Detection of Brain Tau Pathology in Down Syndrome Using Plasma Biomarkers

This cross-sectional study analyzes data from participants in the Alzheimer’s Biomarker Consortium–Down Syndrome study to determine which plasma biomarker combinations can accurately detect Alzheimer disease–related tau pathological brain changes in individuals with Down syndrome.


Participants
In this study, we included 300 participants with DS and 37 non-DS sibling controls with baseline blood samples who were enrolled in the Alzheimer's Biomarker Consortium-Down Syndrome study (ABC-DS; https://www.nia.nih.gov/research/abc-ds) between Jul 13, 2016, and Jan 15, 2019 at multiple enrolling sites. 1 ABC-DS is conducted under IRB approved protocols with participants and/or caregivers providing written informed consent to participate.
Participants with DS received a diagnosis of cognitively stable (DS-CS, n=212), mild cognitive impairment (DS-MCI, n=40), Alzheimer's disease dementia (DS-dementia, n=33) or were classified as "unable to determine" (n=15).Diagnosis was determined by clinical consensus conferences generally in accordance with the recommendations of the AAMR-IASSID Working Group for the Establishment of Criteria for the Diagnosis of Dementia in Individuals with Developmental Disability and was based on medical, clinical, and cognitive testing considered in reference to baseline IQ and any recent major life transitions or events.Neither genetic findings (e.g., APOE status) nor biomarker results were available to the consensus team members.Participants were classified as cognitively stable if they were without cognitive or functional decline, beyond what would be expected with adult aging, per se.Participants were classified as having MCI if they demonstrated some cognitive and/or functional decline over and above what would be expected with aging per se, but not severe enough to indicate the presence of dementia.Participants were categorized as having Alzheimer's disease dementia if there was evidence of substantial progressive declines in cognitive functioning and daily living skills.An "unable to determine" category was utilized to indicate that declines were observed but could be caused by significant life circumstance (e.g., staff changes, family death) or conditions unrelated to AD (e.g., severe sensory loss, poorly resolved hip fracture, psychiatric diagnosis primarily depression).
Cognitive function was evaluated with the Down Syndrome Mental Status Examination (DS-MSE) 2 and Cued Recall Test, 3 two measures included in a larger neuropsychological battery (see Handen et al., 1 for full battery).These measures were selected because they are designed for use with individuals varying widely in their premorbid levels of intellectual functioning and have been shown to be sensitive to early signs of prodromal AD in adults with DS. [4][5][6] DS participants classified as "unable to determine" were included in the analysis examining associations of plasma biomarkers with Aβ-PET, tau-PET and cognition.

Plasma sampling and analysis
Plasma P-tau217 concentration was measured according to the published protocols using immunoassay on a Mesoscale Discovery platform developed by Lilly Research Laboratories. 7,8Briefly, biotinylated-IBA493 was used as a capture antibody and SULFO-TAG-4G10-E2 (anti-Tau) as the detector and samples were diluted 1:2.The assay was calibrated with a synthetic P-tau217 peptide.Plasma GFAP concentration was quantified using Simoa kit (Quanterix, Lexington, MA, USA) according to the manufacturer's instructions.P-tau217 and GFAP were analyzed in plasma samples from 300 participants with DS and 37 non-DS sibling controls at the Clinical Memory Research Unit, Lund University (Sweden); one case with insufficient plasma volume for both P-tau217 and GFAP assays was excluded from the study.All measurements were above the lower limit of detection of the assay (P-tau217, 0.10 pg/ml; GFAP, 0.26 pg/ml).The inter-assay coefficient of variation (CV) for 3-4 quality control samples included in every run were 14.2% and 17.4% in the P-tau217 and GFAP assays, respectively.The intra-assay CV was 5.7% in the P-tau217 assay and 4.0% in the GFAP assay.Plasma Aβ42/Aβ40, NfL and T-tau were analyzed with Simoa 4-plex kit (Quanterix, Lexington, MA, USA) in plasma samples from 258 participants with DS and 28 non-DS sibling controls at the Institute for Translational Research, University of North Texas Health Science Center (Fort Worth, USA) as previously described. 9l samples were analyzed by staff blinded to the clinical and imaging data.

Tau and Aβ PET imaging and processing
Out of 337 participants, 233 (213 DS and 30 non-DS sibling controls) had [ 11 C]Pittsburgh Compound-B (PiB) PET scans and 154 (119 DS and 35 non-DS sibling controls) underwent [ 18 F]AV-1451 PET imaging.PET images were acquired on a Siemens ECAT HR + (University of Wisconsin-Madison, University of Pittsburgh), Siemens 4-ring Biograph mCT (University of Pittsburgh), GE SIGNA (University of Cambridge), and GE Discovery 710 (Barrow Neurological Institute) scanners as previously described.10,11Aβ-PET imaging was performed 50-70 min after the injection of 15 mCi (target dose) of PiB (four 5-minute frames).Following Aβ-PET, tau-PET was performed 80-100 min after the injection of 10 mCi (target dose) of [ 18 F]AV-1451 (four 5-minute frames).PET frames were inspected for motion, and if necessary, re-aligned using PMOD software.Frames were averaged to form a single 50-70 minute [ 11 C]PiB image and a single 80-100 minute [ 18 F]AV-1451 image for each subject.PET images were rigidly registered to their corresponding T1-weighted magnetic resonance (MR) images.
In the AV-1451 analysis, the T1 MR images were segmented into a standard set of regions of interest (ROIs) using FreeSurfer 5.3 (FS) that were transferred to the registered PET.A specific uptake value ratio (SUVR) for each FS ROI was calculated by normalizing regional activity-concentration to cerebellar gray matter activity-concentration.Final tau measures were determined for meta-ROIs formed from groups of FS ROIs, including a temporal meta-ROI (entorhinal cortex, inferior and middle temporal cortices, fusiform gyrus, parahippocampal cortex and amygdala, approximating Braak I/ III/IV) and neocortical meta-ROI (caudal anterior cingulate gyrus, caudal middle frontal gyrus, cuneus, inferior parietal lobule, isthmus cingulate, lateral occipital gyrus, lateral orbitofrontal gyrus, lingual gyrus, medial orbitofrontal gyrus, paracentral gyrus, pars opercularis, pars orbitalis, pars triangularis, pericalcarine cortex, postcentral gyrus, posterior cingulate gyrus, precentral gyrus, precuneus, rostral anterior cingulate gyrus, rostral middle frontal gyrus, superior frontal gyrus, superior parietal lobule, superior temporal gyrus, supramarginal gyrus, frontal pole, temporal pole, transverse temporal lobe, insula, approximating Braak V/VI to capture late-stage tau pathology). 12,13An SUVR for each meta-ROI was determined as a volume weighted average of the constituent FS-ROI SUVRs.
PiB PET was quantified in terms of a global centiloid value using established methodology. 14Briefly, with the T1 MR image as an intermediary, [ 11 C]PiB images were spatially warped to the Montreal Neurological Institute 152 space (MNI152) using SPM8.A global SUVR was determined for each subject from tracer concentration in a standard cortex ROI (including anterior cingulate, frontal cortex, parietal cortex, precuneus, temporal cortex, and striatum) normalized to the concentration in a whole cerebellum ROI.Both ROIs are available at the GAAIN website (http://www.gaain.org/centiloid-project).

Associations with Tau-PET SUVR in the neocortical region
In Aβ-PET positive participants with DS (A+ DS), the association with tau-PET SUVR in the neocortical region was significant for P-tau217 (β=0.715,p<0.001, eFigure 5A) but not for GFAP (β=0.386,p=0.09, eFigure 4B).There were no significant associations between either of the two plasma biomarkers and tau-PET SUVR in the neocortical meta-ROI in A -DS.We found no associations between either NfL or T-tau with tau-PET measures (eFigures 5D-E).Higher plasma Aβ42/Aβ40 was associated with increased tau-PET signal in A+ DS (eFigure 5C), but these associations were no longer significant after excluding two outliers (data not shown).Both tau-PET and plasma biomarker measures were available in 109 participants with DS (eFigure 2A) of whom 10 (9.2%) had abnormal tau-PET signal in the neocortical region.Univariable analysis showed that P-tau217, GFAP, NfL and age were significantly associated with abnormal tau-PET and that the associations were independent of age for both P-tau217 (OR=2.32,p=0.002)) and GFAP (OR=1.59,p=0.028) (eTable 2).The best performing model among the top 4 models was the most parsimonious one including only two predictors, i.e., P-tau217 and age (AUC=0.985,CI [0.962-1.00])(eTable 3, eFigure 4B).

eTable 1 .
Demographic and clinical characteristics of study participants aged ≥35y

eFigure 1 eFigure 1 .
Aβ-PET centiloid values as a function of age in participants with DS.

CRT Models including individual plasma biomarkers β (p value), N β (p value), N
Data are from logistic regression models and ROC curve analysis with tau-PET as outcome.Models are ordered based on AIC (lower values representing better model fit).Odds ratio (p-value) of the variable included in each model are reported.For plasma biomarkers, odds ratios represent increased risk of tau-PET positivity for each SD change in biomarker value.The parsimonious models are highlighted.Data are from logistic regression models and ROC curve analysis with tau-PET status as outcome.For plasma biomarkers, odds ratios represent increased risk of Aβ-PET positivity for each SD change in biomarker value.Model selection and performance for detecting abnormal Aβ-PET in participants with DS using centiloid ≥18 cutoff for Aβ positivity Data are from logistic regression models and ROC curve analysis with tau-PET as outcome.Models are ordered based on AIC (lower values representing better model fit).Odds ratio (p-value) of the variable included in each model are reported.For plasma biomarkers, odds ratios represent increased risk of Aβ-PET positivity for each SD change in biomarker value.The parsimonious models are highlighted.Data are β (standardized coefficient) and p value from linear regression models adjusted for age, sex and the level of premorbid intellectual impairment with cognitive measures as outcomes.Associations with tau-PET in participants with DS aged ≥35y Data are from logistic regression models and ROC curve analysis with tau-PET status as outcome.For plasma biomarkers, odds ratios represent increased risk of tau-PET positivity for each SD change in biomarker value.Associations with Aβ-PET in participants with DS aged ≥35y Data are from logistic regression models and ROC curve analysis with Aβ-PET status as outcome.For plasma biomarkers, odds ratios represent increased risk of Aβ-PET positivity for each SD change in biomarker value.Associations with cognition in participants with DS aged ≥35y Data are β (standardized coefficient) and p value from linear regression models adjusted for age, sex and the level of premorbid intellectual impairment with cognitive measures as outcomes.