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. Author manuscript; available in PMC: 2025 May 1.
Published in final edited form as: Lancet Neurol. 2024 May;23(5):500–510. doi: 10.1016/S1474-4422(24)00084-X

Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer’s disease: a cross-sectional study

Julie K Wisch 1, Nicole S McKay 2, Anna H Boerwinkle 3, James Kennedy 4, Shaney Flores 5, Benjamin L Handen 6, Bradley T Christian 7, Elizabeth Head 8, Mark Mapstone 9, Michael S Rafii 10, Sid E O’Bryant 11, Julie C Price 12, Charles M Laymon 13, Sharon J Krinsky-McHale 14, Florence Lai 15, H Diana Rosas 16,17, Sigan L Hartley 18, Shahid Zaman 19, Ira T Lott 20, Dana Tudorascu 21, Matthew Zammit 22, Adam M Brickman 23, Joseph H Lee 24,25, Thomas D Bird 26, Annie Cohen 27, Patricio Chrem 28, Alisha Daniels 29, Jasmeer P Chhatwal 30, Carlos Cruchaga 31,32, Laura Ibanez 33, Mathias Jucker 34, Celeste M Karch 35,36,37, Gregory S Day 38, Jae-Hong Lee 39, Johannes Levin 40,41,42, Jorge Llibre-Guerra 43, Yan Li 44,45, Francisco Lopera 46, Jee Hoon Roh 47, John M Ringman 48, Charlene Supnet-Bell 49, Christopher H van Dyck 50, Chengjie Xiong 51, Guoqiao Wang 52,53, John C Morris 54, Eric McDade 55, Randall J Bateman 56, Tammie L S Benzinger 57, Brian A Gordon 58,*, Beau M Ances 59,*; Alzheimer’s Biomarker Consortium-Down syndrome†; Dominantly Inherited Alzheimer Network†
PMCID: PMC11209765  NIHMSID: NIHMS1988989  PMID: 38631766

Summary

Background

In people with genetic forms of Alzheimer’s disease, such as in Down syndrome and autosomal-dominant Alzheimer’s disease, pathological changes specific to Alzheimer’s disease (ie, accumulation of amyloid and tau) occur in the brain at a young age, when comorbidities related to ageing are not present. Studies including these cohorts could, therefore, improve our understanding of the early pathogenesis of Alzheimer’s disease and be useful when designing preventive interventions targeted at disease pathology or when planning clinical trials. We compared the magnitude, spatial extent, and temporal ordering of tau spread in people with Down syndrome and autosomal-dominant Alzheimer’s disease.

Methods

In this cross-sectional observational study, we included participants (aged ≥25 years) from two cohort studies. First, we collected data from the Dominantly Inherited Alzheimer’s Network studies (DIAN-OBS and DIAN-TU), which include carriers of autosomal-dominant Alzheimer’s disease genetic mutations and non-carrier familial controls recruited in Australia, Europe, and the USA between 2008 and 2022. Second, we collected data from the Alzheimer Biomarkers Consortium–Down Syndrome study, which includes people with Down syndrome and sibling controls recruited from the UK and USA between 2015 and 2021. Controls from the two studies were combined into a single group of familial controls. All participants had completed structural MRI and tau PET (18F-flortaucipir) imaging. We applied Gaussian mixture modelling to identify regions of high tau PET burden and regions with the earliest changes in tau binding for each cohort separately. We estimated regional tau PET burden as a function of cortical amyloid burden for both cohorts. Finally, we compared the temporal pattern of tau PET burden relative to that of amyloid.

Findings

We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2], 74 [54%] male, and 63 [46%] female), 49 individuals with autosomal-dominant Alzheimer’s disease (mean age 43·9 years [11·2], 22 [45%] male, and 27 [55%] female), and 85 familial controls, pooled from across both studies (mean age 41·5 years [12·1], 28 [33%] male, and 57 [67%] female), who satisfied the PET quality-control procedure for tau-PET imaging processing. 134 (98%) people with Down syndrome, 44 (90%) with autosomal-dominant Alzheimer’s disease, and 77 (91%) controls also completed an amyloid PET scan within 3 years of tau PET imaging. Spatially, tau PET burden was observed most frequently in subcortical and medial temporal regions in people with Down syndrome, and within the medial temporal lobe in people with autosomal-dominant Alzheimer’s disease. Across the brain, people with Down syndrome had greater concentrations of tau for a given level of amyloid compared with people with autosomal-dominant Alzheimer’s disease. Temporally, increases in tau were more strongly associated with increases in amyloid for people with Down syndrome compared with autosomal-dominant Alzheimer’s disease.

Interpretation

Although the general progression of amyloid followed by tau is similar for people Down syndrome and people with autosomal-dominant Alzheimer’s disease, we found subtle differences in the spatial distribution, timing, and magnitude of the tau burden between these two cohorts. These differences might have important implications; differences in the temporal pattern of tau accumulation might influence the timing of drug administration in clinical trials, whereas differences in the spatial pattern and magnitude of tau burden might affect disease progression.

Funding

None.

Introduction

Down syndrome is caused by triplication of chromosome 21, on which the APP gene is located.1,2 Mutations in this gene are implicated in rare forms of genetic Alzheimer’s disease, such as autosomal-dominant Alzheimer’s disease.3 While the exact mechanism differs between these two cohorts in the expression of the APP protein, individuals with these genetic forms of Alzheimer’s disease are also characterised by their young age of symptom onset, which typically occurs in their mid-40s.

Alzheimer’s disease is characterised by the accumulation of amyloid plaques and neurofibrillary tau tangles during a long asymptomatic phase.4,5 The National Institute on Aging and Alzheimer’s Association Research Framework specifies that tau tangles form after a substantial concentration of amyloid plaque has accumulated in the brain.5 Thus, tau is temporally more proximate to changes in cognition than amyloid.6,7

The development of PET radiotracers facilitates in-vivo observation of Alzheimer’s disease neuropathological changes. Furthermore, researchers can now approximate when preclinical changes in amyloid and tau might occur in people with Alzheimer’s disease based on studies in individuals with autosomal-dominant Alzheimer’s disease, using estimated years until symptom onset (EYOs). Previous studies in autosomal-dominant Alzheimer’s disease suggest that amyloid accumulates approximately 15–20 years before symptom onset, whereas tau increases closer to 6 years before cognitive impairment.8 In people with Down syndrome, amyloid accumulates about 17 years before symptom onset,2 and evidence suggests that tau might begin to accumulate before people with Down syndrome become amyloid positive.9

In view of the close relationship between tau and cognitive changes, tau represents a potentially powerful target in clinical trials. Research studies using tau PET as a clinical endpoint will require careful definition of the regions of interest in the brain, to maximise study power. Previous work developing summary measures of tau from PET imaging has primarily been done in cohorts with sporadic Alzheimer’s disease and regularly includes regions from the medial temporal lobe and the amygdala.10 Early evidence in autosomal-dominant Alzheimer’s disease and Down syndrome suggested that these brain regions were affected. However, additional areas of the brain, such as the precuneus, are affected in autosomal-dominant Alzheimer’s disease.11 Within a small cohort of asymptomatic people with Down syndrome (n=12), tau was observed in the lateral temporal, precuneus, and posterior cingulate regions.12 A neuropathological study13 in people with Down syndrome has shown the presence of early tau accumulation in the entorhinal cortex, hippocampus, and subcortical regions, with subsequent tau spread to the occipital lobe. In autosomal-dominant Alzheimer’s disease, tau pathological changes are primarily identified in the medial temporal lobe and hippocampus.11,14

The spatial distribution of tau is not the only important consideration when assessing the severity of Alzheimer’s disease pathophysiology; the magnitude of tau deposition is strongly associated with both atrophy15 and symptom severity.11 A comprehensive assessment of tau in the brain requires consideration of the timing of tau assessment, the spatial distribution of tau, and the overall magnitude of tau burden.6,7

We aimed to characterise the spatial pattern of tau as well as the magnitude of tau pathological changes relative to cortical amyloid burden in people with Down syndrome and people with autosomal-dominant Alzheimer’s disease. We also assessed temporal changes in tau PET relative to amyloid changes in these two genetic forms of Alzheimer’s disease.

Methods

Study design and participants

We did a cross-sectional study to investigate in-vivo tau expression using PET imaging in people with Down syndrome and individuals with autosomal-dominant Alzheimer’s disease. We obtained data for our study from two cohort studies. The Alzheimer’s Biomarker Consortium–Down syndrome (ABC–DS) study recruits adults aged at least 25 years who have Down syndrome, and sibling controls, from ten sites in the UK and USA. The Dominantly Inherited Alzheimer Network (DIAN) studies, DIAN-TU and DIAN-OBS, include adults aged at least 18 years who have a family history of a genetic mutation for autosomal-dominant Alzheimer’s disease. Participants in DIAN are recruited from 21 sites in Australia, Europe, and the USA.16

For inclusion in our study, all participants had to be at least 25 years old and have both structural MRI and tau PET data available. Although ABC–DS and DIAN both collect longitudinal data, we used data from baseline visits only for our study. Data from the ABC–DS study were obtained from the data release 1 dataset (recruited 2015–21), and data from DIAN-OBS were from the datafreeze 16 dataset (recruited 2008–22). Baseline scans from DIAN-TU were also included (recruited 2012–20).

Data obtained via DIAN-OBS and DIAN-TU were independently collected (study protocols were harmonised; co-enrolment was possible) and merged for our study. Sibling controls from ABC–DS and familial controls from DIAN-OBS were pooled. Gender was self-identified and reported. We obtained informed consent directly from participants when possible. When not possible, assent was obtained and informed consent was obtained from the participant’s legally authorised representative. Study protocols were approved by local institutional review boards of all ABC–DS and DIAN sites.

Procedures

Participants with Down syndrome in the ABC–DS study received a clinical diagnosis via consensus conference of either cognitively stable, mild cognitive impairment, dementia due to Alzheimer’s disease, or no consensus. Consensus conference was based on neuropsychological assessments, medical and psychiatric history, and interviews with informants.2 Individuals with diagnoses of mild cognitive impairment or dementia due to Alzheimer’s disease were defined as symptomatic, and those diagnosed as cognitively stable were considered asymptomatic.17 Sibling controls in ABC–DS were deemed asymptomatic but were not clinically assessed. Participants with autosomal-dominant Alzheimer’s disease in DIAN had a clinical dementia assessment using the Clinical Dementia Rating scale.18 A Clinical Dementia Rating score of 0 indicated no impairment (asymptomatic), and a Clinical Dementia Rating score of 0·5, 1, 2, or 3 indicated symptomatic impairment of increasing severity. Familial controls recruited via DIAN received clinical assessment and were classified as asymptomatic.

On recruitment to DIAN or ABC–DS, DNA samples were collected, which were genotyped using KASP assays (LGC Genomics [Beverly, MA, USA]) for ABC–DS participants and a TaqMan assay for DIAN participants (Applied Biosystems [Waltham, MA, USA]). Individuals were classified as APOE ε4 positive if they had at least one copy of the ε4 allele.

MRI was done in ABC–DS and DIAN on 3T scanners (either Siemens [Malvern, PA USA] or GE [Florence, SC, USA]). For our study, we segmented T1-weighted scans with FreeSurfer (version 5.3) using the Desikan–Killiany atlas. Processing of MRI scans was identical for both cohorts, and processing was done at Washington University in St Louis (St Louis, MO, USA).2

Amyloid PET scans were obtained using [11C]-Pittsburgh compound B, using previously described methods.2,10 For our study, we processed images using the PET Unified Pipeline.19,20 We smoothed images to achieve a spatial resolution of 8 mm, minimising inter-scanner differences.20,21 We used a standard image-registration technique to correct for motion,22,23 on the basis of corresponding structural images. Regions of interest were based on MRI parcellation. We assessed the standardised uptake value ratio (SUVR) in each region using the 50–70-min post-injection window in participants with Down syndrome,24 and the 40–70-min post-injection window for participants with autosomal-dominant Alzheimer’s disease.24 We calculated a summary value for cortical amyloid burden25 as the arithmetic mean of the partial volume-corrected SUVR of the precuneus, prefrontal cortex, gyrus rectus, and lateral temporal regions.26 We transformed SUVR summary values to centiloids using previously published methods.2,25

Tau PET imaging was done with 18F-flortaucipir for all studies. We assessed SUVRs using the 80–100-min post-injection window. For all tracers, the whole cerebellum was the reference region,24 with partial volume correction applied via a geometric transfer matrix approach.26,27

Statistical analysis

We compared participant demographics using the R package tableone. We derived p values from χ2 tests of categorical variables, and we assessed continuous variables using ANOVA.

To assess the timing of tau elevation across the brain, we selected regions of interest, identified by structural MRI, that are commonly affected by Alzheimer’s disease. We did exploratory analyses, comparing the mean regional tau burden for symptomatic individuals from the ABC–DS and DIAN cohorts for all bilateral regions in the Desikan–Killiany atlas. The ratio of tau burden in Down syndrome to autosomal-dominant Alzheimer’s disease was nearest to 1 for the fusiform gyrus, suggesting that the magnitude of tau burden in this region is roughly equivalent for both groups. We therefore present information on tau burden in the fusiform gyrus as well as in the entorhinal cortex, a region affected in all forms of Alzheimer’s disease,13 and the precuneus, a region affected particularly early in autosomal-dominant Alzheimer’s disease.11

To identify the spatial pattern of tau expression, we applied Gaussian mixture modelling using the R package mclust, similar to previously described methods.28 We applied this approach separately to the autosomal-dominant Alzheimer’s disease and Down syndrome cohorts, including asymptomatic and symptomatic individuals, as well as the pooled controls. Briefly, for each region from the Desikan–Killiany atlas, we identified either one or two distributions with the best fit assessed by Bayesian information criteria. We visually inspected distributions for normality. If the fit yielded two distributions (as identified in nearly all cases), the lower distribution was referred to as normal and the higher distribution as elevated. We subsequently ordered regions on the basis of the frequency of elevated SUVRs.28 We then grouped regions into stages, where each stage was characterised by having the same number of elevated regions. Each participant was staged on the basis of the spatial spread. Individuals in stage 1 had elevated tau burden in at least half of the regions identified during the modelling process as stage 1. Individuals in stage 2 met the criteria for stage 1 and had elevated tau burden in at least half of the regions identified during the modelling process as stage 2. Equivalent requirements persisted for stage 3 and stage 4. A fundamental assumption of this analysis is that regions with the greatest frequency of elevated SUVR were regions with the earliest accumulation of tau. After staging each participant, we stratified demographics by stage, using one-way ANOVA for continuous variables and the χ2 test for categorical variables.

We assessed regional tau burden as a function of cortical amyloid burden for selected regions. We applied generalised additive models (GAMs) with regional tau SUVR chosen as the response variable and group membership (Down syndrome, autosomal dominant Alzheimer’s disease, or controls), centiloids, and their interaction as regressors. We visually inspected model residuals using the gam.check() function in mgcv. We generated spline-based CIs, because this is a more conservative approach than point-based CIs to interpret GAMs. We applied GAMs rather than linear models because a non-linear relationship between tau and amyloid burden has been hypothesised.29 We also used models that included participant gender and APOE ε4 status, but these covariates were non-significant and overall model fit (as assessed by adjusted R2) did not change meaningfully, so these models were not subsequently presented. Furthermore, for people with Down syndrome only, we compared models with and without site (as a mechanism to account for potential scanner effects). We found no difference in model fit and site was a non-significant covariate, so these results were not included.

Finally, we identified the timing of significant elevations in regional tau in relation to amyloid for Down syndrome and autosomal dominant Alzheimer’s disease using EYOs. EYOs were defined according to previous literature.2,30 Briefly, we calculated EYO as the individual’s age at scan minus the parental age of symptom onset for individuals with autosomal-dominant Alzheimer’s disease. For individuals with Down syndrome, we subtracted the participant’s age at scan from 52·5 years, consistent with previous work.2 We fitted GAMs to amyloid and tau burdens within select regions of interest, with group membership (Down syndrome, autosomal-dominant Alzheimer’s disease, or familial control) as an interaction term. Using the spline-based CIs, we identified the EYOs when the amyloid and tau burden in participants with Down syndrome and autosomal dominant Alzheimer’s disease was significantly greater than in familial controls. We considered pathological burden to be significantly elevated when the spline-based CI of the model fitted for individuals with either Down syndrome or autosomal-dominant Alzheimer’s disease no longer overlapped with the spline-based CI for the model for the familial controls.

Results

We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2]), 49 individuals with autosomal-dominant Alzheimer’s disease (mean age 43·9 years [11·2]), and 85 familial controls (mean age 41·5 years [12·1]) that satisfied the PET quality-control procedure for tau-PET imaging processing. Nearly all individuals (134 [98%] with Down syndrome, 44 [90%] with autosomal dominant Alzheimer’s disease, and 77 [91%] controls) completed an amyloid PET scan within 3 years of tau PET imaging.

Overall, people with Down syndrome were more frequently male compared with participants with autosomal dominant Alzheimer’s disease (table). A higher percentage of participants with autosomal-dominant Alzheimer’s disease (17 [35%]) were symptomatic compared with people with Down syndrome (10 [7%]; p<0·0001; table). The APOE ε4 allele was more prevalent in individuals with autosomal-dominant Alzheimer’s disease (20 [41%]) than in those with Down syndrome (27 [20%]; p=0·015; table).

Table:

Participant demographics

Familial controls (n=85) Down syndrome (n=137) Autosomal-dominant Alzheimer's disease (n=49) p value
Age, years 41⋅5 (12⋅1) 38⋅5 (8⋅2) 43⋅9 (11⋅2) 0⋅013
Gender
 Female 57 (67%) 63 (46%) 27 (55%) 0⋅0091
 Male 28 (33%) 74 (54%) 22 (45%) ..
Cognitive status <0⋅0001
 Asymptomatic 85 (100%) 120 (88%) 32 (65%) ..
 Symptomatic 0 10 (7%) 17 (35%) ..
 No consensus 0 7 (5%) 0 ..
APOE ε4 positive 25 (29%) 27 (20%) 20 (41%) 0⋅015
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Data are n (%) or mean (SD).

Application of the Gaussian mixture modelling-based staging approach (appendix p 2) identified specific spatial patterns of tau spread for Down syndrome and autosomal dominant Alzheimer’s disease. In Down syndrome, the most common regions with elevated tau burden were subcortical areas (amygdala, hippocampus, and pallidum) and the entorhinal cortex (figure 1A, B). For autosomal dominant Alzheimer’s disease, the most common region with elevated tau burden was the fusiform gyrus, followed by the medial temporal lobe. Subcortical tau was observed in a later Gaussian mixure model-derived stage (figure 1C, D).

Figure 1: Tau spatial staging in people with Down syndrome and autosomal-dominant Alzheimer’s disease.

Figure 1:

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Spatial staging shows the magnitude of tau spread in regions of the brain. Stage 1 indicates little spread across the brain, and stage 4 indicates that tau has spread to virtually all parts of the brain. The frequency of abnormally increased tau PET is shown for people with Down syndrome (A) and autosomal-dominant Alzheimer’s disease (C). The spatial pattern of abnormally increased tau PET across the brain is shown for people with Down syndrome (B) and autosomal-dominant Alzheimer’s disease (D). Within people with Down syndrome, the earliest changes in tau burden occurred subcortically (amygdala, hippocampus, and pallidum) and were followed by changes in the entorhinal cortex (B). With autosomal-dominant Alzheimer’s disease participants, the earliest changes in tau burden were seen in the medial temporal lobe with subcortical changes occurring later (D).

People with Down syndrome at higher tau stages were more likely to be older and have a symptomatic diagnosis (appendix p 3). Only one participant in the Down syndrome cohort was discordant, meaning that this individual had elevated tau PET findings in most of the regions associated with stages 2–3, but did not have elevated tau PET findings in most stage 1 regions (appendix p 3). There was no discordance in the autosomal-dominant Alzheimer’s disease cohort, and the frequency of symptomatic diagnosis increased with increasing disease stage (appendix p 4). Generally, the magnitude of the tau burden for individuals with advanced amyloidosis was greater for individuals with Down syndrome compared with those with autosomal-dominant Alzheimer’s disease (figure 2; appendix p 5).

Figure 2: Tau PET burden versus cortical amyloid burden for selected brain regions in people with Down syndrome and autosomal-dominant Alzheimer’s disease.

Figure 2:

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Three representative regions of interest—the entorhinal cortex (A), precuneus (B), and fusiform gyrus (C)—showed a similar pattern for tau burden (measured as SUVR) as a function of amyloid burden (measured as centiloids). For individuals who had very high amyloid burden, the tau PET burden was greater for people with Down syndrome than for those with autosomal-dominant Alzheimer’s disease. Individual dots show individual tau concentrations relative to amyloid and the centred line represents the generalised additive model fit. Shaded regions indicate 95% CIs. SUVR=standardised uptake value ratio.

Finally, we investigated potential differences in the timing of changes in tau burden in relation to amyloid for both Down syndrome and autosomal dominant Alzheimer’s disease (figure 3). Cortical amyloid was significantly higher for people with Down syndrome at EYOs −12 compared with sibling controls, and cortical amyloid was significantly higher for autosomal-dominant Alzheimer’s disease participants at EYOs −17 compared with familial controls. For most regions of interest, we found tau PET changes only after amyloid PET became elevated; however, for people with Down syndrome, tau in the entorhinal cortex was significantly elevated before significant elevation of cortical amyloid (figure 3A, D). Tau PET was greater than in sibling controls at EYOs −15 in the entorhinal cortex, EYOs −6 in the precuneus, and EYOs −10 in the fusiform gyrus for people with Down syndrome (figure 3A, B, C). For individuals with autosomal-dominant Alzheimer’s disease, tau was greater than in familial controls at EYOs −12 in the entorhinal cortex, EYOs −8 in the precuneus, and EYOs −7 in the fusiform gyrus (figure 3D, E, F). In the regions specifically presented here, as well as throughout the brain, amyloid and tau PET elevation occurred in regions more closely together in Down syndrome compared with in autosomal-dominant Alzheimer’s disease (figure 3).

Figure 3: Temporal pattern of pathological changes in amyloid and tau within selected brain regions in people with Down syndrome and autosomal-dominant Alzheimer’s disease.

Figure 3:

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Plots show differences in tau burden (measured as SUVR) between familial controls and people with Down syndrome (A, B, C) and autosomal-dominant Alzheimer’s disease (D, E, F) in three representative regions of interest—the entorhinal cortex (A, D), precuneus (B, E), and fusiform gyrus (C, F). For both Down syndrome and autosomal-dominant Alzheimer’s disease, the timepoint when the burden of amyloid or tau was greater than that of familial controls was calculated with regards to EYOs. When the CI crossed 0, participants with Down syndrome (A, B, and C) and those with autosomal-dominant Alzheimer’s disease (D, E, and F) had greater pathological burden of amyloid or tau compared with familial controls. Cortical amyloid was significantly higher for people with Down syndrome compared with familial controls at EYOs −12 (A, B, C), and at EYOs −17 for people with autosomal-dominant Alzheimer’s disease compared with familial controls (D, E, F). For all regions considered, people with Down syndrome accumulated amyloid and tau PET more closely together temporally compared with people with autosomal dominant Alzheimer’s disease. EYO=estimated years until symptom onset. SUVR=standardised uptake value ratio.

Discussion

In our cross-sectional study, we noted subtle differences in spatial distribution between people with Down syndrome and those with autosomal-dominant Alzheimer’s disease, and we found larger differences with regards to timing and magnitude of tau PET. Spatially, we recorded increases in subcortical tau at an earlier EYO in people with Down syndrome, although other early changes in the medial temporal lobe were seen in people with both Down syndrome and autosomal-dominant Alzheimer’s disease. Temporally, increases in tau were more closely coupled with increases in cortical amyloid in people with Down syndrome than in those with autosomal-dominant Alzheimer’s disease. In terms of magnitude, tau burden was greater for a given concentration of cortical amyloid across the brain in people with Down syndrome compared with those with autosomal-dominant Alzheimer’s disease.

We used a previously published spatial-spreading method28 and showed that the regions of earliest change in people with Down syndrome were primarily within subcortical regions and the entorhinal cortex. These results corroborate autopsy findings.13 The disease stages identified aligned closely with Braak stages, whereby Braak stage I–II regions were included within disease stage 1, Braak stage III regions were included in stages 1–2, and Braak stage VI regions were included in stage 4.31 Tau in the medial temporal lobe was also observed at the earliest stages of pathological development in people with autosomal-dominant Alzheimer’s disease. Consistent with previous findings,11 we found early changes in the precuneus and the parietal lobe in autosomal-dominant Alzheimer’s disease. Although autosomal-dominant Alzheimer’s disease and Down syndrome are both genetic forms of Alzheimer’s disease, in this study Down syndrome was not associated with early pathological changes in the precuneus, suggesting that changes within this region are more specific to autosomal-dominant Alzheimer’s disease.

Excluding subcortical changes, the general patterns of spatial spread were similar for individuals with autosomal-dominant Alzheimer’s disease and Down syndrome. Although similar patterns were observed, the autosomal-dominant Alzheimer’s disease cohort had a greater prevalence of Alzheimer’s disease symptoms (35%) than did the Down syndrome cohort (7%). This finding might affect the interpretation of the spatial-spreading model, as thresholds for elevated tau might be different between cohorts. The number of individuals with elevated tau in a given region should not be compared across cohorts; however, the relative position of regions within cohort is a useful heuristic for understanding the spread of tau.

Review of participant demographics by tau stage provided additional confidence in the validity of the Gaussian mixture modelling-based approach. Only one participant with Down syndrome was discordant with the analytically derived tau staging process. This participant had mosaicism, which might affect expression of tau and, thus, account for the discrepancy. For the remaining participants, an increase in mean age and symptom frequency was seen with increasing disease stage. This finding was generally true for autosomal dominant Alzheimer’s disease, although participants with stage-4 tau spread were younger than participants with stage-2–3 tau spread. This finding supports existing literature that suggests that pathology is more severe for young individuals with cognitive impairment,11 although we found few individuals with substantial tau burden within the autosomal dominant Alzheimer’s disease cohort.

The prevalence of subcortical tau in people with Down syndrome has important implications for both clinical trials and the mechanistic understanding of the spread of tau. In clinical trials for individuals with Down syndrome, development of a summary tau outcome measure specific to Down syndrome might be advantageous, given its distinct pattern compared with autosomal-dominant Alzheimer’s disease. Furthermore, because the pattern of tau spread is somewhat regular and predictable, it is likely to be associated with a biological process. The nature of this biological process is unclear; however, understanding the source of tau spread is crucial for understanding the development of Alzheimer’s disease. Work investigating network-based models of tau spread as well as studies considering gene expression should consider early subcortical tau deposition in Down syndrome.

Across the brain, we found differences in the magnitude of tau burden relative to cortical amyloid in people with Down syndrome compared with those with autosomal-dominant Alzheimer’s disease. We found wide CIs for the autosomal-dominant Alzheimer’s disease cohort. This finding probably reflects both the heterogeneity of multiple genetic mutations (APP, PSEN1, and PSEN2) as well as the greater prevalence of individuals with advanced disease in the autosomal-dominant Alzheimer’s disease cohort than in the Down syndrome cohort. The few individuals with substantial cortical amyloid burden in the Down syndrome cohort, and even fewer individuals with advanced Alzheimer’s disease, could have led to reduced variability in the estimates of tau for high amyloid, and the narrow CIs probably reflect limitations of cohort size rather than biology. Although interpretation of the CIs should be approached with caution, these general trends align with work by Zammit and colleagues9 that showed that people with Down syndrome accumulate tau rapidly in Braak stages I–III once sufficient amyloid is present. Zammit and colleagues’ investigation of tau spread relative to amyloid chronicity suggests an earlier time course for the development of tau burden compared with tau burden in sporadic Alzheimer’s disease and spurred our subsequent comparison of tau spread relative to amyloid for Down syndrome and autosomal-dominant Alzheimer’s disease.

Increases in amyloid PET occurred at approximately 12 years before symptom onset in people with Down syndrome, and they happened slightly later in individuals with autosomal-dominant Alzheimer’s disease, for whom changes occurred approximately 17 years before symptom onset. These findings are consistent with previous work reporting that regions included within the cortical amyloid summary value exceeded suprathreshold values 15–20 years before symptom onset for people with autosomal-dominant Alzheimer’s disease, but only 10–12 years before symptom onset for individuals with Down syndrome.2,19 This work extends these observations for tau PET and shows that increases in entorhinal tau PET occur either concurrently or slightly before the development of cortical amyloid in people with Down syndrome. By contrast, in individuals with autosomal-dominant Alzheimer’s disease, increases in entorhinal tau PET were seen after amyloid positivity was attained and occurred around 12 years before symptom onset. Although exact age estimates (and their subsequently derived CIs) are subject to the limitations of the size of the cohort and the relative numbers of individuals with advanced disease, qualitatively, our results suggest that the increase of amyloid occurs more closely temporally with increases in tau in individuals with Down syndrome than in those with autosomal-dominant Alzheimer’s disease.

Taken together, our results suggest that people with Down syndrome experience a delayed onset of spatially diffuse amyloid combined with a more rapid accumulation of tau burden compared with individuals with autosomal-dominant Alzheimer’s disease. The reasons for the stronger association between amyloid and tau aggregation in Down syndrome compared with autosomal-dominant Alzheimer’s disease are unknown, but they could be related to many comorbidities specific to Down syndrome, including metabolic dysregulation, oxidative stress, inflammation, or differences in genetic expression.32 Furthermore, triplication of APP in Down syndrome could lead to different downstream effects other than the several point mutations observed in autosomal-dominant Alzheimer’s disease. Future longitudinal studies of larger cohorts of people with Down syndrome and autosomal-dominant Alzheimer’s disease are needed to ascertain the specific biological mechanisms associated with the more proximate onset of amyloid and tau pathology in individuals with Down syndrome.

The more rapid onset of tau pathology relative to amyloid plaque presence, as well as the greater magnitude of tau burden relative to amyloid, suggests a shorter overall time course for Alzheimer’s disease pathological progression in people with Down syndrome compared with autosomal-dominant Alzheimer’s disease. These findings are consistent with clinical observations of a compressed time course to death seen in Down syndrome (average 4·6 years vs 11·6 years).33,34 Furthermore, these results are important for designing clinical treatment trials for Alzheimer’s disease for the Down syndrome population. As tau pathology is temporally closer to cognitive decline than amyloid,11,15 therapy with both amyloid-lowering treatments in combination with anti-tau agents might be necessary. Moreover, these interventions might need to be administered sooner in the disease course, or in a narrower window, in people with Down syndrome compared with in autosomal-dominant Alzheimer’s disease.

Our study has several key limitations. It is cross-sectional in nature, and longitudinal data would provide a greater understanding of the temporal aspects of tau propagation in Alzheimer’s disease. Furthermore, we included few individuals with severe tauopathy in both the Down syndrome and autosomal-dominant Alzheimer’s disease cohorts. Future studies should emphasise recruitment and retention of people with more advanced disease. Increased recruitment of individuals with autosomal-dominant Alzheimer’s disease would also facilitate stratification by mutation type. Co-enrolment in DIAN OBS and DIAN TU was possible, and because of anonymisation procedures, individuals in both studies could not be detected. Future work comparing individuals with autosomal-dominant Alzheimer’s disease carrying the APP mutation with individuals with Down syndrome would be of particular interest. Unfortunately, this comparison was not feasible in our analysis given the limited sample size. The inclusion of more individuals with advanced disease would also facilitate in-depth analysis of the spatial patterns of tau spread, allowing for analysis of heterogeneity. Subcortical tau, an identified area of great interest in Down syndrome, is particularly sensitive to off-target binding. Although we applied partial volume correction to mitigate the risk of tracer spillover, off-target binding remains a known issue. Neurodevelopmental differences in this brain area for individuals with Down syndrome might also complicate interpretation of findings. Future work with other tau-PET tracers that are less susceptible to off-target binding within these regions could offer promising insights into early pathological development of tau in Down syndrome. Notably, autopsy studies have also identified increased prevalence of subcortical tau in individuals with Down syndrome.13 Also, the use of 52·5 years as the time of symptom onset for all individuals with Down syndrome is an approximate estimate. Although previous work2 has investigated this proxy and found it robust to adjustments of a few years in either direction, longitudinal follow-up of asymptomatic individuals in ABC–DS that allows for exact dating of time to symptom onset is important. Finally, this cohort represents a convenience sample of individuals with genetic forms of Alzheimer’s disease and caution should be taken in generalising to a broader clinical demographic.

In conclusion, although the general progression of amyloid followed by tau is similar for Alzheimer’s disease in people with Down syndrome and in those with autosomal-dominant Alzheimer’s disease, differences in the spatial distribution, timing, and magnitude of tau burden exist for these two genetic forms of Alzheimer’s disease. Tau spread occurs earliest in subcortical regions in people with Down syndrome, but otherwise follows patterns similar to those observed in individuals with autosomal-dominant Alzheimer’s disease. Tau begins to accumulate with lower concentrations of amyloid in multiple regions across the brain in people with Down syndrome, and increases in a shorter timeframe as compared with individuals with autosomal-dominant Alzheimer’s disease. These differences might have important implications because differences in the temporal pattern for tau spread might affect the timing of drug administration in clinical trials, and differences in spatial patterning and magnitude of tau burden might affect disease progression.

Supplementary Material

MMC1

Research in context.

Evidence before this study

We searched PubMed for articles published between Jan 1, 2017, and Nov 1, 2022, relating to deposition of tau in Down syndrome and autosomal-dominant Alzheimer’s disease. Search terms included “tau”, “tau PET”, “autosomal dominant Alzheimer disease”, “Down syndrome”, and “positron emission tomography”, without language restrictions. Previous studies identified early tau presence in the hippocampus and medial temporal lobe with substantial subcortical tau involvement also present. Most previous studies were limited in sample size and were primarily ex-vivo pathology studies rather than in-vivo PET studies.

Added value of this study

The spatial spread of tau in Down syndrome and autosomal-dominant Alzheimer’s disease has historically been observed in a few people and not directly compared between cohorts. Our cross-sectional study is, to the best of our knowledge, the largest comparison of tau burden and spatial spread using PET in people with Down syndrome (n=137), autosomal-dominant Alzheimer’s disease (n=49), and familial controls (n=85).

Implications of all the available evidence

We observed similar spatial patterning but significant differences in tau burden with regards to magnitude and timing relative to amyloid burden between people with Down syndrome and those with autosomal-dominant Alzheimer’s disease. The greater increases in magnitude and distinct temporal pattern of tau accumulation in Down syndrome compared with in autosomal-dominant Alzheimer’s disease might help explain the accelerated overall time course of Alzheimer’s disease seen in people with Down syndrome. These differences might also affect when potential anti-tau agents should be administered in future clinical trials in people with Down syndrome.

Acknowledgments

Data collection and sharing for this project was supported by the ABC–DS (U01AG051406 and U01AG051412), funded by the National Institute on Aging and the Eunice Kennedy Shriver National Institute of Child Health and Human Development. We are grateful to the adults with Down syndrome, their siblings, and their families and care providers, as well as the ABC–DS research and support staff, for their invaluable contributions to this study. Data collection and sharing for this project was also supported by DIAN (U19AG032438), funded by the National Institute on Aging, the Alzheimer’s Association (SG-20–690363-DIAN), the German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, Spanish Institute of Health Carlos III, Canadian Institutes of Health Research, Canadian Consortium of Neurodegeneration and Aging, Brain Canada Foundation, and Fonds de Recherche du Québec—Santé. This manuscript has been reviewed by DIAN study investigators for scientific content and consistency of data interpretation with previous DIAN study publications. We acknowledge the altruism of the participants and their families and the contributions of the DIAN research and support staff at each of the participating sites. This research was also supported by the National Institute for Health and Care Research Cambridge Biomedical Research Centre (BRC-1215–20014*). Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health (U01AG042791, U01AG042791-S1 [Foundation for the National Institutes of Health and Accelerating Medicines Partnership], R1AG046179, R01/R56 AG053267, and R01AG053267-S1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This research was also supported by the Alzheimer’s Association, Eli Lilly and Company, F Hoffman-LaRoche, Janssen Pharmaceuticals, Avid Radiopharmaceuticals (a wholly owned subsidiary of Eli Lilly and Company), GHR Foundation, an anonymous organisation, Cogstate, and Signant. Avid Radiopharmaceuticals, Inc., a wholly owned subsidiary of Eli Lilly and Company, enabled use of the 18F-flortaucipir tracer by providing precursor, but did not provide direct funding and was not involved in data analysis or interpretation. The DIAN-TU has received funding from the DIAN-TU Pharma Consortium. We acknowledge the altruism of the participants and their families and contributions of the DIAN, DIAN Expanded Registry, and DIAN-TU research and support staff at each of the participating sites for their contributions to this study. The views expressed in this Article are those of the authors and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care. We also acknowledge the additional support provided by the Barnes-Jewish Hospital Foundation, the Charles F and Joanne Knight Alzheimer’s Research Initiative, the Hope Center for Neurological Disorders, the Mallinckrodt Institute of Radiology, the Paula and Rodger Riney fund, and the Daniel J Brennan MD fund.

Role of the funding source

There was no funding source for this study.

Footnotes

Declaration of interests

TLSB has received funding from the National Institutes of Health and Siemens; has a licensing agreement from Sora Neuroscience but receives no financial compensation; has received honoraria for lectures, presentations, speakers bureaus, or educational events from Biogen and Eisai Genetech; has served on a scientific advisory board for Biogen; holds a leadership role in other board, society, committee, or advocacy groups for the American Society for Neuroradiology (unpaid) and Quantitative Imaging Biomarkers Alliance (unpaid); and has participated in radiopharmaceuticals and technology transfers with Avid Radiopharmaceuticals, Cerveau, and LMI. EMD received support from the National Institute on Aging, an anonymous organisation, the GHR Foundation, the DIAN-TU Pharma Consortium, Eli Lilly, and F Hoffmann La-Roche; has received speaking fees from Eisai and Eli Lilly; and is on the data safety and monitoring board and advisory boards of Eli Lilly, Alector, and Alzamend. WS has received research funding from the National Institute on Aging and the Eunice Kennedy Shriver National Institute of Child Health and Human Development. JPC serves as the chair of the American Neurological Association Dementia and Aging Special Interest Group and is on the medical advisory board of Humana Healthcare. CC has received consulting fees from GSK and Alector. AMF reports personal fees from Roche Diagnostics, Araclon/Grifols, and Diadem Research and grants from Biogen, outside the submitted work. BLH has received research funding from Roche and Autism Speaks; receives royalties from Oxford University Press for book publications; and is the chair of the data safety and monitoring board for the US Department of Defense-funded study Comparative Effectiveness of EIBI and MABA (NCT04078061). BTC receives research funding from the National Institutes of Health. EH receives research funding from the National Institutes of Health and the BrightFocus Foundation. FL is supported by grants from the National Institute on Aging. HDR has received funding from the National Institutes of Health and is on the scientific advisory committee for the Hereditary Disease Foundation. J-HL has received research funding from the National Institutes of Health and the National Institute on Aging. RJP receives research funding from the National Institutes of Health and the National Institute on Aging. RJB is Director of DIAN-TU and Principal Investigator of DIAN-TU001; receives research support from the National Institute on Aging of the National Institutes of Health, DIAN-TU trial pharmaceutical partners (Eli Lilly, F Hoffmann-La Roche, Janssen, Eisai, Biogen, and Avid Radiopharmaceuticals), the Alzheimer’s Association, the GHR Foundation, an anonymous organisation, the DIAN-TU Pharma Consortium (active members Biogen, Eisai, Eli Lilly, Janssen, and F Hoffmann-La Roche/Genentech; previous members AbbVie, Amgen, AstraZeneca, Forum, Mithridion, Novartis, Pfizer, Sanofi, and United Neuroscience), the NfL Consortium (F Hoffmann-La Roche, Biogen, AbbVie, and Bristol Myers Squibb), and the Tau SILK Consortium (Eli Lilly, Biogen, and AbbVie); has been an invited speaker and consultant for AC Immune, F Hoffmann-La Roche, the Korean Dementia Association, the American Neurological Association, and Janssen; has been a consultant for Amgen, F Hoffmann-La Roche, and Eisai; and has submitted the US non-provisional patent application named Methods for Measuring the Metabolism of CNS Derived Biomolecules In Vivo (13/005,233 [RJB and DH]) and a provisional patent application named Plasma Based Methods for Detecting CNS Amyloid Deposition (PCT/UC2018/030518 [VO and RJB]). BMA receives research funding from the National Institutes of Health and has a patent (Markers of Neurotoxicity in CAR T Patients). MSR has received consulting fees from AC Immune, Embic, and Keystone Bio and has received research support from the National Institutes of Health, Avid, Baxter, Eisai, Elan, Genentech, Janssen, Lilly, Merck, and Roche. JHR has received funding from the Korea Dementia Research Project through the Korea Dementia Research Center, funded by the Ministry of Health & Welfare and the Ministry of Science and ICT, South Korea (HU21C0066). All other authors declare no competing interests.

Contributor Information

Julie K Wisch, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Nicole S McKay, Department of Radiology, Washington University in St Louis, St Louis, MO, USA.

Anna H Boerwinkle, McGovern Medical School, University of Texas in Houston, Houston, TX, USA.

James Kennedy, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Shaney Flores, Department of Radiology, Washington University in St Louis, St Louis, MO, USA.

Benjamin L Handen, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.

Bradley T Christian, Department of Medical Physics and Psychiatry, University of Wisconsin–Madison, Madison, WI, USA.

Elizabeth Head, Department of Pathology, Gillespie Neuroscience Research Facility, University of California, Irvine, CA, USA.

Mark Mapstone, Department of Neurology, University of California Irvine School of Medicine, Irvine, CA, USA.

Michael S Rafii, Alzheimer’s Therapeutic Research Institute, Keck School of Medicine of USC, Los Angeles, CA, USA.

Sid E O’Bryant, Institute for Translational Research Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA.

Julie C Price, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.

Charles M Laymon, Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.

Sharon J Krinsky-McHale, Department of Psychology, New York State Institute for Basic Research in Developmental Disabilities, New York, NY, USA.

Florence Lai, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.

H Diana Rosas, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.

Sigan L Hartley, Waisman Center, University of Wisconsin–Madison, Madison, WI, USA.

Shahid Zaman, Cambridge Intellectual and Developmental Disabilities Research Group, University of Cambridge, Cambridge, UK.

Ira T Lott, Department of Pediatrics, University of California Irvine School of Medicine, Irvine, CA, USA.

Dana Tudorascu, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.

Matthew Zammit, Department of Medical Physics and Psychiatry, University of Wisconsin–Madison, Madison, WI, USA.

Adam M Brickman, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.

Joseph H Lee, Department of Epidemiology, Columbia University Irving Medical Center, New York, NY, USA; Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.

Thomas D Bird, Department of Neurology, University of Washington, Seattle, WA, USA.

Annie Cohen, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.

Patricio Chrem, Centro de Memoria y Envejecimiento, Buenos Aires, Argentina.

Alisha Daniels, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Jasmeer P Chhatwal, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.

Carlos Cruchaga, Hope Center for Neurological Disorders, Washington University in St Louis, St Louis, MO, USA; Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA.

Laura Ibanez, Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA.

Mathias Jucker, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.

Celeste M Karch, Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA; German Center for Neurodegenerative Diseases, Tübingen, Germany.

Gregory S Day, Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA.

Jae-Hong Lee, Department of Neurology, University of Ulsan College of Medicine, Asian Medical Center, Seoul, South Korea.

Johannes Levin, Department of Neurology, LMU University Hospital, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases, site Munich, Munich, Germany; Munich Cluster for Systems Neurology, Munich, Germany.

Jorge Llibre-Guerra, Hope Center for Neurological Disorders, Washington University in St Louis, St Louis, MO, USA.

Yan Li, Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA.

Francisco Lopera, Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia.

Jee Hoon Roh, Departments of Physiology and Neurology, Korea University College of Medicine, Seoul, South Korea.

John M Ringman, Alzheimer’s Therapeutic Research Institute, Keck School of Medicine of USC, Los Angeles, CA, USA.

Charlene Supnet-Bell, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Christopher H van Dyck, School of Medicine, Yale University, New Haven, CT, USA.

Chengjie Xiong, Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA.

Guoqiao Wang, Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA.

John C Morris, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Eric McDade, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Randall J Bateman, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Tammie L S Benzinger, Department of Radiology, Washington University in St Louis, St Louis, MO, USA.

Brian A Gordon, Department of Radiology, Washington University in St Louis, St Louis, MO, USA.

Beau M Ances, Department of Neurology, Washington University in St Louis, St Louis, MO, USA.

Data sharing

The data used in this analysis are available on request from the respective studies (ABC–DS and DIAN), provided data-request applications are approved by the committees of the studies. The data request application is available for ABC–DS at https://pitt.co1.qualtrics.com/jfe/form/SV_cu0pNCZZlrdSxUN. Data for DIAN can be requested from DIAN-OBS at https://dian.wustl.edu/our-research/for-investigators/dian-observational-study-investigatorresources/data-request-form/ and DIAN-TU at https://dian.wustl.edu/our-research/for-investigators/diantu-investigator-resources/.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

MMC1
NIHMS1988989-supplement-MMC1.pdf (636.2KB, pdf)

Data Availability Statement

The data used in this analysis are available on request from the respective studies (ABC–DS and DIAN), provided data-request applications are approved by the committees of the studies. The data request application is available for ABC–DS at https://pitt.co1.qualtrics.com/jfe/form/SV_cu0pNCZZlrdSxUN. Data for DIAN can be requested from DIAN-OBS at https://dian.wustl.edu/our-research/for-investigators/dian-observational-study-investigatorresources/data-request-form/ and DIAN-TU at https://dian.wustl.edu/our-research/for-investigators/diantu-investigator-resources/.

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