Plasma P-tau217, APOE Genotype, and the Timing of Cognitive Impairment in individuals Across Diverse Racial and Ethnic Groups: A Pooled Analysis of Prospective Cohort Studies

Yuexuan Xu; Tamil Iniyan Gunasekaran; Yian Gu; Dolly Reyes-Dumeyer; Angel Piriz; Danurys Sanchez; Diones Rivera Mejia; Martin Medrano; Rafael A Lantigua; Alzheimer’s Disease Neuroimaging Initiative; Lawrence Honig; Rachael Wilson; Ramiro Eduardo Rea Reyes; Jennifer J Manly; Adam M Brickman; Corinne D Engelman; Sterling Johnson; Sanjay Asthana; David A Bennett; Melissa Petersen; Sid O’Bryant; Badri N Vardarajan; Richard Mayeux

doi:10.1016/S1474-4422(26)00313-3

. Author manuscript; available in PMC: 2026 Sep 27.

Published before final editing as: Lancet Neurol. 2026 Sep 9:S1474-4422(26)00313-3. doi: 10.1016/S1474-4422(26)00313-3

Plasma P-tau217, APOE Genotype, and the Timing of Cognitive Impairment in individuals Across Diverse Racial and Ethnic Groups: A Pooled Analysis of Prospective Cohort Studies

Yuexuan Xu ^1,², Tamil Iniyan Gunasekaran ^1,², Yian Gu ^1,^2,³, Dolly Reyes-Dumeyer ^1,², Angel Piriz ¹, Danurys Sanchez ¹, Diones Rivera Mejia ^4,⁵, Martin Medrano ⁶, Rafael A Lantigua ^1,^7,^†; Alzheimer’s Disease Neuroimaging Initiative^*, Lawrence Honig ^1,^2,^3,^†, Rachael Wilson ^8,⁹, Ramiro Eduardo Rea Reyes ^8,⁹, Jennifer J Manly ^1,^2,^3,^†, Adam M Brickman ^1,^2,^3,^†, Corinne D Engelman ^10,^†, Sterling Johnson ^8,^9,^†, Sanjay Asthana ^8,^9,^†, David A Bennett ^11,^12,^†, Melissa Petersen ^13,¹⁴, Sid O’Bryant ^13,^14,^†, Badri N Vardarajan ^1,^2,^#, Richard Mayeux ^1,^2,^3,^**,^†,^#

^1.Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA

^2.G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA

^3.Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, and the New York Presbyterian Hospital, New York, NY, USA

^4.CEDIMAT, Santo Domingo, Dominican Republic

^5.Universidad Pedro Henríquez Urena, Santo Domingo, Dominican Republic

^6.Pontificia Universidad Católica Madre y Maestra (PUCMM), Santiago, Dominican Republic

^7.Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, and the New York Presbyterian Hospital, New York, NY, USA

^8.Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

^9.Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

^10.Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

^11.Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA.

^12.Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.

^13.Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, Texas, USA

^14.Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, Texas, USA

Contributed equally to the manuscript.

^†

Indicates a full professor.

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete list of ADNI investigators can be found in Appendix 2.

Author contributions:

Yuexuan Xu: conceptualization, methodology, formal analysis, validation, visualization, data interpretation, writing – original draft, and writing – review and editing. Tamil Iniyan Gunasekaran: data curation and writing – review and editing. Yian Gu: methodology, data interpretation, and writing – review and editing. Dolly Reyes-Dumeyer, Angel Piriz, Danurys Sanchez, and Diones Rivera Mejia: data collection and/or curation, project administration, and writing – review and editing. Martin Medrano: data collection, resources, and writing – review and editing. Rafael A. Lantigua: resources, supervision, and writing – review and editing. The Alzheimer’s Disease Neuroimaging Initiative: data collection, data curation, resources, and funding acquisition. Lawrence Honig: clinical assessment, resources, data interpretation, and writing – review and editing. Rachael Wilson and Ramiro Eduardo Rea Reyes: data curation, resources, and writing – review and editing. Jennifer J Manly and Adam M. Brickman: resources, supervision, data interpretation, and writing – review and editing. Corinne D. Engelman, Sterling Johnson, Sanjay Asthana, David A. Bennett, and Sid O’Bryant: resources, funding acquisition, supervision, and writing – review and editing. Melissa Petersen: project administration, data curation, resources, and writing – review and editing. Badri N. Vardarajan: conceptualization, methodology, validation, supervision, project administration, funding acquisition, data interpretation, writing – original draft, and writing – review and editing. Richard Mayeux: conceptualization, resources, supervision, project administration, funding acquisition, data interpretation, writing – original draft, and writing – review and editing. Yuexuan Xu, Badri N. Vardarajan, and Richard Mayeux accessed and verified the underlying data. The other authors did not directly access the underlying individual-level data because data access was restricted to authorized investigators in accordance with the participating cohort’s data-use agreement. All authors had full access to the study results, reviewed and approved the final manuscript, and accept responsibility for the decision to submit the manuscript for publication.

^**

Correspondence: Richard Mayeux, MD, Neurological Institute, Columbia University, 710 West 168^th Street, New York, NY 10032, Fax: (212)305-0241, rpm2@cumc.columbia.edu

PMCID: PMC13615659 NIHMSID: NIHMS2211502 PMID: 42716078

ABSTRACT/SUMMARY

Background:

Plasma phosphorylated tau217 (P-tau217), a biomarker of Alzheimer’s disease (AD), can increase before overt symptoms. However, individuals with similar P-tau217 levels but different genetic backgrounds might differ in their risk or timing of cognitive decline. We aimed to determine whether APOE genotype provides additional prognostic information beyond plasma P-tau217 for estimating the risk and timing of cognitive impairment.

Methods:

We did a pooled analysis of participant-level data from seven multi-ethnic prospective cohorts of older adults spanning the Alzheimer’s disease clinical spectrum, from cognitively unimpaired individuals at elevated risk to those with mild cognitive impairment or dementia, at sites in the USA, Canada, and the Dominican Republic. Data were collected from 1992 through 2025. The primary outcome was cognitive impairment, defined as mild cognitive impairment or dementia. Baseline analyses included all eligible participants (those with data for plasma P-tau217, clinical diagnosis, APOE genotype, and required covariates); longitudinal analyses were restricted to participants who were cognitively unimpaired at baseline and had at least one follow-up clinical assessment. We evaluated whether APOE-ε4 carrier status modifies the risk and timing of cognitive impairment associated with continuous plasma P-tau217. Associations of baseline P-tau217 with prevalent and incident cognitive impairment were assessed using logistic regression and Cox models, stratified by APOE-ε4 and with interaction terms. Timing and prognostic performance were evaluated using survival analyses, discrimination measures, and random survival forests.

Findings:

Among 8,873 participants with available plasma P-tau217 and clinical data who were examined for eligibility, 8,582 participants (5,652 female and 2,930 male) were included in, the baseline analysis. Of these, 4,569 participants (3,173 female and 1,396 male) were cognitively unimpaired at baseline and were included in the longitudinal analysis. Increase in P-tau217 levels was associated with cognitive impairment at baseline and with incident cognitive impairment in APOE-ε4 carriers compared to non-carriers (OR = 2.25, 95% CI 1.52–3.34 vs 1.52, 95% CI 1.35–1.72; HR = 1.76, 95% CI 1.36–2.26 vs 1.26, 95% CI 1.12–1.42, for 1-SD increase of P-tau217 levels). Each 1-SD increase in P-tau217 levels was accompanied by a 24% shorter period to cognitive impairment among APOE-ε4 carriers, compared to 13% among non-carriers. Differences in cognitive-impairment-free survival by P-tau217 level emerged three to four years before symptom onset.

Interpretation:

Plasma P-tau217 levels, considered together with APOE genotype, may help stratify risk and estimate the timing of future cognitive impairment in asymptomatic individuals who are at elevated risk for Alzheimer’s disease, such as those with a family history of dementia or known APOE-ε4 carrier status. These findings support the use of biomarker-informed approaches to guide monitoring and therapeutic intervention in appropriately selected at-risk individuals before the onset of clinical symptoms.

INTRODUCTION

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with a long preclinical period before the onset of overt cognitive symptoms¹. Plasma tau phosphorylated at threonine 217 (P-tau217) is a biomarker of AD-related amyloid and tau pathology² and is strongly correlated with amyloid positron emission tomography (PET) uptake³. Elevated plasma P-tau217 precedes clinical symptoms and predicts amyloid pathology^4–6, and recent Alzheimer’s Association criteria⁷ have suggested including P-tau217 positivity in the definition of preclinical AD.

The apolipoprotein E (APOE) ε4 allele is the strongest genetic contributor to risk for AD. Individuals carrying APOE-ε4 develop AD pathology and symptoms earlier than non-carriers^8,9 and progress more rapidly¹⁰. Because plasma P-tau217 reflects ongoing AD-related pathology, it may help identify the likely time interval before the symptom onset. The age at plasma P-tau217 positivity predicts an estimated age at onset of AD symptoms¹¹ using analyses relying on model-based predicted ages rather than observed time-to-event. However, the period of time an individual remains free of cognitive impairment after biomarker assessment remains unknown. In addition, individuals with similar P-tau217 levels may differ substantially in the timing of symptoms onset according to genetic background, particularly the APOE ε4 status.

We hypothesized that APOE genotype modifies the prognostic value of plasma P-tau217 for estimating the risk and timing of cognitive impairment¹². This would provide an opportunity to identify a window for therapeutic intervention or clinical trials. We aimed to determine whether APOE genotype improved the prognostic value of plasma P-tau217 for estimating the risk and timing of cognitive impairment.

METHODS

Study design and participants

In this pooled multi-cohort study, longitudinal clinical data, APOE genotypes, plasma P-tau217 levels, and AD-related clinical outcomes, were included from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), the Wisconsin Alzheimer’s Disease Research Center (Wisconsin-ADRC), the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Estudio Familiar de Influencia Genética en Alzheimer (EFIGA), the Washington Heights, Hamilton Heights, Inwood Columbia Aging Project (WHICAP), the Religious Orders Study/Rush Memory and Aging Project (ROSMAP), and the Health & Aging Brain Study - Health Disparities (HABS-HD)^13–19. The contributing cohorts included participants recruited at sites in the United States, Canada, and the Dominican Republic. An in-depth overview of all methods is provided in Appendix 1 (pp 3–18). The study design for each cohort has been described previously^13–20. We harmonized WRAP and the Wisconsin-ADRC into a single Wisconsin cohort to increase sample size of symptomatic individuals. Data for this study were collected across the contributing cohorts from 1992 through 2025. The Columbia University Medical Center Institutional Review Board approved the study. Data from each cohort was accessed with approval from the respective principal investigators and their data access committees. All analyses were conducted in accordance with the STROBE guidelines²¹.

Participants were selected from the subsets of each contributing cohort with available plasma P-tau217 measurements. Eligibility for the baseline analyses included available clinical diagnosis, APOE genotype, and complete data for the covariates required for the analyses. For longitudinal analysis, participants were additionally required to be cognitively unimpaired at baseline and to have at least one subsequent clinical assessment. Because the proportion of missing data was small, participants with missing APOE genotype or data for any covariate required for a given analysis were excluded from that analysis; no imputation of missing data was performed. The same harmonized eligibility criteria were applied across cohorts. The study size was determined by including all available participants across the seven contributing cohorts who met the prespecified eligibility criteria. Sex and race and ethnicity were self-reported by participants in each cohort and harmonized across cohorts for the present analyses. Sex was categorized as female or male. Ethnic group identify was collected within each cohort, but distributions varied substantially. Wisconsin, ADNI, and ROSMAP included predominantly non-Hispanic White participants, followed by Black participants, with few Hispanic participants or participants from other ethnic groups. EFIGA included only Hispanic participants, whereas WHICAP and HABS-HD had more balanced racial and ethnic distributions. Hispanic participants and those from other ethnic groups were therefore combined to ensure adequate sample sizes, reduce sparse categories, and improve model convergence. To reduce potential selection and measurement bias, we applied harmonized eligibility criteria, standardized plasma P-tau217 within each cohort, adjusted analyses for prespecified covariates, and conducted cohort-specific and sensitivity analyses.

Procedures

Genotypes were generated using high-throughput platforms across cohorts, with quality control procedures described previously^22–24. For analysis, participants were grouped as ε4 carriers (ε3/ε4, ε4/ε4) and non-carriers (ε2/ε3, ε3/ε3). Individuals with ε2/ε2 and ε2/ε4 genotypes were excluded due to rarity and potentially protective effects of the ε2 allele.

The acquisition and processing of plasma P-tau217 for each cohort was described previously^25–30. Briefly, plasma P-tau217 was measured using ALZpath-based assays in WRAP/Wisconsin ADRC, HABS-HD, WHICAP, and EFIGA, Lumipulse in ADNI, and the Meso Scale Discovery platform in ROSMAP. Cohort-specific means and standard deviations (SD) of plasma P-tau217 for the full analytic sample and longitudinal sample are reported in Table 1 and Appendix 1 (p 20), respectively. Validated thresholds for plasma P-tau217 positivity were not available for all cohorts thus positivity-based analyses were not used as the primary analytical approach. Instead, we used continuous P-tau217 levels as the primary exposure. For cross-sectional analyses, P-tau217 values were log₁₀-transformed and z-standardized within the full sample of each cohort to harmonize measurement scales across platforms regardless of amyloid status; for longitudinal analyses, log transformation and standardization were performed using only cognitively unimpaired individuals at baseline within each cohort. As secondary analyses, we applied a three-range positivity approach using two cohort-specific thresholds (Appendix 1, p 19), one maximizing sensitivity and one maximizing specificity, to classify P-tau217 status. For cohorts without an established threshold, a proxy threshold was used.

Table 1.

Baseline characteristics of participants included in the analysis

Characteristic	ADNI	EFIGA	HABS-HD	ROSMAP	WHICAP	Wisconsin
Characteristic	N = 985¹	N = 1,426¹	N = 2,479¹	N = 1,035¹	N = 1,018¹	N = 1,639¹
Age at baseline (years)	74 (8)	72 (8)	66 (9)	79 (8)	76 (7)	63 (8)
Sex ²
Female	479 (49%)	960 (67%)	1,546 (62%)	844 (82%)	700 (69%)	1,123 (69%)
Male	506 (51%)	466 (33%)	933 (38%)	191 (18%)	318 (31%)	516 (31%)
Education (years)	16 (3)	6 (5)	13 (4)	16 (4)	11 (5)	16 (2)
Initial P-tau217 (pg/mL)	0.32 (0.34)	0.46 (0.42)	0.43 (0.46)	0.69 (0.62)	0.45 (0.35)	0.38 (0.27)
Initial P-tau217 positivity (sensitivity-cutoff)
Negative	484 (49%)	922 (65%)	1,698 (68%)	371 (36%)	628 (62%)	1,177 (72%)
Positive	501 (51%)	504 (35%)	781 (32%)	664 (64%)	390 (38%)	462 (28%)
Initial P-tau217 positivity (specificity-cutoff)
Negative	669 (68%)	1,255 (88%)	2,230 (90%)	758 (73%)	908 (89%)	1,452 (89%)
Positive	316 (32%)	171 (12%)	249 (10%)	277 (27%)	110 (11%)	187 (11%)
Initial cognitive status ³
Prevalent cognitive impairment	464 (47%)	606 (42%)	627 (25%)	285 (28%)	205 (20%)	80 (4.9%)
Cognitively unimpaired at baseline & No Follow-up	120 (12%)	491 (34%)	683 (28%)	18 (1.7%)	13 (1.3%)	421 (26%)
Cognitively unimpaired at baseline & Has Follow-up	401 (41%)	329 (23%)	1,169 (47%)	732 (71%)	800 (79%)	1,138 (69%)
Ethnic Group ⁴
Black	109 (11%)	0 (0%)	529 (21%)	380 (37%)	260 (26%)	102 (6.2%)
Non-Hispanic White	816 (83%)	0 (0%)	956 (39%)	574 (55%)	256 (25%)	1,454 (89%)
Others and Hispanics	60 (6.1%)	1,426 (100%)	994 (40%)	81 (7.8%)	502 (49%)	83 (5.1%)
*APOE* ⁵
2/3	80 (8.1%)	164 (12%)	258 (10%)	139 (13%)	146 (14%)	148 (9.0%)
3/3	472 (48%)	796 (56%)	1,563 (63%)	618 (60%)	625 (61%)	883 (54%)
3/4	343 (35%)	405 (28%)	595 (24%)	251 (24%)	228 (22%)	530 (32%)
4/4	90 (9.1%)	61 (4.3%)	63 (2.5%)	27 (2.6%)	19 (1.9%)	78 (4.8%)

Open in a new tab

Mean (SD); n (%)

Baseline characteristics of the biomarker analytic sample across cohorts. Biomarker availability and longitudinal follow-up varied by cohort; the baseline sample reflects the first available plasma biomarker measurement for each participant. For cohorts with only cross-sectional biomarker data, this represents a single time point; for longitudinal cohorts, it corresponds to the first time a biomarker was collected. There were no missing data for the variables presented in Table 1 because eligibility for the analytic sample required complete data for all variables included in the analyses.

Sex was self-reported by participants and categorized as male or female.

Baseline cognitive status was defined at the time when plasma biomarker data first became available for each participant. Cognitive impairment at follow-up reflects diagnoses made after this baseline assessment; any cognitive status assessments prior to biomarker availability were not considered.

⁴

Ethnic group identity was collected in detail within each cohort; however, the distribution varied substantially across studies. For example, Wisconsin, ADNI, and ROSMAP were predominantly composed of non-Hispanic White participants, followed by Black individuals, with very few Hispanic or other ethnic groups. In contrast, EFIGA included only Hispanic participants, while WHICAP and HABS-HD had a more balanced ethnic composition. To facilitate analysis and ensure adequate sample sizes within each category, we combined Hispanic and other ethnic groups into a single category. This grouping also helped stabilize regression models by reducing sparse categories and improving model convergence.

⁵

APOE ε2/ε2 and ε2/ε4 genotypes were excluded due to their low frequency and the potentially protective effects of the ε2 allele, which may confound interpretation of APOE-ε4–related risk associations.

All individuals were clinically evaluated and diagnosed during study visits in each cohort^13,20,31,32, without use of the plasma, CSF, or PET biomarkers. Cohorts varied substantially in age at recruitment and in the number of individuals with clinically diagnosed dementia, AD, and MCI (Table 1). To increase comparability across the AD clinical spectrum and elucidate the earliest changes in AD, the primary outcome was defined as cognitive impairment, combining MCI and dementia, including AD dementia, whereas individuals without cognitive impairment were classified as cognitively unimpaired.

Statistical Analysis

WHICAP and EFIGA had only cross-sectional plasma P-tau217 biomarker data, whereas most participants in ADNI, ROSMAP, and HABS-HD had only one or two longitudinal biomarker assessments, and Wisconsin had more extensive follow-up. All cohorts, however, included longitudinal clinical assessment data. To maximize comparability across cohorts, baseline was defined as the first visit with plasma biomarker measurement.

We characterized baseline plasma P-tau217 levels and examined their cross-sectional association with cognitive impairment. We subsequently summarized P-tau217 levels by cognitive status, age group (<=70 and >70), and APOE-ε4 status. Cohort-specific associations between baseline P-tau217 and cognitive impairment were evaluated using logistic regression adjusted for age, sex, education, ethnic group, and APOE genotype. To assess how associations differed by APOE-ε4 status, analyses were repeated within APOE-ε4 strata and the APOE-ε4 by P-tau217 interaction was formally tested.

We next examined the longitudinal prognostic value of baseline P-tau217 for incident cognitive impairment among individuals who were cognitively unimpaired at baseline. Cox proportional hazards models adjusted for age, sex, education, ethnic group, and APOE genotype were used to assess the association between baseline P-tau217 levels and subsequent onset of cognitive impairment. Incident events were dated from baseline to onset of cognitive impairment; all others were censored at last evaluation. The proportional hazards assumption was assessed using Schoenfeld residuals³³. We repeated the longitudinal analyses stratified by APOE-ε4 status and tested the APOE-ε4 *P-tau217 interaction to determine whether APOE-ε4 modified the prognostic value of baseline P-tau217. Random-effects meta-analyses were used to combine cohort-specific estimates for the cross-sectional association between baseline P-tau217 and cognitive impairment, and for the hazard ratio (HR) relating baseline P-tau217 to incident cognitive impairment, overall and within APOE-ε4 strata.

To characterize timing of progression and prognosis, we conducted pooled post hoc analyses across all cohorts, including cohort as a covariate to account for between-cohort differences. Parallel post hoc analyses using P-tau217 positivity defined by cohort-specific thresholds were performed as secondary analyses and are reported in Appendix 1 (pp 23–28, 31–34, 37–40, 43–64, and 66). Covariate-adjusted survival curves were used to identify the earliest divergence in cognitive-impairment free survival across clinically relevant P-tau217 levels (e.g., the mean and +1 SD within each cohort)^34–40. To quantify differences in time to impairment, we then compared the time at which adjusted survival reached 0.75 (S(t) = 0.75), a measure less influenced by incomplete follow-up in younger cohorts that did not reach the median survival time (e.g., Wisconsin). The proportional hazards assumption was violated in the pooled continuous P-tau217 models, likely due to between-dataset heterogeneity, thus these pooled Cox models were stratified²⁴.

We quantified average time free of cognitive impairment over time using restricted mean survival time (RMST) and fitted accelerated failure time (AFT) models to formally evaluate whether baseline P-tau217, and its interaction with APOE-ε4, were associated with earlier onset of cognitive impairment. AFT models were fitted using a lognormal distribution, which was selected based on model fit assessed by the Akaike information criterion. The lognormal AFT model provides a timing-based framework appropriate for right-skewed time-to-event data with a long tail and does not require the proportional hazards assumption. These models provided time-ratio estimates that complemented the Cox-based analyses⁴¹. For the survival curve and RMST analyses, we applied the Benjamini–Yekutieli false discovery rate (BY-FDR) procedure, which accommodates dependence among tests. For comparability across strata and to reduce overinterpretation of later estimates where fewer cohorts contributed data, survival and RMST estimates were truncated at 10 years.

Finally, we evaluated whether combining P-tau217 with APOE-ε4 improved prognostic performance using area under the ROC curve (AUC) and incremental R² in the cross-sectional models, and Harrell’s concordance index (C-index) in the longitudinal analyses. In the pooled longitudinal sample, stratification was applied for covariates that violated the proportional hazards assumption.

As sensitivity analyses, we repeated the survival, RMST, and discrimination analyses within each cohort. We also explored predictive performance over follow-up using non-parametric random forest models⁴². As an exploratory post-hoc analysis, we examined APOE associations with P-tau217 across different disease stages and with disease status, jointly defined by clinical cognitive impairment and P-tau217 positivity, to facilitate interpretation of between-cohort heterogeneity. We also tested whether the associations of APOE-ε4, P-tau217, and their interaction with cognitive impairment differed by race/ethnicity. All analyses were performed in R (v4.2.2).

Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

RESULTS

Among 8,873 participants with available plasma P-tau217 and clinical data who were examined for eligibility, 276 (3.1%) were excluded because of APOE-ε2/ε2 or ε2/ε4 genotypes and 15 (0.2%) because of missing APOE genotype or required covariate data. The remaining 8,582 participants (5,652 female and 2,930 male) comprised the full analytic sample, and their baseline descriptive statistics are presented in Table 1. Of these, 4,569 (53.2%) participants (3,173 female and 1,396 male) were cognitively unimpaired at baseline, had at least one follow-up clinical assessment, and comprised the longitudinal analytic subset described in Appendix 1 (pp 20–21). Overall, P-tau217 concentrations were higher in cognitively impaired individuals compared to individuals without cognitive impairment (Figure 1). Differences were more pronounced among APOE-ε4 carriers, including younger participants, with greater elevations in both magnitude and statistical significance across most datasets. In additional comparisons of cognitively unimpaired individuals with and without longitudinal follow-up, baseline characteristics were generally similar, although age and racial and ethnic composition differed in some cohorts (Appendix 1, p 22).

Figure 1. — Analyses were conducted at the time of the first available biomarker measurement. Data are shown separately for the Wisconsin, ADNI, EFIGA, WHICAP, ROSMAP, and HABS-HD cohorts. Cognitive status was classified as cognitively unimpaired or Impaired (mild cognitive impairment [MCI], dementia, or Alzheimer’s disease [AD]). Participants were further stratified by *APOE* genotype (ε2/ε3, ε3/ε3 or ε3/ε4, ε4/ε4) and by age group (<=70 years vs >70 years). Boxplots display the median, interquartile range, and individual participant values (dots). Differences in plasma P-tau217 levels between groups were assessed using Welch’s t-test. Wisconsin: a combined sample from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center; ADNI: Alzheimer’s Disease Neuroimaging Initiative; EFIGA: Estudio Familiar de Influencia Genética en Alzheimer; WHICAP: Washington Heights-Hamilton Heights-Inwood Columbia Aging Project; ROSMAP: The Religious Order Study and Memory and Aging Project; HABS-HD: The Health and Aging Brain Study–Health Disparities.

Statistically significant associations between P-tau217 and baseline cognitive impairment were observed in the Wisconsin, ADNI, EFIGA, ROSMAP, and HABS-HD datasets (Figure 2A). In meta-analyses across cohorts, each 1-unit increase in within-cohort standardized, log-transformed P-tau217 level was associated with higher odds of cognitive impairment (OR = 1.77, 95% CI: 1.42–2.20). When stratified by APOE-ε4 status, the association remained significant in both groups but was stronger among APOE-ε4 carriers (OR = 2.25, 95% CI: 1.52–3.34) than non-carriers (OR = 1.52, 95% CI: 1.35–1.72). Consistent with this pattern, the combined-sample model showed a significant P-tau217 by APOE-ε4 interaction (p<0.0001). Similar interaction effects were observed in Wisconsin and ADNI and were marginal in EFIGA (Figure 2B–2C).

Figure 2. — (A) Harmonized plasma P-tau217 and clinical cognitive impairment, along with meta-analytic estimates in the full sample and stratified subgroups. We also evaluated *APOE*ε4 × P-tau217 interactions in the combined sample, which included all cohorts (B), as well as within each individual cohort (C). Analyses were conducted at the time of first available biomarker measurement. For cohorts with only cross-sectional data, this corresponds to a single time point; for longitudinal cohorts, it reflects the earliest available measure. Plasma P-tau217 was harmonized using log₁₀ transformation and z-standardization within each cohort. Associations were tested using logistic regression models adjusted for age, sex, education, ethnic group, cohort (in the combined sample only), and *APOE* genotype; Within each full or stratified subgroup, random-effects meta-analyses were performed using cohort-specific ORs. Cognitive impairment was defined as a diagnosis of MCI, dementia, or AD.

Higher baseline P-tau217 levels were associated with an increased risk of incident cognitive impairment in the Wisconsin, ADNI, WHICAP, ROSMAP, and HABS-HD datasets (Figure 3A). In meta-analyses across cohorts, each 1-unit increase in within-cohort standardized, log-transformed P-tau217 level was associated with incident cognitive impairment (HR = 1.41, 95% CI: 1.22–1.64). When stratified by APOE-ε4 status, the association was stronger among APOE-ε4 carriers (HR = 1.76, 95% CI: 1.36–2.26) than among non-carriers (HR = 1.26, 95% CI: 1.12–1.42). Consistent with this pattern, the pooled sample showed a statistically significant P-tau217 by APOE-ε4 interaction (p=0.00066). In cohort-specific analyses, significant interactions were observed only in Wisconsin and ADNI, both of which were enriched for non-Hispanic White participants (Appendix 1, p 23). Results from positivity-based analyses are presented in Appendix 1 (p 31).

Figure 3. — Cohort-specific hazard ratios for incident cognitive impairment in the full sample and *APOE*-stratified subgroups using harmonized continuous P-tau217 measures, along with meta-analytic estimates (A) and adjusted survival probabilities (B) illustrating the temporal dynamics of P-tau217 in the pooled cohort. Analyses were restricted to individuals who were cognitively unimpaired at baseline and had follow-up data. P-tau217 values were log₁₀ transformed and standardized within each cohort. Cox models were adjusted for age, sex, education, race and ethnicity, and *APOE* genotype. The proportional hazards assumption was tested using Schoenfeld residuals. Within each full or stratified subgroup, random-effects meta-analyses were conducted using cohort-specific estimates. In Panel B, covariate-adjusted survival curves were plotted at pre-defined P-tau217 levels, including the cohort-specific mean and +1 SD above the mean. The black vertical dashed line marks the earliest follow-up year at which the two adjusted survival curves began to diverge after correction for multiple testing. The red horizontal dashed line indicates an adjusted cognitive-impairment-free survival probability of 0.75. Incident cognitive impairment was defined as conversion from cognitively unimpaired to either MCI, dementia, or AD.

In pooled analyses using continuous P-tau217, adjusted survival curves showed little difference in cognitive-impairment-free survival during the first 3 years between individuals with P-tau217 levels 1 SD above the mean and those at the mean, suggesting a delay between elevated baseline P-tau217 and clinically detectable cognitive impairment (BY-FDR–adjusted, Figure 3B). However, at year 4, APOE-ε4 carriers with P-tau217 levels 1 SD above the mean began to diverge from those at the mean, indicating the onset of cognitive impairment, whereas no clear separation was observed among non-carriers. RMST analyses showed a similar overall pattern (Appendix 1, p 33), supporting a delayed emergence of the association between higher P-tau217 and cognitive-impairment-free survival, particularly among APOE-ε4 carriers.

Higher P-tau217 levels were associated with earlier cognitive impairment, with a larger shift in timing among APOE-ε4 carriers than among non-carriers. Relative to individuals at the mean P-tau217 level, those with P-tau217 levels 1 SD above the mean reached the 75% cognitive-impairment–free survival threshold 1.8 years earlier among APOE-ε4 carriers (95% bootstrap CI 1.0–3.0) and 0.8 years earlier among non-carriers (95% bootstrap CI 0.3–1.4). Consistent with these findings, pooled AFT models showed a significant APOE-ε4 by P-tau217 interaction, indicating that the association between higher P-tau217 and earlier cognitive impairment was stronger among APOE-ε4 carriers than among non-carriers (Appendix 1, p 24). Specifically, each 1-SD increase in within-cohort standardized, log-transformed P-tau217 level was associated with a 24% shorter time to cognitive impairment among APOE-ε4 carriers, compared with a 13% shorter time among non-carriers. Similar patterns were observed in the positivity analyses (Appendix 1, pp 24 and 31–34), and results were broadly consistent across cohorts (Survival curve: Appendix 1, pp 35–40; RMST: Appendix 1, pp 41–46).

Discrimination analyses showed strong baseline predictive performance for continuous P-tau217 (AUC = 0.795) and APOE-ε4 (AUC = 0.774) (Appendix 1, p 25). Notably, continuous P-tau217 among APOE-ε4 carriers yielded the highest discrimination (AUC = 0.835), exceeding that of the additive model including both continuous P-tau217 and APOE-ε4, suggesting that stratification by APOE-ε4 status may better capture risk differences than modeling the two markers additively. Similar patterns were observed for incremental R² and longitudinal C-index estimates (Figure 4). These findings were generally consistent across most cohorts. Results from the positivity-based analyses are presented in Appendix 1 (pp 47–48), and cohort-specific results are shown in Appendix 1 (pp 49–60). Findings were also generally consistent when positivity was defined using a uniform cut-off derived from the Wisconsin cohort and applied across datasets (Appendix 1, pp 61–64). In a separate leave-one-cohort-out external validation analysis, random survival forest models including P-tau217 and covariates showed the best predictive performance among APOE-ε4 carriers compared with the other models. Time-dependent AUC values exceeded 0.75 at most time points, and the magnitude of performance improvement increased with longer follow-up time across cohorts (Appendix 1, p 65).

Figure 4. — Receiver operating characteristic (ROC) curves were obtained from logistic regression models adjusted for age, sex, education, cohort, and ethnic group. Models included *APOE*-ε4 status with covariates (“*APOE*-ε4”); plasma P-tau217 with covariates (“P-tau217”); plasma P-tau217 and *APOE*-ε4 entered additively with covariates (“P-tau217 + *APOE*-ε4 [additively]”); and the plasma P-tau217 with covariates fitted separately within *APOE*-ε4 carrier (“P-tau217 [ε4 carriers]”) and non-carrier groups (“P-tau217 [ε4 non-carriers]”). Incremental Nagelkerke’s R² was defined as the increase in model fit when adding the predictor or the predictors of interest to the covariate-only model. Harrell’s C-indices were obtained from Cox proportional hazards models adjusted for the same covariates and specified analogously.

We examined cross-cohort and race-ethnicity-related heterogeneity. Study-specific analyses showed that APOE-ε4 was significantly associated with P-tau217 levels in all cohorts, including among cognitively unimpaired individuals within each cohort (Appendix 1, pp 66–67). APOE-ε4 was also associated with P-tau217 levels among biomarker negative individuals, regardless of whether negativity was defined using sensitivity- or specificity-based threshold. Similarly, APOE-ε4 was associated with P-tau217 positivity under both threshold definitions (Appendix 1, pp 66–67). Notably, these associations showed essentially no heterogeneity across cohorts among cognitively unimpaired and P-tau217–negative individuals when the sensitivity-based cutoff was applied.

The proportion of cognitively impaired who were P-tau217 positive differed substantially across racial/ethnic groups. Under the sensitivity-based cutoff, 74% of cognitively impaired individuals among non-Hispanic white participants were P-tau217 positive, compared with 52% among Black individuals, and 45% among Hispanic individuals. Under the specificity-based cutoff, the corresponding proportions were 51%, 24%, and 22%, respectively. Within each racial/ethnic group, P-tau217 positivity was generally higher among APOE-ε4-carriers than non-carriers, particularly among cognitively impaired individuals (Appendix 1, p 26). When APOE-ε4 associations were examined across groups jointly defined by clinical status and P-tau217 positivity, the strongest associations were consistently observed in P-tau217-positive groups across racial/ethnic groups. In contrast, the association between APOE-ε4 and clinically impaired but P-tau217-negative individuals was substantially weaker, with only minor variation across racial/ethnic groups (Appendix 1, pp 27–28). We further evaluated whether the associations of APOE-ε4 and P-tau217 with cognitive impairment differed by race/ethnicity using likelihood ratio tests. We found evidence that the associations of APOE-ε4 and P-tau217 with cognitive impairment varied across racial/ethnic groups. However, the P-tau217 × APOE-ε4 interaction did not significantly differ by race/ethnicity. Similar patterns were observed in the longitudinal survival sample using AFT models (Appendix 1, pp 29–30). In addition, meta-analysis of cohort-specific P-tau217 × APOE-ε4 interaction term showed a similar overall significant interaction in both the baseline logistic regression and longitudinal AFT models, with interactions estimates generally in the same direction across cohorts (Appendix 1, p 68). No significant interactions were observed among P-tau217, APOE-ε4, and sex.

DISCUSSION

In this study of 8,582 individuals from seven cohorts with diverse ethnic backgrounds, we found that plasma P-tau217 informs, not only the risk of cognitive impairment, but provides time window over which cognitive impairment may emerge that differs by APOE genotype. Although higher baseline P-tau217 was associated with prevalent cognitive impairment, greater risk of incident impairment, and earlier onset of symptoms overall, individuals with similar P-tau217 levels did not follow the same clinical trajectory. In particular, the prognostic effect of elevated P-tau217 was stronger among APOE-ε4 carriers than non-carriers, indicating that APOE genotype can augment the clinical interpretation of a given P-tau217 level. Importantly, elevated baseline P-tau217 preceded clinically detectable divergence in cognitive impairment free survival time by approximately three years in the full sample and about four years among APOE-ε4 carriers. Together, these findings suggest that P-tau217 alone does not fully capture near-term progression risk, and that incorporating APOE genotype improves estimations of risk and timing of cognitive impairment. These conclusions were further supported by complementary prediction analyses, in which plasma P-tau217 showed the strongest discrimination among APOE-ε4 carriers, outperforming either marker alone, their additive combination, and corresponding models among non-carriers, in baseline logistic AUC analyses and in out-of-sample random survival forest prediction of future cognitive impairment.

Elevated plasma P-tau217 is widely viewed as a marker of underlying AD pathology, but our findings suggest that this prognostic interpretation is not uniform across individuals. At a similar level of baseline P-tau217, APOE-ε4 carriers have a steeper transition toward cognitive impairment, a shorter impairment-free interval, and a higher risk of progression than non-carriers. These findings indicate that comparable P-tau217 levels may reflect different stages or rates of disease progression depending on genetic background. One possible interpretation is APOE-ε4 may accelerate downstream consequences of AD pathology. Prior studies have suggested that APOE-ε4 impairs lipid transport in the brain, promotes glial lipid droplet accumulation, reduces microglial phagocytic function, and increases neurotoxic responses^43–45. APOE-ε4 may also contribute through vascular and blood-brain barrier pathways, which could lower resilience to amyloid and tau pathology and promote earlier clinical progression^46,47. All these mechanisms could favor amyloid buildup, tau spread, and subsequent neuronal injury. Our findings are consistent with previous studies showing faster progression and greater risk associated with elevated P-tau217 among APOE-ε4 carriers¹².

The observations here show that elevated P-tau217 preceded the clinically detectable separation in the incidence of cognitive impairment, particularly among APOE-ε4 carriers. This indicates that plasma P-tau217 can be used to identify a clinically meaningful pre-symptomatic window albeit when AD pathology accumulates. In contrast, there was no clear separation was observed among non-APOE-ε4 carriers over the same interval. Prior studies have linked plasma P-tau217 positivity to age at symptoms onset, supporting the idea that plasma P-tau217 carries temporal information about disease progression¹¹. However, these studies were mainly prediction-focused and asked when symptoms may occur. Our results add a more clinically relevant perspective by showing the earliest time point at which elevated baseline P-tau217 becomes associated with measurable divergence in cognitive outcomes. In this way, the lag window is defined not by predicted symptom-onset age, but by the emergence of detectable differences in cognitive impairment during follow-up. This provides a conservative estimate of the preclinical interval before symptoms become clinically apparent, which would be useful for prevention trials and early intervention strategies using existing AD biomarkers.

The prognostic value of plasma P-tau217 for cognitive impairment varied across cohorts with different ethnic compositions, appearing more robust in non-Hispanic white individuals compared to ethnically diverse individuals, consistent with previous findings^48,49. This heterogeneity could partly reflect differences in the pathological basis of cognitive impairment across these cohorts. Cognitive impairment is a syndromic outcome rather than a pathology-specific diagnosis, and the extent to which it reflects underlying AD pathology may differ by cohort and racial/ethnic composition. In cohorts where clinically defined impairment more closely reflects underlying AD, elevated P-tau217 may provide a clearer estimate of the interval before clinically detectable divergence in outcomes, whereas in cohorts with greater pathological heterogeneity, the corresponding lag period may be less distinct.

Formal interaction analyses further supported this interpretation. The associations of APOE-ε4 and P-tau217 with cognitive impairment varied by ethnicity and appeared stronger among non-Hispanic White individuals than among other racial and ethnic groups^49,50. However, the P-tau217 by APOE-ε4 interaction did not significantly differ by ethnicity in either cross-sectional logistic models or longitudinal AFT models. Although the statistical significance of the interaction varied across cross-sectional and longitudinal analyses, the direction of the interaction was generally consistent across racial/ethnic groups. In addition, meta-analysis of cohort-specific P-tau217 × APOE-ε4 interaction across cohorts with varying racial/ethnic compositions showed a significant overall interaction, further supporting the robustness of this modifying effect. Together, these findings suggests that although the overall prognostic strength of each marker may vary across populations, the modifying effect of APOE-ε4 on the relationship between P-tau217 and cognitive impairment was broadly consistent. Nevertheless, because sample sizes were limited in some racial/ethnic groups, several groups had to be combined. Future studies with larger and more diverse samples should evaluate whether these findings vary across more specific racial/ethnic subpopulations, particularly among Hispanic populations.

The distinction between biomarker-defined and clinical-defined disease may also help explain heterogeneity in APOE-ε4 associations across cohorts and racial/ethnic groups. APOE-ε4 associations with P-tau217 appeared more consistent at earlier disease stages, before cognitive symptoms had occurred, and became more heterogeneous once symptomatic individuals were included. This pattern indicates that cross-cohort differences are less likely to arise from the relationship between APOE and early AD-related biomarker changes themselves, and more likely to emerge at the stage of clinically defined impairment. This may also help explain previously observed heterogeneity in APOE associations with clinically defined AD and cognitive impairment when biomarker information was not incorporated into diagnosis⁵⁰. Consistent with this interpretation, APOE associations were stronger among P-tau217-positive individuals regardless of clinical diagnosis but were weaker among clinically impaired individuals who were P-tau217-negative. The APOE association in P-tau217-positive impaired individuals, relative to cognitively unimpaired biomarker negative controls, was similar to that reported in neuropathologically confirmed AD, indicating that this biomarker-defined group more closely reflects underlying AD pathology⁵¹.

These findings have direct implications for clinical trial recruitment and enrichment strategies. Among cognitively unimpaired individuals, APOE-ε4 carriers who are P-tau217-positive represent a high-priority population for prevention trials, as they are likely to progress to cognitive impairment over a shorter interval than P-tau217-positive with other APOE genotypes. Trials targeting preclinical AD may therefore maximize efficiency by prioritizing recruitment of APOE-ε4 carriers with P-tau217 positivity, as this combination identifies individuals at greatest near-term risk. For studies that include individuals with lower APOE-ε4 frequencies, investigators may anticipate longer follow-up periods or larger sample sizes to observe incident cognitive impairment.

Our study has some limitations. We measured P-tau217 at a single time point in most cohorts, which prevented us from capturing longitudinal within-person changes and our ability to define the lag period in relation to biomarker progression itself. P-tau217 positivity may have clinical utility because binary classification could provide a more intuitive framework for risk stratification than continuous biomarker analyses alone. However, threshold for positivity likely varied across cohorts because of assay and population differences, and appropriate cutoffs remain uncertain in several settings. As a result, binary classification may have introduced additional heterogeneity and reduced the robustness and generalizability of cutoff-based estimates across cohorts. We therefore treated positivity analyses as secondary and based our primary analyses on cohort-standardized continuous P-tau217 measures, while recognizing that broadly validated thresholds would be important for future clinical implementation. Although cohort-standardization of continuous P-tau217 reduced differences in scale and distribution across assays, it may not fully account for assay-specific differences in calibration, analytical sensitivity, dynamic range, or measurement error, which could contribute to heterogeneity in cohort-specific estimates.

Adjusted survival curves and RMST analyses suggested that the effect of P-tau217 on risk of cognitive impairment may not be constant over time. Limited sample sizes and events within individual cohorts, together with substantial heterogeneity in baseline hazards and follow-up structures, made precise estimation of time-dependent hazard ratios challenging, even in the combined sample. Nevertheless, the main findings were robust in accelerated failure time models, which provided a complementary framework for assessing differences in timing of progression without relying on the proportional hazards assumption. The present study focused specifically on the modifying effect of APOE-ε4 on the prognostic value of P-tau217. However, APOE-ε4 represents only a single genetic factor, and future studies should examine whether broader genetic profiles, including polygenic risk scores and other susceptibility variants, to further refine the relationship between P-tau217 and the risk and timing of cognitive impairment. Differences in the definitions of conversion to cognitive impairment, particularly mild cognitive impairment may have introduced heterogeneity across cohorts. The outcome also captured cognitive impairment of any type, limiting our ability to distinguish incident AD dementia from non-AD dementia.

Although the cohorts were multi-ethnic, they were based in the Western Hemisphere, and some racial/ethnic groups were combined because of sample-size constraints; broader global validation is needed. In addition, some differences between cohorts may remain, including differences in recruitment, follow-up, clinical assessment, and assay implementation, which may have contributed to heterogeneity in cohort-specific estimates. Differential availability and duration of longitudinal follow-up may have introduced selection bias, particularly in cohorts where fewer participants had repeated cognitive assessments. Although baseline characteristics were generally similar between cognitive unimpaired individuals with and without follow-up, differences in racial and ethnic composition and age were observed in some cohorts, and unmeasured differences related to memory concerns, family history, comorbidity burden, or health status cannot be excluded. Renal function and other comorbidities may influence plasma P-tau217 concentrations⁵², but these measures were not available in all cohorts. Therefore, residual confounding by renal function and comorbidity burden cannot be excluded and should be evaluated in future studies with harmonized clinical laboratory and comorbidity data. This study focused on plasma P-tau217 as one of the most promising blood-based biomarkers for AD-related pathology, but future studies should evaluate whether broader genetic profiles interact with multiple AD-related biomarkers, including amyloid, tau, neurodegeneration, and inflammatory markers, to further refine prediction of cognitive impairment. Lastly, residual confounding by unmeasured socioeconomic and structural factors may have influenced the observed differences across racial and ethnic groups.

Assays for plasma P-tau217 and APOE genotyping are broadly available. As demonstrated, when used together, plasma P-tau217 and APOE genotyping provide robust early predictions of cognitive impairment, particularly among APOE-ε4 carriers. These findings suggest that combined biomarker and genetic information may help identify individuals at elevated risk before symptom onset, thereby informing monitoring and potential therapeutic timing in appropriately selected clinical or research contexts. Although prognostic strength for clinical diagnosis varied by ethnicity, the relationships of APOE genotyping and P-tau217 with the underlying biological process appeared broadly consistent across ethnic groups, supporting further evaluation of their utility for early detection and treatment planning.

Supplementary Material

NIHMS2211502-supplement-1.docx^{(9.8MB, docx)}

NIHMS2211502-supplement-2.docx^{(51.3KB, docx)}

Research in context.

Evidence before this study:

We searched PubMed and Google Scholar from database inception to April 30, 2026, for observational studies examining plasma P-tau217 in relation to cognitive decline, cognitive impairment, dementia, timing of symptom onset, or Alzheimer’s disease related biomarkers, including studies that considered APOE genotype. We used combinations of the search terms “plasma P-tau217”, “phosphorylated tau 217”, “blood biomarker”, “APOE”, “cognitive decline”, “cognitive impairment”, “dementia”, “Alzheimer’s disease”, “survival”, “time to event”, and “age at symptom onset”. The search was not restricted by language. The available evidence was of moderate quality but was limited by observational designs, heterogeneous assays and thresholds, a predominant focus on non-Hispanic White populations, and insufficient assessment of APOE-related effect modification and observed time-to-event outcomes.

Prior studies have shown that elevated P-tau217 levels are associated with greater risk of cognitive decline and may be elevated years before overt symptoms. However, whether APOE-ε4 carrier status modifies the risk and timing of cognitive impairment at a given P-tau217 level remains unclear, particularly using directly observed time-to-event rather than model-based age at symptom onset and in racially and ethnically diverse populations.

Added value of this study:

Using data from 8,582 individuals across several multi-ethnic cohorts, we examined whether APOE-ε4 carrier-status influenced the clinical prognosis using plasma P-tau217 for future cognitive impairment. By using observed clinical follow-up rather than predicted age at onset, we were able to evaluate both risk and timing in a more clinically relevant setting.

Implications of all the available evidence:

Plasma P-tau217 appears to provide prognostic information not only about whether cognitive impairment is likely to occur, but also about the likely time window before symptom onset. APOE-ε4 carrier status refines this interpretation. Together, these findings support a more personalized prognostic framework in which the clinical meaning of a given P-tau217 level depends in part on genetic background, with potential relevance for risk stratification, trial enrichment, and timing of early intervention in diverse population groups.

Acknowledgements:

We acknowledge the participants from the Washington Heights-Inwood Columbia Aging Project (WHICAP), the Genetic Studies of Alzheimer’s Disease in Caribbean Hispanics (EFIGA), the Wisconsin Registry for Alzheimer’s Prevention (WRAP), the Wisconsin Alzheimer’s Disease Research Center (Wisconsin ADRC), the Health & Aging Brain Study – Health Disparities (HABS-HD), the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) for their invaluable contributions and dedication. This study would not be possible without their continued participation. We also thank the researchers and study teams involved in WHICAP, EFIGA, WRAP, the Wisconsin ADRC, HABS-HD, ROSMAP, and ADNI for their longstanding efforts in data collection and subject follow-up over the years. The authors wish to acknowledge and thank the physicians in the Dominican Republic, Belisa Soriano, MD, Yahaira Franco, MD, Zoraida Dominguez Coronado, MD, Patricia Recio, MD for their help recruiting individuals in the EFIGA study. Author YX would also like to thank Yuanyuan Qiao for her patience, support, and assistance with proofreading this study. All individuals named in the Acknowledgements section provided written consent to be acknowledged.

The original and revised versions of this manuscript were prepared using Microsoft Word. Microsoft Editor and the built-in ChatGPT add-in for Microsoft Word were used solely to assist with grammar, punctuation, spelling, and language clarity. ChatGPT was also used to check the consistency of reported values with those presented in figures and tables and to identify formatting inconsistencies, such as whether p values were formatted correctly to two significant figures. No scientific content, figures, tables, analyses, interpretations, or conclusions were generated by these tools. All suggested edits were reviewed by the authors. Following revision, the manuscript was independently reviewed by two senior authors. The final manuscript was reviewed, approved, and accepted by all authors, who take full responsibility for its content.

Funding:

This work was supported by the National Institute on Aging (NIA) and the National Institutes of Health (NIH). Data collection and research activities were funded through multiple grants, including support for the Genetic Studies of Alzheimer’s Disease in Caribbean Hispanics (EFIGA: R01 AG067501), the Washington Heights-Inwood Columbia Aging Project (WHICAP: R01 AG072474, RF1 AG066107), and the National Center for Advancing Translational Sciences (NCATS) through Grant Number UL1TR001873. Additional support was provided for the Wisconsin Registry for Alzheimer’s Prevention (WRAP: R01 AG027161), the Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer’s Disease (R01 AG054047, RF1 AG054047), and the Wisconsin Alzheimer’s Disease Research Center (P30 AG062715). Data collection and sharing for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were funded by the National Institute on Aging (National Institutes of Health Grant U19AG024904). The grantee organization is the Northern California Institute for Research and Education. ADNI has also received support from the National Institute of Biomedical Imaging and Bioengineering, the Canadian Institutes of Health Research, and private sector contributions through the Foundation for the National Institutes of Health (FNIH), including generous donations from the following organizations: AbbVie; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers 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. We are deeply grateful to all participants, researchers, and collaborators who contributed to this study. ROSMAP is supported by P30AG10161, P30AG72975, R01AG17917. R01 AG015819, R01 AG34374, U01 AG072572, and U01 AG046152. Research reported for HABS-HD was supported by the NIA/NIH under Award Numbers R01AG054073, R01AG058533, R01AG070862, P41EB015922 and U19AG078109.

Conflict of interest:

Dr. Lawrence Honig reports grants or contracts paid to his institution from the New York State Department of Health, Lewy Body Disease Association, CurePSP, Acumen, Alector, Alnylam, Biogen, Bristol-Myer Squibb, Cervomed/EIP, Cognition, Eisai, Ferrer, Genentech/Roche, Janssen/Johnson & Johnson, Transposon Therapeutics, UCB, and Vaccinex. He has received personal consulting fees from Beckman, BioArctic, Biogen, BMS, Eisai, Genentech/Roche, and New Amsterdam; personal honoraria from Medscape and Integritas; and has participated on data safety monitoring or advisory boards for VectorY, Eli Lilly, and Lighthouse. Dr. Rachael Wilson reports that donor funds from the UW Initiative to End Alzheimer’s were provided to her institution to purchase equipment used for sample analysis. Dr. Jennifer Manly reports grants from the NIH/NIA and NIH/NINDS paid to her institution, consulting fees from RAND and the University of Mississippi, and support from the Alzheimer’s Association for meeting registration. Dr. Adam Brickman reports consulting fees from Cogstate, Cognito Therapeutics, Cognition Therapeutics, IQVIA, Tau Biosciences, and Xincere Medtech; personal honoraria from Celdara; a US patent (No. 9,867,566) and a pending patent application (publication No. 20230298170); participation on a data safety monitoring or advisory board for the University of Illinois Urbana-Champaign; service as a section editor for Alzheimer’s & Dementia; and stock options in CogniScreen and Tau Biosciences. Dr. Sterling Johnson reports personal consulting fees from Alamar Bio, Eisai, Enigma Biomedical, Lilly, Lantheus, Trillium Bio, and Roche Diagnostics. Dr. Sanjay Asthana reports NIH/NIA grants, including P30 funding for the Wisconsin ADRC, an R01 grant, a T32 training grant, and an R24 grant; royalties as an editor of Hazzard’s Geriatrics and Gerontology from McGraw Hill; and travel support from the University of Wisconsin-Madison to attend scientific meetings. Dr. Richard Mayeux reports a provisional, non-public patent application (USPTO No. 63/705,622). All other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Ethics statement:

The studies involving humans were approved by the Institutional Review Board of the Columbia University Medical Center. The studies were conducted in accordance with the local legislation and institutional requirements. Subjects from WRAP, Wisconsin-ADRC, ADNI, WHICAP, EFIGA, ROSMAP, and HABS-HD have provided signed informed consent before participation.

Data availability statement:

The data analyzed in this study are subject to the following licenses/restrictions: datasets can be requested through formal research applications to the Washington Heights-Inwood Columbia Aging Project and the Genetic Studies of Alzheimer’s Disease in Caribbean Hispanics (EFIGA). Requests to access these datasets should be directed to the Columbia University Alzheimer’s Disease Research Center: https://www.neurology.columbia.edu/research/research-centers-and-programs/alzheimers-disease-research-center-adrc/investigators/investigator-resources.

Data may also be requested from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center via: https://wrap.wisc.edu/data-requests-2/, from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): https://adni.loni.usc.edu/data-samples/adni-data/, from the Health & Aging Brain Study (HABS-HD): https://apps.unthsc.edu/itr/our/, and from the Religious Orders Study and Memory and Aging Project (ROSMAP): https://www.radc.rush.edu/requests.htm.

Reference

1.Jia J, Ning Y, Chen M, et al. Biomarker Changes during 20 Years Preceding Alzheimer’s Disease. New England Journal of Medicine 2024; 390: 712–22. [DOI] [PubMed] [Google Scholar]
2.Therriault J, Ashton NJ, Pola I, et al. Comparison of two plasma p-tau217 assays to detect and monitor Alzheimer’s pathology. eBioMedicine 2024; 102. DOI: 10.1016/j.ebiom.2024.105046. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Ashton NJ, Brum WS, Di Molfetta G, et al. Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology. JAMA Neurol 2024; 81: 255–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Yakoub Y, Gonzalez-Ortiz F, Ashton NJ, et al. Plasma p-tau217 identifies cognitively normal older adults who will develop cognitive impairment in a 10-year window. Alzheimer’s & Dementia 2025; 21: e14537. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.De Meyer S, Schaeverbeke JM, Luckett ES, et al. Plasma pTau181 and pTau217 predict asymptomatic amyloid accumulation equally well as amyloid PET. Brain Commun 2024; 6: fcae162. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Jiang X, Hoang TD, Shaw LM, et al. Alzheimer’s disease neuropathology plasma biomarkers and cognition in midlife: a community-based cohort study. The Lancet 2026; 407: 2208–16. [DOI] [PubMed] [Google Scholar]
7.Jack CR Jr., Andrews JS, Beach TG, et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimer’s & Dementia 2024; 20: 5143–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Liu C-C, Zhao N, Fu Y, et al. ApoE4 accelerates early seeding of amyloid pathology. Neuron 2017; 96: 1024–1032.e3. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Lim YY, Mormino EC, For the Alzheimer’s Disease Neuroimaging Initiative. APOE genotype and early β-amyloid accumulation in older adults without dementia. Neurology 2017; 89: 1028–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Polsinelli AJ, Lane KA, Manchella MK, Logan PE, Gao S, Apostolova LG. APOE ε4 is associated with earlier symptom onset in LOAD but later symptom onset in EOAD. Alzheimer’s & Dementia 2023; 19: 2212–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Petersen KK, Milà-Alomà M, Li Y, et al. Predicting onset of symptomatic Alzheimerʼs disease with plasma p-tau217 clocks. Nat Med 2026;: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Woo MS, Macedo AC, Hosseini SA, et al. APOE ɛ4 carriership determines a faster plasma p-tau217 progression in Aβ-positive individuals. Alzheimers Dement 2025; 21: e71048. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Johnson SC, Koscik RL, Jonaitis EM, et al. The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions. Alzheimers Dement (Amst) 2018; 10: 130–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Ma Y, Mora Pinzon MC, Buckingham WR, et al. Comparison of sample characteristics of Wisconsin Alzheimer’s Disease Research Center participants with the Wisconsin state population—An evaluation of the recruitment effort. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 2025; 11: e70036. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Rivera Mindt M, Arentoft A, Calcetas AT, et al. The Alzheimer’s Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core: A culturally informed, community-engaged research (CI-CER) model to advance brain health equity. Alzheimer’s & Dementia 2024; 20: 8279–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Gu Y, Honig LS, Kang MS, et al. Risk of Alzheimer’s disease is associated with longitudinal changes in plasma biomarkers in the multi-ethnic Washington Heights–Hamilton Heights–Inwood Columbia Aging Project (WHICAP) cohort. Alzheimer’s & Dementia 2024; 20: 1988–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Vardarajan BN, Faber KM, Bird TD, et al. Age-Specific Incidence Rates for Dementia and Alzheimer Disease in NIA-LOAD/NCRAD and EFIGA Families. JAMA Neurol 2014; 71: 315–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious Orders Study and Rush Memory and Aging Project. Journal of Alzheimer’s Disease 2018; 64: S161–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Petersen ME, Zhou Z, Hall JR, et al. Health and Aging Brain Study–Health Disparities (HABS-HD) methods and partner characteristics. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 2025; 11: e70140. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Xu Y, Qiao M, Gunasekaran TI, et al. Evaluating polygenic risk score prediction performance for Alzheimer’s disease in a population-based Hispanic cohort using single- and multi-ancestry models. The Lancet Regional Health – Americas 2025; 49. DOI: 10.1016/j.lana.2025.101198. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Cuschieri S. The STROBE guidelines. Saudi J Anaesth 2019; 13: S31–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Xu Y, Qiao M, Gunasekaran TI, et al. Polygenic Scores of Core-1 Alzheimer’s Disease Biomarkers Predict Early Cognitive and Pathological Change. 2025;: 2025.07.12.25331438. [DOI] [PMC free article] [PubMed]
23.Pérez-González AP, García-Kroepfly AL, Pérez-Fuentes KA, et al. The ROSMAP project: aging and neurodegenerative diseases through omic sciences. Front Neuroinform 2024; 18. DOI: 10.3389/fninf.2024.1443865. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.O’Bryant SE, Petersen M, Hall J, Johnson L. APOEε4 Genotype Is Related to Brain Amyloid Among Mexican Americans in the HABS-HD Study. Front Neurol 2022; 13: 834685. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Du L, Langhough RE, Wilson RE, et al. Longitudinal plasma phosphorylated-tau217 and other related biomarkers in a non-demented Alzheimer’s risk-enhanced sample. Alzheimer’s & Dementia 2024; 20: 6183–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Brickman AM, Manly JJ, Honig LS, et al. Plasma p-tau181, p-tau217, and other blood-based Alzheimer’s disease biomarkers in a multi-ethnic, community study. Alzheimer’s & Dementia 2021; 17: 1353–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Eissman JM, Ma Y, Qiao M, et al. Biological Age and Age Acceleration Predict Alzheimer’s Disease Plasma Biomarker Levels. 2025;: 2025.05.22.25328181. [DOI] [PMC free article] [PubMed]
28.Shaw LM, Korecka M, Lee EB, et al. ADNI Biomarker Core: A review of progress since 2004 and future challenges. Alzheimer’s & Dementia 2025; 21: e14264. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Yu L, Boyle PA, Janelidze S, et al. Plasma p-tau181 and p-tau217 in discriminating PART, AD and other key neuropathologies in older adults. Acta Neuropathol 2023; 146: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Yu L, Du L, Wang T, et al. Neuropathologic correlates of distinct plasma biomarker profiles in community-living older adults. Brain 2025; 148: 3340–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Aisen PS, Donohue MC, Raman R, Rafii MS, Petersen RC, Initiative for the ADN. The Alzheimer’s Disease Neuroimaging Initiative Clinical Core. Alzheimer’s & Dementia 2024; 20: 7361–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Karpouzian-Rogers T, Ho E, Novack M, et al. Baseline characterization of the ARMADA (Assessing Reliable Measurement in Alzheimer’s Disease) study cohorts. Alzheimer’s & Dementia 2023; 19: 1974–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Schoenfeld D. Chi-Squared Goodness-of-Fit Tests for the Proportional Hazards Regression Model. Biometrika 1980; 67: 145–53. [Google Scholar]
34.Coory M, Lamb KE, Sorich M. Risk-difference curves can be used to communicate time-dependent effects of adjuvant therapies for early stage cancer. Journal of Clinical Epidemiology 2014; 67: 966–72. [DOI] [PubMed] [Google Scholar]
35.Andersen PK, Syriopoulou E, Parner ET. Causal inference in survival analysis using pseudo-observations. Statistics in Medicine 2017; 36: 2669–81. [DOI] [PubMed] [Google Scholar]
36.Andersen PK, Pohar Perme M. Pseudo-observations in survival analysis. Stat Methods Med Res 2010; 19: 71–99. [DOI] [PubMed] [Google Scholar]
37.Perperoglou A, Sauerbrei W, Abrahamowicz M, Schmid M. A review of spline function procedures in R. BMC Medical Research Methodology 2019; 19: 46. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Overgaard M, Parner ET, Pedersen J. Pseudo-observations under covariate-dependent censoring. Journal of Statistical Planning and Inference 2019; 202: 112–22. [Google Scholar]
39.Binder N, Gerds TA, Andersen PK. Pseudo-observations for competing risks with covariate dependent censoring. Lifetime Data Anal 2014; 20: 303–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Denz R, Klaaßen-Mielke R, Timmesfeld N. A comparison of different methods to adjust survival curves for confounders. Statistics in Medicine 2023; 42: 1461–79. [DOI] [PubMed] [Google Scholar]
41.Omaku PE, Braimah JO, Correa MF. Bayesian Accelerated Failure Time Model for Zero-Inflated Survival Data With Application to Liver Cirrhosis. Journal of Probability and Statistics 2025; 2025: 5562074. [Google Scholar]
42.Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. The Annals of Applied Statistics 2008; 2: 841–60. [Google Scholar]
43.Orr AL, Kim C, Jimenez-Morales D, et al. Neuronal Apolipoprotein E4 Expression Results in Proteome-Wide Alterations and Compromises Bioenergetic Capacity by Disrupting Mitochondrial Function. J Alzheimers Dis 2019; 68: 991–1011. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Haney MS, Pálovics R, Munson CN, et al. APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia. Nature 2024; 628: 154–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Yin Z, Rosenzweig N, Kleemann KL, et al. APOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints. Nat Immunol 2023; 24: 1839–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Laing KK, Fialova N, Wardlaw J, Montagne A. Impact of Apolipoprotein E4 on blood-brain barrier integrity in target replacement murine models: a systematic review and meta-analysis. Alz Res Therapy 2026; 18: 119. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Tai LM, Thomas R, Marottoli FM, et al. The role of APOE in cerebrovascular dysfunction. Acta Neuropathol 2016; 131: 709–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Nayyar A, Li M, Sotelo V, et al. Influence of cognitive impairment and race on plasma p-Tau217 in two diverse cohorts. Alzheimer’s & Dementia 2025; 21: e14585. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Shadyab AH, Zhang B, LaCroix AZ, et al. Plasma Phosphorylated Tau 217 and Incident Mild Cognitive Impairment and Dementia in Older Women. JAMA Netw Open 2026; 9: e261295. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Belloy ME, Andrews SJ, Le Guen Y, et al. APOE Genotype and Alzheimer Disease Risk Across Age, Sex, and Population Ancestry. JAMA Neurology 2023; 80: 1284–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Reiman EM, Arboleda-Velasquez JF, Quiroz YT, et al. Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5,000-person neuropathological study. Nat Commun 2020; 11: 667. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Bornhorst JA, Lundgreen CS, Weigand SD, et al. Quantitative Assessment of the Effect of Chronic Kidney Disease on Plasma P-Tau217 Concentrations. Neurology 2025; 104: e210287. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

NIHMS2211502-supplement-1.docx^{(9.8MB, docx)}

NIHMS2211502-supplement-2.docx^{(51.3KB, docx)}

Data Availability Statement

[R1] 1.Jia J, Ning Y, Chen M, et al. Biomarker Changes during 20 Years Preceding Alzheimer’s Disease. New England Journal of Medicine 2024; 390: 712–22. [DOI] [PubMed] [Google Scholar]

[R2] 2.Therriault J, Ashton NJ, Pola I, et al. Comparison of two plasma p-tau217 assays to detect and monitor Alzheimer’s pathology. eBioMedicine 2024; 102. DOI: 10.1016/j.ebiom.2024.105046. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R3] 3.Ashton NJ, Brum WS, Di Molfetta G, et al. Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology. JAMA Neurol 2024; 81: 255–63. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Yakoub Y, Gonzalez-Ortiz F, Ashton NJ, et al. Plasma p-tau217 identifies cognitively normal older adults who will develop cognitive impairment in a 10-year window. Alzheimer’s & Dementia 2025; 21: e14537. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.De Meyer S, Schaeverbeke JM, Luckett ES, et al. Plasma pTau181 and pTau217 predict asymptomatic amyloid accumulation equally well as amyloid PET. Brain Commun 2024; 6: fcae162. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Jiang X, Hoang TD, Shaw LM, et al. Alzheimer’s disease neuropathology plasma biomarkers and cognition in midlife: a community-based cohort study. The Lancet 2026; 407: 2208–16. [DOI] [PubMed] [Google Scholar]

[R7] 7.Jack CR Jr., Andrews JS, Beach TG, et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimer’s & Dementia 2024; 20: 5143–69. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Liu C-C, Zhao N, Fu Y, et al. ApoE4 accelerates early seeding of amyloid pathology. Neuron 2017; 96: 1024–1032.e3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Lim YY, Mormino EC, For the Alzheimer’s Disease Neuroimaging Initiative. APOE genotype and early β-amyloid accumulation in older adults without dementia. Neurology 2017; 89: 1028–34. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Polsinelli AJ, Lane KA, Manchella MK, Logan PE, Gao S, Apostolova LG. APOE ε4 is associated with earlier symptom onset in LOAD but later symptom onset in EOAD. Alzheimer’s & Dementia 2023; 19: 2212–7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Petersen KK, Milà-Alomà M, Li Y, et al. Predicting onset of symptomatic Alzheimerʼs disease with plasma p-tau217 clocks. Nat Med 2026;: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Woo MS, Macedo AC, Hosseini SA, et al. APOE ɛ4 carriership determines a faster plasma p-tau217 progression in Aβ-positive individuals. Alzheimers Dement 2025; 21: e71048. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Johnson SC, Koscik RL, Jonaitis EM, et al. The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions. Alzheimers Dement (Amst) 2018; 10: 130–42. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Ma Y, Mora Pinzon MC, Buckingham WR, et al. Comparison of sample characteristics of Wisconsin Alzheimer’s Disease Research Center participants with the Wisconsin state population—An evaluation of the recruitment effort. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 2025; 11: e70036. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Rivera Mindt M, Arentoft A, Calcetas AT, et al. The Alzheimer’s Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core: A culturally informed, community-engaged research (CI-CER) model to advance brain health equity. Alzheimer’s & Dementia 2024; 20: 8279–93. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Gu Y, Honig LS, Kang MS, et al. Risk of Alzheimer’s disease is associated with longitudinal changes in plasma biomarkers in the multi-ethnic Washington Heights–Hamilton Heights–Inwood Columbia Aging Project (WHICAP) cohort. Alzheimer’s & Dementia 2024; 20: 1988–99. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Vardarajan BN, Faber KM, Bird TD, et al. Age-Specific Incidence Rates for Dementia and Alzheimer Disease in NIA-LOAD/NCRAD and EFIGA Families. JAMA Neurol 2014; 71: 315–23. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious Orders Study and Rush Memory and Aging Project. Journal of Alzheimer’s Disease 2018; 64: S161–89. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Petersen ME, Zhou Z, Hall JR, et al. Health and Aging Brain Study–Health Disparities (HABS-HD) methods and partner characteristics. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 2025; 11: e70140. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Xu Y, Qiao M, Gunasekaran TI, et al. Evaluating polygenic risk score prediction performance for Alzheimer’s disease in a population-based Hispanic cohort using single- and multi-ancestry models. The Lancet Regional Health – Americas 2025; 49. DOI: 10.1016/j.lana.2025.101198. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Cuschieri S. The STROBE guidelines. Saudi J Anaesth 2019; 13: S31–4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Xu Y, Qiao M, Gunasekaran TI, et al. Polygenic Scores of Core-1 Alzheimer’s Disease Biomarkers Predict Early Cognitive and Pathological Change. 2025;: 2025.07.12.25331438. [DOI] [PMC free article] [PubMed]

[R23] 23.Pérez-González AP, García-Kroepfly AL, Pérez-Fuentes KA, et al. The ROSMAP project: aging and neurodegenerative diseases through omic sciences. Front Neuroinform 2024; 18. DOI: 10.3389/fninf.2024.1443865. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.O’Bryant SE, Petersen M, Hall J, Johnson L. APOEε4 Genotype Is Related to Brain Amyloid Among Mexican Americans in the HABS-HD Study. Front Neurol 2022; 13: 834685. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Du L, Langhough RE, Wilson RE, et al. Longitudinal plasma phosphorylated-tau217 and other related biomarkers in a non-demented Alzheimer’s risk-enhanced sample. Alzheimer’s & Dementia 2024; 20: 6183–204. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Brickman AM, Manly JJ, Honig LS, et al. Plasma p-tau181, p-tau217, and other blood-based Alzheimer’s disease biomarkers in a multi-ethnic, community study. Alzheimer’s & Dementia 2021; 17: 1353–64. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Eissman JM, Ma Y, Qiao M, et al. Biological Age and Age Acceleration Predict Alzheimer’s Disease Plasma Biomarker Levels. 2025;: 2025.05.22.25328181. [DOI] [PMC free article] [PubMed]

[R28] 28.Shaw LM, Korecka M, Lee EB, et al. ADNI Biomarker Core: A review of progress since 2004 and future challenges. Alzheimer’s & Dementia 2025; 21: e14264. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Yu L, Boyle PA, Janelidze S, et al. Plasma p-tau181 and p-tau217 in discriminating PART, AD and other key neuropathologies in older adults. Acta Neuropathol 2023; 146: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Yu L, Du L, Wang T, et al. Neuropathologic correlates of distinct plasma biomarker profiles in community-living older adults. Brain 2025; 148: 3340–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Aisen PS, Donohue MC, Raman R, Rafii MS, Petersen RC, Initiative for the ADN. The Alzheimer’s Disease Neuroimaging Initiative Clinical Core. Alzheimer’s & Dementia 2024; 20: 7361–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Karpouzian-Rogers T, Ho E, Novack M, et al. Baseline characterization of the ARMADA (Assessing Reliable Measurement in Alzheimer’s Disease) study cohorts. Alzheimer’s & Dementia 2023; 19: 1974–82. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Schoenfeld D. Chi-Squared Goodness-of-Fit Tests for the Proportional Hazards Regression Model. Biometrika 1980; 67: 145–53. [Google Scholar]

[R34] 34.Coory M, Lamb KE, Sorich M. Risk-difference curves can be used to communicate time-dependent effects of adjuvant therapies for early stage cancer. Journal of Clinical Epidemiology 2014; 67: 966–72. [DOI] [PubMed] [Google Scholar]

[R35] 35.Andersen PK, Syriopoulou E, Parner ET. Causal inference in survival analysis using pseudo-observations. Statistics in Medicine 2017; 36: 2669–81. [DOI] [PubMed] [Google Scholar]

[R36] 36.Andersen PK, Pohar Perme M. Pseudo-observations in survival analysis. Stat Methods Med Res 2010; 19: 71–99. [DOI] [PubMed] [Google Scholar]

[R37] 37.Perperoglou A, Sauerbrei W, Abrahamowicz M, Schmid M. A review of spline function procedures in R. BMC Medical Research Methodology 2019; 19: 46. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Overgaard M, Parner ET, Pedersen J. Pseudo-observations under covariate-dependent censoring. Journal of Statistical Planning and Inference 2019; 202: 112–22. [Google Scholar]

[R39] 39.Binder N, Gerds TA, Andersen PK. Pseudo-observations for competing risks with covariate dependent censoring. Lifetime Data Anal 2014; 20: 303–15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Denz R, Klaaßen-Mielke R, Timmesfeld N. A comparison of different methods to adjust survival curves for confounders. Statistics in Medicine 2023; 42: 1461–79. [DOI] [PubMed] [Google Scholar]

[R41] 41.Omaku PE, Braimah JO, Correa MF. Bayesian Accelerated Failure Time Model for Zero-Inflated Survival Data With Application to Liver Cirrhosis. Journal of Probability and Statistics 2025; 2025: 5562074. [Google Scholar]

[R42] 42.Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. The Annals of Applied Statistics 2008; 2: 841–60. [Google Scholar]

[R43] 43.Orr AL, Kim C, Jimenez-Morales D, et al. Neuronal Apolipoprotein E4 Expression Results in Proteome-Wide Alterations and Compromises Bioenergetic Capacity by Disrupting Mitochondrial Function. J Alzheimers Dis 2019; 68: 991–1011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Haney MS, Pálovics R, Munson CN, et al. APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia. Nature 2024; 628: 154–61. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Yin Z, Rosenzweig N, Kleemann KL, et al. APOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints. Nat Immunol 2023; 24: 1839–53. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Laing KK, Fialova N, Wardlaw J, Montagne A. Impact of Apolipoprotein E4 on blood-brain barrier integrity in target replacement murine models: a systematic review and meta-analysis. Alz Res Therapy 2026; 18: 119. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Tai LM, Thomas R, Marottoli FM, et al. The role of APOE in cerebrovascular dysfunction. Acta Neuropathol 2016; 131: 709–23. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Nayyar A, Li M, Sotelo V, et al. Influence of cognitive impairment and race on plasma p-Tau217 in two diverse cohorts. Alzheimer’s & Dementia 2025; 21: e14585. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 49.Shadyab AH, Zhang B, LaCroix AZ, et al. Plasma Phosphorylated Tau 217 and Incident Mild Cognitive Impairment and Dementia in Older Women. JAMA Netw Open 2026; 9: e261295. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Belloy ME, Andrews SJ, Le Guen Y, et al. APOE Genotype and Alzheimer Disease Risk Across Age, Sex, and Population Ancestry. JAMA Neurology 2023; 80: 1284–94. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 51.Reiman EM, Arboleda-Velasquez JF, Quiroz YT, et al. Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5,000-person neuropathological study. Nat Commun 2020; 11: 667. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 52.Bornhorst JA, Lundgreen CS, Weigand SD, et al. Quantitative Assessment of the Effect of Chronic Kidney Disease on Plasma P-Tau217 Concentrations. Neurology 2025; 104: e210287. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Plasma P-tau217, APOE Genotype, and the Timing of Cognitive Impairment in individuals Across Diverse Racial and Ethnic Groups: A Pooled Analysis of Prospective Cohort Studies

Yuexuan Xu, PhD

Tamil Iniyan Gunasekaran, PhD

Yian Gu, PhD

Dolly Reyes-Dumeyer, BS

Angel Piriz, MD

Danurys Sanchez, MS

Diones Rivera Mejia, MD

Martin Medrano, MD

Rafael A Lantigua, MD

Lawrence Honig, MD

Rachael Wilson, PhD

Ramiro Eduardo Rea Reyes, PhD

Jennifer J Manly, PhD

Adam M Brickman, PhD

Corinne D Engelman, PhD

Sterling Johnson, PhD

Sanjay Asthana, MD

David A Bennett, MD

Melissa Petersen, PhD

Sid O’Bryant, PhD

Badri N Vardarajan, PhD

Richard Mayeux, MD

ABSTRACT/SUMMARY

Background:

Methods:

Findings:

Interpretation:

INTRODUCTION

METHODS

Study design and participants

Procedures

Table 1.

Statistical Analysis

Role of the funding source

RESULTS

Figure 1. Raw plasma P-tau217 levels by cognitive status, APOE genotype, and age group across cohorts.

Figure 2. Cohort-specific associations between plasma P-tau217 and clinical cognitive impairment, with meta-analysis estimates.

Figure 3. Cohort-specific and meta-analytic hazard ratios for incident cognitive impairment and adjusted survival probabilities.

Figure 4. Baseline and longitudinal prognostic performance of APOE-ε4 carrier status and plasma P-tau217 levels for cognitive impairment.

DISCUSSION

Supplementary Material

Research in context.

Evidence before this study:

Added value of this study:

Implications of all the available evidence:

Acknowledgements:

Funding:

Conflict of interest:

Footnotes

Data availability statement:

Reference

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases