Ursula S Sandau1, Jack T Wiedrick2, Sierra J Smith1, Trevor J McFarland1, Theresa A Lusardi3, Babett Lind4, Christina A Harrington5, Jodi A Lapidus2,6, Douglas R Galasko7, Joseph F Quinn8,9, Julie A Saugstad1. 1. Department of Anesthesiology & Perioperative Medicine, Oregon Health & Science University, Portland, OR, USA. 2. Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA. 3. Knight Cancer Institute Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA. 4. Department of Neurology, Layton Aging and Alzheimer's Center, Oregon Health & Science University, Portland, OR, USA. 5. Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR, USA. 6. Oregon Health & Science University- Portland State University School of Public Health, Portland, OR, USA. 7. Department of Neurosciences, University of California San Diego, La Jolla, CA, USA. 8. Parkinson Center and Movement Disorders Program, School of Medicine, Oregon Health & Science University, Portland, OR, USA. 9. Portland VAMC Parkinson's Disease Research, Education, and Clinical Center, Portland, OR, USA.
Abstract
BACKGROUND: Cerebrospinal fluid (CSF) microRNA (miRNA) biomarkers of Alzheimer's disease (AD) have been identified, but have not been evaluated in prodromal AD, including mild cognitive impairment (MCI). OBJECTIVE: To assess whether a set of validated AD miRNA biomarkers in CSF are also sensitive to early-stage pathology as exemplified by MCI diagnosis. METHODS: We measured the expression of 17 miRNA biomarkers for AD in CSF samples from AD, MCI, and cognitively normal controls (NC). We then examined classification performance of the miRNAs individually and in combination. For each miRNA, we assessed median expression in each diagnostic group and classified markers as trending linearly, nonlinearly, or lacking any trend across the three groups. For trending miRNAs, we assessed multimarker classification performance alone and in combination with apolipoprotein E ɛ4 allele (APOEɛ4) genotype and amyloid-β42 to total tau ratio (Aβ42:T-Tau). We identified predicted targets of trending miRNAs using pathway analysis. RESULTS: Five miRNAs showed a linear trend of decreasing median expression across the ordered diagnoses (control to MCI to AD). The trending miRNAs jointly predicted AD with area under the curve (AUC) of 0.770, and MCI with AUC of 0.705. Aβ42:T-Tau alone predicted MCI with AUC of 0.758 and the AUC improved to 0.813 (p = 0.051) after adding the trending miRNAs. Multivariate correlation of the five trending miRNAs with Aβ42:T-Tau was weak. CONCLUSION: Selected miRNAs combined with Aβ42:T-Tau improved classification performance (relative to protein biomarkers alone) for MCI, despite a weak correlation with Aβ42:T-Tau. Together these data suggest that that these miRNAs carry novel information relevant to AD, even at the MCI stage. Preliminary target prediction analysis suggests novel roles for these biomarkers.
BACKGROUND: Cerebrospinal fluid (CSF) microRNA (miRNA) biomarkers of Alzheimer's disease (AD) have been identified, but have not been evaluated in prodromal AD, including mild cognitive impairment (MCI). OBJECTIVE: To assess whether a set of validated AD miRNA biomarkers in CSF are also sensitive to early-stage pathology as exemplified by MCI diagnosis. METHODS: We measured the expression of 17 miRNA biomarkers for AD in CSF samples from AD, MCI, and cognitively normal controls (NC). We then examined classification performance of the miRNAs individually and in combination. For each miRNA, we assessed median expression in each diagnostic group and classified markers as trending linearly, nonlinearly, or lacking any trend across the three groups. For trending miRNAs, we assessed multimarker classification performance alone and in combination with apolipoprotein E ɛ4 allele (APOEɛ4) genotype and amyloid-β42 to total tau ratio (Aβ42:T-Tau). We identified predicted targets of trending miRNAs using pathway analysis. RESULTS: Five miRNAs showed a linear trend of decreasing median expression across the ordered diagnoses (control to MCI to AD). The trending miRNAs jointly predicted AD with area under the curve (AUC) of 0.770, and MCI with AUC of 0.705. Aβ42:T-Tau alone predicted MCI with AUC of 0.758 and the AUC improved to 0.813 (p = 0.051) after adding the trending miRNAs. Multivariate correlation of the five trending miRNAs with Aβ42:T-Tau was weak. CONCLUSION: Selected miRNAs combined with Aβ42:T-Tau improved classification performance (relative to protein biomarkers alone) for MCI, despite a weak correlation with Aβ42:T-Tau. Together these data suggest that that these miRNAs carry novel information relevant to AD, even at the MCI stage. Preliminary target prediction analysis suggests novel roles for these biomarkers.
Authors: Theresa A Lusardi; Jay I Phillips; Jack T Wiedrick; Christina A Harrington; Babett Lind; Jodi A Lapidus; Joseph F Quinn; Julie A Saugstad Journal: J Alzheimers Dis Date: 2017 Impact factor: 4.472
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Authors: Eric Falke; Jonathan Nissanov; Thomas W Mitchell; David A Bennett; John Q Trojanowski; Steven E Arnold Journal: Am J Pathol Date: 2003-10 Impact factor: 4.307
Authors: Ursula S Sandau; Trevor J McFarland; Sierra J Smith; Douglas R Galasko; Joseph F Quinn; Julie A Saugstad Journal: Front Cell Dev Biol Date: 2022-04-27
Authors: Theresa A Lusardi; Ursula S Sandau; Nikita A Sakhanenko; Sarah Catherine B Baker; Jack T Wiedrick; Jodi A Lapidus; Murray A Raskind; Ge Li; Elaine R Peskind; David J Galas; Joseph F Quinn; Julie A Saugstad Journal: Front Neurosci Date: 2021-09-09 Impact factor: 4.677