and represent an example of how informed decision - making can be based on metabolomics data , a randomized study design will have to be employed in order to authenticate these results . it is clear that further research on translating metabolomics results into clinical applications is necessary . in order for this to be successful , a robust set of relevant biomarkers must be defined , considering also that the metabolites have to be measured cost - effectively in a standard clinical environment . in any case , it may be feasible for metabolomic biomarkers to be used in addition to established clinical tests for t2d and its comorbidities . day - to - day variations in the metabolomic profile , which occur due to differences in diet , physical activity , and so on , may be minimized when analyzing serial time points of the same individual to create an average metabolic profile . additionally , insights from metabolomics studies may be the basis for genome - wide association studies ( gwas ) [ 7375 ] , possibly identifying genetic backgrounds of the observed effects and further strengthening the personalized aspects of diagnosis and treatment . the field of metabolomics is still relatively young and exists at a basic research level , where the issues of measurements and data analysis often require novel solutions . moreover , metabolomics data often lacks standardization in its reporting , thus making it difficult to compare results from independent studies . in addition , differences in protocols and instrumentation can yield varied sets of detectable metabolites , often with little overlap to other designs . even when similar methods are used , different statistical approaches might lead to contradictory results . despite these potential limitations , metabolomic studies have the potential to determine a unique set of metabolites that are predictive of both pd and t2d , often years or decades ahead of disease onset . this powerful information may shed light on disease development and may improve patients ' health , as shown in the pilot study by varvel et al . , who used a set of metabolites in a clinical setting to inform physicians on t2d risk factors . although there is much work left to do , the evidence of metabolomics benefitting t2d care makes its clinical application inevitable .
type 2 diabetes ( t2d ) and its comorbidities have reached epidemic proportions , with more than half a billion cases expected by 2030 . 
 metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions , which are most effective against t2d and its comorbidities . in metabolomics , 
 the abundance of a comprehensive set of small biomolecules ( metabolites ) is measured , thus giving insight into disease - related metabolic alterations . 
 this review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of t2d . 
 we summarized key metabolites changing in response to t2d . despite large variations in predictive biomarkers , 
 many studies have replicated elevated plasma levels of branched - chain amino acids and their derivatives , aromatic amino acids and -hydroxybutyrate ahead of t2d manifestation . 
 in contrast , glycine levels and lysophosphatidylcholine c18:2 are depressed in both predictive studies and with overt disease . the use of metabolomics for predicting t2d comorbidities is gaining momentum , as are our approaches for translating basic metabolomics research into clinical applications . as a result , 
 metabolomics has the potential to enable informed decision - making in the realm of personalized medicine .