type 2 diabetes is estimated to affect > 550 million people worldwide by 2030 ( 1 ) . given the personal and health care system costs associated with this growing epidemic , it is critical to identify high - risk individuals as a first step in providing effective preventive interventions for diabetes ( 2 ) . although the medical history and standard laboratory testing provide clues as to an individual s future risk of diabetes ( 3 ) , many of these predictors emerge only after years of subclinical metabolic dysfunction ( 4 ) . novel markers may help to elucidate aspects of metabolic dysfunction contributing to diabetes risk and improve the early identification of individuals who might benefit from preventive therapies for diabetes . genetic information , specifically , single nucleotide polymorphisms ( snps ) grouped into a genetic risk score ( grs ) , has been shown to predict type 2 diabetes incidence alone and in the context of clinical prediction models within the framingham offspring study ( 5,6 ) as well as within multiple independent cohorts ( 710 ) . however , genetic information alone generally has low predictive performance for type 2 diabetes incidence ( 11 ) and only modestly improves accuracy of prediction by traditional clinical risk factors for type 2 diabetes . circulating metabolite biomarkers , specifically , branched - chain and aromatic amino acids ( 12 ) , lipid species of particular acyl chain length composition and saturation ( 13 ) , the glutamine - to - glutamate ratio ( 14 ) , and 2-aminoadipic acid ( 15 ) , have also been shown to predict diabetes risk independent of clinical factors such as sex , bmi , and fasting glucose . these metabolite associations have been demonstrated in the framingham offspring study ( 1215 ) and validated in independent cohorts ( 12,1417 ) . while the majority of diabetes - related snps with known physiological associations have been linked to -cell function ( 18,19 ) , amino acid and lipid metabolites have been strongly associated with measures of insulin resistance ( 12,13,16,2024 ) . we hypothesized that genetic and metabolite biomarkers of diabetes risk , by capturing different pathogenic elements for type 2 diabetes development , would provide complementary information to each other regarding future diabetes risk and possibly improve clinical models of diabetes prediction in aggregate . we therefore evaluated the ability of a type 2 diabetes grs , containing 62 diabetes - related snps derived from the most recent association findings ( 25 ) , and circulating metabolites involved in cardiometabolic risk pathways , separately and together , to predict diabetes and improve discrimination of incident type 2 diabetes compared with conventional clinical predictors . the framingham offspring study was initiated in 1971 ( 26 ) , and participants were examined approximately every 4 years . the fifth examination of the framingham offspring study , which was conducted between 1991 and 1995 , was considered the baseline assessment for the current study . genotyping and metabolite profiling were performed on archived plasma samples collected from participants with at least one exam cycle of follow - up from the baseline examination ( 26 ) . individuals with known diabetes ( defined as fasting plasma glucose 7.0 mmol / l , glucose at 2 h after a standard 75-g oral glucose load 11.1 mmol / l , or use of antidiabetes therapy ) at the baseline examination or without a sample for genotyping or metabolite profiling were excluded . diabetes incidence was defined by plasma glucose level 7.0 mmol / l or use of antidiabetes therapy , and time to diabetes incidence was derived from the time of the baseline examination . measurement methods for amino acid and intermediary metabolites ( 12,14,15 ) , as well as lipid metabolites ( 13 ) , have previously been reported . a previous study has documented concordance in amino acid metabolite measures between archived samples from the framingham offspring study and freshly obtained samples ( 27 ) . peak areas of internal standards were monitored for quality control , and individual samples with peak areas from individual samples differing from the group mean by more than two sds were reanalyzed . metabolite peaks were manually reviewed for quality of integration and compared against a known standard to confirm identity . replicates derived from a single pooled plasma sample were run after every 30 experimental samples , enabling detection of temporal drift in instrument performance . in age- and sex - adjusted logistic regression models testing the association of amino acid metabolites or lipid metabolites with incident diabetes , backward stepwise regression was used to remove metabolite traits sequentially until each remaining trait was associated with diabetes at p value < 0.05 . the initial models containing all amino acid and lipid metabolite traits and those following stepwise regression are shown in supplementary tables 1 and 2 , respectively ; the metabolite traits retained following backward stepwise regression were used in subsequent prediction models . sixty - two autosomal snps , each previously associated with type 2 diabetes at genome - wide significance ( 25 ) , were genotyped or imputed , including 22 new snps from prior diabetes prediction analyses ( 6 ) . genotypes were obtained from affymetrix array data in the framingham offspring study or from de novo genotyping on the iplex ( sequenom ) platform . as in prior reports ( 5,6 ) , minimum call rates were 97% for affymetrix and 96.9% for iplex snps . the number of risk alleles at each snp ( 0 , 1 , or 2 ) was multiplied by its published -coefficient for diabetes risk ( 28 ) , and then these weighted alleles were summed across the 62 loci ( 19 ) . this 62 snp grs has been associated with type 2 diabetes in another study from the framingham offspring study ( n = 3,869 ) and the coronary artery risk development in young adults ( n = 2,470 ) study ( 25 ) . statistical association of each snp , independently , with type 2 diabetes in the cohort examined in the current study was not required for inclusion into the grs . the snp components of the grs with published -coefficient are displayed in supplementary table 3 . association tests were performed in sex- and age - adjusted models containing the 62-snp grs , amino acid metabolites , lipid metabolites , and previously validated clinical predictors of incident diabetes : family history of diabetes , bmi , systolic blood pressure , hdl cholesterol , triglycerides , and fasting blood glucose ( 3 ) . logistic regression models tested the association of these variables with diabetes onset in the overall cohort and in subgroups stratified by age ( < 50 and 50 years ) and bmi ( < 30 and 30 kg / m ) . within - study model validity was assessed using a jackknife procedure with 10 random samples of 90% of the cohort , as has been done previously ( 3 ) . linear regression was used to test the association of the same variables with the log - transformed homeostasis model assessment of insulin resistance ( log homa - ir ) ( 29 ) and -cell function ( log homa - b ) ( 29 ) at the baseline assessment in age- , sex - adjusted models . model discrimination and reclassification were evaluated using areas under the receiver operating characteristic curve ( auc ) and continuous net reclassification improvement indices ( nri ) , respectively ( 30,31 ) . an nri of 0.2 was interpreted as weak improvement , nri of 0.4 as intermediate moderate improvement , and nri of 0.8 as strong improvement ( 31 ) . analyses were conducted using sas software ( version 9.2 ; sas institute , cary , nc ) . the institutional review boards at boston university and the partners human research committee approved the study . baseline characteristics of the 1,622 participants with clinical , genotype , and metabolite profiling measurements and at least 4 years of follow - up information from the framingham offspring study are displayed in table 1 . in total , there were 206 new diabetes cases over an average follow - up of 13.5 years . the mean ( sd ) time to diabetes incidence was 9.2 ( 4.3 ) years . when examined in aggregate , three amino acids ( isoleucine , tyrosine , and phenylalanine ) and six lipid metabolites ( c18:2 lysophosphatidylcholine [ lpc ] , c38:6 phosphatidylcholine [ pc ] , c44:1 triacylglycerol [ tag ] , c48:0 tag , c52:1 tag , and c56:9 tag ) remained associated with incident diabetes and were included in subsequent prediction models . the auc was greater for age- and sex - adjusted models containing amino acid or lipid measurements than for the model containing the grs ( p < 0.0001 for both comparisons ) . the auc for the model containing both the grs and metabolite measurements was greater than the auc for models with either set of predictors alone ( p < 0.0001 for grs alone vs. grs and metabolites ; p=0.01 for metabolites alone vs. grs and metabolites ) . the reclassification capacity of the metabolite prediction model was improved by the addition of the grs ( nri = 0.421 , p < 0.0001 ) , and the reclassification capacity of the grs prediction model was improved by the addition of the metabolites ( nri = 0.796 , p < 0.0001 ) . all models are adjusted for age and sex , and the line indicates the auc for the model containing only age and sex as predictors . addition of metabolomic information alone but not the grs alone increased the auc of the model containing clinical risk factors ( p = 0.002 for addition of all metabolites and p = 0.08 for addition of grs ) . addition of metabolomic information increased the auc of the model containing the grs and clinical risk factors ( p = 0.007 ) . within - study validity testing demonstrated a narrow range of aucs for each model ( 0.22 ) ; in each case , the auc from the model in the entire cohort was at or near the center of this range ( supplementary table 4 ) . the ability of the standard clinical model in reclassifying the risk for type 2 diabetes was improved by the addition of the grs alone ( nri = 0.247 , p = 0.0009 ) , by metabolites alone ( nri = 0.442 , p < 0.0001 ) , and by grs and metabolites together ( nri = 0.576 , p < 0.0001 ) . the auc of each prediction model , including that incorporating clinical factors , metabolites , and grs , was greater for participants < 50 vs. 50 years old ( supplementary fig . 1 ) and for participants with bmi < 30 vs. 30 kg / m ( supplementary fig . 2 ) . within the model containing clinical , grs , and metabolite information , participants in the lowest and highest deciles of predicted diabetes risk had actual diabetes rates of 1.2 and 56.0% , respectively ( supplementary fig . odds ratios ( ors ) for the grs and metabolite traits within prediction models , separately and jointly , are shown in table 2 . the or for the association of the grs with incident type 2 diabetes was not attenuated by the addition of metabolite data and remained significant when considered alone or in the combined grs and metabolites model . similarly , the or for the association of metabolite traits was not attenuated by the addition of the grs , and phenylalanine , c18:2 lpc , c38:6 pc , c44:1 tag , and c52:1 tag were significant when considered in the metabolites - only model or the combined grs and metabolites model . the ors for the grs and metabolite traits with incident type 2 diabetes remained consistent when considered with clinical factors ( supplementary table 5 ) . or for incident type 2 diabetes associated with a grs and serum metabolites in separate and combined models lipid metabolites notation is cxx : yy , where cxx is the number of carbons in the acyl chain and yy is the number of double bonds . auc ( 95% ci ) for grs : 0.641 ( 0.603 , 0.679 ) . auc ( 95% ci ) for metabolites : 0.803 ( 0.773 , 0.832 ) . auc ( 95% ci ) for grs + metabolites : 0.820 ( 0.792 , 0.848 ) . when considered individually , the grs ( p = 0.0005 ) and all metabolites , individually , were associated with log homa - b ( p < 0.0001 for all metabolites except c38:6 pc , p = 0.03 ) . when both sources of information were examined in the same age- and sex - adjusted model , the grs remained associated with log homa - b ( p < 0.0001 ) , though fewer metabolites ( p > 0.05 for phenylalanine and c44:1 tag ) remained associated with log homa - b ( table 3 ) . when considered individually , all metabolites were associated with log homa - ir ( p < 0.0001 for all metabolites except c38:6 pc , p = 0.008 ) . in contrast , the grs was not associated with log homa - ir ( p = 0.43 ) . when both sources of information were examined together in age- and sex- adjusted models , all metabolites remained associated with log homa - ir ( p < 0.05 for all ) , and the grs was not associated with log homa - ir ( p = 0.29 ) ( table 3 ) . estimates for homa - ir or homa - b associated with a grs and serum metabolites in combination -coefficient estimates provided from age- and sex - adjusted regression models . lipid metabolites notation is cxx : yy , where cxx is the number of carbons in the acyl chain and yy is the number of double bonds . this study integrates novel assessment of the combined effects of genetic and circulating metabolite measurements on predictive models for incident type 2 diabetes . while the association between genetic traits or metabolite levels and future type 2 diabetes has been demonstrated separately in the framingham offspring study ( 5,6,1215,25 ) and validated in several independent cohorts ( 79,12,1417,25 ) , we show that metabolite and genetic traits together contribute information both distinct and complementary to each other for the prediction of future type 2 diabetes among adults in the framingham offspring study . within - study replication was used to demonstrate high reliability of accuracy for incident type 2 diabetes for each of the models presented , and two different methods of assessing model performance , auc and nri , showed consistent results . all metabolites and clinical risk factors were measured at a common baseline examination , and the time to diabetes incidence or diabetes - free follow - up was derived from that assessment . thus , strengths of this study 's design are the measurement of genetic information and baseline metabolite concentrations in the same well - phenotyped cohort with longitudinal information ( avoiding confounding from extant disease ) , use of the most current grs for diabetes prediction , and use of metabolites currently validated against future risk of type 2 diabetes . our results extend prior studies demonstrating the capacity of the grs ( 5,6,9 ) and metabolite measurements ( 1214 ) , separately , to predict type 2 diabetes risk . consistent with prior studies ( 5,7,10 ) and a recent meta - analysis ( 11 ) , we show that the addition of genetic information only slightly improves accuracy of type 2 diabetes prediction over traditional clinical risk factors . the increase in predictive accuracy from incorporating metabolomic data with or without genetic information to clinical risk factors is greater . these findings suggest that both metabolomic and genetic information may have utility for type 2 diabetes prediction in specific subpopulations . in agreement with prior reports from de miguel - yanes et al . ( 8,9 ) demonstrating that the grs adds more information to a diabetes prediction model in younger versus older individuals , we show that both genetic and metabolomic measurements yield improved accuracy for type 2 diabetes prediction in individuals younger than 50 years versus older than 50 years and in nonobese versus obese individuals . further work is needed to test whether genetic and metabolite markers , separately and together , are more informative with respect to type 2 diabetes risk in individuals who have not yet developed traditional clinical risk factors for the disease , such as older age and obesity , than those who have . the large difference in diabetes incidence between individuals in the lowest predicted and highest predicted risk decile suggests a model using all sources of information may have clinical applicability in the identification of individuals who are at high risk of developing type 2 diabetes . importantly , by comparing the effect of the grs and metabolite measures on diabetes prediction in the same individuals , we were able to show that the discrimination and reclassification for models containing metabolites , with or without clinical predictors , were greater than for similar models containing the grs among adults in the framingham offspring study . furthermore , we show that , of the metabolites previously associated with incident type 2 diabetes , separately , nine remain significant predictors when considered in aggregate . this subset of metabolites , along with any newly discovered metabolite biomarkers , may be sufficient to capture the currently known metabolomic information on diabetes risk in future studies . indeed , the auc for each predictive model is similar whether all metabolites previously associated with type 2 diabetes or only the subset of nine metabolites is used ( supplementary fig . notably , the association between the grs and incident diabetes was not attenuated by the addition of metabolomic information and retained significance in all prediction models tested . this may indicate that the grs and metabolites represent different aspects of type 2 diabetes risk . the majority of diabetes - related snps with known physiological associations have been linked to -cell function ( 18,19 ) . by contrast , amino acids and lipids have been associated principally with measures of insulin resistance but also with measures of insulin secretion ( 12,13,16,2024 ) . thus , the ability of metabolites and grs to provide information on diabetes prediction complementary to each other may be related to their capturing different aspects of type 2 diabetes development . supporting this hypothesis , our results show that the grs is consistently associated with an estimate of -cell function , while all the metabolite traits are consistently associated with an estimate of insulin resistance . in contrast to a prior report that demonstrated similar correlations between individual amino acid metabolites and either homa - ir or homa - b in a case - control experiment ( 12 ) , we find that , for the subset of amino acid and lipid metabolites that were associated with both traits , the relationship of all but one metabolite ( c48:0 tag ) was stronger with homa - ir than with homa - b . this difference may be explained by our examining the relationship of all metabolites with each trait in aggregate models , as opposed to separately , or by our having performed the analyses in a longitudinal cohort design as opposed to in a case - control design . although the relationship between individual metabolites and homa - b could be attenuated by the additional metabolite or clinical variables , the relationship between the grs and homa - b was not affected by these other sources of information . in exploratory analyses , we found that incorporating homa - ir into models with clinical factors and metabolites did not alter the auc or the ors of metabolites with type 2 diabetes risk ( not shown ) . this finding is consistent with the prior observation that adjustment for homa - ir does not reduce the association of amino acid metabolites with type 2 diabetes risk ( 12 ) . together , these findings suggest that the grs is a robust indicator of -cell function and that circulating metabolites may be marking several aspects of diabetes risk , including the fasting insulin resistance as estimated by homa - ir . still , further studies are warranted to investigate whether the genetic and metabolomic data are signaling different biological etiologies for diabetes development . detailed physiological tools , such as insulin clamps , were not available to discern the relative contribution of the metabolite signature and grs to either insulin resistance or -cell function in this study , which used archived samples from an established cohort first , participants were all of european ancestry and of average risk for type 2 diabetes development . while amino acids and snp components of the grs have been associated with insulin resistance or diabetes incidence , respectively , in nonwhite populations ( 23,3236 ) , the relative contribution of these sources of information to type 2 diabetes prediction is not known outside of this study . therefore , these findings should be tested in nonwhites and populations at high risk of diabetes . second , we used within - study statistical techniques to demonstrate reliability of the prediction models and the range of aucs that might be expected in other data sets . while the grs and metabolite predictors have each been validated independently , formal replication in a different cohort , ideally using data collected in a prospective manner , would further support the reproducibility of these findings . third , the snps and metabolites used in these analyses do not fully represent the genetic or metabolomic contribution to diabetes risk . it is anticipated that new genetic variants and circulating metabolites associated with diabetes risk will be validated , at which time they can be incorporated into the existing type 2 diabetes prediction models established here . in summary , specific measurements from genomic and metabolomic platforms , distilled here into the most predictive determinants , provide complementary information for prediction of type 2 diabetes , and , together , modestly improve the accuracy of standard clinical models of diabetes prediction . future studies should test the effect of a combined type 2 diabetes prediction model in high - risk and multi - ethnic populations and seek to understand how this information may be used to target clinical interventions for the prevention of type 2 diabetes in subpopulations with the highest metabolic risk .
objectivea genetic risk score ( grs ) comprised of single nucleotide polymorphisms ( snps ) and metabolite biomarkers have each been shown , separately , to predict incident type 2 diabetes . 
 we tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and , together , improve the accuracy of prediction models containing clinical traits.research design and methodsdiabetes risk was modeled with a 62-snp grs , nine metabolites , and clinical traits . 
 we fit age- and sex - adjusted logistic regression models to test the association of these sources of information , separately and jointly , with incident type 2 diabetes among 1,622 initially nondiabetic participants from the framingham offspring study . 
 the predictive capacity of each model was assessed by area under the curve ( auc).resultstwo hundred and six new diabetes cases were observed during 13.5 years of follow - up . 
 the auc was greater for the model containing the grs and metabolite measurements together versus grs or metabolites alone ( 0.820 vs. 0.641 , p < 0.0001 , or 0.820 vs. 0.803 , p = 0.01 , respectively ) . 
 odds ratios for association of grs or metabolites with type 2 diabetes were not attenuated in the combined model . 
 the auc was greater for the model containing the grs , metabolites , and clinical traits versus clinical traits only ( 0.880 vs. 0.856 , p = 0.002).conclusionsmetabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes . 
 these novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors .