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1 Wang et al., Metabolite Profiles and the Risk of Developing Diabetes Supplementary Figure 1 Mass Spec Intensity (a.u.) 2.E+07 2.E+07 1.E+07 5.E+06 x Leucine R= E+07 1.E+07 1.E+07 8.E+06 6.E+06 4.E+06 2.E+06 x Isoleucine R= E Concentration ( M) 0.E Concentration ( M) Representative dose-response studies of isotope-labeled standards for two branched-chain amino acids in normal pooled human plasma are shown. Boxes represent mean data from calibration curves run at the beginning, middle, and end of each analytical batch of ~150 samples. The median concentration of the endogenous plasma metabolite in the control samples as assessed by the LC-MS method is denoted with an x and an arrow. For all metabolites, peak areas were >2 orders of magnitude above the lower limit of quantitation (as defined as a discrete peak 10-fold greater than noise, lowest dose with a closed box) and fell well within the linear range of the dose-response relationship. 1

2 Supplementary Table 1: Metabolite profiling in individuals with and without incident diabetes (Framingham Heart Study) Metabolite Type of variable P-value Phenylalanine Continuous < Tyrosine Continuous < Isoleucine Continuous Leucine Continuous Valine Continuous Ornithine Continuous Tryptophan Continuous Proline Continuous Histidine Continuous 0.03 Cotinine Continuous '-Adenosylhomocysteine Continuous 0.04 Alanine Continuous 0.04 Glycine Continuous 0.05 Niacinamide Continuous 0.05 camp Discrete 0.09 Choline Continuous 0.10 Xanthosine Continuous 0.10 Trimethylamine-N-oxide Continuous 0.11 Lysine Continuous 0.12 NMMA Continuous 0.13 Phosphocholine Discrete 0.18 Taurine Continuous 0.20 Citrulline Continuous 0.23 Carnitine Continuous 0.24 Serine Continuous 0.26 Creatinine Continuous 0.30 cis/trans-hydroxyproline Continuous 0.30 N-Carbamoyl-β-alanine Continuous 0.31 Methionine Continuous 0.33 Threonine Continuous 0.34 Adenosine Discrete 0.35 Triiodothyronine Discrete 0.35 Cytosine Discrete Hydroxytryptophan Discrete 0.38 Dimethylglycine Continuous 0.39 Asparagine Continuous 0.40 aminoisobutyric acid Continuous

3 Anthranilic acid Continuous 0.47 Creatine Continuous 0.47 Thyroxine Continuous 0.49 Acetylcholine Discrete 0.53 Spermine Continuous 0.58 Allantoin Continuous 0.60 Glutamine Continuous 0.65 GABA Continuous 0.68 Glycerol Continuous 0.71 Glutamic acid Continuous '-Deoxyadenosine Discrete 0.80 Aspartic Acid Continuous 0.80 ADMA/SDMA Continuous 0.82 Serotonin Continuous 0.84 Arginine Continuous 0.86 Kynurenic acid Continuous 0.87 Spermidine Discrete 0.88 Phosphoethanolamine Discrete HIAA Continuous 0.90 α-glycerophosphocholine Continuous 0.96 Betaine Continuous OH-Anthranilic acid Discrete '-Deoxycytidine Discrete 1.00 Thiamine Discrete 1.00 P-values are from paired t-tests for each continuous variable, and McNemar tests for each discrete (dichotomous) variable. 3

4 Supplementary Table 2: Correlation of selected amino acids with measures of insulin resistance and sensitivity Isoleucine Leucine Valine Tyrosine Phenylalanine Fasting insulin 0.26 (0.0003) 0.26 (0.0003) 0.26 (0.0003) 0.38 (<.0001) 0.23 (0.0013) HOMA-IR 0.24 (0.0007) 0.24 (0.0009) 0.24 (0.0008) 0.37 (<.0001) 0.23 (0.0019) HOMA-B 0.28 (<.0001) 0.28 (0.0001) 0.28 (0.0001) 0.37 (<.0001) 0.24 (0.0009) OGTT (2-hr glucose) 0.07 (0.35) 0.13 (0.07) 0.15 (0.04) 0.03 (0.67) (0.15) Pearson correlation coefficients (r) shown, from pairwise correlations between the variables shown in the control sample (n=189). Values in parentheses are p-values. HOMA-IR: homeostasis model assessment of insulin resistance. HOMA-IR: homeostasis model assessment of beta cell function. OGTT: oral glucose tolerance test. 4

5 Supplementary Table 3: Prediction of incident diabetes, with various combinations of amino acids, in high-risk and low-risk individuals Model Description High-risk sample* -2 log-likelihood ratio c-statistic Random sample* -2 log-likelihood ratio c-statistic Clinical model: age, BMI, glucose Clincal plus biomarker model, with 3 amino acids (isoleucine, phenylalanine, tyrosine) Clinica plus biomarker model, with 5 amino acids (isoleucine, valine, leucine, phenylalanine, tyrosine) First model (age, sex, body mass index, fasting glucose) denotes the basic clinical model. Higher numbers for log-likelihood ratio indicate better model fit. *High-risk sample includes cases and matched controls from Framingham. Random cohort includes cases and random controls from Framingham. Clinical characteristics of the study samples are shown in Table 1. 5

6 Supplementary Table 4: Absolute quantitation of amino acids Framingham Heart Study Ciba-Geigy reference Leucine (123.4, 165.8) 157 Isoleucine 85.1 (72.3, 102.5) 84 Phenylalanine 62.0 (57.0, 67.9) 56 Valine (172.2, 213.0) 209 Amino acid concentrations are in M. In Framingham Heart Study controls, median with upper and lower quartile cutpoints in parentheses are shown. In Ciba-Geigy samples, median values are shown. 6

7 Supplementary Methods Clinical evaluation and definition of diabetes. Participants in the Framingham Offspring Study are examined approximately every four years. At each visit, participants undergo a physician-administered physical examination and medical history, and routine laboratory tests that include fasting glucose. At the baseline examination, participants were administered a standard 2-hour oral glucose tolerance test (OGTT) after a 12-hour overnight fast, using 75 grams of glucose in solution. Information on dietary intake of protein and amino acids and total calories was systematically obtained from a detailed, validated food frequency questionnaire 1. The presence of diabetes was ascertained at every Framingham Heart Study visit, and defined by a fasting glucose 126 mg/dl or the use of insulin or other anti-diabetes medications 2. Individuals with a 2-hour glucose 200 mg/dl on the OGTT administered at the baseline examination were also considered to have diabetes. Individuals with prevalent diabetes (based on the criteria detailed above) were excluded from the present investigation. In MDC (replication cohort), diabetes at baseline was defined as self report of a physician diagnosis or use of diabetes medication or fasting glucose 126 mg/dl. New-onset diabetes diagnosed after the baseline examination until December 2005 (mean follow-up time 12.6 years) was assessed in subjects free from diabetes at baseline by three registers: the Malmö HbA1c register (MHR), the nationwide Swedish National Diabetes Register (NDR) 3, and the regional Diabetes 2000 register of the Scania region of which Malmö is the largest city 4. The NDR and 7

8 Diabetes 2000 registers required a physician diagnosis according to established diagnostic criteria (fasting glucose 126 mg/dl, measured on two different occasions). Nested case-control design. In the Framingham Offspring Study, a total of 201 individuals developed new-onset diabetes over a 12-year follow-up period (e.g., three follow-up examinations) after the baseline examination. These individuals were designated as cases. All incident diabetes diagnoses were type 2 diabetes. Propensity matching was used to select paired controls. Sex-specific, logistic regression models were used to generate the propensity scores. For these models, diabetes was the outcome variable, and the following variables were used as covariates: age, body mass index, fasting glucose, and hypertension (defined as blood pressure 140/90 mm Hg or use of anti-hypertensive therapy). Six separate logistic regression models were estimated, one for each follow-up examination of each gender. Each case was matched to the control with the closest exam- and gender-specific propensity score, provided the difference in propensity scores was < 0.10 (on a scale of 0.0 to 1.0). A control could only be used once. Using this approach, a propensity-matched control was identified for all but 12 cases. Thus, the final study sample comprised 189 cases and 189 controls. Using an identical approach in MDC, we identified an additional 163 cases and 163 controls for the replication analyses. 8

9 Random cohort sample. We also selected 400 individuals randomly from all eligible individuals in the Framingham Offspring cohort who were not already identified as cases. This sub-sample (referred to as the random cohort ) was used in secondary analyses, performed to assess the predictive value of metabolites in a more heterogeneous, lower-risk sample. Metabolic profiling. A total of 756 pre- and post-ogtt plasma samples from matched case and control participants in Framingham were queued for analysis in a blinded, random order. The samples were processed for analysis in four groups of 152 samples and one group of 148 samples. Liquid chromatography-tandem mass spectrometry (LC-MS) data were acquired using a 4000 QTRAP triple quadrupole mass spectrometer (Applied Biosystems/Sciex) that was coupled to a multiplexed LC system comprised of two 1200 Series pumps (Agilent Technologies) and an HTS PAL autosampler (Leap Technologies) equipped with two injection ports and a column selection valve. The two pumps were similarly configured for hydrophillic interaction chromatography (HILIC) using 150 x 2.1 mm Atlantis HILIC columns (Waters) and with the same mobile phases (mobile phase A: 10 mm ammonium formate and 0.1% formic acid, v/v; mobile phase B: acetonitrile with 0.1% formic acid, v/v). Multiplexing was used to enable the measurement of 61 metabolite transitions divided between the 2 LC systems, and each sample was injected once on each. Each column was eluted isocratically with 5% mobile phase A for one minute followed by a linear gradient to 60% mobile phase A over 10 minutes. MS analyses were carried out using electrospray ionization (ESI) and multiple reaction monitoring (MRM) scans in the positive ion mode. Declustering potentials and collision energies were optimized for 9

10 each metabolite by infusion of reference standards prior to sample analyses. The dwell time for each transition was 30 ms, the ion spray voltage was 4.5 kv, and the source temperature was 425ºC. Internal standard peak areas were monitored for quality control and individual samples with peak areas differing from the group mean by more than two standard deviations were reanalyzed. MultiQuant software (Version 1.1; Applied Biosystems/Sciex) was used for automated peak integration and metabolite peaks were manually reviewed for quality of integration and compared against a known standard to confirm identity. For the validation studies in the MDC, data were acquired using a slightly modified version of the LC-MS method in which the HILIC liquid chromatography run time was shortened to 32 minutes and the list of MRM-targeted endogenous metabolites was limited to five branched chain and aromatic amino acids, based on the Framingham results. For all isotope measurements using these LC/MS methods, peak areas were greater than two orders of magnitude above the lower limit of quantitation (as defined as a discrete peak 10-fold greater than noise) and fell well within the linear range of the dose-response relationship (representative data for two branched-chain amino acids are shown in Supplementary Figure 1). Using available reference standards for four amino acids (phenylalanine, isoleucine, leucine, valine), quartile cutpoints were determined for each amino acid in Framingham controls, and were shown to be comparable to previously published data on amino acid levels in normal volunteers (Supplementary Table 4). We also performed preliminary studies using sample preparation and mass spectrometry replicates of human samples to assess the coefficient of variation (CV) for the metabolites in the 10

11 platform. This analysis showed that 54% of the analytes had CV 10% (including branched chain and aromatic amino acids), and 74% of the analytes had CV 20%. In a pilot study, we previously documented concordance in metabolite profiles during OGTT between archived samples from Framingham and freshly-obtained samples from volunteers 5. Statistical analyses and multiple testing. To account for multiple testing, we used a corrected p-value threshold of (=0.05/48) to account for the 48 metabolites analyzed as continuous variables. The 13 metabolites with binary data were analyzed in separate analyses, using a correction factor of 13. This tiered approach was taken to distinguish analyses in which metabolite levels were detectable in all participants (continuous metabolites) from those in which metabolite levels were detactable only in some participants (dichotomous metabolites). Because many metabolites were correlated and clustered within well-defined groups (e.g. amino acids, tryptophan derivatives, urea cycle metabolites, etc.; Figure 1), the number of independent tests was substantially lower than the nominal number of tests. We performed separate tests in a replication cohort (MDC) to further reduce the risk of false positive findings. All statistical analyses were performed using SAS software version (SAS Institute). 11

12 References for Supplementary Methods 1. Rimm, E.B., et al. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am.J Epidemiol. 135, (1992). 2. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 20, (1997). 3. Cederholm, J., et al. Risk prediction of cardiovascular disease in type 2 diabetes: a risk equation from the Swedish National Diabetes Register. Diabetes care 31, (2008). 4. Lindholm, E., Agardh, E., Tuomi, T., Groop, L. & Agardh, C.D. Classifying diabetes according to the new WHO clinical stages. European journal of epidemiology 17, (2001). 5. Shaham, O., et al. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol Syst Biol 4, 214 (2008). 12