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