Synergistic associations of amyloid-β and phosphorylated tau with tau aggregation and cognitive decline in Alzheimer's disease

Abstract

Background

The pathological hallmarks of Alzheimer's disease (AD) include the amyloid-β (Aβ) plaques and phosphorylated tau (p-tau) forming neurofibrillary tangles. Understanding the pathophysiological cascade related to Aβ and tau process is crucial.

Objective

To investigate the impact of Aβ positron emission tomography (PET) and cerebrospinal fluid (CSF) p-tau on tau pathology and cognitive decline in AD.

Methods

We analyzed 319 older individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent Aβ (¹⁸F-florbetapir or ¹⁸F-florbetaben) and tau (¹⁸F-flortaucipir) PET scans, along with CSF and cognitive assessments. Aβ positivity (A+) was determined by global standardized uptake value ratio thresholds of ≥1.11 for ¹⁸F-florbetapir or ≥1.08 for ¹⁸F-florbetaben, while p-tau positivity (T+) was defined as CSF p-tau181 levels ≥23 pg/ml. Linear mixed regression models were used to assess the effects of PET Aβ and CSF p-tau181 levels on tau accumulation in predefined Braak regions and cognitive function over time.

Results

Our results revealed significant differences in PET tau pathology and cognitive decline between A + and A− individuals. We observed that interactions between Aβ and p-tau proteins were associated with tau accumulation and cognitive decline. Additionally, A−/T + individuals exhibited higher levels of tau accumulation in all Braak regions compared to A−/T− counterparts, suggesting a potential independent role of p-tau in tau pathology in the absence of Aβ.

Conclusions

Our findings suggest that Aβ positivity and elevated CSF p-tau181 levels were associated with tau accumulation and cognitive decline, highlighting the relevance of soluble p-tau as a potential biomarker for further investigation.

Keywords

Alzheimer's disease amyloid-β cognitive decline phosphorylated tau tau pathology

Introduction

Alzheimer's disease (AD) is characterized by two principal pathological features: the presence of extracellular amyloid-β (Aβ) plaques and intracellular neurofibrillary tangles (NFTs).¹ The Aβ cascade hypothesis posits that Aβ accumulation and deposition trigger pathological cascades associated with AD, including tau hyperphosphorylation, neuroinflammation, and loss of synaptic neuronal cells.^2,3 Over the past 20 years, significant resources have been devoted to interventions targeting the production and deposition of Aβ. However, neuropathological and imaging evidence indicates that tau pathology is more closely associated with neurodegeneration and cognitive deficits among individuals within the AD continuum.^4–8 Thus, tau has become an attractive therapeutic target.^9,10

The AT(N) classification system recognizes three biomarkers of AD: amyloid-β (A), tau (T), and neurodegeneration (N).¹¹ Within the AT(N) biomarker framework, tau positivity is defined by either increased levels of phosphorylated tau (p-tau) in cerebrospinal fluid (CSF) or cortical tau positron emission tomography (PET) ligand binding,^12–15 suggesting that CSF p-tau levels and tau-PET binding can be used interchangeably to assess tau pathology. However, there are important differences in the aspects of tau pathology represented by CSF and PET. CSF p-tau, an indicator of soluble tau variants, represents a specific pathological hallmark of AD¹⁶ and exhibits elevated levels across all stages of AD, including the asymptomatic phase, where abnormalities may not be detected by tau-PET scans.^17–19 In contrast, tau-PET imaging identifies insoluble tau filaments primarily in symptomatic stages of AD, correlated with cerebral atrophy and cognitive impairments.^20,21

Recent investigations propose that soluble p-tau seeds might initiate a cascade of tau aggregation in animal models^22–24 while potentially mediating the effect of Aβ pathology on subsequent tau aggregation.¹⁸ CSF p-tau might be more sensitive in detecting early-stage AD, with its potential as a therapeutic target against insoluble tau aggregation and cognitive decline.

The objective of this study is to investigate the synergistic associations between Aβ and p-tau in tau tangle accumulation and cognitive decline in AD. The study employs a combined approach using cross-sectional PET Aβ, CSF p-tau levels, and longitudinal PET tau along with cognitive assessments. The goal is to highlight targeting soluble p-tau levels as a strategy to mitigate tau aggregation and cognitive decline in AD and offer a new direction for clinical trials aimed at halting AD progression.

Methods

Study design

The data used in this study were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in October 2023. The ADNI, launched in 2003, is an ongoing observational study formed as a public-private partnership. The primary objective of ADNI has been to assess whether a combination of neuroimaging biomarkers, clinical, biological, and neuropsychological markers can effectively measure the progression of mild cognitive impairment (MCI) and early AD. The ADNI was approved by the Institutional Review Board at each Center of ADNI. Informed consent was obtained from each participant before the study was conducted.

Cohort

The study enrolled 319 participants across three diagnostic categories: cognitively normal (CN), MCI, and dementia. CN participants were characterized by Mini-Mental Status Examination (MMSE) scores ranging from 24 to 30, Clinical Dementia Ratings (CDR) of 0.5, and no reported memory concerns. Individuals with MCI scored between 24 and 30 on the MMSE, had a CDR of 0.5, exhibited objective memory impairment, and preserved activities of daily living. Dementia was defined for participants who showed memory decline on the Wechsler Memory Scale, had an MMSE score between 20 and 26, a CDR of 0.5 or 1.0, and received a probable AD clinical diagnosis according to criteria from the National Institute of Neurological and Communicative Disorders and Stroke, and the Alzheimer's Disease and Related Disorders Association. More detailed inclusion and exclusion criteria are available at http://www.adni-info.org.

APOE ε4 genotyping

To determine the APOE ε4 genotype of the study participants, a peripheral blood sample of 10 ml was collected from each individual. Individuals with at least one ε4 allele were classified as APOE ε4 carriers (APOE ε4+), while those with no APOE ε4 alleles were classified as APOE ε4 non-carriers (APOE ε4−).

CSF biomarkers

CSF measurements of phosphorylated tau at threonine 181 were assessed using the multiplex xMAP Luminex platform (Luminex, Austin, TX, USA) with INNOBIA AlzBio3 (Innogenetics, Ghent, Belgium) immunoassay kit-based reagents. The cut-off value for CSF p-tau181 positivity (T+) was determined using pre-established cut-offs at 23 pg/ml.^25,26 Further information regarding the CSF methods employed in ADNI can be accessed at: http://adni.loni.usc.edu/data-samples/clinical-data/.

Neuroimaging acquisition and PET preprocessing

Aβ-PET assessment used ¹⁸F-florbetapir or ¹⁸F-Florbetaben, while tau-PET employed ¹⁸F-flortaucipir. Tau-PET was recorded in 6 × 5 min frames, 75–105 min post-injection of ¹⁸F-flortaucipir. Aβ-PET was acquired 50–70 min after ¹⁸F-Florbetaben injection in 4 × 5 min frames or 90–110 min after ¹⁸F-Florbetaben injection in 4 × 5 min frames. PET images were co-registered, averaged, and further standardized regarding their orientation, voxel size, and intensity by the ADNI PET core.²⁷

For Aβ-PET, the MRI-derived FreeSurfer parcellation²⁸ was applied to the co-registered PET images to extract global Aβ-PET standardized uptake value ratio (SUVR) values with intensity normalized to the whole cerebellum, as described previously.²⁹ Amyloid-β positivity (A+) was considered if the global SUVRs of (¹⁸F-florbetapir ≥ 1.11 or ¹⁸F-Florbetaben ≥1.08, respectively.^30,31

For tau-PET, images were co-registered with structural MRI to extract mean Freesurfer region-of-interest (ROI) values. SUVRs were normalized to the inferior cerebellar cortex according to a previously established approach.³²

ROI-level tau-PET SUVRs were computed for three pre-defined composite regions, including Braak 1 region (entorhinal cortex); Braak 3 + 4 regions (parahippocampal gyri, fusiform gyri, lingual gyri, amygdala, middle and inferior temporal gyri, insula, anterior/posterior/isthmus cingulate, and temporal poles), and Braak 5 + 6 regions (frontal poles, superior/middle frontal gyri, lateral/medial orbitofrontal gyri, pars opercularis/orbitalis/triangularis, supramarginal gyri, superior and inferior parietal lobules, lateral occipital lobes, superior temporal gyri, banks of the superior temporal sulcus, precuneus, transverse temporal gyri). Notably, the Braak-stage 2 region (hippocampus) was excluded from the analyses due to potential spill-in from off-target binding to the choroid plexus.

Assessment of cognitive changes

The composite scores were used to reflect episodic memory (ADNI-MEM),³³ executive function (ADNI-EF),³⁴ language-related (ADNI-LAN) and visuospatial domains (ADNI-VS).³⁵ The average cognitive follow-up time was 2 years (range = 1–4 years).

Statistical analysis

Group differences were assessed with t-tests for continuous variables and χ² tests for dichotomous variables. When the assumptions of normality or homogeneity of variance were not met, the Wilcoxon rank-sum test was employed.

To investigate the contributions of PET Aβ and CSF p-tau181 to tau accumulation and cognitive functions over time, we performed linear mixed-effects regression models. These models integrated interaction terms for Aβ status with time and CSF p-tau181 with time, alongside a critical exploration of their combined interaction with time. All models included the main effects of baseline age, sex, clinical diagnosis status, APOE ε4 status, and years of education (when assessing cognitive changes), and their interactions with time, as well as a random intercept for each participant. Further exploration of interactions between CSF p-tau181 and Aβ status was conducted, assessing tau aggregate accumulation across all pairwise group contrasts (A−/T−, A−/T+, A+/T−, and A+/T+).

All statistical analyses were conducted using R software (version 4.3.1, R Foundation for Statistical Computing). Two-sided p < 0.05 indicated statistical significance. A false discovery rate (FDR) of 0.05 was also applied using the Benjamini-Hochberg approach for multiple comparison correction.

Results

Baseline demographics and clinical characteristics

Participant characteristics and demographic data are summarized in Table 1. The cohort comprised 319 participants predominantly classified as CN (59.9%), with a balanced gender distribution (56.1% female) and a mean age of 70.2 years. At baseline, A + individuals were older (p < 0.001) with a higher proportion of APOE ε4 carriers (p < 0.001) compared to A− individuals. There were no significant differences in gender or years of education between the two groups.

Table 1.

Clinical characteristics of participants.

	Amyloid-β negative (A−)	Amyloid-β positive (A+)	p
	(N = 182)	(N = 137)
CN/MCI/Dementia, n (%)	131/45/6 (72%/25%/3.3%)	60/52/25 (18%/38%/44%)	<0.001*
Female, n (%)	104 (57%)	75 (55%)	0.73
Age	69.54 ± 6.58	72.39 ± 7.33	<0.001*
Years of education	16.74 ± 2.30	16.40 ± 2.31	0.14
APOE ε4 carrier	44 (24%)	91 (66%)	<0.001*
CDR-SB	0.51 ± 1.10	1.37 ± 1.75	<0.001*
MMSE	28.87 ± 1.59	27.25 ± 3.06	<0.001*
ADNI-MEM	1.09 ± 0.84	0.43 ± 1.12	<0.001*
ADNI-EF	1.07 ± 0.84	0.46 ± 1.09	<0.001*
ADNI-LAN	0.89 ± 0.84	0.44 ± 0.93	<0.001*
ADNI-VS	0.10 ± 0.73	−0.18 ± 0.95	0.005*
CSF p-tau181, pg/ml	18.11 ± 6.19	31.09 ± 14.74	<0.001*
CSF p-tau181 positivity(T+)	35 (19%)	92 (67%)	<0.001*
Braak 1 SUVR	1.09 ± 0.12	1.33 ± 0.29	<0.001*
Braak 3 + 4 SUVR	1.10 ± 0.08	1.30 ± 0.29	<0.001*
Braak 5 + 6 SUVR	1.02 ± 0.07	1.17 ± 0.27	<0.001*
Follow-up time (y)	2.03 ± 1.17	1.61 ± 0.813	0.011*

Data are presented as mean ± SD or n (%).

A–; amyloid-β-negative; A+; amyloid-β-positive; CN; cognitively normal; MCI; mild cognitive impairment; p-tau; phosphorylated tau; CSF: cerebrospinal fluid; APOE: apolipoprotein E; SUVR: standardized uptake value ratio; CDR-SB: Clinical Dementia Rating Sum of Boxes; MMSE: Mini-Mental State Examination; ADNI-MEM: Alzheimer's Disease Neuroimaging Initiative Memory Composite Score; ADNI-EF: Alzheimer's Disease Neuroimaging Initiative Executive Function Composite Score; ADNI-LAN: Alzheimer's Disease Neuroimaging Initiative Language Composite Score; ADNI-VS: Alzheimer's Disease Neuroimaging Initiative Visuospatial Composite Score. *p value statistically significant.

Moreover, A + participants exhibited markedly higher tau-PET SUVRs and CSF p-tau181 concentrations (p < 0.001 for both). Compared to A− individuals, A + individuals demonstrated substantial cognitive decline across multiple domains, including global cognition (assessed by the MMSE), memory (assessed by ADNI-MEM), executive function (assessed by ADNI-EF), language (assessed by ADNI-LAN), and visuospatial abilities (assessed by ADNI-VS) (p < 0.01 for all domains).

Longitudinal change models

In analyzing longitudinal changes, linear mixed-effects regression models were performed to investigate the contributions of PET Aβ and CSF p-tau181 to tau accumulation and cognitive decline over time. Key findings from these analyses, as presented in Tables 2 and 3, provide an in-depth examination of tau pathology and cognitive trajectories within our cohort.

Table 2.

Linear mixed models for change in ROI tau-PET SUVRs.

	BRAAK 1 SUVR			BRAAK 3 + 4 SUVR			BRAAK 5 + 6 SUVR
Model predictors	Estimate	SE	p	Estimate	SE	p	Estimate	SE	p
Age × time	0.0002	0.0007	0.7905	−0.0003	0.0005	0.5965	−0.0002	0.0005	0.5991
Male sex × time	−0.0020	0.0088	0.8185	−0.0026	0.0067	0.6974	−0.0004	0.0057	0.9400
Dementia × time	0.0089	0.0171	0.6025	0.0095	0.0130	0.4644	0.0087	0.0111	0.4350
MCI × time	−0.0030	0.0110	0.7883	0.0077	0.0084	0.3585	0.0049	0.0071	0.4944
APOE ε4 carrier × time	−0.0107	0.0104	0.3079	−0.0024	0.0079	0.7596	−0.0016	0.0068	0.8129
CSF p-tau181 × time	−0.0002	0.0010	0.8545	−0.0003	0.0007	0.7228	−0.0006	0.0006	0.3512
Aβ positive × time	0.0188	0.0230	0.4141	−0.0228	0.0175	0.1937	−0.0325	0.0149	0.0305*
Aβ positive × CSF p-tau181 × time	0.0002	0.0011	0.8486	0.0017	0.0008	0.0367*	0.0020	0.0007	0.0049*

MCI: mild cognitive impairment; Aβ: amyloid-β; p-tau: phosphorylated tau; CSF: cerebrospinal fluid; APOE: apolipoprotein E; SUVR: standardized uptake value ratio; ROI, region-of-interest; PET, positron emission tomography. Linear mixed models were examined for change in ROI tau-PET SUVRs including Braak 1 region, Braak 3 + 4 region, Braak 5 + 6 region. First, changes related to Aβ status and CSF p-tau181 were assessed in the same model. Then, an interaction between Aβ status and CSF p-tau181 was added. Main effects of independent variables are included in each model (estimates not shown). Estimates are unstandardized values, reflecting the amount of change in each dependent variable per year. *p value statistically significant.

Table 3.

Linear mixed models for change in composite scores.

	ADNI-MEM			ADNI-EF			ADNI-VS			ADNI-LAN
Model predictors	Estimate	SE	p	Estimate	SE	p	Estimate	SE	p	Estimate	SE	p
Age × time	−0.0060	0.0037	0.1046	−0.0016	0.0045	0.7252	0.0020	0.0055	0.7130	−0.0091	0.0046	0.0495*
Male sex × time	0.1111	0.0463	0.0174*	0.0371	0.0555	0.5051	0.0499	0.0686	0.4677	0.0712	0.0581	0.2223
Dementia × time	−0.2277	0.0925	0.0149*	−0.0341	0.1188	0.7742	−0.1911	0.1400	0.1738	−0.1001	0.1168	0.3925
MCI × time	−0.1920	0.0590	0.0014*	−0.1911	0.0709	0.0079*	−0.1245	0.0877	0.1574	−0.1948	0.0742	0.0095*
Education × time	0.0012	0.0092	0.9006	−0.0015	0.0111	0.8957	0.0040	0.0137	0.7737	−0.0020	0.0116	0.8603
APOE ε4 carrier × time	−0.0401	0.0538	0.4580	−0.0617	0.0649	0.3434	0.0294	0.0805	0.7151	0.0573	0.0677	0.3983
CSF p-tau181 × time	0.0121	0.0050	0.0173*	−0.0047	0.0060	0.4430	0.0028	0.0075	0.7107	0.0049	0.0063	0.4353
Aβ positive × time	0.2437	0.1198	0.0436*	0.0675	0.1440	0.6401	−0.0611	0.1780	0.7320	0.0888	0.1506	0.5562
Aβ positive × CSF p-tau181 × time	−0.0138	0.0056	0.0140*	0.0018	0.0067	0.7931	−0.0003	0.0083	0.9688	−0.0095	0.0070	0.1771

MCI: mild cognitive impairment; Aβ: amyloid-β; p-tau: phosphorylated tau; CSF: cerebrospinal fluid; APOE: apolipoprotein E; SUVR: standardized uptake value ratio; ADNI-MEM: Alzheimer's Disease Neuroimaging Initiative Memory Composite Score; ADNI-EF: Alzheimer's Disease Neuroimaging Initiative Executive Function Composite Score; ADNI-LAN: Alzheimer's Disease Neuroimaging Initiative Language Composite Score; ADNI-VS: Alzheimer's Disease Neuroimaging Initiative Visuospatial Composite Score. Linear mixed models were examined for change in composite scores including ADNI-MEM, ADNI-EF, ADNI-VS, ADNI-LAN. First, changes related to Aβ status and CSF p-tau181 were assessed in the same model. Then, an interaction between Aβ status and CSF p-tau181 was added. Main effects of independent variables are included in each model (estimates not shown). Estimates are unstandardized values, reflecting the amount of change in each dependent variable per year. *p value statistically significant.

Our analysis revealed significant interactions among Aβ status, CSF p-tau181 levels, and time, indicating increases in tau pathology within Braak 3 + 4 and Braak 5 + 6 regions (p = 0.0367 and p = 0.0049, respectively). Furthermore, the interaction between Aβ positivity and elevated CSF p-tau181 levels was associated with memory decline over time (p = 0.014).

To understand these interactions, we directly compared groups based on Aβ status and CSF p-tau181 status (A−/T−, A−/T+, A+/T−, and A+/T+), as shown in Figure 1 and detailed in Table 4. Specifically, A+/T + individuals exhibited significantly greater tau accumulation across all Braak regions compared to other groups, suggesting that Aβ positivity and elevated p-tau levels are associated with increased tau pathology. When comparing A−/T− individuals to A+/T− individuals, notable differences in tau accumulation were observed at the Braak 1 region, suggesting that the impact of Aβ on tau accumulation may be more pronounced in the early stages. Moreover, individuals with A+/T + status exhibited significant differences in tau accumulation in the Braak stages 3 + 4 and 5 + 6 regions compared to those with A+/T− status, indicating a potential association between Aβ positivity and elevated p-tau levels in relation to tau accumulation. The comparison further revealed that A−/T + individuals exhibited increased tau accumulation in all Braak regions compared to their A−/T− counterparts, indicating a potential role of p-tau in tau accumulation in the absence of Aβ deposition. However, the analysis did not reveal statistically significant differences in cognitive domains.

Figure 1.

Change in tau accumulation over time by joint Aβ (A) and CSF p-tau181 (T).

Table 4.

Comparisons across Aβ/CSF p-tau181 groups in tau accumulation.

	BRAAK 1 SUVR			BRAAK 3 + 4 SUVR			BRAAK 5 + 6 SUVR
Contrast	Estimate	SE	p	Estimate	SE	p	Estimate	SE	p
(A−/T− × time) vs. (A−/T + × time)	−0.0976	0.0345	0.0075*	−0.1058	0.0326	0.0026*	−0.1105	0.0270	0.0002*
(A−/T− × time) vs. (A+/T− × time)	−0.0941	0.0349	0.0088*	−0.0391	0.0329	0.2363	−0.0236	0.0272	0.3872
(A−/T− × time) vs. (A+/T + × time)	−0.2505	0.0302	<0.0001*	−0.2030	0.0286	<0.0001*	−0.1391	0.0236	<0.0001*
(A−/T + × time) vs. (A+/T− × time)	0.0035	0.0428	0.9351	0.0667	0.0404	0.1203	0.0870	0.0334	0.0146*
(A−/T + × time) vs. (A+/T + × time)	−0.1529	0.0361	0.0001*	−0.0972	0.0342	0.0073*	−0.0285	0.0283	0.3765
(A+/T− × time) vs. (A+/T + × time)	−0.1564	0.0392	0.0002*	−0.1639	0.0370	<0.0001*	−0.1155	0.0306	0.0004*

Aβ: amyloid-β; p-tau: phosphorylated tau; CSF: cerebrospinal fluid; A–: amyloid-β-negative; A+: amyloid-β-positive; T–: CSF p-tau181-negative; T+: CSF p-tau181- positive. Estimates are unstandardized values, reflecting the amount of change in each dependent variable per year. * Significance at false discovery rate–corrected p < 0.05.

Discussion

The current longitudinal PET study evaluates the relationship between Aβ and soluble p-tau in driving tau aggregates and cognitive decline over time in AD. Our study presents several significant findings: Firstly, significant differences in PET tau pathology and cognitive decline were observed between A + and A− individuals, corroborating the amyloid cascade hypothesis that posits Aβ as the initial trigger in the AD pathological sequence. Secondly, the interactions between Aβ and p-tau protein were associated with tau accumulation and cognitive decline. Finally, tau may have an Aβ-independent role in neuronal integrity,^36,37 as our results indicate that elevated CSF p-tau181 levels are associated with increased tau accumulation in all Braak regions and cognitive decline in the absence of Aβ deposition.

The clinical progression of AD is characterized by specific neuropathological signatures, including the extracellular deposition of Aβ, intracellular formation of NFTs, and extensive neuronal loss. Recent advances in biomarker identification and validation have significantly improved our understanding of the dynamic pathophysiological changes underlying AD, enabling the in vivo detection of AD pathophysiology.³⁸ The AT(N) biomarker classification framework underscores the pivotal roles of Aβ and tau in AD pathogenesis as well as their associations with neurological dysfunctions.¹¹ According to the updated amyloid cascade hypothesis, Aβ initiates AD by triggering a series of pathological events, including tau hyperphosphorylation, neuroinflammation, and neurodegeneration, leading to cognitive impairments.³⁹ Our study supports the amyloid cascade hypothesis by demonstrating that Aβ not only acts as a catalyst in AD but also correlates with cognitive decline through its associations with tau pathology. Elevated levels of both Aβ and p-tau are associated with neuronal damage, which may contribute to subsequent cognitive deficits.⁴⁰ Preclinical studies provide strong support that molecular interactions between Aβ and tau lead to synaptic and neural circuit dysfunctions, impair axonal transport, and accelerate neuronal death.^41,42

Furthermore, our results extend these observations by indicating that tau pathology may be associated with increased Aβ toxicity rather than simply being a consequence of Aβ accumulation, suggesting a potential bidirectional relationship between Aβ and tau in AD pathogenesis. Recent reports have proposed that Aβ synergistically interacts with tau to influence disease progression.^43,44 In our study, we found that individuals who were Aβ-positive with elevated levels of CSF p-tau181 showed a greater association with increased tau accumulation and cognitive decline. Our findings reveal a complex relationship between Aβ and p-tau, where higher levels of both proteins are linked to greater tau pathology, suggesting a potential exacerbation of tau pathology rather than a purely additive effect.

The Aβ cascade model of AD assumes a sequential cascade of events, with abnormal elevations in Aβ leading to tau hyperphosphorylation.⁴⁵ However, emerging evidence challenges this linear causality, suggesting that tau hyperphosphorylation and Aβ accumulation may proceed through independent pathways.^36,46,47 Post-mortem studies reveal a stronger correlation between NFTs pathology with neurodegeneration and cognitive impairment, while Aβ plaque density does not. The development of PET tracers specific to paired helical filaments offers a new method for in vivo tracking of tauopathy levels within the brain. Recent imaging studies in humans have suggested that the aggregation of tau into NFTs, rather than Aβ, is closely associated with clinical status, with tau-PET patterns recapitulating domain-specific cognitive dysfunctions.^4,48 Further studies have revealed a direct correlation between tau aggregation and cognitive decline in AD.⁴⁹ Our study aligns with these observations, showing that elevated levels of CSF p-tau are associated with increased tau aggregation and cognitive decline in the absence of Aβ deposition. This supports the hypothesis that tau phosphorylation is associated with tau aggregation and cognitive decline, suggesting that these processes may not be fully dependent on Aβ mechanisms. These findings underscore the central role of tau protein in the pathophysiology of AD a and suggest that independent tau pathogenic mechanisms should receive greater attention in the development of early diagnostic and treatment strategies for AD. The direct impacts of tau phosphorylation and aggregation on neurological function and cognitive decline, as indicated by our findings, call for a more in-depth exploration of the underlying mechanisms.

Our analyses have several limitations. First, we included all CN, MCI, and AD participants with tau-PET imaging from ADNI in the analyses due to the relatively small sample sizes of subgroups. To ensure that the results were not driven by the effect of clinical status, we adjusted the models for clinical diagnosis. Second, our analysis was limited to data on demographic indicators, cognitive scores, CSF biomarkers, Aβ-PET, and tau-PET from the ADNI database. Future studies can further investigate plasma biomarkers with unified specifications and clinical application prospects. In addition, our study design was cross-sectional and longitudinal, which might not fully reveal a causal relationship between Aβ and tau phosphorylation. Future studies are needed to validate and extend these findings with larger samples and a longer follow-up time.

In conclusion, the intricate relationship between Aβ and p-tau is critical for understanding the progression of AD, while emphasizing the need for early diagnostic and therapeutic approaches targeting both biomarkers. Our results not only reinforce the importance of Aβ and p-tau in AD pathology but also suggest the potential for developing personalized treatment strategies that address tau pathology independently of Aβ. Further exploration into the mechanisms of interactions among these biomarkers will be vital for advancing targeted therapeutic strategies for AD.

Footnotes

Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; BristolMyers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

ORCID iD

Maowen Ba

Ethical considerations

The ADNI study was approved by the institutional review boards of all participating centers (), including: Oregon Health and Science University; University of Southern California; University of California—San Diego; University of Michigan; Mayo Clinic, Rochester; Baylor College of Medicine; Columbia University Medical Center; Washington University, St. Louis; University of Alabama at Birmingham; Mount Sinai School of Medicine; Rush University Medical Center; Wien Center; Johns Hopkins University; New York University; Duke University Medical Center; University of Pennsylvania; University of Kentucky; University of Pittsburgh; University of Rochester Medical Center; University of California, Irvine; University of Texas Southwestern Medical School; Emory University; University of Kansas, Medical Center; University of California, Los Angeles; Mayo Clinic, Jacksonville; Indiana University; Yale University School of Medicine; McGill University, Montreal-Jewish General Hospital; Sunnybrook Health Sciences, Ontario; U.B.C. Clinic for AD & Related Disorders; Cognitive Neurology—St. Joseph's, Ontario; Cleveland Clinic Lou Ruvo Center for Brain Health; Northwestern University; Premiere Research Inst (Palm Beach Neurology); Georgetown University Medical Center; Brigham and Women's Hospital; Stanford University; Banner Sun Health Research Institute; Boston University; Howard University; Case Western Reserve University; University of California, Davis—Sacramento; Neurological Care of CNY; Parkwood Hospital; University of Wisconsin; University of California, Irvine—BIC; Banner Alzheimer's Institute; Dent Neurologic Institute; Ohio State University; Albany Medical College; Hartford Hospital, Olin Neuropsychiatry Research Center; Dartmouth-Hitchcock Medical Center; Wake Forest University Health Sciences; Rhode Island Hospital; Butler Hospital; UC San Francisco; Medical University South Carolina; St Joseph's Health Care Nathan Kline Institute; University of Iowa College of Medicine; Cornell University and University of South Florida: USF Health Byrd Alzheimer's Institute. Written informed consent was obtained from all participants or their authorized representatives. The investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found online (https://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf).

Consent to participate

Not applicable

Consent for publication

Not applicable

Author contributions

Chunhua Zhang (Conceptualization; Data curation; Formal analysis; Methodology; Writing – original draft); Yaojun Tai (Conceptualization; Writing – review & editing); Min Kong (Data curation; Formal analysis; Funding acquisition; Methodology); Pengyuan Jia (Data curation; Supervision); Guozhao Ma (Conceptualization; Methodology; Writing – review & editing); Maowen Ba (Conceptualization; Funding acquisition; Methodology; Project administration; Writing – review & editing).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by Shandong Provincial key research and development project (No.2018GSF118235) and Chinese National Natural Science Foundation (No. 81571234).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during this study.

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