Browsing by Subject "Down Syndrome"
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Item Acute Regression in Down Syndrome(MDPI, 2021-08-23) Handen, Benjamin; Clare, Isabel; Laymon, Charles; Petersen, Melissa E.; Zaman, Shahid; O'Bryant, Sid E.; Minhas, Davneet; Tudorascu, Dana; Brown, Stephanie; Christian, BradleyAcute regression has been reported in some individuals with Down syndrome (DS), typically occurring between the teenage years and mid to late 20s. Characterized by sudden, and often unexplained, reductions in language skills, functional living skills and reduced psychomotor activity, some individuals have been incorrectly diagnosed with Alzheimer's disease (AD).|This paper compares five individuals with DS who previously experienced acute regression with a matched group of 15 unaffected individuals with DS using a set of AD biomarkers.|While the sample was too small to conduct statistical analyses, findings suggest there are possible meaningful differences between the groups on proteomics biomarkers (e.g., NfL, total tau). Hippocampal, caudate and putamen volumes were slightly larger in the regression group, the opposite of what was hypothesized. A slightly lower amyloid load was found on the PET scans for the regression group, but no differences were noted on tau PET.|Some proteomics biomarker findings suggest that individuals with DS who experience acute regression may be at increased risk for AD at an earlier age in comparison to unaffected adults with DS. However, due to the age of the group (mean 38 years), it may be too early to observe meaningful group differences on image-based biomarkers.Item 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, NicoleIntroduction: 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.Item 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.