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. 2025 Aug 29;21(9):e70625. doi: 10.1002/alz.70625

Association of plasma Alzheimer's disease biomarkers with cognitive decline in cognitively unimpaired individuals

Petrice M Cogswell 1,✉, Heather J Wiste 2, Terry M Therneau 2, Michael E Griswold 3, Niklas Mattsson‐Carlgren 4,5, Sebastian Palmqvist 4,5, Alexa Pichet Binette 4, Erik Stomrud 4,5, Randall J Bateman 6, Nicolas Barthelemy 6, Joel B Braunstein 7, Tim West 7, Philip B Verghese 7, Mary M Machulda 8, Jonathan Graff‐Radford 9, Alicia Algeciras‐Schimnich 10, Val J Lowe 1, Christopher G Schwarz 1, Matthew L Senjem 1,11, Jeffrey L Gunter 1, David S Knopman 9, Prashanthi Vemuri 1, Ronald C Petersen 2,9, Oskar Hansson 4, Clifford R Jack Jr 1
PMCID: PMC12397066  PMID: 40883967

Abstract

INTRODUCTION

Plasma biomarkers’ utility for predicting incident mild cognitive impairment (MCI) remains unclear. We evaluated associations of plasma Alzheimer's disease (AD) biomarkers and amyloid positron emission tomography (PET) with transitions from cognitively unimpaired (CU) to MCI in the Mayo Clinic Study of Aging (MCSA) and BioFINDER‐2 studies.

METHODS

Associations of continuous baseline plasma biomarker levels and amyloid PET Centiloid with progression to MCI, adjusting for age, sex, and education, were evaluated with Cox proportional hazards models.

RESULTS

The study included 381 MCSA and 584 BioFINDER‐2 participants. Amyloid PET and percent phosphorylated to non‐phosphorylated tau217 (%p‐tau217) were strong predictors of progression to MCI in both cohorts: hazard ratios of 1.49 and 1.23 in the MCSA and 1.72 and 1.65 in BioFINDER, respectively. Amyloid beta 42/40 was a significant predictor in BioFINDER‐2 only (hazard ratio 2.20).

DISCUSSION

Plasma %p‐tau217 was associated with progression from CU to MCI in both cohorts, although differences in biomarker associations may be related to differences in the two cohorts.

Highlights

  • Mass‐spectrometry‐based plasma phosphorylated tau217 was associated with cognitively unimpaired to mild cognitive impairment (MCI) progression.

  • Plasma amyloid beta 42/40 was a significant predictor in BioFINDER but not the Mayo Clinic Study of Aging (MCSA).

  • Amyloid positron emission tomography (PET) was the strongest predictor of progression to MCI in the MCSA.

  • Plasma had added value to amyloid PET in BioFINDER but not the MCSA.

  • Biomarker performance may vary with cohort and biomarker measurement differences.

Keywords: Alzheimer's disease, amyloid beta 42/40, amyloid beta positron emission tomography, mild cognitive impairment, phosphorylated tau 217, plasma biomarkers

1. BACKGROUND

With the availability of amyloid beta (Aβ) targeted therapies 1 , 2 , 3 and ongoing investigation of other targeted Alzheimer's disease (AD) therapies, predicting which individuals are likely to experience cognitive decline is increasingly important. Identifying early disease changes in cognitively unimpaired (CU) individuals and if these are associated with future cognitive decline are of particular interest. This information would aid in prognosis and in enrichment for clinical trials. 4 , 5 , 6

Biomarker profiles and demographics are important components of clinical risk profiles; amyloid and tau positron emission tomography (PET) levels are associated with future cognitive decline, in addition to older age and apolipoprotein E (APOE) ε4 carriership. 7 Relatively less is known about the performance of plasma AD biomarkers for clinical prognostication. Plasma phosphorylated tau (p‐tau)217 and Aβ42/40 are the most widely implemented analytes to date. Aβ42/40 is of interest as one of the earliest changing biomarkers, shown to decrease before or near the time of amyloid PET based on cerebrospinal fluid and plasma studies. 8 , 9 P‐tau217 is a somewhat later changing plasma biomarker, shown to be associated with amyloid and tau PET and to become abnormal between the PET biomarkers. 10 , 11 , 12 , 13 , 14 Both plasma p‐tau217 and Aβ42/40 predict PET progression and cognitive decline, with p‐tau217 performing better than Aβ42/40. 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 Much of the work in predicting cognitive outcomes has been performed in highly selected, enriched research cohorts and/or did not use top‐performing p‐tau analytes or assays. To facilitate broader application of plasma AD biomarkers, a better understanding of how the best available biomarkers perform in different populations is needed.

The goals of this study were to evaluate the following in two separate cohorts with different recruitment strategies and compositions: (1) associations between mass spectrometry–based plasma p‐tau217 concentration, p‐tau217 represented as a ratio (%p‐tau217), and Aβ42/40 and the rate of clinical transitions from CU to mild cognitive impaired (MCI); (2) how plasma biomarkers compare to amyloid PET in predicting clinical transitions; and (3) if Aβ42/40 or amyloid PET provide complementary predictive information to p‐tau217 and %p‐tau217.

2. METHODS

2.1. Participants

We included participants in the Mayo Clinic Study of Aging (MCSA) and BioFINDER‐2 cohorts. The MCSA is a longitudinal population‐based study among a sample of individuals residing in Olmsted County, Minnesota, USA. A random set of individuals are invited to participate in the study via age‐ and sex‐based stratification. A subset had plasma AD biomarkers measured. BioFINDER‐2 (hereafter referred to as BioFINDER) recruits CU healthy controls from the Malmo Offspring study and the Malmo Diet and Cancer study and individuals with subjective cognitive decline (SCD), MCI, and dementia from memory clinics in Sweden (www.biofinder.se). 24 The CU and cognitively impaired samples are enriched for APOE ε4 carriers, with ≈ 50% of the BioFINDER cohort carrying at least one APOE ε4 allele. For inclusion in this study, participants were required to have one or more clinical follow‐up visits, mass spectrometry‐based %p‐tau217 and amyloid PET at the index (baseline) date, APOE ε4 genotype available, be age ≥ 50 years, and have a diagnosis of CU at the baseline visit. In BioFINDER, participants with a diagnosis of SCD, a diagnosis not used in the MCSA, were included as CU participants. The discrimination between SCD and MCI has been previously published. 25

RESEARCH IN CONTEXT

  1. Systematic review: The authors reviewed the literature using traditional sources (e.g., PubMed) and online content (e.g., meeting abstracts). Plasma Alzheimer's disease (AD) biomarkers have shown utility for prediction of cognitive decline, though limited data exist on conversion from cognitively unimpaired (CU) to mild cognitive impairment (MCI) and within community‐based compared to selected research cohorts.

  2. Interpretation: Our findings suggest that plasma AD biomarkers, particularly phosphorylated tau217 (p‐tau217) and percent p‐tau217, and amyloid positron emission tomography are important predictors of progression from CU to MCI, in addition to age, education, and apolipoprotein E ε4 carriership. The composition of the cohort as well as biomarker measurement technique may affect biomarker predictive value.

  3. Future directions: This study supports plasma AD biomarkers for detection of clinically significant early cognitive changes. Like much of the existing literature, findings support group‐level associations. In the future it will be important to evaluate how plasma biomarkers perform in other cohorts and for individual‐level predictions.

2.2. Plasma assays

Plasma samples were collected and stored as previously described. 26 , 27 In BioFINDER, plasma was analyzed at Washington University (WU) using an in‐house mass spectrometry assay. 28 In the MCSA, plasma was analyzed via mass spectrometry at C2N Diagnostics (p‐tau217 assay version 1) using an adapted form of the WU assay, previously commercially licensed to C2N. In both cohorts, plasma assays included Aβ42, Aβ40 (analyzed as the Aβ42/40 ratio), non‐phosphorylated tau217 (np‐tau217), and phosphorylated tau217 (p‐tau217; analyzed as the ratio of p‐tau217/np‐tau217 × 100, %p‐tau217). The %p‐tau217 was on a similar scale in the two cohorts, but not the raw p‐tau217 level or Aβ42/40. For MCSA p‐tau217, results below the limit of quantification (< 0.5 pg/mL) were assigned a value of 0.25. The MCSA cohort also had the Amyloid Probability Score 2 (APS2), a value ranging from 0 to 100 indicating likelihood of amyloid PET positivity via a proprietary algorithm by C2N. 29

2.3. Amyloid PET

In the MCSA, amyloid PET was performed using Pittsburgh compound B (PiB) and processed using standard in‐house pipelines. 30 , 31 The amyloid PET meta‐region of interest standardized uptake value ratios (SUVRs) were derived via the voxel number weighted average of the median uptake in each of the prefrontal, orbitofrontal, parietal, temporal, anterior and posterior cingulate, and precuneus regions normalized to the cerebellar crus gray matter. SUVR values were converted to Centiloid values. 32 , 33

In the BioFINDER cohort, amyloid PET was performed using [18F]flutemetamol 34 and processed using previously described pipelines. 35 The neocortical composite SUVR values were derived via composition of the prefrontal, parietal, temporal lateral, anterior cingulate, posterior cingulate, and precuneus volumes of interest normalized to the mean uptake in the cerebellar cortex. SUVR values were converted to Centiloids.

2.4. Cognitive assessments

In the MCSA, participants underwent assessments at, on average, 15‐month intervals that included a neurological examination and neuropsychological evaluation. Based on these data, a clinical exam, and patient and informant interviews, an expert panel determined a consensus diagnosis of CU, MCI, or dementia blinded to diagnoses on prior visits and according to published criteria. 36 , 37 , 38

In BioFINDER, neurological examinations and neuropsychological evaluations were performed every year for participants with a suspected neurocognitive disorder and every 2 years for those without a suspected neurocognitive disorder. Participants were classified as CU (healthy controls and SCD), MCI, or dementia based on the neuropsychological battery, clinical symptoms, and loss of independence in activities of daily living. 25 , 39

2.5. Statistical analysis

Overall rates of progression from CU to MCI were estimated using Poisson regression models fit separately within the MCSA and BioFINDER cohorts and were reported as number of events per 100 person‐years. We used Cox proportional hazards models to evaluate the association of continuous baseline AD biomarker levels with time to progression from CU to MCI after adjusting for other covariates. Analyses were performed separately in the MCSA and BioFINDER cohorts due to differences in the makeup of the cohorts and the biomarkers. Time was defined as years from the first visit with amyloid PET and plasma biomarkers (i.e., baseline, or time 0) to the first visit with MCI (event) or the last visit with a cognitive assessment of CU (censored). We first fit a “base model” including only baseline age, sex, and education as predictors. Next, to evaluate individual associations between baseline biomarkers and MCI progression, we fit a “base + PET” model that included age, sex, education, and PET along with several “base + plasma” models that included age, sex, education, and one of the following: plasma Aβ42/40, %p‐tau217, p‐tau217, or APS2. Third, we fit “base + plasma combination” models including Aβ42/40 and either %p‐tau217 or p‐tau217 to evaluate independent, additive effects of the plasma biomarkers. Finally, we fit models with age, sex, education, amyloid PET, and each plasma biomarker as predictors to evaluate independent, additive effects of the plasma and amyloid PET biomarkers. Models were fit both without and with adjustment for APOE ε4 carriership (at least one APOE ε4 allele). We estimated the concordance (C) statistic for each of the Cox models. To aid in interpretation of the hazard ratios for continuous variables in the model, we summarized the model estimates using the following contrasts: a 10 year increase in age, a difference of 25 Centiloids higher for amyloid PET, a difference of 0.75 higher for %p‐tau217, and a difference of 0.015 lower for Aβ42/40 in both cohorts. The raw p‐tau217 level was also used in the models and a separate contrast was used for each cohort given differences in the biomarker scales, 0.75 pg/mL for the MCSA (same as %p‐tau217) and 1.5 pg/ml for BioFINDER. In the MCSA, an APS2 contrast of 20 points higher was used. The biomarker contrasts were informed by the interquartile ranges (IQRs). All of the MCSA models included weights to account for oversampling of events in this study (see Supplemental Methods in supporting information).

All analyses were performed using the R Language and Environment for Statistical Computing version 4.4.1. Cox proportional hazards models and C statistics were computed using the survival package version 3.8‐3.

2.6. Sensitivity analyses

To assess differences in effects between APOE ε4 carriers and non‐carriers, we fit models with an APOE ε4 interaction for all variables. In BioFINDER, in which Aβ42/40 was missing for 214 (37%) participants, we fit models in the subset with Aβ42/40 to assess if the results were similar to those fit among the full BioFINDER cohort. Finally, within the BioFINDER cohort, we also evaluated differences in baseline characteristics between SCD and healthy controls and fit models with an SCD interaction for all variables to assess differences in the associations with MCI progression between SCD and healthy controls.

3. RESULTS

3.1. Participants

The study included 381 MCSA participants with a median (IQR) age of 73 (65, 78) years, 46% females, and 31% APOE ε4 carriers and 584 BioFINDER participants with a median (IQR) age of 70 (60, 77), 55% females, 50% APOE ε4 carriers (Table 1). At the baseline visit, median amyloid PET Centiloid was lower in the BioFINDER cohort than the MCSA cohort, −5 (−10, 17) versus 14 (9, 21). Individuals who progressed to MCI had on average more abnormal baseline biomarker levels; however, differences in baseline biomarker values between progressors and non‐progressors were larger in the BioFINDER cohort than the MCSA cohort (Table S1 in supporting information). The characteristics of those observed to progress versus not over the observation period are provided for reference; however, groups cannot be directly compared as values do not account for follow‐up time, which differed among individuals.

TABLE 1.

Characteristics of MCSA and BioFINDER participants.

MCSA (N = 381) BioFINDER (N = 584)
Age
Median (Q1, Q3) 73 (65, 78) 70 (60, 77)
Range 52–92 50–92
Sex
Female 176 (46%) 320 (55%)
Male 205 (54%) 264 (45%)
Education
≤12 years 106 (28%) 308 (53%)
≥13 years 275 (72%) 276 (47%)
APOE genotype
Non‐carrier 261 (69%) 290 (50%)
Carrier 120 (31%) 294 (50%)
MMSE a
Median (Q1, Q3) 29 (28, 29) 29 (28, 30)
Range 23–30 24–30
Memory z score a
Median (Q1, Q3) 0.26 (‐0.53, 0.91) –
Range −3.07 – 3.02 –
ADAS delayed word recall score a
Median (Q1, Q3) – 3 (1, 4)
Range – 0 ‐ 10
Amyloid PET Centiloid
Median (Q1, Q3) 14 (9, 21) −5 (‐10, 17)
Range −17 – 153 −27 – 131
Amyloid PET status
<25 Centiloid 297 (78%) 455 (78%)
≥25 Centiloid 84 (22%) 129 (22%)
Aβ42/40 a
Median (Q1, Q3) 0.092 (0.084, 0.103) 0.117 (0.111, 0.125)
Range 0.060–0.152 0.092–0.154
p‐tau217
Median (Q1, Q3) 0.75 (0.25, 1.26) 1.71 (1.28, 2.79)
Range 0.25–5.91 0.49–19.74
%p‐tau217
Median (Q1, Q3) 0.79 (0.39, 1.29) 0.79 (0.63, 1.26)
Range 0.14–4.50 0.31–7.47
Amyloid Probability Score 2 a
Median (Q1, Q3) 12 (7, 27) –
Range 0–100 –
Time from index visit to last clinical visit, years
Median (Q1, Q3) 8.9 (6.5, 10.3) 4.0 (2.1, 4.3)
Range 1.2–14.0 1.0–6.5
Progressed to MCI over observed follow‐up b
N 100 90
Rate (95% CI) per 100 person‐years 2.4 (2.1, 2.6) 4.5 (3.6, 5.5)
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Abbreviations: Aβ, amyloid beta; ADAS, Alzheimer's Disease Assessment Scale; APOE, apolipoprotein E; CI, confidence interval; MCI, mild cognitive impairment; MCSA, Mayo Clinic Study of Aging; MMSE, Mini‐Mental State Examination; PET, positron emission tomography; p‐tau, phosphorylated tau.

a

MMSE was missing for 2 MCSA individuals; memory z score was missing for 1 MCSA individual and not available for BioFINDER; ADAS delayed word recall score was missing for 4 BioFINDER individuals and not available for MCSA; Aβ42/40 was missing for 214 BioFINDER individuals; Amyloid Probability Score 2 was not available for BioFINDER.

b

Rates of progression to MCI were calculated from Poisson regression.

The empirical cumulative distribution functions (eCDF) are shown for the biomarkers in both cohorts in Figure S1 in supporting information. Amyloid PET and %p‐tau217 are comparable between cohorts, with the exception of a shift at the lower levels; for %p‐tau217 this is related to MCSA participants with values below the limit of quantification (n = 125). The p‐tau217 and Aβ42/40 distributions are different, shifted to higher values in BioFINDER versus the MCSA.

3.2. Rates of progression

In the MCSA cohort, 100 individuals progressed from CU to MCI, and the overall rate of progression was 2.4 (95% confidence interval [CI]: 2.1–2.6) events per 100 person‐years; 16 individuals progressed to dementia after the incident MCI. In the BioFINDER cohort, 90 individuals progressed from CU to MCI with an overall rate of progression of 4.5 (95% CI: 3.6–5.5) events per 100 person‐years; 28 individuals progressed to dementia after the incident MCI in the BioFINDER cohort.

3.3. Associations of participant characteristics with progression to MCI: base model

In the base model (Figure 1), a 10 year older age was associated with progression to MCI with relative hazard ratios (HRs) of 2.57 (95% CI: 1.91–3.46, p < 0.001) for MCSA and 2.01 (95% CI: 1.59–2.55, p < 0.001) for BioFINDER. Sex was not significant in either cohort (M:F HR 1.20, 95% CI: 0.76–1.89, p = 0.44 for MCSA and HR 1.11, 95% CI: 0.73–1.69, p = 0.62 for BioFINDER). Having ≤ 12 years of education and having APOE ε4 carriership were associated with progression to MCI in the MCSA models (HR 2.32, 95% CI: 1.46–3.70, p < 0.001 and HR 1.95, 95% CI: 1.20–3.16, p = 0.007, respectively), but not the BioFINDER models (HR 1.30, 95% CI: 0.84–2.02, p = 0.24 and HR 1.33, 95% CI: 0.88–2.03, p = 0.18, respectively).

FIGURE 1.

FIGURE 1

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Associations of demographic variables and biomarkers with progression from cognitively unimpaired to mild cognitive impairment. Hazard ratio (95% confidence interval) estimates are from Cox proportional hazards models fit separately within the MCSA and BioFINDER cohorts. The base model included age, sex, and education. PET and plasma biomarkers were added to the base model, one biomarker at a time, and additional models were fit with the base model plus Aβ42/40 and %p‐tau217 (or p‐tau217). All models were fit with and without adjusting for APOE ε4 carrier status. Aβ, amyloid beta; APOE, apolipoprotein E; APS2, Amyloid Probability Score 2; CI, confidence interval; HR, hazard ratio; MCSA, Mayo Clinic Study of Aging; PET, positron emission tomography; p‐tau, phosphorylated tau.

3.4. Associations of continuous baseline biomarker levels with progression to MCI

Figure 1 summarizes the HRs of the Cox models for progression from CU to MCI with the biomarkers as predictors, adjusted for age, sex, education level, and with or without adjusting for APOE ε4 carriership. Results below are described for the models adjusted for APOE ε4 carriership but are similar to those without adjusting for APOE. Baseline amyloid PET was a strong biomarker predictor with relative HRs of 1.49 (95% CI: 1.25–1.79, p < 0.001) in the MCSA and 1.72 (95% CI: 1.51–1.96, p < 0.001) in BioFINDER. Higher plasma %p‐tau217 and p‐tau217 were also significant predictors of progression to MCI with relative HRs of 1.23 (95% CI: 1.02–1.48, p = 0.03) and 1.21 (95% CI: 1.02–1.45, p = 0.03) in the MCSA and 1.65 (95% CI: 1.48–1.83, p < 0.001) and 1.48 (95% CI: 1.36–1.60, p < 0.001) in BioFINDER. In the MCSA, APS2 showed a similar effect size as %p‐tau217, 1.21 (95% CI: 1.01–1.44, p = 0.03). Lower Aβ42/40 was associated with risk of progression to MCI in BioFINDER but not in the MCSA with relative HRs of 2.20 (95% CI: 1.44–3.35, p < 0.001) and 0.95 (95% CI: 0.74–1.22, p = 0.68), respectively. The primary models demonstrate effect sizes for representative biomarkers contrasts, chosen to be comparable across the biomarkers and informed by the biomarker IQRs in these cohorts. The patterns observed are similar, regardless of the chosen contrast (Figure S2 in supporting information).

When both Aβ42/40 and %p‐tau217 were included in the “base + plasma combination” models (Figure 1), the relative HRs were very similar in the MCSA with Aβ42/40 HR near 1.0. In BioFINDER, the %p‐tau217 HR remained similar, 1.53 (95% CI: 1.34–1.75, p < 0.001) and that of Aβ42/40 decreased to 1.52 (95% CI: 0.98–2.35, p = 0.06).

In the models including both amyloid PET and a plasma biomarker (Figure 2) as predictors, %p‐tau217, p‐tau217, and APS2 were no longer significant predictors of progression in the MCSA. For example, in the model with amyloid PET and %p‐tau217 adjusted for APOE ε4 carriership, HRs were 1.55 (95% CI: 1.23–1.95, p < 0.001) for amyloid PET and 0.95 (95% CI: 0.74–1.21, p = 0.67) for %p‐tau217. In BioFINDER, amyloid PET and plasma biomarker HRs were reduced when both were included in a model. In the model with both amyloid PET and %p‐tau217, both were statistically significant predictors of progression to MCI with similar HRs, amyloid PET 1.39 (95% CI: 1.16–1.67, p < 0.001) and %p‐tau217 1.36 (95% CI: 1.16–1.59, p < 0.001). Aβ42/40 was no longer a significant predictor when included in a model with amyloid PET; however, the HR was of similar size as the amyloid PET HR: 1.46 (95% CI: 0.93–2.31, p = 0.10) versus 1.58 (1.33–1.88, p < 0.001).

FIGURE 2.

FIGURE 2

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PET and plasma associations with progression from cognitively unimpaired to mild cognitive impairment from models including both PET and plasma measures. Hazard ratio (95% confidence interval) estimates are from Cox proportional hazards models fit separately within the MCSA and BioFINDER cohorts. All models included age, sex, education, and amyloid PET. Separate models were fit for each plasma biomarker, one at a time, or combinations of Aβ42/40 and %p‐tau217 (or p‐tau217). All models were fit with and without adjusting for APOE ε4 carrier status. Aβ, amyloid beta; APOE, apolipoprotein E; APS2, Amyloid Probability Score 2; CI, confidence interval; HR, hazard ratio; MCSA, Mayo Clinic Study of Aging; PET, positron emission tomography; p‐tau, phosphorylated tau.

The C statistics of the models ranged from 0.76 for the base model to 0.79 for the base model plus amyloid PET in the MCSA (Table S2 in supporting information). In BioFINDER, the C statistics ranged from 0.67 for the base model to 0.83 for base model plus amyloid PET and 0.84 for the base model plus amyloid PET and the plasma biomarkers.

3.5. Sensitivity analyses

In the models that included interactions with APOE ε4 carriership (Figure 3), there was a significant interaction between sex and APOE ε4 carriership in the MCSA with males having higher risk of progression among non‐carriers and females having higher risk of progression among carriers (p = 0.03). In BioFINDER, the interaction of sex and APOE ε4 carriership was the opposite but did not reach statistical significance (p = 0.25).

FIGURE 3.

FIGURE 3

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Associations of demographic variables and biomarkers with progression from cognitively unimpaired to mild cognitive impairment stratified by APOE ε4 carrier status. Hazard ratio (95% confidence interval) estimates are from Cox proportional hazards models fit separately within the MCSA and BioFINDER cohorts. The base model included age, sex, education, and APOE ε4 carrier status. PET and plasma biomarkers were added to the base model, one biomarker at a time, and additional models were fit with the base model plus Aβ42/40 and %p‐tau217 (or p‐tau217). All models included interactions with APOE and the other predictor variables in the model. The interaction P values are shown above, indicating if the associations were significantly different among APOE ε4 carriers versus non‐carriers. Aβ, amyloid beta; APOE, apolipoprotein E; APS2, Amyloid Probability Score 2; CI, confidence interval; HR, hazard ratio; MCSA, Mayo Clinic Study of Aging; PET, positron emission tomography; p‐tau, phosphorylated tau.

The interaction of APOE ε4 carriership with biomarker levels was also different between cohorts. In the MCSA, there was a general pattern of higher risk of progression among APOE ε4 carriers for Aβ42/40 (p = 0.15), %p‐tau217 (p = 0.43), and APS2 (p = 0.15), though none of the interactions were significant. In BioFINDER, the Aβ42/40 HR was greater among APOE ε4 non‐carriers than carriers (p = 0.10) and when adjusting for %p‐tau217, Aβ42/40 was only associated with higher risk of progression in non‐carriers (p = 0.02). The MCSA amyloid PET and the BioFINDER amyloid PET, %p‐tau217, and p‐tau217 effects were similar within APOE ε4 non‐carriers and carriers.

Models fit within the BioFINDER subgroup with non‐missing Aβ42/40 showed similar results to those in the full cohort (Figure S3 in supporting information).

The baseline characteristics for the BioFINDER healthy controls and SCD participants are shown in Table S3 in supporting information. Demographic characteristics were similar with the exception that the SCD participants were more likely to be male than the healthy controls (p = 0.03). While median (IQR) Mini‐Mental State Examination values were the same between the groups, biomarker values were more abnormal among the SCD versus healthy controls (p ≤ 0.01 for all) and the SCD participants had a ≈ 5x greater rate of progression to MCI (p < 0.001). In the interaction models (Figure S4 in supporting information), there were no significant differences in associations with progression to MCI between healthy controls and SCD. The p‐tau217 HRs were very similar between the groups while the Aβ42/40 HRs tended to be greater for SCD participants versus healthy controls; however, there was more uncertainty in the Aβ42/40 estimates.

4. DISCUSSION

In this study, we evaluated the performance of plasma biomarkers compared to amyloid PET for prediction of conversion from CU to MCI in two independent cohorts. The primary findings were: (1) higher p‐tau217, %p‐tau217, and APS2 were associated with risk of progression from CU to MCI in both cohorts, while lower Aβ42/40 was associated with risk of progression to MCI only in BioFINDER; (2) p‐tau217 and %p‐tau217 HRs were similar to amyloid PET in BioFINDER but lower than amyloid PET in the MCSA; (3) Aβ42/40 and %p‐tau217 provided additive associations with progression when included in the same model for BioFINDER; (4) when amyloid PET was included in the model with each plasma biomarker, %p‐tau217 remained a significant predictor of progression in BioFINDER but not the MCSA. Finally, in general, effect sizes were larger in BioFINDER than the MCSA, likely related to cohort composition differences and potentially also differences in biomarker measurements.

In the single biomarker Cox models, plasma %p‐tau217 and p‐tau217 were strong predictors of progression from CU to MCI in both cohorts. However, in the MCSA, the plasma biomarkers did not perform as well as amyloid PET (lower HRs). The %p‐tau217 results correspond with prior work in different cohorts showing that p‐tau (p‐tau217 and p‐tau181) is a predictor of future cognitive decline 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 40 , 41 and support the use of %p‐tau217 and p‐tau217 as early indicators of AD pathology and incipient cognitive decline. This work differs from and adds to the existing literature by use of mass spectrometry %p‐tau217 (one of the best‐performing p‐tau217 assays to date 42 ), the focus on individuals who are CU at baseline, and the comparison of two cohorts, one that is representative of the local community (MCSA) and one that is a more selected research sample (BioFINDER). In prior studies focusing on the conversion from CU to MCI, plasma p‐tau181 and p‐tau217 were associated with conversion in some cases, 40 , 41 though not all, 43 with differences likely related to use of less sensitive assays, smaller sample sizes, and low number of events. Similarly, in this study, as seen for all biomarkers, the HRs for %p‐tau217 and p‐tau217 were higher in BioFINDER than the MCSA, likely related to differences between the cohorts and assays. Our results help clarify the utility of plasma p‐tau in predicting conversion from CU to MCI and verify generalizable to community samples with lower rates of cognitive progression than selected pre‐clinical AD cohorts.

Aβ42/40 was a significant predictor of progression to MCI in BioFINDER but not the MCSA. In BioFINDER, the Aβ42/40 HRs were on average higher than %p‐tau217 but with a broader CI and lower C statistic likely related to greater degree of biological and measurement noise in the Aβ42/40 assays. 44 When both Aβ42/40 and %p‐tau217 were included in the same model, both remained important predictors, indicating independent information and utility of both biomarker measurements. In the MCSA, Aβ42/40 was not a significant predictor of cognitive decline with HR of ≈ 1.0. The BioFINDER Aβ42/40 results are more consistent with prior studies that show Aβ42/40 is associated with cognitive decline 23 , 40 and in some cohorts provides some additive value to p‐tau in predicting cognitive decline. 18 , 41 The differences in Aβ42/40 performance between cohorts in this study may reflect differences in the cohorts as well as assays.

When amyloid PET was included in models with each of the plasma biomarkers, the plasma biomarkers remained important predictors in BioFINDER but not the MCSA. Additionally, when Aβ42/40 and %p‐tau217 were included in a model, both retained predictive power with HRs ≈ 1.4, though Aβ42/40 was no longer statistically significant. These results suggest that in some cohorts, use of multiple biomarkers may aid prediction of future cognitive decline.

Differences in effect sizes in primary models as well as effect sizes of plasma biomarkers when used along with amyloid PET are perhaps to be expected given differences in the cohorts, plasma assays, amyloid PET tracers, and sampling variability. In particular, there are several notable differences in the cohorts. The MCSA is an age‐ and sex‐stratified population‐based sample of individuals in Olmsted County, Minnesota with rates of APOE ε4 carriership and cognitive impairment representative of the community. These individuals may have AD and other pathologies commonly seen in the community (e.g., renal failure, hypertension, diabetes, cardiovascular disease, or cerebrovascular disease) contributing to cognitive decline and/or plasma assay non‐specificity. 45 BioFINDER is enriched for APOE ε4 carriership and AD, and by design this cohort had a higher percentage of APOE ε4 carriers than the MCSA (50% vs. 31%). Additionally, the BioFINDER cohort includes healthy controls and participants with SCD in the CU sample. As seen in the sensitivity analyses, inclusion of the SCD participants did not affect the p‐tau217 estimates but may account for some of the differences in performance of Aβ42/40 between the MCSA and BioFINDER cohorts. Differences in performance of biomarkers in different populations are important to consider when evaluating generalizability of findings. These differences are not recognized when data from multiple cohorts are combined in analyses. Additionally, there are many components to cohort differences, and those related to racial and ethnic composition are often the focus. Both cohorts used in this study are of primary northern European ancestry, yet there are important differences based on recruitment strategies.

In terms of potential differences in the biomarkers, the plasma assays used in both cohorts are mass spectrometry and designed to be essentially the same despite some differences in implementation of the commercial product versus in‐house assay on which it was based. Data comparing the performance of these assays are not available, to our knowledge. For amyloid PET, different tracers were used, PiB in the MCSA and [18F]flutemetamol in BioFINDER, both converted to Centiloids. Although head‐to‐head comparisons have shown similar performance between these tracers, 46 , 47 PiB has been proposed to have higher sensitivity for early changes given lower levels of off‐target white matter binding. 46 This may contribute to the differences in the eCDFs between cohorts, BioFINDER with many individuals with negative Centiloids versus many individuals with low levels of amyloid (10–20 Centiloids) in the MCSA. In general, measurement errors in explanatory variables can lead to attenuation of estimated associations toward the null, and differences in the measurement properties of several correlated explanatory variables in a model can make it difficult to identify the strongest predictors. 48 In our study, measurement error differences could be contributing to differences in relative effect sizes with larger amyloid PET versus plasma effect sizes in the MCSA versus more comparable amyloid PET and plasma effect sizes in BioFINDER.

There are limitations to this study. The goal was to study two cohorts with comparable plasma biomarker measures. Despite both being mass spectrometry assays of the same analytes, there may be differences in assay performance (particularly Aβ42/40) that contribute to the differences in results, in addition to the cohort differences. The MCSA cohort was a selected sample of the population‐based study, and this was accounted for via weighting back to the at‐risk population. Many BioFINDER participants were missing Aβ42/40, and sensitivity analyses showed similar results in the full cohort and the subset with non‐missing Aβ42/40 results.

In conclusion, plasma AD biomarkers were important predictors of progression from CU to MCI, in addition to age, education, and APOE ε4 carriership, and the composition of the cohort affected biomarker associations. Plasma %p‐tau217 and p‐tau217 were strong predictors of conversion from CU to MCI in both the MCSA and BioFINDER cohorts, though with HRs less than that of amyloid PET in MCSA. Aβ42/40 also showed value depending on the cohort and likely the plasma assay. Plasma %p‐tau217 and Aβ42/40 performed better in BioFINDER and showed additive value to amyloid PET in predicting conversion to MCI in this sample enriched for AD compared to the MCSA sample that was more representative of the community. This study supports the potential for AD biomarkers in early disease detection, prognosis, and clinical trial enrichment, and highlights the need to consider differences in biomarker performance based on measurement technique and population.

CONFLICT OF INTEREST STATEMENT

P.M.C. has consulted for Eli Lilly and received honoria from Eisai Inc., American Academy of Neurology, Kaplan, Medical Learning Institute, Peerview, and Medscape. She serves on Eisai and Lilly data safety monitoring boards but receives no compensation to self or institution. H.J.W., T.M.T., M.E.G., A.P.B., E.S., and J.L.G. have no disclosures. N.M.C. has received consultancy/speaker fees from Biogen, Eli Lilly, Owkin, and Merck. S.P. has acquired research support (for the institution) from Avid and ki elements through ADDF. In the past 2 years, he has received consultancy/speaker fees from Bioartic, Biogen, Eisai, Eli Lilly, Novo Nordisk, and Roche. R.J.B. has equity ownership interest in C2N Diagnostics; receives income from C2N Diagnostics for serving on the scientific advisory board; may receive income based on technology licensed by Washington University to C2N Diagnostics; is an unpaid scientific advisory board member of Roche and Biogen; and receives research funding from Avid Radiopharmaceuticals, Janssen, Roche or Genentech, Eli Lilly, Eisai, Biogen, AbbVie, Bristol Myers Squibb, and Novartis. N.R.B. may receive income based on technology licensed by Washington University to C2N Diagnostics. J.B.B., T.W., and P.B.V. are paid employees of C2N Diagnostics. M.M.M. receives research funding from the NIH. J.G.R. serves as an associate editor for JAMA Neurology and receives research support from the NIH. He serves on the data safety monitoring board for NINDS StrokeNET. He is site investigator for Cognition Therapeutics and Eisai. A.A.‐S. has participated in advisory boards for Roche Diagnostics, Fujirebio Diagnostics, and Siemens Healthineers. She also has received honorarium from Roche Diagnostics and Eli Lilly. V.J.L. is a consultant for AVID Radiopharmaceuticals, Eisai Co. Inc., Bayer Schering Pharma, GE Healthcare, Piramal Life Sciences, and Merck Research, and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals, and NIH (NIA, NCI). C.G.S. receives research support from the NIH. M.L.S. holds stock in medical related companies, unrelated to the current work: Align Technology, Inc., LHC Group, Inc., Medtronic, Inc., Mesa Laboratories, Inc., Natus Medical Inc., and Varex Imaging Corporation. He has also owned stock in these medical related companies within the past 3 years, unrelated to the current work: CRISPR Therapeutics, Gilead Sciences, Inc., Globus Medical Inc., Inovio Biomedical Corp., Ionis Pharmaceuticals, Johnson & Johnson, Medtronic, Inc., Oncothyreon, Inc., Parexel International Corporation. D.S.K. serves on a data safety monitoring board for the Dominantly Inherited Alzheimer Network Treatment Unit study sponsored by Washington University St Louis, and for the SMART‐HS clinical trial (Univ of Kentucky). He was an investigator in Alzheimer's disease clinical trials sponsored by Biogen, Lilly Pharmaceuticals, and the University of Southern California, which have ended, and is currently an investigator in a trial in frontotemporal degeneration with Alector. He has served as a consultant for Roche, AriBio, Linus Health, Biovie, and Alzeca Biosciences but receives no personal compensation. He receives funding from the NIH. P.V. receives research support from the NIH. R.C.P. serves as a consultant for Roche Inc., Merck Inc., and Biogen, Inc. He serves on the data safety monitoring board for Genentech, Inc. and receives royalty from Oxford University Press and UpToDate. O.H. is an employee of Lund University and Eli Lilly. C.R.J. receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic. Author disclosures are available in the supporting information.

CONSENT STATEMENT

For the MCSA, the study was approved by the Mayo Clinic and Olmsted Medical Center institutional review boards. For BioFINDER, ethical approval was granted by the Regional Ethical Committee in Lund, Sweden. All participants provided informed written consent.

Supporting information

Supporting Information

ALZ-21-e70625-s002.docx (1.2MB, docx)

Supporting Information

ALZ-21-e70625-s001.pdf (1.1MB, pdf)

ACKNOWLEDGMENTS

The MCSA data collection and analyses for this study were supported by the National Institutes of Health (U01 AG006786, P50 AG016574, R37 AG011378, RO1 AG041851, R01 NS097495, R01 AG056366, RF1 AG069052). The BioFINDER‐2 study was supported by the National Institute on Aging (R01AG083740), European Research Council (ADG‐101096455), Alzheimer's Association (ZEN24‐1069572, SG‐23‐1061717), GHR Foundation, Swedish Research Council (2022‐00775, 2021‐02219 and 2018‐02052), ERA PerMed (ERAPERMED2021‐184), Knut and Alice Wallenberg foundation (2022‐0231), Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson's disease) at Lund University, Swedish Alzheimer Foundation (AF‐980907, AF‐1011799, and AF‐1011949), Swedish Brain Foundation (FO2021‐0293, FO2023‐0163, and FO2024‐0284), WASP and DDLS Joint call for research projects (WASP/DDLS22‐066), Parkinson foundation of Sweden (1412/22), Cure Alzheimer's fund, Rönström Family Foundation, Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, Skåne University Hospital Foundation (2020‐O000028), Regionalt Forskningsstöd (2022‐1259), and Swedish federal government under the ALF agreement (2022‐Projekt0080, 2022‐Projekt0107). Mass spectrometry measures in BioFINDER‐2 were supported by NIA RF1AG061900 and Rainwater Charitable Foundation.

Cogswell PM, Wiste HJ, Therneau TM, et al. Association of plasma Alzheimer's disease biomarkers with cognitive decline in cognitively unimpaired individuals. Alzheimer's Dement. 2025;21:e70625. 10.1002/alz.70625

Oskar Hansson and Clifford R. Jack Jr. are co‐senior authors.

DATA AVAILABILITY STATEMENT

Data from the Mayo Clinic Study of Aging are available to qualified academic and industry researchers by request to the MCSA Executive Committee (https://www.mayo.edu/research/centers‐programs/alzheimers‐disease‐research‐center/research‐activities/mayo‐clinic‐study‐aging/for‐researchers/data‐sharing‐resources). Pseudonymized BioFINDER‐2 data 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 Swedish Ethical Review Authority and Region Skåne, which should be regulated in a material transfer agreement.

REFERENCES

  • 1. van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in early Alzheimer's Disease. N Engl J Med. 2023;388:9‐21. doi: 10.1056/NEJMoa2212948 [DOI] [PubMed] [Google Scholar]
  • 2. Cummings J, Apostolova L, Rabinovici GD, et al. Lecanemab: Appropriate use recommendations. J Prev Alzheimers Dis. 2023;10(3):362‐377. doi: 10.14283/jpad.2023.30 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Mintun MA, Lo AC, Duggan Evans C, et al. Donanemab in Early Alzheimer's Disease. N Engl J Med. 2021;384:1691‐1704. doi: 10.1056/NEJMoa2100708 [DOI] [PubMed] [Google Scholar]
  • 4. Ramanan VK, Armstrong MJ, Choudhury P, et al. Antiamyloid Monoclonal antibody therapy for Alzheimer Disease: Emerging issues in neurology. Neurology. 2023;101(19):842‐852. doi: 10.1212/WNL.0000000000207757 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Wolz R, Schwarz AJ, Gray KR, Yu P, Hill DLG. Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration. Neurology. 2016;87:1235‐1241. doi: 10.1212/WNL.0000000000003126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Ballard C, Atri A, Boneva N, et al. Enrichment factors for clinical trials in mild‐to‐moderate Alzheimer's disease. Alzheimers Dement (N Y). 2019;5:164‐174. doi: 10.1016/j.trci.2019.04.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Caselli RJ, Dueck AC, Osborne D, et al. Longitudinal modeling of age‐related memory decline and the APOE ε4 effect. N Engl J Med. 2009;361:255‐263. doi: 10.1056/NEJMoa0809437 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Bateman RJ, Barthelemy NR, Benzinger TLS, et al. CSF and blood plasma mass spectrometry measures of Aβ, tau, and NfL species and longitudinal relationship to preclinical and clinical staging of amyloid and tau aggregation and clinical stage of Alzheimer's disease. Alzheimer's & Dementia. 2021;17:e050711. doi: 10.1002/alz.050711 [DOI] [Google Scholar]
  • 9. Palmqvist S, Mattsson N, Hansson O, Alzheimer's Disease Neuroimaging Initiative . Cerebrospinal fluid analysis detects cerebral amyloid‐β accumulation earlier than positron emission tomography. Brain. 2016;139:1226‐1236. doi: 10.1093/brain/aww015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Mattsson‐Carlgren N, Janelidze S, Bateman RJ, et al. Soluble P‐tau217 reflects amyloid and tau pathology and mediates the association of amyloid with tau. EMBO Mol Med. 2021;13:e14022. doi: 10.15252/emmm.202114022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Janelidze S, Berron D, Smith R, et al. Associations of plasma phospho‐Tau217 levels with tau positron emission tomography in early Alzheimer Disease. JAMA Neurol. 2021;78:1‐8. doi: 10.1001/jamaneurol.2020.4201 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Mattsson‐Carlgren N, Andersson E, Janelidze S, et al. Aβ deposition is associated with increases in soluble and phosphorylated tau that precede a positive Tau PET in Alzheimer's disease. Sci Adv. 2020;6:eaaz2387. doi: 10.1126/sciadv.aaz2387 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Therriault J, Vermeiren M, Servaes S, et al. Association of phosphorylated tau biomarkers with amyloid positron emission tomography vs tau positron emission tomography. JAMA Neurol. 2022;80(2):188. doi: 10.1001/jamaneurol.2022.4485 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Cogswell PM, Lundt ES, Therneau TM, et al. Modeling the temporal evolution of plasma p‐tau in relation to amyloid beta and tau PET. Alzheimer's & Dementia. 2024;20:1225‐1238. doi: 10.1002/alz.13539 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Cullen NC, Leuzy A, Janelidze S, et al. Plasma biomarkers of Alzheimer's disease improve prediction of cognitive decline in cognitively unimpaired elderly populations. Nat Commun. 2021;12:3555. doi: 10.1038/s41467-021-23746-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Jack CR, Wiste HJ, Algeciras‐Schimnich A, et al. Comparison of plasma biomarkers and amyloid PET for predicting memory decline in cognitively unimpaired individuals. Alzheimers Dement. 2024;20:2143‐2154. doi: 10.1002/alz.13651 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Mattsson‐Carlgren N, Salvadó G, Ashton NJ, et al. Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers. JAMA Neurol. 2023;80:360‐369. doi: 10.1001/jamaneurol.2022.5272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Meyer P‐F, Ashton NJ, Karikari TK, et al. Plasma p‐tau231, p‐tau181, PET Biomarkers, and cognitive change in older adults. Ann Neurol. 2022;91:548‐560. doi: 10.1002/ana.26308 [DOI] [PubMed] [Google Scholar]
  • 19. Pereira JB, Janelidze S, Stomrud E, et al. Plasma markers predict changes in amyloid, tau, atrophy and cognition in non‐demented subjects. Brain. 2021;144:2826‐2836. doi: 10.1093/brain/awab163 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Rauchmann BS, Schneider‐Axmann T, Perneczky R. Associations of longitudinal plasma p‐tau181 and NfL with tau‐PET, Aβ‐PET and cognition. J Neurol Neurosurg Psychiatry. 2021;92:1289‐1295. doi: 10.1136/jnnp-2020-325537 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Smirnov DS, Ashton NJ, Blennow K, et al. Plasma biomarkers for Alzheimer's Disease in relation to neuropathology and cognitive change. Acta Neuropathol. 2022;143:487‐503. doi: 10.1007/s00401-022-02408-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Tropea TF, Waligorska T, Xie SX, et al. Plasma phosphorylated tau181 predicts cognitive and functional decline. Annals of Clinical and Translational Neurology. 2023;10(1):18‐31. doi: 10.1002/acn3.51695 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Chatterjee P, Pedrini S, Doecke JD, et al. Plasma Aβ42/40 ratio, p‐tau181, GFAP, and NfL across the Alzheimer's disease continuum: a cross‐sectional and longitudinal study in the AIBL cohort. Alzheimers Dement. 2023;19:1117‐1134. doi: 10.1002/alz.12724 [DOI] [PubMed] [Google Scholar]
  • 24. Palmqvist S, Tideman P, Mattsson‐Carlgren N, et al. Blood biomarkers to detect Alzheimer Disease in primary care and secondary care. JAMA. 2024;332(15):1245‐1257. doi: 10.1001/jama.2024.13855 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Palmqvist S, Rossi M, Hall S, et al. Cognitive effects of Lewy body pathology in clinically unimpaired individuals. Nat Med. 2023;29:1971‐1978. doi: 10.1038/s41591-023-02450-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Mielke MM, Hagen CE, Xu J, et al. Plasma phospho‐tau181 increases with Alzheimer's disease clinical severity and is associated with tau‐PET and amyloid‐PET. Alzheimers Dement. 2018;14:989‐997. doi: 10.1016/j.jalz.2018.02.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Palmqvist S, Janelidze S, Quiroz YT, et al. Discriminative accuracy of plasma phospho‐tau217 for Alzheimer Disease vs other neurodegenerative disorders. JAMA. 2020;324:772‐781. doi: 10.1001/jama.2020.12134 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Barthélemy NR, Li Y, Joseph‐Mathurin N, et al. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer's disease. Nat Med. 2020;26:398‐407. doi: 10.1038/s41591-020-0781-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Meyer MR, Kirmess KM, Eastwood S, et al. Clinical validation of the PrecivityAD2 blood test: a mass spectrometry‐based test with algorithm combining %p‐tau217 and Aβ42/40 ratio to identify presence of brain amyloid. Alzheimers Dement. 2024;20(5):3179‐3192. doi: 10.1002/alz.13764 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Jack CR, Wiste HJ, Weigand SD, et al. Defining imaging biomarker cut points for brain aging and Alzheimer's disease. Alzheimers Dement. 2017;13:205‐216. doi: 10.1016/j.jalz.2016.08.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Schwarz CG, Gunter JL, Lowe VJ, et al. A Comparison of partial volume correction techniques for measuring change in serial Amyloid PET SUVR. J Alzheimers Dis. 2019;67:181‐195. doi: 10.3233/JAD-180749 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Klunk WE, Koeppe RA, Price JC, et al. The centiloid project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 2015;11:1‐15.e4. doi: 10.1016/j.jalz.2014.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Schwarz CG, Tosakulwong N, Senjem ML, et al. Considerations for performing level‐2 centiloid transformations for amyloid PET SUVR values. Sci Rep. 2018;8:7421. doi: 10.1038/s41598-018-25459-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Koole M, Lewis DM, Buckley C, et al. Whole‐body biodistribution and radiation dosimetry of 18F‐GE067: A radioligand for in vivo brain amyloid imaging. J Nucl Med. 2009;50:818‐822. doi: 10.2967/jnumed.108.060756 [DOI] [PubMed] [Google Scholar]
  • 35. Lundqvist R, Lilja J, Thomas BA, et al. Implementation and validation of an adaptive template registration method for 18F‐Flutemetamol imaging data. J Nucl Med. 2013;54:1472‐1478. doi: 10.2967/jnumed.112.115006 [DOI] [PubMed] [Google Scholar]
  • 36. Roberts RO, Geda YE, Knopman DS, et al. The mayo clinic study of aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology. 2008;30:58‐69. doi: 10.1159/000115751 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256:183‐194. doi: 10.1111/j.1365-2796.2004.01388.x [DOI] [PubMed] [Google Scholar]
  • 38. Blacker D, Albert MS, Bassett SS, Go RC, Harrell LE, Folstein MF. Reliability and validity of NINCDS‐ADRDA criteria for Alzheimer's disease. The National Institute of Mental Health Genetics Initiative. Arch Neurol. 1994;51:1198‐1204. doi: 10.1001/archneur.1994.00540240042014 [DOI] [PubMed] [Google Scholar]
  • 39. Quadalti C, Palmqvist S, Hall S, et al. Clinical effects of Lewy body pathology in cognitively impaired individuals. Nat Med. 2023;29:1964‐1970. doi: 10.1038/s41591-023-02449-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Bilgel M, An Y, Shah I, et al. Comparison of amyloid positron emission tomography, hippocampal volume, and plasma biomarkers in predicting conversion to mild cognitive impairment. Alzheimers Dement. 2025;20:e093667. doi: 10.1002/alz.093667 [DOI] [Google Scholar]
  • 41. 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: 10.1002/alz.14537 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Janelidze S, Bali D, Ashton NJ, et al. Head‐to‐head comparison of 10 plasma phospho‐tau assays in prodromal Alzheimer's disease. Brain. 2022;146(4):1592‐1601. doi: 10.1093/brain/awac333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Mendes AJ, Ribaldi F, Lathuiliere A, et al. Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non‐demented memory clinic patients. Alzheimer's Research & Therapy. 2024;16:110. doi: 10.1186/s13195-024-01478-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Benedet AL, Brum WS, Hansson O, et al. The accuracy and robustness of plasma biomarker models for amyloid PET positivity. Alzheimers Res Ther. 2022;14:26. doi: 10.1186/s13195-021-00942-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Mielke MM, Dage JL, Frank RD, et al. Performance of plasma phosphorylated tau 181 and 217 in the community. Nat Med. 2022;28:1398‐1405. doi: 10.1038/s41591-022-01822-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Lowe VJ, Lundt E, Knopman D, et al. Comparison of [18F]Flutemetamol and [11C]Pittsburgh Compound‐B in cognitively normal young, cognitively normal elderly, and Alzheimer's disease dementia individuals. Neuroimage Clin. 2017;16:295‐302. doi: 10.1016/j.nicl.2017.08.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Rowe CC, Pejoska S, Mulligan RS, et al. Head‐to‐Head Comparison of 11C‐PiB and 18F‐AZD4694 (NAV4694) for β‐Amyloid Imaging in Aging and Dementia. J Nucl Med. 2013;54:880‐886. doi: 10.2967/jnumed.112.114785 [DOI] [PubMed] [Google Scholar]
  • 48. Gustafson P. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. CRC Press; 2003. [Google Scholar]

Associated Data

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

Supplementary Materials

Supporting Information

ALZ-21-e70625-s002.docx (1.2MB, docx)

Supporting Information

ALZ-21-e70625-s001.pdf (1.1MB, pdf)

Data Availability Statement

Data from the Mayo Clinic Study of Aging are available to qualified academic and industry researchers by request to the MCSA Executive Committee (https://www.mayo.edu/research/centers‐programs/alzheimers‐disease‐research‐center/research‐activities/mayo‐clinic‐study‐aging/for‐researchers/data‐sharing‐resources). Pseudonymized BioFINDER‐2 data 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 Swedish Ethical Review Authority and Region Skåne, which should be regulated in a material transfer agreement.

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