Figure e-1.

Figure e-1. Metabolomic profile of stroke recurrence (SR) and large artery atherosclerosis (LAA) in TIA patients. A. Heat map representation of hierarchical clustering of molecular features found in each sample. Each line of this graphic represents an accurate mass ordered

by retention time, colored by its abundance intensity and baselining to median/mean across the samples (cohort 2). The scale from –13.6 blue (low abundance) to +13.6 red (high abundance) represents this normalized abundance in arbitrary units. B. Tridimensional PLS-DA graphs demonstrated that SR (I) and TIA temporal patterns recurrence (II) determines a plasma metabolome. I: Blue spots represent SR plasma samples while red ones represent non SR samples. II: Early recurrence (<90 days) is represented in blue spots, medium (>90 days and <1 any) in red and late (>1 year) in brown. C. Tridimensional PLS-DA graphs show differences between patients with LAA. Blue spots represent LAA and red ones Non LAA plasma samples. D. The inclusion of unidentified compound “X” (accurate mass 734.267, retention time: 11.66) levels to ABCD2 and large artery atherosclerosis (LAA) score to ROC curve increase the predictive power of early stroke recurrence (Areas: ABCD2 = 0.623, p=0.12; ABCD2+LAA =0.670, p=0.032; ABCD2+LAA+X = 0.712, p=0.008).

Table e-1. Differential metabolites identified (p<0.05) between SR groups according time after TIA.

Compound p-value Fold change

([1 year] vs [90 days])

Regulation

([1 year] vs [90 days])

Fold change

([1 year] vs [>1 year])

Regulation

([1 year] vs [>1 year])

Fold change

([90 days] vs [>1 year])

Regulation

([90 days] vs [>1 year])

10-hydroxy capric acid 0.004946 -1 down -261.004 down -261.004 down

1-Monopalmitin 0.04731 540.9529 up 387.9943 up -1.39423 down

2-Hexyldecanoic acid 0.049197 3.898534 up 376.2647 up 96.5144 up

2-hydroxyhexadecanoic acid 0.024344 -321.573 down -3.76825 down 85.33727 up

5alpha-dihydroprogesterone 0.00409 -47.1216 down -12836 down -272.403 down

6-Phosphogluconic acid 0.027761 485.0714 up 1113.566 up 2.295676 up

Arachidonic acid 0.021668 -217.184 down -3.24109 down 67.00941 up

DL-Ornithine 0.003342 1354.707 up 111.5821 up -12.1409 down

Epinephrine (adrenaline) 0.040817 1.337346 up -1.1178 down -1.49489 down

Glutamine 0.049488 3.015914 up 612.3105 up 203.0265 up

Kynurenine 0.002082 797.5383 up 1.47238 up -541.666 down

L-Norleucine 0.020661 -50.7492 down 6.174435 up 313.3476 up

Stearic acid 0.022383 -36.2047 down 5.775481 up 209.0995 up

Vitamin E (Alpha-Tocopherol) 0.017923 966.007 up 3705.88 up 3.836286 up

Table e-2. Sequential cox proportional hazards regression model to assess risk of stroke recurrence

Model 1 Model 2 Model 3Variables HR (CI) P HR (CI) p HR (CI) PABCD2 1.27

(0.89-1.82) 0.187 - - 1.25(0.95-1.66) 0.117

LAA 2.18(0.89-5.33) 0.089 - - 2.04

(0.83-5.03) 0.119

LysoPC 20:4 - - 3.64(0.85-15.71) 0.083 3.19

(0.74-13.85) 0.121

e-Methods

Cohorts description

We defined two cohorts of patients. The first one included 131 patients recruited from January

2008 to January 2010 and cohort 2 included 162 patients recruited from January 2010 to

January 2012. Both cohorts of patients shared the same methodology. TIA was defined

according to the classical definition as acute onset of focal cerebral or monocular symptoms

lasting <24 hours and thought to be attributable to a brain ischemia1. Peripheral venous

samples were obtained within the first 24 hours after symptoms onset, and plasma was

separated and stored at -80ºC. Patients with brain haemorrhages or tumors on the computed

tomography scan performed in the Emergency Department were excluded. A neurologist

treated all patients within the first 48 hours after the onset of symptoms. We excluded

patients with a modified Rankin Scale Score (mRS) >3. The mRS was always measured at

baseline after symptom resolution.

Ultrasound protocol

Transcranial doppler recordings were performed on admission, within the first 48 hours after

symptoms onset, with the use of a Multi-Dop-T/TCD device (DWL Elektronische Systeme

GmbH) in the first cohort and with the use of a Toshiba applio device in the second cohort.

Intracranial stenoses were diagnosed if the mean blood flow velocity at a circumscribed

insonation depth was >80 cm/s, with side-to-side differences >30 cm/s and signs of disturbed

flow2. Baseline cervical internal carotid artery (ICA) atherosclerosis was categorized by Eco

Doppler Micromaxx (FUJIFILM SonoSite, Inc., Madrid, Spain) device in the first cohort and on

Toshiba applio device (Toshiba, Japan) in the second cohort, as follows: absent; mild, if one or

both ICAs had <50% stenosis; moderate, when any of the ICA presented 50–70% stenosis; and

severe if any ICA had >70% stenosis according to Society of Radiologists in Ultrasound

Consensus Conference criteria3.

Patients that were classified as having LAA if a moderate to severe intracranial or extracranial

stenosis was recorded after doing ultrasonography study and being confirmed by angioMRI.

LAA required TIA symptoms to be attributable to the location and side of the stenosis.

All patients underwent routine blood biochemistry, electrocardiography, cervical duplex

ultrasonography, transcranial doppler (TCD) and neuroimaging. Transthoracic/transesophageal

echocardiography and Holter monitoring or monitoring ECG were performed in all patients

with clinical or neuroimaging findings presumably due to an embolus arising from the heart.

Neuroimaging protocol

All cases were studied with non-enhanced cranial tomography. Patients with a non-ischemic

brain lesion were excluded. Patients without medical contraindications or very early

subsequent stroke underwent MRI within 7 days (3.7 [SD 2.1] days) following the protocol

published previously4.

Metabolomic analysis

For non-targeted metabolomics analysis, metabolites were extracted from plasma samples

with methanol according to previously described methods5. Samples were randomized and 90

µl of cold methanol were added to 30 µl of plasma, incubated 1h at -20ºC and centrifuged 3

min at 12000g. The supernatant were recovered, evaporated using a Speed Vac (Thermo

Fisher Scientific, Barcelona, Spain) and resuspended in water 0.4% acetic acid/methanol

(50/50).

We used an ultra-high pressure liquid chromatography (UHPLC) scheme with an Agilent 1290

LC system coupled to an electrospray-ionization quadrupole time of flight (Q-TOF) mass

spectrometer 6520 instrument (Agilent Technologies, Barcelona, Spain). A column with 1.8 μM

particle size was employed and we performed the preliminary identification of differential

metabolites by using the database PCDL from Agilent (Agilent Technologies, Barcelona, Spain),

which uses retention times, exact mass and isotope distribution in an standardized

chromatographic system as an orthogonal searchable parameter to complement accurate

mass data (AMRT approach) according to previously published works6. MS/MS analyses were

used to confirm identities with authentic standards (Sigma-Aldrich, St. Louis, MO). All samples

were randomized before metabolomics analyses and the study was made in a double-blinded

fashion. In order to avoid inter-batch confounding effects, all batches contained quality control

samples as well as the inclusion of deuterated internal standards in samples.

The ConsensusPathDB-human7 integrates interaction networks in Homo sapiens metabolome

were used for calculation of pathway impact, as described recently 8. Briefly, this platform

collates pathways from several public databases of protein interactions, signaling and

metabolic pathways as well as gene regulation in humans. We applied our analysis to the

following databases: KEGG, Reactome, Netpath, Biocarta, HumanCyc and the pathway

interaction database (PID), Signalink, Inoh, Wikipathways, Pharmgkb, Humancyc and Ehmn,

thus reducing bias by potentially enhancing coverage.

Multivariate statistics

Hierarchical heatmap clustering and Partial least discriminate analysis (PLS-DA) was performed

using Mass Hunter Mass Profiler Professional software (Agilent Technologies, Barcelona,

Spain). Briefly, the number of components chosen for PLS-DA was 4, and data were scaled

using an auto scaling algorithm. Validation of the model was achieved with a N-fold validation

type with 3 folds and 10 repeats as validation parameters. In all cases, significance was

considered for p<0.05.

Statistical analysis

Statistical significance for intergroup differences was assessed using the Χ2 test for categorical

variables and the Student’s t-test and Mann– Whitney U-test for continuous variables.

Univariate analyses were performed to detect variables associated with the occurrence of SR.

For the establishment of a multiple comparison correction, a Bonferroni correction was

applied to all significant associations to reduce the risk of finding false-positive associations.

Receiver operating characteristic (ROC) curves for metabolomic data was performed using the

ROCCET platform9. In these analyses, normalization and processing for unbalanced data, was

performed according Monte Carlo random sampling to produce balanced sub-samples for

training data, allowing for diminishing confounding effects. Further we used ROC to establish

optimal cutoff points of the biomarkers to predict the occurrence of stroke recurrence during

the follow up. Moreover, ROC curves were plotted for comparing the predictive accuracy of

ABCD2 score and ABCD2 score in addition to the BM identified after the metabolomic analysis.

For this purpose we used the Hmisc Package in the R environment

(http://biostat.mc.vanderbilt.edu/wiki/Main/Hmisc), containing the Improveprob command,

after obtaining general lineal models for each one of the examined prediction models. For the

sake of comparison, we only used those cases where all measures were available. In this set of

samples, we performed the Net Reclassification Improvement (NRI) and the Integrated

Discrimination Improvement (IDI) tests10, as well as the Hosmer-Lemeshow test for calibration

of the risk prediction models Finally, we compared the cumulative event-free rates for the time

to a SR according to the metabolomics pattern using the Kaplan-Meier product limit method.

Supplementary Results

When comparing models only with ABCD2 with those ABCD2+LAA, both NRI (0.73, 4.09, 4.31e-

05 for NRI index, Z and 2P values, respectivelly) and IDI (0.023, 3.43, 0.0004 for IDI index, Z and

2P values, respectivelly) tests indicated significant improvement. When comparing models only

with ABCD2 with those ABCD2+LAA, both NRI (0.73, 4.09, 4.31e-05 for NRI index, Z and 2P

values, respectively) and IDI (0.023, 3.43, 0.0004 for IDI index, Z and 2P values, respectively)

tests indicated significant improvement. When comparing the model ABCD2+LAA with the

same adding the LysoPC(20:4) values we had a significant improvement in NRI (0.48, 3.51,

0.0004 for NRI index, Z and 2P values, respectively) and IDI (0.024, 3.76, 0.000168 for IDI index,

Z and 2P values, respectively) tests. However, when using the same model (ABCD2+LAA)

adding the LysoPC(16:0), no significant improvement was obtained neither in the NRI (-0.308,-

1.69,0.091 for NRI index, Z and 2P values, respectively) nor in the IDI tests (-0.029, -2.03, 0.041

for IDI index, Z and 2P values, respectively), suggesting that information explained by

LysoPC(16:0) is biologically related to clinical factors implicit in ABCD2+LAA score

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