Browsing by Subject "Plasma"

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Cell-Free mtDNA Quantification in Alzheimer's Patients from the Mexican American Population
(2020-05) House, Sara R.; Phillips, Nicole R.; Hodge, Lisa M.; Zascavage, Roxanne R.
Abstract Background AD is a continuous problem in the 65+ population but it is especially challenging in the Hispanic population where not only is it more prevalent but more severe than Caucasian populations. This study explores the efficacy of using peripheral blood plasma as an alternative tissue for testing as well as the usefulness for future research assisting in identifying the population structure most at risk for developing AD based upon CF-mtDNA quantity results. Materials and Methods Samples tested included a total cohort (Mexican American and Caucasian) of 177 individuals (AD=45, MCI=74, NC=58). The Mexican American subset contained 92 individuals (AD=21, MCI=53, and NC=18). Peripheral blood plasma was collected from the TARCC biobank and quantified. CF-mtDNA was then tested for significance using correlation analyses, logistic and linear regression models. Results CF-mtDNA was significantly negatively correlated with education, age, sex, and hypertensive samples in the total and Mexican American populations. The greatest difference was expected to be in CF-mtDNA quantity from NC to AD samples. Instead, the most significant difference was between MCI and NC samples. As CF-mtDNA quantity increased, the MMSE and CDRSOB scores were less impaired. Conclusion In conclusion, CF-mtDNA is an easily accessible and easily tested molecular marker of diseases that are relevant to studies for cognitive decline. Although our findings were inconsistent with current literature, they bring to light the weight of confounding factors within limited sample studies. With the completion of the full sample set associated with this study, more power is needed to overcome these issues.
Proteomic profiles of incident mild cognitive impairment and Alzheimer's disease among adults with Down syndrome
(Wiley Periodicals, Inc., 2020-05-21) O'Bryant, Sid E.; Zhang, Fan; Silverman, Wayne; Lee, Joseph H.; Krinsky-McHale, Sharon J.; Pang, Deborah; Hall, James R.; Schupf, Nicole
Introduction: We sought to determine if proteomic profiles could predict risk for incident mild cognitive impairment (MCI) and Alzheimer's disease (AD) among adults with Down syndrome (DS). Methods: In a cohort of 398 adults with DS, a total of n = 186 participants were determined to be non-demented and without MCI or AD at baseline and throughout follow-up; n = 103 had incident MCI and n = 81 had incident AD. Proteomics were conducted on banked plasma samples from a previously generated algorithm. Results: The proteomic profile was highly accurate in predicting incident MCI (area under the curve [AUC] = 0.92) and incident AD (AUC = 0.88). For MCI risk, the support vector machine (SVM)-based high/low cut-point yielded an adjusted hazard ratio (HR) = 6.46 (P < .001). For AD risk, the SVM-based high/low cut-point score yielded an adjusted HR = 8.4 (P < .001). Discussion: The current results provide support for our blood-based proteomic profile for predicting risk for MCI and AD among adults with DS.
Proteomic profiles of prevalent mild cognitive impairment and Alzheimer's disease among adults with Down syndrome
(Wiley Periodicals, Inc., 2020-04-17) Petersen, Melissa E.; Zhang, Fan; Krinsky-McHale, Sharon J.; Silverman, Wayne; Lee, Joseph H.; Pang, Deborah; Hall, James R.; Schupf, Nicole; O'Bryant, Sid E.
Introduction: We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease (AD) in the general population would apply to adults with Down syndrome (DS). Methods: Plasma samples were obtained from 398 members of a community-based cohort of adults with DS. A total of n = 186 participants were determined to be non-demented and without mild cognitive impairment (MCI) at baseline and throughout follow-up; n = 50 had prevalent MCI; n = 42 had prevalent AD. Results: The proteomic profile yielded an area under the curve (AUC) of 0.92, sensitivity (SN) = 0.80, and specificity (SP) = 0.98 detecting prevalent MCI. For detecting prevalent AD, the proteomic profile yielded an AUC of 0.89, SN = 0.81, and SP = 0.97. The overall profile closely resembled our previously published profile of AD in the general population. Discussion: These data provide evidence of the applicability of our blood-based algorithm for detecting MCI/AD among adults with DS.