Hongqian Yang1, Yaroslav Lyutvinskiy1, Sanna-Kaisa Herukka2, Hilkka Soininen2, Dorothea Rutishauser1, Roman A Zubarev3. 1. Division of Physiological Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. 2. Department of Neurology, School of Medicine, University of Eastern Finland, Kuopio, Finland. 3. Division of Physiological Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden SciLifeLab, Stockholm, Sweden.
Abstract
BACKGROUND: Patients with mild cognitive impairment (MCI) have varying risks of progression to Alzheimer's disease (AD). OBJECTIVE: To test the utility of the relative abundances of blood plasma polypeptides for predicting the risk of AD progression. METHODS: 119 blood plasma samples of patients with MCI with different outcomes (stable MCI and progressive MCI) were analyzed by untargeted, label-free shotgun proteomics. Predictive biomarkers of progressive MCI were selected by multivariate analysis, followed by cross-validation of the predictive model. RESULTS: The best model demonstrated the accuracy of ca. 79% in predicting progressive MCI. Sex differences of the predictive biomarkers were also assessed. We have identified some sex-specific protein biomarkers, e.g., alpha-2-macrogloblin (A2M), which strongly correlates with female AD progression but not with males. CONCLUSION: Significant sex bias in AD-specific biomarkers underscores the necessity of selecting sex-balanced cohort in AD biomarker studies, or using sex-specific models. Blood protein biomarkers are found to be promising for predicting AD progression in clinical settings.
BACKGROUND:Patients with mild cognitive impairment (MCI) have varying risks of progression to Alzheimer's disease (AD). OBJECTIVE: To test the utility of the relative abundances of blood plasma polypeptides for predicting the risk of AD progression. METHODS: 119 blood plasma samples of patients with MCI with different outcomes (stable MCI and progressive MCI) were analyzed by untargeted, label-free shotgun proteomics. Predictive biomarkers of progressive MCI were selected by multivariate analysis, followed by cross-validation of the predictive model. RESULTS: The best model demonstrated the accuracy of ca. 79% in predicting progressive MCI. Sex differences of the predictive biomarkers were also assessed. We have identified some sex-specific protein biomarkers, e.g., alpha-2-macrogloblin (A2M), which strongly correlates with female AD progression but not with males. CONCLUSION: Significant sex bias in AD-specific biomarkers underscores the necessity of selecting sex-balanced cohort in AD biomarker studies, or using sex-specific models. Blood protein biomarkers are found to be promising for predicting AD progression in clinical settings.
Entities:
Keywords:
Biomarkers; human blood plasma; label-free quantification; mass spectrometry
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