Urinary proteomics to support diagnosis of stroke - PubMed (original) (raw)

Urinary proteomics to support diagnosis of stroke

Jesse Dawson et al. PLoS One. 2012.

Abstract

Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69), compared to controls (n = 33), and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value <0.004) or 35 biomarkers (nominal p-value <0.01). When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (-0.30 to 1.25) in cases compared to -1.04 (IQR -1.86 to -0.09) in controls, p<0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a test in large prospective clinical trials.

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Conflict of interest statement

Competing Interests: H. Mischak is the co-founder and co-owner of Mosaiques Diagnostics. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Figures

Figure 1

Figure 1. Urinary polypeptide signatures in cases and controls.

Normalized molecular weight (800–20 000 Da) in logarithmic scale is plotted against normalized migration time (18–45 minutes). The mean signal intensity of the polypeptide peak is given in 3-dimensional depiction. n = 26 controls and 33 cases.

Figure 2

Figure 2. Polypeptide signatures of the 35 biomarker model in cases and controls.

Normalized molecular weight (800–20 000 Da) in logarithmic scale is plotted against normalized migration time (18–45 minutes). The mean signal intensity of the polypeptide peak is given in 3-dimensional depiction. n = 26 controls and 33 cases.

Figure 3

Figure 3. Receiver operating characteristics curve for the 14 and 35 biomarker models.

Figure 4

Figure 4. Scatter plot of National Institute of Health of Stroke Scale (NIHSS) score and the biomarker model classifier (modA).

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References

    1. Libman RB, Wirkowski E, Alvir J, Rao TH. Conditions that mimic stroke in the emergency department. Implications for acute stroke trials. Arch. Neurol. 1995;52:1119–1122. - PubMed
    1. Dawson J, Lamb E, Quinn TJ, Horvers M, Verrijth MJ, et al. A Recognition Tool for Transient Ischaemic Attack. Quarterly Journal of Medicine. 2009;102:43–49. - PubMed
    1. Harbison J, Hossain O, Jenkinson D, Nor AM, Davis J, et al. Diagnostic accuracy of stroke referrals from primary care, emergency room physicians, and ambulance staff using the Face Arm Speech Test. Stroke. 2003;34:71–76. - PubMed
    1. Nor AM, Davis J, Sen B, Shipsey D, Louw SJ, et al. The recognition of stroke in the emergency room (ROSIER) scale: development and validation of a stroke recognition instrument. 5. Lancet Neurol. 2005;4:727–34. - PubMed
    1. Chalela JA, Kidwell CS, Nentwich LM, Luby M, Butman JA, et al. Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet. 2007;27:293–98. - PMC - PubMed

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