CSF MTBR-tau243 is a specific biomarker of tau tangle pathology in Alzheimer’s disease

Aggregated insoluble tau is one of two defining features of Alzheimer’s disease. Because clinical symptoms are strongly correlated with tau aggregates, drug development and clinical diagnosis need cost-effective and accessible specific fluid biomarkers of tau aggregates; however, recent studies suggest that the fluid biomarkers currently available cannot specifically track tau aggregates. We show that the microtubule-binding region (MTBR) of tau containing the residue 243 (MTBR-tau243) is a new cerebrospinal fluid (CSF) biomarker specific for insoluble tau aggregates and compared it to multiple other phosphorylated tau measures (p-tau181, p-tau205, p-tau217 and p-tau231) in two independent cohorts (BioFINDER-2, n = 448; and Knight Alzheimer Disease Research Center, n = 219). MTBR-tau243 was most strongly associated with tau-positron emission tomography (PET) and cognition, whereas showing the lowest association with amyloid-PET. In combination with p-tau205, MTBR-tau243 explained most of the total variance in tau-PET burden (0.58 ≤ R2 ≤ 0.75) and the performance in predicting cognitive measures (0.34 ≤ R2 ≤ 0.48) approached that of tau-PET (0.44 ≤ R2 ≤ 0.52). MTBR-tau243 levels longitudinally increased with insoluble tau aggregates, unlike CSF p-tau species. CSF MTBR-tau243 is a specific biomarker of tau aggregate pathology, which may be utilized in interventional trials and in the diagnosis of patients. Based on these findings, we propose to revise the A/T/(N) criteria to include MTBR-tau243 as representing insoluble tau aggregates (‘T’).

Aggregated insoluble tau is one of two defining features of Alzheimer's disease. Because clinical symptoms are strongly correlated with tau aggregates, drug development and clinical diagnosis need cost-effective and accessible specific fluid biomarkers of tau aggregates; however, recent studies suggest that the fluid biomarkers currently available cannot specifically track tau aggregates. We show that the microtubule-binding region (MTBR) of tau containing the residue 243 (MTBR-tau243) is a new cerebrospinal fluid (CSF) biomarker specific for insoluble tau aggregates and compared it to multiple other phosphorylated tau measures (p-tau181, p-tau205, p-tau217 and p-tau231) in two independent cohorts (BioFINDER-2, n = 448; and Knight Alzheimer Disease Research Center, n = 219). MTBR-tau243 was most strongly associated with tau-positron emission tomography (PET) and cognition, whereas showing the lowest association with amyloid-PET. In combination with p-tau205, MTBR-tau 243 explained most of the total variance in tau-PET burden (0.58 ≤ R 2 ≤ 0.75) and the performance in predicting cognitive measures (0.34 ≤ R 2 ≤ 0.48) approached that of tau-PET (0.44 ≤ R 2 ≤ 0.52). MTBR-tau243 levels longitudinally increased with insoluble tau aggregates, unlike CSF p-tau species. CSF MTBR-tau243 is a specific biomarker of tau aggregate pathology, which may be utilized in interventional trials and in the diagnosis of patients. Based on these findings, we propose to revise the A/T/(N) criteria to include MTBR-tau243 as representing insoluble tau aggregates ('T').
Given the growing interest in tau-targeted therapeutics for Alzheimer's disease (AD), there is a critical need for reliable and specific biomarkers of insoluble, aggregated tau to understand AD pathophysiology and to evaluate the effects of treatments 1 . PET with radio ligands that bind to fibrillar forms of tau reflect the burden of insoluble AD-specific tau aggregates in the brain, including neurofibrillary tangles (NFTs) and neuropil threads [2][3][4][5][6] . Tau-PET imaging studies have shown that insoluble tau aggregates are strongly associated with cognitive decline even during the early pre-symptomatic stages of AD 7 and tau-PET is the most accurate prognostic marker of Article https://doi.org/10.1038/s41591-023-02443-z unimpaired (171, 78%): 83 CU−, 88 CU+, 35 very mild AD and 13 AD+. The average age was 71.2 ± 6.6 years, 112 (51.1%) were women and 96 (43.8%) were APOE ε4 carriers (Extended Data Table 1). CSF biomarkers were measured in the BioFINDER-2 and the Knight ADRC cohorts, including MTBR-tau243 concentration, as well as the phosphorylation occupancy at different tau residues (percent pT181/T181, pT205/ T205, pT217/T217 and pT231/T231). The phosphorylation occupancy represents the percentage of soluble tau phosphorylated at a certain amino acid position (Methods), which is a more specific measure of phosphorylation not confounded with total tau concentrations and superior to the corresponding p-tau concentration in prediction of abnormal Aβ status 29,38 . In Extended Data Fig. 1 and Supplementary Table 1, we compared the CSF levels of all biomarkers in all diagnostic groups in the BioFINDER-2 cohort. We observed that MTBR-tau243 concentrations were not increased in other non-AD tauopathies such as progressive supranuclear palsy or frontotemporal dementia (FTD), thus suggesting a high specificity for AD-related tau. Further, we did not observe any significant difference between MTBR-tau243 concentrations in CU+ compared to CU−. Of note, we found that two outliers (one in CU− and the other in FTD) that had very high levels of MTBR-tau243 were MAPT R406W mutation carriers who were amyloid negative, but clearly tau-PET positive (indicated in Extended Data Fig. 1).

Association between CSF marker and amyloid or tau measure
CSF MTBR-tau243, pT181/T181, pT205/T205, pT217/T217 and pT231/ T231 were assessed for association with amyloid-PET and tau-PET measures of pathology using linear regression models adjusting for age and sex. All participants were compared, in addition to the amyloid-positive-only subgroup, to separate out amyloid from tau pathology effects (Fig. 1). The phosphorylation occupancy at T217 (pT217/T217) was the CSF measure most strongly correlated with amyloid-PET (BioFINDER-2, β = 0.81, 95% confidence interval (CI) 0.74-0.88; Knight ADRC, β = 0.87, 0.79-0.95; all P < 0.001; Fig. 1a and Extended Data Table 2). MTBR-tau243 concentration was the CSF measure most strongly associated with tau-PET in all participants  Table 2). Notably, the CSF MTBR-tau243 concentration was significantly more strongly associated with tau-PET when compared to pT217/T217 (BioFINDER-2, β = 0.77, 0.71-0.83, P comp < 0.001; Knight ADRC, β = 0.61, 0.49-0.73, P comp < 0.001) in all participants and in amyloid-positive particip = ants (BioFINDER-2, β0.76, 0.69-0.84, P comp = 0.001; Knight ADRC, β = 0.58, 0.43-0.73, P comp = 0.001; Extended Data  Table 3). AD available today 8 ; however, PET imaging is highly expensive and needs a complex infrastructure, which reduces its use to only highly specialized centers. In contrast, fluid biomarkers are less expensive and are more clinically accessible. The most widely used fluid biomarkers of tau are N-terminal or mid-domain total tau (t-tau) and phosphorylated tau species resulting from cleavage near residue 224 of tau 9,10 , including tau phosphorylated at residues 181, 217 and 231 (p-tau181, p-tau217 and p-tau231) (refs. 11-17). But, these biomarkers are strongly associated with increasing burden of amyloid plaques more than insoluble tau aggregates [18][19][20] . For instance, plasma and CSF concentrations of these p-tau species are already increased in preclinical AD many years before widespread insoluble tau aggregates in the neocortex are observed [21][22][23][24] . Further, recent clinical trials have demonstrated substantial reductions of CSF or plasma concentrations of t-tau, p-tau181 and p-tau217 (refs. 25-28) in response to anti-amyloid passive immunotherapies, which substantially remove amyloid plaques. Neuropathological and imaging studies have also reported strong associations between these fluid biomarkers and amyloid plaques 19,20,29 . In addition, animal studies have found that CSF t-tau and p-tau are increased in mouse models with amyloid β (Aβ) pathology, even when no aggregated tau pathology is observed 23,[30][31][32] . Taken together, these findings indicate that plasma and CSF concentrations of N-terminal to mid-domain t-tau and p-tau do not directly represent insoluble tau aggregates, but rather reflect a response to amyloid plaque pathology. Thus, there is currently no fluid biomarker that specifically reflects AD-related tau pathology.
In this study, we therefore evaluated a new CSF biomarker of insoluble tau aggregates. Notably, tau species that contain MTBR-tau are a major component of insoluble tau aggregates in the brain [33][34][35][36][37] , but these fragments have been poorly investigated as candidate biomarkers. In an initial study, with a small sample size of controls and AD patients (n = 35), we showed preliminary results that MTBR-tau was present in human CSF and that a specific MTBR-tau species containing residue 243 (MTBR-tau243) was strongly associated with tau-PET and disease progression 33 . Here, we expanded these results to two large independent sporadic AD cohorts, the Swedish BioFINDER-2 study and the Charles F and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), covering the whole AD continuum, with available amyloid-PET and tau-PET images. In this study, we compared the performance of MTBR-tau243 to other CSF phosphorylated tau measures, including p-tau181, p-tau205, p-tau217 and p-tau231 phosphorylation occupancies (% p-tau to total tau ratio), which are also reported as biomarkers to recapitulate AD pathologies 21,29 and we showed that MTBR-tau243 was the fluid biomarker most strongly associated with tau-PET. We also investigated the proportion of variation in CSF biomarker levels explained by amyloid-PET and tau-PET measures of pathology. Then, we evaluated longitudinal CSF biomarker changes to investigate their rate of change based on the presence or absence of amyloid and tau pathologies to indicate which are increasing with amyloid versus tau pathologies. Finally, we assessed whether prediction of continuous AD-related measures could be improved by the combination of multiple biomarkers and found that MTBR-tau243, together with p-tau205, could optimally predict tau-PET measures and cognitive impairment.

Longitudinal change in CSF biomarkers
Longitudinal data from the BioFINDER-2 cohort was used to examine changes in CSF biomarkers stratified by amyloid (A) and tau (T) pathology status (+ and −). Characteristics of the 220 participants with longitudinal CSF measurements are described in Supplementary Table 4. Linear mixed models were used to compare CSF longitudinal trajectories among groups (A−/T−, A+/T− and A+/T+) using post hoc pairwise Wilcoxon test when the interaction with   5. CSF pT217/T217, pT181/T181 and pT231/T231 had their greatest rate of increase in the A+T− group and a lower rate of increase in the A+T+ group, indicating that the rate of increase of these biomarkers was plateauing at later stages of disease when tau pathology was increasing most (Fig. 3). In contrast, CSF pT205/T205 and MTBR-tau243 had their greatest rate of increase in the A+T+ group, corresponding to matching increases in tau pathology. Notably, CSF MTBR-tau243 was increasing faster in the A+T+ group than the A+T− group (versus A−T−, Cohen's d = 1.48, P < 0.001; versus A+T−, Cohen's d = 1.13, P < 0.001), whereas the rate of increase in pT205/ T205 was not significantly different in the A+T+ and A+T− groups (Cohen's d = 0.08, P = 0.788; Fig. 3 and Supplementary Table 5). This suggests that CSF MTBR-tau243 would best reflect AD progression in tau-PET positive individuals. As a sensitivity analysis, we repeated this analysis using amyloid-PET rather than CSF Aβ42/40 for classifying participants, using a previously validated threshold 39 . We found that the longitudinal trajectories for all CSF biomarkers were replicated, with pT205/ T205, but especially MTBR-tau243, rates of change increasing with progressing A/T status and the rest of biomarkers having the highest rate of change at A+T− status (Supplementary Fig. 3 and Supplementary Table 5).

Association of CSF and PET biomarkers with MMSE scores
We assessed associations of CSF and PET biomarkers with a common clinical assessment of dementia, the Mini Mental State Examination (MMSE) 40 , which was assessed in both cohorts, using linear regression models that adjusted for age, sex and years of education. MTBR-tau243 was the CSF biomarker most strongly associated with MMSE scores in all participants (BioFINDER-2, β = −0.    Association P values were derived from two-sided tests and bootstrapping P values were obtained from one-sided tests, all without adjustment for multiple comparisons. All P values from associations between CSF biomarkers and amyloid-PET and tau-PET were <0.001.

Best predictors of AD-related measures and cognitive function
Finally, we aimed to determine whether combinations of CSF biomarkers could be used as accurate quantitative surrogates for amyloid-PET, tau-PET or cognitive measures. We first evaluated the variance explained by each individual biomarker for each outcome. Next, we used the least absolute shrinkage and selection operator (LASSO) procedure to select which combination of CSF biomarkers were optimal for each outcome and then, we compared this new model to the ones from the individual biomarkers. CSF pT217/T217 was the individual biomarker that best predicted amyloid-PET (BioFINDER-2, R 2 = 0.73, corrected Akaike information criterion (AICc) = 404.5; Knight ADRC, R 2 = 0.73, AICc = 265.2; Fig. 5 and Supplementary Table 7). Based on the LASSO regressions, we found that combining CSF pT217/T217 with pT205/T205 and Aβ42/40 significantly improved prediction of amyloid-PET in both cohorts (BioFINDER-2,  Table 4).
MTBR-tau243 was the individual biomarker that best predicted MMSE scores (BioFINDER-2,  Table 4). Tau-PET predicted MMSE scores better than a combination of CSF biomarkers, but the difference was not  Table 7 and Extended Data Table 4).

Discussion
In this study, we found that a new CSF biomarker, MTBR-tau243, was strongly associated with tau pathology, whereas it was minimally associated with Aβ pathology, in two large independent sporadic AD cohorts. We also found that CSF MTBR-tau243 has a significantly higher correlation with cognitive measures than phosphorylated tau measures (for example, pT217/T217 and pT181/T181), which indicates its potential utility in the clinical setting. Further, we found that CSF MTBR-tau243 is the biomarker with the largest rate of increase in participants that are already positive for both amyloid and tau pathologies, suggesting that CSF MTBR-tau243 best reflects disease progression in late stages. We further extended these findings by combining CSF MTBR-tau243 with phosphorylated tau measures to predict Aβ pathology, tau pathology and cognitive measures in the AD continuum. We found that CSF MTBR-tau243 in combination with pT205/T205 can accurately predict continuous tau-PET measures and has similar predictive accuracy for cognitive measures as tau-PET. Based on these results, our study suggests that CSF MTBR-tau243 may be a viable alternative to tau-PET for use as a pre-screening tool or a tau pathology end point surrogate for clinical trials and also as an accurate diagnostic measure of tau pathology.
Our first objective was to characterize MTBR-tau243 concentration and compare it to four phosphorylated tau measures by looking at their associations with Aβ and tau pathologies measured by PET. Notably, MTBR-tau243 was the tau biomarker that demonstrated the highest correlation with tau-PET and the lowest correlation with amyloid-PET, not only in the whole group, but also in Aβ-positive group.  Article https://doi.org/10.1038/s41591-023-02443-z that MTBR-tau243 is a biomarker that specifically reflects aggregated tau pathology independent of amyloid pathology. Although pT217/T217 was also well correlated with tau-PET, there was a nonlinear relationship and a substantial increase in pT217/T217 before tau-PET pathology was elevated, which plateaued once the tau-PET threshold was exceeded. This may indicate that pT217/T217 is primarily associated with tau pathology through its quantitative relationship with the amount of Aβ pathology. This is further supported by the observation that Aβ pathology explained a significantly larger proportion of variation of pT217/T217 levels than tau, when including both amyloid-PET and tau-PET measures in the model. Notably, while pT205/T205 levels demonstrated a high correlation with tau-PET, they also showed the second highest correlation with amyloid-PET, after pT217/T217. In combined models, both amyloid-PET and tau-PET explained similar proportion of variation of pT205/T205 levels, suggesting that it is an intermediate biomarker affected by both Aβ and tau pathologies.
Regarding the other p-tau measures, pT181/T181 and pT231/T231 were highly correlated with amyloid-PET, while the correlations with tau-PET were significantly lower than the other three CSF tau biomarkers, suggesting that they mainly reflect Aβ-pathology. These results are in line with several recent studies suggesting that p-tau181, p-tau217 and p-tau231 may be more related to amyloid pathology than tau. This is supported by their increased levels, both measured in CSF or in plasma, in early stages 13,14,17,21,22,[41][42][43] , and by being more tightly associated with amyloid-PET than tau-PET 18,23,44 or to actual amyloid pathology in postmortem studies 20,45 . Finally, we found that MTBR-tau243 was particularity increased in two cases of MAPT R406W mutation carriers that were amyloid negative but had high tau-PET binding. Tau pathology on MAPT R406W mutation carriers is known to be similar to AD tau pathology 46,47 and reactive to AD tau-PET tracers 4,48-50 , further supporting our finding that MTBR-tau243 is a specific biomarker to AD-like tau pathology.
Longitudinal CSF biomarkers changes were also investigated to understand how these biomarkers change at different stages of the disease. Most notably, among the five CSF tau biomarkers, only MTBR-tau243 exhibited a significant increase in the rate of change between A+T− and A+T+ groups, suggesting that it enables longitudinal disease tracking during the phase of the disease characterized by neocortical tau aggregates, which mainly occurs in the symptomatic phase of AD. On the other hand, there was no major difference in the rate of change between A+T− and A+T+ for pT205/T205, although it still demonstrated a positive rate of changes at this late stage, suggesting a lower but still significant increase after tau deposition. Notably, for the other phosphorylated tau measures (pT181/T181, pT217/T217 and pT231/T231), there was a pronounced increase in the rate of change during the transition from A−T− to A+T−, consistent with a previous report showing that phosphorylated tau (especially p-tau217) is an optimal marker for disease monitoring during the very early (preclinical) stages of the disease 51 . Of note, here we found either no significant increase in the rate of change of phosphorylated tau occupancy during the transition from A+T− to A+T+ or a significant decrease in the rate of change, consistent with previous reports 21 . These results suggest that rate of change in these phosphorylated tau measures may plateau or decline at advanced disease stages, when insoluble tau aggregates are depositing in the neocortex, indicating they are discordant longitudinally and that the classic p-tau measures are not direct measures of AD tau pathology 52 . Altogether, the findings of CSF p-tau measures are consistent with previous clinical observational studies and preclinical mouse models 22,32 , where these biomarkers seem to be driven by Aβ pathology. These results further support recent proposals to revise the A/T/(N) criteria system, in which any p-tau biomarker can be used as a tau (T) marker 53 .
As a relevant question for clinical practice, we also investigated the relationship between these CSF biomarkers and a cognitive measure. As expected by the observed associations with tau pathology, MTBR-tau243 was the measure most strongly associated with MMSE, a cognitive test frequently used in the clinical setting. Notably, this association was not significantly different from tau-PET, thus supporting the idea that CSF MTBR-tau243 could be a viable alternative to tau-PET for clinical purposes. Although pT205/T205 had a lower correlation with MMSE than MTBR-tau243, pT205/T205 was also well correlated with MMSE and not significantly different from tau-PET. In contrast, other CSF biomarkers such as pT217/T217 or pT181/T181 showed significantly lower associations.
An unmet need is to determine not just who has amyloid or tau pathology, but if the symptoms are due to those pathologies. Because tau pathology is most highly correlated with cognitive and clinical impairment, an important question is how well CSF biomarkers can predict tau pathology or cognitive impairment. Thus, we next examined whether combining CSF biomarkers would improve prediction of MTBR-tau243 pT181/T181 pT205/T205 pT217/T217 pT231/T231 Trajectories for each group are displayed as boxplots, which were generated using linear mixed models (the central band represents the median, the lower and upper hinges correspond to the first and third quartiles and the whiskers depict the maximum/minimum value or 1.5 × interquartile range from the hinge, whichever is lower). Differences among all groups were assessed using Kruskal-Wallis tests and pairwise Wilcoxon tests were employed for post hoc comparisons. Asterisks indicate the P values from two-sided tests without correction for multiple comparisons. Longitudinal CSF data was available only in BioFINDER-2. Amyloid-positive participants were identified using a previously validated cutoff for CSF Aβ42/40 (CSF Aβ42/40 < 0.08). Tau positivity was determined based on tau-PET SUVR in the meta-ROI (Braak I-IV, SUVR > 1.32). ROI, region of interest. The actual P values for A−T− versus A+T− were P = 0.011 (pT205/T205); for A−T− versus A+T+ were P = 0.011 (pT181/T181), P = 0.014 (pT205/T205), P = 0.617 (pT217/T217) and P = 0.980 (pT231/T231); and for A+T− versus A+T+ were P = 0.788 (pT205/T205), P = 0.007 (pT231/T231). All other comparisons yielded P < 0.001. *P < 0.050; **P < 0.010; ***P < 0.001. Article https://doi.org/10.1038/s41591-023-02443-z Aβ or tau pathologies or cognitive measures. Based on a data-driven approach, we observed that the combination of pT205/T205, Aβ42/40 and pT217/T217 was optimal for predicting amyloid-PET continuous measures and significantly improved the performance of any individual measure. We also found that the combination of MTBR-tau243 and pT205/T205 in a single model improved prediction of tau-PET burden compared to any other single-fluid biomarker. The fact that such high predictive accuracy for both amyloid-PET and tau-PET imaging can be achieved by CSF biomarkers indicates that CSF assays can potentially be an alternative to PET measures, which are costly and have limited accessibility. Notably, MTBR-tau243 and pT205/T205 were also the optimal combination for predicting a cognitive measure (MMSE), suggesting potential clinical applications of this biomarker combination in predicting not only tau pathology but also cognitive impairment. For broader use, the translation of these biomarkers into blood-based biomarkers will be of utmost importance.
The main strength of this study is that we replicated our key findings in two large independent cohorts that represented different types of populations, used different PET tracers and also that we measured collected samples prospectively together with predefined outcome measures. Although further research is needed in a more diverse and generalizable population to implement our findings in the clinic, it is important to highlight that BioFINDER-2 participants were consecutively recruited from a secondary Clinical Memory in Sweden. As such, this cohort is a representative of memory clinical patients in Sweden and include both AD and also non-AD dementia patients. Limitations include that the magnitude of the trend differed between the two cohorts in some analyses although similar trends were shown. Potential reasons include that the BioFINDER-2 cohort includes more tau-PET-positive participants with AD dementia than the Knight ADRC cohort, as well as more participants in advanced stages of the disease, which may have affected the results with tau-PET and MMSE. Another limitation is that relatively few participants with AD dementia in the BioFINDER-2 cohort had an amyloid-PET scan per study design, although these participants all had CSF Aβ42/40. Thus, we used CSF Aβ42/40 instead of amyloid-PET as a marker of Aβ pathology in a sensitivity analysis and confirmed that this limitation did not affect the overall results and interpretations. Further, we acknowledge that our measures of Aβ and tau pathologies are only surrogate biomarkers and not actual measures of pathology, but both amyloid-PET and tau-PET markers have been validated against neuropathological measures of insoluble Aβ and tau aggregates, respectively 2,3,6,54-57 . Future studies

Fig. 4 | Associations between CSF biomarkers and MMSE. a,b, Associations between CSF biomarkers and MMSE are depicted for BioFINDER-2 (a, n = 342)
and Knight ADRC (b, pT231/T231: n = 184, rest: n = 219) participants. The first two columns display scatter-plots of MTBR-tau243 (first column) and pT217/T217 (second column) against MMSE, color-coded by diagnosis and amyloid status. In the BioFINDER-2 cohort, orange dots represent MCI+ participants, while in the Knight ADRC cohort, they represent individuals with very mild AD. Linear regression models, adjusted for age, sex and years of education were utilized to obtain β coefficients, P values (asterisks) and R 2 values shown in the plots. Scatter-plots for all biomarkers in both cohorts can be found in Extended Data Fig. 4. The third column shows the standardized β coefficients for all biomarkers, along with the associations of amyloid-PET and CSF Aβ42/40 (reversed) and tau-PET for comparison. Solid and dashed lines represent the standardized β coefficients (central dot) and 95% CI when including all participants or only amyloid-positive participants (BioFINDER-2, n = 261; Knight ADRC, n = 136, except for pT231/T231, where n = 117), respectively. Asterisks (crosses) indicate the highest or not significantly different standardized β coefficients in all (amyloid-positive only) participants within each cohort and outcome, based on bootstrapping. Non-AD participants from BioFINDER-2 were excluded from these analyses. Amyloid-positive participants were selected using previously validated cutoffs for CSF Aβ42/40 (CSF Aβ42/40 < 0.08 in BioFINDER-2 and CSF Aβ42/40 < 0.0673 in Knight ADRC). Association P values were derived from two-sided tests and bootstrapping p values were obtained from one-sided tests, all without adjustment for multiple comparisons. All p values for associations between CSF biomarkers and MMSE were <0.001. Article https://doi.org/10.1038/s41591-023-02443-z using animal models and neuropathological measures will be important to further validate the results here presented.
In conclusion, these findings confirm that CSF MTBR-tau243 specifically reflects changes in aggregated tau pathology that occur at a late stage of AD progression and are associated with clinical and cognitive symptoms. Thus, we suggest that MTBR-tau243 should replace the commonly used p-tau measures as the fluid biomarker representing insoluble tau aggregate pathology (T) in defining AD pathology and in future versions of the commonly used A/T/(N) criteria for AD 52 . As such, MTBR-tau243 could be used to assess AD tauopathy and track the effects of drug treatment independent of amyloid effects. The combination of CSF MTBR-tau243 and pT205/ T205 is nearly equivalent to tau-PET measures and predicts MMSE almost as accurately as tau-PET, which indicates clinical utility of a biomarker panel containing MTBR-tau243. Compared to biomarkers altered by amyloidosis that are often abnormal in older cognitively normal individuals, CSF MTBR-tau243 could enable confirmation of tau pathology and provide greater certainty that cognitive symptoms are due to AD, as proposed in the latest clinical AD criteria requiring biomarker evidence of both amyloid and tau pathology to diagnose AD with high likelihood 58 . These findings add to the improving biomarker diagnostic accuracy for AD and to strategies to develop new AD therapies.

Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41591-023-02443-z. The base model included age and sex (and years of education for MMSE) as predictors. The parsimonious model was derived by using LASSO regression to identify the optimal combination of CSF biomarkers and demographic factors (age, sex and/or years of education). Biomarkers included in the parsimonious models are indicated by a black border and their names are shown in bold. The other models solely employed individual CSF biomarkers as predictors. For comparison, CSF Aβ42/40 and tau-PET were used as predictors in independent models for predicting all outcomes and cognition only, respectively. Model comparisons were conducted using an F-test for nested models or Vuong's test for non-nested models. Non-AD cases were excluded from the BioFINDER-2 cohort for the cognition analyses.  59 and the MMSE 40 . Individuals with a CDR of 0.5 or greater were considered to have a dementia syndrome and the probable etiology of the dementia syndrome was formulated by clinicians based on clinical features in accordance with standard criteria and methods 60 .
In the BioFINDER-2 cohort, participants were divided in CU as either Aβ negative or positive (CU− and CU+, respectively), patients with MCI Aβ positive (MCI+), patients with AD dementia Aβ positive (AD+) or patients with non-AD neurodegeneration, regardless of their Aβ status. Two participants (one in CU− and the other in non-AD) were MAPT R406W mutation carriers with Aβ negative and tau positive. In the Knight ADRC cohort, participants were divided in CU with CDR of 0 either Aβ negative or positive (CU− and CU+, respectively), patients with very mild AD with CDR of 0.5 Aβ positive and patients with AD dementia with CDR ≥ 1 Aβ positive (AD+). In accordance with the research framework by the National Institute on Aging-Alzheimer's Association study, patients with SCD and cognitively normal controls were considered the CU group 5 . All participants gave written informed consent and ethical approval was granted by the Regional Ethical Committee in Lund, Sweden and the Washington University Human Research Protection Office, respectively.

Anti-tau antibody generation
Antibodies HJ32.11 and HJ34.8 were generated by immunizing tau knockout mice (The Jackson Laboratory) with either keyhole limpet hemocyanin (KLH) fused to amino acids 225-242 of tau to generate antibody HJ32.11 or to KLH fused to amino acids 226-264 to generate antibody HJ34.8. Spleen cells from immunized mice were fused with P3 hybridoma cells and expanded. Clones were screened by direct ELISA.

CSF measurements
Measurement of CSF tau species, including p-tau and MTBR-tau243 was performed at Washington University in both cohorts using the newly developed immunoprecipitation/mass spectrometry (IP/MS) method. We developed two new monoclonal antibodies to immune-purify CSF MTBR-tau243 (HJ32.11, which binds near residue 243 and HJ34.8, which binds near residue 260). The procedure of CSF tau analysis is described in Supplementary Fig. 5. The calculation of percent phosphorylation was performed by measuring the phosphorylated peptide and the non-phosphorylated peptide in the same injection and calculating the percent phosphorylation occupancy as % p-tau/t-tau (ref. 21).
Additionally, CSF Aβ42/40 levels were used in both cohorts to assess Aβ positivity. In the BioFINDER-2 cohort, CSF levels of Aβ42/40 were measured as previously explained 13 . A threshold of 0.080, based on a Gaussian mixture modeling, determined Aβ positivity 39 . In the Knight ADRC cohort, CSF Aβ42/40 levels were measured as explained previously 61 . The threshold (0.0673) had the maximum combined sensitivity and specificity in distinguishing amyloid-PET status.

Imaging acquisition and quantification
In the BioFINDER-2 cohort, amyloid and tau-PET acquiring methods have been previously reported 13 . Briefly, amyloid-PET was acquired using [ 18 33 . Amyloid-PET was measured in a neocortical meta-ROI using cerebellar gray as a reference region. In the BioFINDER-2 cohort, Centiloids were calculated using the Computational Analysis of PET from AIBL (CapAIBL) pipeline 62 . For tau-PET, SUVRs were calculated using the inferior cerebellum cortex as reference region and binding from a temporal meta-ROI were used for main analyses (Braak I-IV), to capture the regions most affected by tau. In supplementary analyses, we also quantified tau-PET in early (Braak I), intermediate (Braak III-IV) and late (Braak V-VI) regions of tau deposition 63 . Tau positivity was assessed based on tau-PET in all cases. In the Braak I-IV region, cutoff for positivity was set at SUVR > 1.32 both in BioFINDER-2 and in the Knight ADRC cohorts 64,65 .

Cognitive tests
MMSE was used as a measure of global cognition in both cohorts.

Statistical analyses
Differences in CSF biomarker levels by diagnostic groups were tested using ANCOVA adjusted for age and sex. Post hoc analyses were performed using the Tuckey test. Linear regression models were used to assess the association between amyloid-PET and tau-PET (independent variable) and each of the CSF biomarkers (dependent variable), after adjusting for age and sex. For cognition, we additionally used years of education as covariate in the linear regression models. All standardized β values were compared to the highest for each outcome and cohort, by building a distribution of the β values' difference and using that to infer significance using a bootstrapping approach (n = 500) with the boot package. Proportion of variation of CSF levels by amyloid and tau measures were assessed using linear regression models with both amyloid and tau as predictors, CSF levels as outcomes and age and sex as covariates. We calculated the partial R 2 of amyloid and tau, raw and as a percentage of the total R 2 of the model using the rsq package. This was used as a measure of proportion of variance explained by amyloid and tau. Next, prediction of amyloid and tau continuous measures was assessed with linear regression models, where amyloid-PET and tau-PET measures were used as outcomes in independent models and individual CSF biomarkers as predictors. A basic model was also created with only covariates (age and sex) as predictors. Additionally, a parsimonious model was constructed to optimally predict (highest accuracy with lower number of predictors) each of these measures, independently for each cohort. To this aim, LASSO regression models were used (glmnet package), initially including all CSF biomarkers and covariates. Only those predictors selected by the LASSO regression and with a significant contribution (P < 0.1) in the model were finally included in the parsimonious model. Similar methods were used for predicting cognition (MMSE in the two cohorts and CDR in Knight ADRC) additionally including years of education as covariate. In these cases, we compared the parsimonious model to one including only tau-PET as predictor. F-tests were used to compare nested models (including the same subset of predictors). When comparing models with different predictors we used the Vuong's test using the nonnest2 package. Finally, CSF longitudinal changes by baseline amyloid and tau status were assessed in the BioFINDER-2 cohort. Individual participant slopes were calculated using linear regression models to calculate https://doi.org/10.1038/s41591-023-02443-z rate of change differences (mean percentage change) and compare them between groups using a Kruskal-Wallis test. Further, we created group trajectories with linear mixed models using the lme4 package for visualization. Here, CSF biomarkers were used as outcome, interaction between time and baseline amyloid and tau status as predictor and age and sex main effects as covariates, using random intercepts and fixed time slopes due to low number of time points. CSF and amyloid-PET and tau-PET measures were log-transformed in linear regression analyses. A two-sided P value < 0.05 was considered statistically significant. R v.4.1.0 was used for all statistical analyses.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding authors (R.J.B. and O.H.). We will share datasets within the restrictions of institutional review board ethics approvals, upon reasonable request. Pseudonymized data from the BioFINDER-2 will be shared by request from a qualified academic investigator for the sole purpose of replicating procedures and results presented in the article and as long as data transfer is in agreement with EU legislation on the General Data Protection Regulation and decisions by the Ethical Review Board of Sweden and Region Skåne, which should be regulated in a material transfer agreement. Knight ADRC data are available to qualified investigators who have a proposal approved by an institutional committee (https://knightadrc.wustl.edu/ Research/ResourceRequest.htm) that meets monthly. The study must be approved by an institutional review board to ensure ethical research practices and investigators must agree to the terms and conditions of the data use agreement, which includes not distributing the data without permission.