Mild behavioral impairment-apathy and Alzheimer's disease plasma phosphorylated tau biomarker levels

Abstract

Background

Mild behavioral impairment (MBI), characterized by later-life emergence of persistent neuropsychiatric symptoms (NPS), is an early clinical indicator of dementia risk. Global MBI has been associated with Alzheimer's disease (AD) pathology; studies have also explored MBI domains. Prior work has linked MBI-apathy to AD cerebrospinal fluid (CSF) biomarkers, but whether associations are detectable using plasma-based biomarkers such as phosphorylated tau (p-tau) is unknown. Establishing such relationships is critical, as plasma biomarkers are more accessible than CSF.

Objective

To explore cross-sectional and longitudinal associations between MBI-apathy and plasma p-tau₁₈₁ levels using Alzheimer's Disease Neuroimaging Initiative data.

Methods

Older adults with normal cognition or mild cognitive impairment were categorized as MBI-apathy (n = 69), non-MBI NPS (n = 112), and no-NPS (n = 215) based on Neuropsychiatric Inventory scores and symptom persistence over one year. Linear regression modelled cross-sectional associations between NPS group and plasma p-tau₁₈₁, adjusting for age, sex, education, apolipoprotein E4 status, and Mini-Mental State Examination score. Hierarchical linear mixed-effects modelling assessed associations over two and three years, including time-by-NPS group interactions.

Results

MBI-apathy was associated with significantly higher plasma p-tau₁₈₁ levels at baseline (24.05% [6.06–45.08%]; adjusted p = 0.014), and over two (26.46% [7.24–49.12%]; adjusted p = 0.012) and three years (29.28% [10.17–51.72%]; adjusted p = 0.004) compared to no-NPS. No significant associations were observed for non-MBI NPS. In sensitivity analyses, non-MBI apathy was not associated with plasma p-tau₁₈₁ at baseline (−9.96% [−32.69–20.44%]; unadjusted p = 0.478).

Conclusions

MBI-apathy is associated with elevated plasma p-tau₁₈₁ cross-sectionally and longitudinally, supporting MBI-apathy as a potential proxy marker of tau pathology for early AD detection.

Keywords

Alzheimer's disease apathy behavioral and psychological symptoms of dementia biomarkers cognitive dysfunction dementia mild behavioral impairment neuropsychiatric symptoms plasma

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative condition characterized by the accumulation of pathological tau and amyloid-β proteins (Aβ).¹ Biomarker-based criteria are now central to the diagnosis and staging of AD, shifting the field towards incorporating AD-specific biology into what was primarily a clinical definition of the disease.¹ In the Alzheimer's Association 2024 revised AD criteria,¹ the biological definition of AD is grounded in a two-tiered biomarker classification system for diagnosis and staging. Core 1 biomarkers are essential for diagnosis and include measures of amyloid and tau. Specifically, these markers include A for Aβ (i.e., cerebrospinal fluid [CSF] and blood plasma Aβ₄₂, as well as amyloid positron emission tomography [PET]) and T1 for phosphorylated tau [p-tau], (i.e., CSF and blood plasma p-tau_181/217/231). In contrast, Core 2 biomarkers are not required for diagnosis but can support staging and further biological characterization. Core 2 biomarkers reflect later-stage tau pathology (T2), including microtubule-binding region tau₂₄₃, other phosphorylated tau forms (e.g., p-tau₂₀₅), non-phosphorylated mid-region tau fragments, and tau PET imaging.

One of the earliest detectable stages of AD pathophysiology in biomarker-based models is marked by amyloid accumulation.² As soluble Aβ is sequestered into plaques, CSF Aβ₄₂ levels decline, and amyloid PET signal increases, often years before the onset of clinical symptoms.^3–8 Following amyloid plaque deposition, tau becomes aberrantly phosphorylated at multiple mid-region residues, reflecting downstream effects of Aβ-driven kinase dysregulation and glial activation.^1,3–5,9 Distinct phospho-epitopes, including threonine 181 (p-tau₁₈₁) and threonine 217 (p-tau₂₁₇), show overlapping but temporally distinct trajectories.^3–5 P-tau₂₁₇ rises earliest and more steeply with emerging Aβ pathology, whereas p-tau₁₈₁ increases slightly later and plateaus more gradually during the prodromal phase.¹⁰ Phosphorylation at threonine 181 likely represents an early but downstream response to amyloid plaque formation, preceding the development of insoluble tau aggregates detectable by tau PET.^1,5,11 Consequently, phosphorylated tau species function as intermediate markers linking amyloid deposition to later neurodegenerative changes. Although p-tau₂₁₇ has demonstrated superior sensitivity and specificity for detecting early AD pathology,^3,11–17 p-tau₁₈₁ remains a valuable biomarker for longitudinal designs, as its trajectory levels are known to increase across preclinical and prodromal stages. The reliable detectability of p-tau₁₈₁ in plasma enhances scalability for early identification of AD pathophysiology, especially in settings where access to neuroimaging or CSF collection is limited. The integration of fluid biomarkers into diagnostic workflows represents a pivotal shift toward earlier and more efficient identification of individuals at risk.

Although cognitive symptoms have traditionally provided the impetus for AD assessment and work up, neuropsychiatric symptoms (NPS) are recognized as early manifestations of the disease. In particular, mild behavioral impairment (MBI) is a validated framework for dementia detection and prognostication that leverages risk associated with later-life emergent and persistent NPS. Multiple studies have demonstrated that MBI is associated with incident cognitive decline and dementia.^18–22 MBI comprises five behavioral domains, including apathy, affective dysregulation, impulse dyscontrol, social inappropriateness, and psychosis, some of which have also been studied longitudinally for risk of incident cognitive decline and dementia.^23–26 Among these domains, apathy has consistently shown particularly strong associations with clinical progression and AD-related pathology.^24,27 Apathy in neurocognitive disorders is defined as diminished interest, initiative, and/or emotional reactivity,^28–31 and is particularly salient given its strong associations with tau pathology and progression to AD dementia.^24,27 In earlier work, we demonstrated that MBI-apathy was associated with greater risk of incident dementia, even in the absence of cognitive impairment.²⁴ More recently, we found that MBI-apathy is associated with greater CSF tau pathology.²⁷ These findings support a domain-specific, hypothesis-driven focus on apathy as a behavioral phenotype closely linked to AD-related pathology. Few studies, however, have examined whether these associations extend to plasma-based biomarkers in non-dementia populations.^32,33 Clarifying the relationship between MBI-apathy and plasma biomarkers is important, as these biomarkers are less invasive and more scalable than CSF or imaging, making them well suited for real-world clinical implementation. As MBI-apathy is a clinically observable and easily screenable behavioral syndrome, establishing alignment with plasma tau pathology could provide a pragmatic avenue for early risk stratification in pre-dementia populations.

Accordingly, the objective of this study was to investigate associations between MBI-apathy and plasma p-tau₁₈₁ in individuals who are cognitively normal (CN) or have mild cognitive impairment (MCI). We hypothesized that MBI-apathy is associated with elevated plasma p-tau₁₈₁ levels at baseline and that this association persists longitudinally over two- and three-year follow-up periods.

Methods

Study cohort and data source

This study utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; https://adni.loni.usc.edu/), a large, multicenter, longitudinal study launched in 2003 under the leadership of Principal Investigator Michael W. Weiner, MD. Supported by both public and private funding, ADNI was established to develop standardized clinical, imaging, genetic, and biomarker methodologies for assessing individuals across the cognitive spectrum, from normal cognition to mild AD. The dataset includes comprehensive demographic, diagnostic, neurological, genetic, and neuropathological assessments collected at six- to twelve-month intervals. ADNI adheres to Good Clinical Practice guidelines, the ethical principles of the Declaration of Helsinki, and U.S. regulatory requirements (21 CFR Part 50: Protection of Human Subjects and Part 56: Institutional Review Boards). The study protocol was approved by the Institutional Review Boards at all participating sites. All participants provided written informed consent for participation and publication prior to the initiation of any study procedures. Participant privacy was protected through the use of coded research identifiers; no personally identifying information was linked to the data used in this analysis. Data and samples were collected from September 2005 to September 2023. The available ADNI data used in this work were accessed and downloaded on March 11, 2024.

Participant inclusion criteria and classification

The study flow for primary data analysis is shown in Figure 1. Cognitive diagnoses (CN or MCI) were based on the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). CN participants had an MMSE score ≥24 and a CDR score of 0. Participants with MCI had an MMSE score ≥24, a CDR score of 0.5, memory complaints, and objective memory impairment based on education-adjusted scores from the Wechsler Memory Scale Logical Memory II, with preserved activities of daily living.

The classification of participant groups in this study follows a previously established methodology.²⁷ Participants were included if they had complete Neuropsychiatric Inventory (NPI)³⁴ (from ADNI-2, 3) or Neuropsychiatric Inventory Questionnaire (NPI-Q)³⁵ (from ADNI-1, GO, 2, 3) domain scores necessary for determining NPS status and at least two study visits within their first year to assess symptom persistence.

As previously described,²⁷ MBI status was determined by transforming NPI and NPI-Q scores into MBI domain scores using a published algorithm.³⁶ Since the NPI and NPI-Q assess NPS over the previous four weeks, rather than over the ≥6-month period required for MBI, multiple study visits were used to approximate the MBI persistence criterion of ≥6 months. MBI-apathy was operationalized based on the NPI and NPI-Q apathy domain scores. The presence of apathy at a given visit was defined as an apathy domain score >0. To meet MBI-apathy criteria, apathy had to be present at two or more of the three visits within the first year (i.e., 0 and 6 months, 0 and 12 months, 6 and 12 months, or all three time points). Individuals were included in the MBI-apathy group irrespective of concurrent NPS in other MBI domains. Participants who met MBI criteria without apathy (Non-apathy MBI) or who exhibited apathy at only one visit but met MBI criteria in another domain (MBI-transient-apathy), were excluded from primary analyses.

Participants who exhibited NPS in any of the five MBI domains (apathy, affective dysregulation, impulse dyscontrol, social inappropriateness, psychosis) at only one of the three visits within the first year were classified as the non-MBI NPS group, as they did not meet the MBI symptom persistence criterion. For sensitivity analyses, individuals with apathy present at only one of three visits and no persistent symptoms in other MBI domains were classified as non-MBI apathy. The no-NPS group included participants without any NPS (MBI total score = 0) at all first-year visits.

Biomarker analysis: plasma p-tau₁₈₁

Participants were required to have baseline plasma p-tau₁₈₁ measurements for inclusion in this analysis. ADNI plasma p-tau₁₈₁ concentrations (ng/L) were collected annually, with baseline blood collection occurring at or near the same scheduled study visit as the baseline NPI/NPI-Q assessment used to define initial NPS status. Samples were analyzed in a blinded manner between May and December 2019 using the Single Molecule Array (Simoa) HD-1 platform (Quanterix, Billerica, MA, USA) in the Clinical Neurochemistry Laboratory at the University of Gothenburg, Sweden, according to established protocols.³⁷ The assay employs Tau12 and AT270 monoclonal antibodies to detect N-terminal to mid-domain forms of p-tau₁₈₁. Further details on analytical procedures and assay validation are available elsewhere.³⁷

Statistical analyses

Baseline demographic characteristics comprised age, sex, and years of education. Clinical data encompassed NPI/NPI-Q and MMSE scores, and biomarker measures included apolipoprotein E4 (APOE4) carrier status and plasma p-tau₁₈₁ levels. Comparisons between the MBI-apathy and non-MBI NPS group versus the no-NPS group were performed using χ² tests for categorical variables and one-way ANOVAs for continuous variables.

Both cross-sectional and longitudinal associations of NPS status and plasma p-tau₁₈₁ were examined. For the cross-sectional analysis, a linear regression model was employed with NPS status as the predictor and plasma p-tau₁₈₁ level as the continuous outcome variable. NPS status was categorized into three groups: MBI-apathy, non-MBI NPS, and no-NPS, with the no-NPS group serving as the reference. As a sensitivity analysis, the cross-sectional model was repeated with non-MBI apathy and MBI-apathy compared against the no-NPS reference group to evaluate whether symptom persistence influenced associations with plasma p-tau₁₈₁. The models were adjusted for age, sex, years of education, APOE4 carrier status, and MMSE score. APOE4 carrier status was dichotomized, with individuals carrying one or more APOE4 alleles classified as APOE4 carriers and those without APOE4 alleles as non-carriers, with non-APOE4 carriers as the reference. Male was the reference group for sex. Additional sensitivity analyses were performed to evaluate the robustness of the primary findings and are reported in the Supplemental Material.

To reduce the influence of extreme values, plasma p-tau₁₈₁ levels were first Winsorized at the 5^th and 95^th percentiles. The Winsorized values were then log-transformed to address right-skewness before inclusion in regression analyses. The transformed distribution was then visually inspected with diagnostic plots (Residuals versus Fitted, Normal Q-Q, Scale-Location, and Residuals versus Leverage) to evaluate linearity, normality of errors, homoscedasticity, and the influence of potential outliers. The visual checks confirmed that assumptions required for regression analyses were adequately met. To facilitate interpretation, regression coefficients from the log-transformed models were exponentiated and converted to percentage differences in plasma p-tau₁₈₁ using the formula (e^β − 1) × 100, where β represents the regression coefficient from the model.

Longitudinal associations between NPS status (predictor) and plasma p-tau₁₈₁ levels (outcome variable) over two and three years were assessed using hierarchical linear mixed-effects (LME) models. Plasma p-tau₁₈₁ levels were treated as repeated measures, whereas all other variables were assessed at baseline. To account for individual variability, participant ID was modeled with random intercepts and slopes. Fixed effects included NPS status, age, sex, years of education, APOE4 carrier status, and MMSE score. Additionally, an interaction term between NPS status and time in months was included to test whether biomarker trajectories differed across NPS groups relative to the no-NPS group.

All statistical analyses were performed using R v4.4.0. Linear models were implemented with the stats package, LME models with the lmer package, and group comparisons over time were examined using the emmeans package. To account for multiple testing and control the false discovery rate (FDR), we applied the Benjamini-Hochberg procedure to the two primary group comparisons of interest within each model (Non-MBI NPS versus no-NPS and MBI-apathy versus no-NPS). Statistical significance was defined as an adjusted p-value < 0.05.

Results

Participant demographics and characteristics

The current study included 396 participants, with 215 in the no-NPS group, 112 in the non-MBI NPS group, and 69 in the MBI-apathy group. On average, participants were 72.5 years old, 49.5% female, and 41.4% CN. In unadjusted descriptive comparisons to the no-NPS group, individuals with MBI-apathy were of similar age and education level, but were more likely to be male, APOE4 carriers, have MCI, and higher plasma p-tau₁₈₁ levels (Table 1).

Table 1.

Baseline participant characteristics.

	No-NPS (n = 215)	Non-MBI NPS (n = 112)	MBI-apathy (n = 69)	p
Age
Mean (SD)	72.6 (6.63)	72.4 (7.72)	72.5 (7.57)	0.982^b
Median [Min, Max]	72.0 [56.2, 90.1]	73.0 [55.0, 88.3]	73.1 [55.0, 88.6]
Sex
Male	105 (48.8%)	47 (42.0%)	48 (69.6%)	0.001^a
Female	110 (51.2%)	65 (58.0%)	21 (30.4%)
Years of education
Mean (SD)	16.6 (2.61)	15.9 (2.70)	16.0 (2.53)	0.036^b
Median [Min, Max]	17.0 [12.0, 20.0]	16.0 [11.0, 20.0]	16.0 [9.00, 20.0]
APOE4 Carrier Status				<0.001 ^a
Non-APOE4 Carrier	144 (67.0%)	68 (60.7%)	28 (40.6%)
APOE4 Carrier	71 (33.0%)	44 (39.3%)	41 (59.4%)
Cognitive Status
CN	127 (59.1%)	30 (26.8%)	7 (10.1%)	<0.001^a
MCI	88 (40.9%)	82 (73.2%)	62 (89.9%)
MMSE Scores
Mean (SD)	28.8 (1.48)	28.4 (1.63)	28.0 (1.74)	0.001^b
Median [Min, Max]	29.0 [24.0, 30.0]	29.0 [24.0, 30.0]	28.0 [24.0, 30.0]
p-tau₁₈₁ (ng/L)
Mean (SD)	18.32 (32.39)	17.85 (11.36)	20.77 (11.77)	0.727^b
Median (IQR)	13.50 (8.94, 20.57)	14.04 (10.63, 21.98)	18.17 (13.17, 24.96)

p-tau: phosphorylated tau; APOE: Apolipoprotein E; CN: cognitively normal; MCI: mild cognitive impairment; MBI: mild behavioral impairment; MMSE: Mini-Mental State Examination; NPS: neuropsychiatric symptoms; SD: standard deviation. ^a χ² tests, ^b one-way ANOVA. Boldface indicates statistically significant p-values (p < 0.05).

A violin plot for baseline plasma p-tau₁₈₁ level by NPS group is presented in Figure 2.

Cross-sectional and longitudinal associations between MBI-apathy and plasma p-Tau₁₈₁

The cross-sectional linear regression model revealed that MBI-apathy status was associated with 24.05% higher baseline plasma p-tau₁₈₁ levels compared to the no-NPS group (percentage difference β [95%CI], 24.05% [6.06%–45.08%]; FDR-adjusted p = 0.014) (Table 2). Comparatively, non-MBI NPS status was not significantly associated with plasma p-tau₁₈₁ levels (β = 10.64% [−2.62%–25.70%]; FDR-adjusted p = 0.120) at baseline.

Table 2.

Cross-sectional association between NPS group and plasma p-tau₁₈₁ modelled using linear regression adjusted for covariates.

Predictors	%Difference in plasma p-tau₁₈₁	95% CI	p	FDR-adjusted p
Non-MBI NPS	10.64%	−2.62%–25.70%	0.120	0.120
MBI-apathy	24.05%	6.06%–45.08%	0.007	0.014
Age	1.57%	0.76%–2.38%	<0.001
Sex (Female)	−1.27%	−12.23%–11.06%	0.830
Years of Education	0.50%	−1.73%–2.79%	0.663
APOE4 Carrier Status (APOE4 Carrier)	23.69%	9.84%–39.28%	<0.001
MMSE Score	−2.65%	−6.18%–1.02%	0.154

Exponentiated regression coefficients for the NPS group variables (highlighted in grey) represent the estimated percent difference in p-tau₁₈₁ compared to the no-NPS group at baseline. This model is adjusted for age, sex, years of education, APOE4 carrier status, and MMSE score. APOE: Apolipoprotein E; CI: confidence interval; FDR: false discovery rate; MBI: mild behavioral impairment; MMSE: Mini-Mental State Examination; NPS: neuropsychiatric symptoms. Boldface indicates statistically significant p-values (p < 0.05).

In the sensitivity analysis, non-MBI apathy was not associated with plasma p-tau₁₈₁, whereas MBI-apathy remained significantly associated with higher plasma p-tau₁₈₁ levels (Supplemental Table 1). Additional sensitivity analyses are reported in Supplemental Tables 2–6.

Longitudinal biomarker data for plasma p-tau₁₈₁ were available for 328/396 participants at the two-year mark (Table 3 and Figure 3).

Table 3.

Longitudinal association between NPS group and plasma p-tau₁₈₁ over two years modelled using linear mixed effect models adjusted for covariates.

Predictors	%Difference in plasma p-tau₁₈₁	95% CI	p	FDR-adjusted p
Non-MBI NPS	11.18%	−2.84%–27.23%	0.126	0.126
MBI-apathy	26.46%	7.24%–49.12%	0.006	0.012
Time (Months)	0.15%	−0.14%–0.43%	0.318
Age	1.96%	1.17%–2.75%	<0.001
Sex (Female)	−5.77%	−15.92%–5.60%	0.310
Years of Education	0.12%	−2.04%–2.33%	0.914
APOE4 Carrier Status (APOE4 Carrier)	22.23%	8.93%–37.15%	0.001
MMSE Score	−2.53%	−5.96%–1.03%	0.165
NPS status (Non-MBI NPS) × Time	−0.14%	−0.63%–0.35%	0.580
NPS status (MBI-apathy) × Time	0.22%	−0.38%–0.82%	0.479

Exponentiated regression for the NPS group variables (highlighted in grey) represent the estimated percent difference in p-tau₁₈₁ compared to the no-NPS group over two years. Tests of interaction between NPS group and time were non-significant. This model is adjusted for age, sex, years of education, APOE4 carrier status, MMSE score, and an interaction term between NPS status and number of months. APOE: Apolipoprotein E; CI: confidence interval; FDR: false discovery rate; MBI: mild behavioral impairment; MMSE: Mini-Mental State Examination; NPS: neuropsychiatric symptoms. Boldface indicates statistically significant p-values (p < 0.05).

Participants missing plasma p-tau₁₈₁ data at two years were older (74.63 [6.77] versus 72.05 [7.10] years, p = 0.005) and had lower MMSE scores (28.03 [1.88] versus 28.62 [1.51], p = 0.017) compared to those with data available at that timepoint. Sex distribution (Female: 52.94% versus 48.78%, p = 0.623), years of education (15.79 ± 2.86 versus 16.44 ± 2.58, p = 0.087), and APOE4 carriership (48.53% versus 37.50%, p = 0.119) did not significantly differ between groups. On average, plasma p-tau₁₈₁ levels did not significantly change over two years across the full sample (percentage change β [95%CI], 0.15% [−0.14%–0.43%]; p = 0.318). However, two-year hierarchal LME analyses revealed significant between-group differences. Compared to the no-NPS group, MBI-apathy status was associated with significantly higher p-tau₁₈₁ levels over two years (26.46% [7.24%–49.12%]; FDR-adjusted p = 0.012), while non-MBI NPS status was not (11.18% [−2.84%–27.23]; FDR-adjusted p = 0.126). Tests of interactions between NPS group and time were not statistically significant (p > 0.05), suggesting that, within this two-year period, there were no detectable differences in the slopes of change in p-tau₁₈₁ between the groups.

Longitudinal biomarker data for plasma p-tau₁₈₁ were available for 186/396 participants at the three-year mark (Table 4 and Figure 3).

Table 4.

Longitudinal association between NPS group and plasma p-tau₁₈₁ over three years modelled using linear mixed effect models adjusted for covariates.

Predictors	%Difference in plasma p-tau₁₈₁	95% CI	p	FDR-adjusted p
Non-MBI NPS	10.78%	−2.81%–26.27%	0.128	0.128
MBI-apathy	29.28%	10.17%–51.72%	0.002	0.004
Time (Months)	0.18%	−0.04%–0.40%	0.108
Age	1.94%	1.17%–2.71%	<0.001
Sex (Female)	−4.61%	−14.66%–6.64%	0.411
Years of Education	0.05%	−2.06%–2.21%	0.961
APOE4 Carrier Status (APOE4 Carrier)	22.58%	9.52%–37.19%	<0.001
MMSE Score	−2.69%	−6.04%–0.78%	0.130
NPS status (Non-MBI NPS) × Time	−0.09%	−0.45%–0.26%	0.617
NPS status (MBI-apathy) × Time	−0.09%	−0.51%–0.33%	0.680

Exponentiated regression for the NPS group variables (highlighted in grey) represent the estimated percent difference in p-tau₁₈₁ compared to the no-NPS group over three years. Tests of interaction between NPS group and time were non-significant. This model is adjusted for age, sex, years of education, APOE4 carrier status, MMSE score, and an interaction term between NPS status and number of months. APOE: Apolipoprotein E; CI: confidence interval; FDR: false discovery rate; MBI: mild behavioral impairment; MMSE: Mini-Mental State Examination; NPS: neuropsychiatric symptoms. Boldface indicates statistically significant p-values (p < 0.05).

Participants missing plasma p-tau₁₈₁ data at three years were older (73.60 [6.70] versus 71.25 [7.36] years, p = 0.001) and had higher MMSE scores (28.84 [1.45] versus 28.15 [1.67], p < 0.001) compared to those with data available at that timepoint. Sex distribution (Female: 46.67% versus 52.69%, p = 0.273), years of education (16.32 [2.64] versus 16.34 [2.64], p = 0.925), and APOE4 carriership (35.71% versus 43.55%, p = 0.136) did not significantly differ between groups. On average, plasma p-tau₁₈₁ did not significantly change over three years across the full sample (0.18% [−0.04%–0.40%]; p = 0.108). However, three-year hierarchal LME analyses revealed that compared to the No-NPS group, MBI-apathy was associated with higher p-tau₁₈₁ over three years (29.28% [10.17%–51.72%]; FDR-adjusted p = 0.004), while non-MBI NPS was not (10.78% [−2.81%–26.27]; FDR-adjusted p = 0.128). Tests of interactions between NPS group and time were not statistically significant (p > 0.05), suggesting that, within the limits of our study, there are no detectable differences in the slopes of changes in p-tau₁₈₁ between the groups.

Discussion

In this study of 396 dementia-free older adults, we examined associations between MBI-apathy and blood plasma p-tau₁₈₁ levels. Associations were assessed cross-sectionally and longitudinally over two and three years, comparing individuals with MBI-apathy and non-MBI NPS to those without NPS. Apathy symptoms meeting MBI criteria were consistently associated with higher plasma p-tau₁₈₁ levels at baseline and follow-up, whereas non-MBI NPS showed no significant association.

Prior work from our group showed that, in CN and MCI individuals, MBI-apathy was associated with greater CSF tau pathology.²⁷ In ADNI, individuals with MBI-apathy exhibited significantly higher CSF p-tau₁₈₁ and CSF p-tau₁₈₁/Aβ₄₂ at baseline and over two years, while non-MBI NPS did not significantly differ from no-NPS. A four-year analysis in ADNI and MEMENTO cohorts supports this pattern at the global MBI level, although apathy was not examined specifically.³⁸ Across four years, MBI was associated with higher CSF p-tau₁₈₁ and CSF p-tau₁₈₁/Aβ₄₂, while non-MBI NPS showed no significant association. More recently, MBI has also been significantly associated with CSF Aβ₄₂ and p-tau₁₈₁ positivity, as well as with the NIA-AA defined AD continuum biomarker profile, whereas non-MBI NPS showed no associations.³⁹ Concordant results were obtained with plasma markers for p-tau₁₈₁ and p-tau₂₁₇.^40,41 Global MBI was associated with significantly higher plasma p-tau₁₈₁ at baseline and over four years, while non-MBI NPS again showed no difference.⁴⁰ Global MBI was also significantly associated with higher plasma p-tau₂₁₇ at baseline compared to those without MBI.⁴¹ Similarly, higher MBI Checklist (MBI-C) scores have been associated with both elevated CSF p-tau₁₈₁ and higher tau-PET signal in the entorhinal cortex and hippocampus among CN Aβ-positive individuals.⁴² Extending these findings, MBI status has been associated with greater tau tracer uptake in early AD cortical regions among Aβ-positive but not Aβ-negative individuals, reinforcing its role as a behavioral correlate of AD-related tau pathology.⁴³ These biomarker results align with autopsy studies showing that MBI in the five years before death is significantly associated with pathologically confirmed AD, but not with non-AD neuropathologies, underscoring its specificity as a prodromal feature of AD.^44,45

Together, these findings show that applying the MBI framework to all later-life NPS helps identify the NPS subgroup most likely to have tau-related neurodegeneration. In the present study, individuals with MBI-apathy had the highest levels of plasma p-tau₁₈₁, supporting the potential utility of this behavioral phenotype for identifying preclinical and prodromal stages of the AD continuum. A key MBI feature is symptom persistence. Across all cohorts, elevated tau was observed only when symptoms had later-life onset and persisted for at least six months. In contrast, impersistent NPS, such as those captured in the non-MBI NPS group, can reflect situational, psychological, or comorbid medical factors unrelated to neurodegeneration.¹⁸ Pooling impersistent with persistent symptoms weakens observed biomarker associations, while excluding episodic cases sharpens the signal. In practice, applying MBI criteria does more than refine a case definition, it isolates a behavioral phenotype that more reliably indexes early AD pathology. Therefore, future biomarker studies and clinical trials should distinguish MBI from non-MBI NPS, as these groups differ in likelihood of underlying AD pathology. While most biomarker studies have examined MBI globally, the present findings highlight the importance of considering domain-specific behavioral phenotypes. In particular, the association between MBI-apathy and plasma p-tau₁₈₁ supports prior evidence suggesting that apathy may be especially closely linked to AD-related pathology. Building on our previous CSF findings in the same cohort,²⁷ the current results extend this domain-specific relationship to plasma tau biomarkers, revealing that persistent apathy may represent a behavioral signal of underlying AD pathology detectable using scalable blood-based measures.

Plasma p-tau₁₈₁ levels remained higher in the MBI-apathy group over time; however, our longitudinal LMEs did not identify significant interaction effects between NPS group and time. As shown in Figure 3, the MBI-apathy group had roughly parallel trajectories to the other groups. The non-significant interaction suggests that, within the study timeframe, group trajectories in p-tau₁₈₁ remained relatively stable and did not diverge significantly. The observed stability is consistent with AD biomarker staging models, which suggest that plasma p-tau₁₈₁ rises after detectable amyloid accumulation and begins to plateau during late-MCI to early-dementia.^1,5,11,37 In contrast, longitudinal data from sporadic and autosomal dominant cohorts show that p-tau₂₁₇ increases earlier in the AD cascade.^1,4,5,11,46 As p-tau₁₈₁ increases later in the disease course, it may better reflect downstream progression rather than initial onset. The absence of a significant interaction however, does not necessarily confirm identical slopes across groups. Absence of slope differences may indicate a biological inflection point occurring before our baseline. This inflection point coincides with global MBI, the construct for which was developed explicitly for early detection; MBI can initially manifest as the emergence and persistence of an undifferentiated constellation of symptoms prior to an identifiable MBI domain.^27,40,47 Group-level differences in p-tau₁₈₁ trajectories may therefore have already stabilized by enrolment, limiting our ability to detect divergence over the observed two- to three-year period. Short follow-up duration, modest assay sensitivity for detecting subtle change in plasma p-tau₁₈₁, inter-individual variability, and attrition-related reductions in sample size could have further contributed to the absence of slope differences. Nonetheless, the consistently elevated p-tau₁₈₁ levels observed in the MBI-apathy group, independent of trajectory acceleration, underscores its potential utility as a behavioral marker of early tau pathology. Within this context, emerging neuropathological evidence suggests that early tau changes may precede detectable amyloid accumulation, particularly in brainstem nuclei, such as the locus coeruleus, highlighting the need for future studies integrating emerging biomarkers of early tau species to further refine current models of AD pathophysiology.^48–50

The elevated risk profile of the MBI-apathy group is further underscored by its demographic characteristics. In our sample, those with MBI-apathy were more likely to carry the APOE4 allele, have MCI, and be male, features that, while adjusted for in all models, highlight clinical differences that may contribute to the early identification of at-risk individuals. These findings align with prior work from our group. For example, in a National Alzheimer's Coordinating Center analysis, MBI-apathy cases were more often male (65.0%) and had higher MCI prevalence (58.5%) than those without apathy (39.7% male, 20.0% with MCI) and no NPS (37.4% male, 13.4% with MCI).²⁴ Similarly, in our ADNI CSF biomarker study, more APOE4 carriers (59.6%) and males (67.3%) were observed among those with MBI-apathy compared to those with no NPS (48.8% male, 33.0% APOE4 carriers) and non-MBI NPS (45.7% male, 31.9% APOE4 carriers).²⁷ Although male sex is not a risk factor for AD, and women have a higher lifetime risk,⁵¹ several studies suggest that apathy may be more common or severe in males.^19,52,53 A meta-analysis showed that across AD clinical stages, apathy severity was higher in men, in contrast to depression or anxiety, which were more prevalent in women.⁵² Sex differences may reflect differential involvement of fronto-striatal circuits or dopaminergic tone, both of which are implicated in apathy.⁵⁴ Differences in symptom reporting or recognition may also contribute, as men may be less likely to report affective complaints, instead presenting with motivational withdrawal. In addition, study partner characteristics may shape symptom detection and endorsement. Female study partners are more likely than male study partners to report NPS, which may influence observed sex differences.⁵⁵ Taken together, these demographic patterns suggest that MBI-apathy clusters with other established indicators of elevated AD risk. Understanding the intersection of behavioral, genetic, and cognitive features could improve our ability to stratify risk and tailor early intervention strategies.

Limitations

This study has a few relevant limitations that should be mentioned. First, the ADNI dataset is derived from a highly curated research cohort that was designed to be enriched for AD pathology. As such, participants are selected based on strict criteria, including minimal vascular burden and elevated risk of AD, making the cohort less representative of the general aging population. Additionally, the sample has limited ethnocultural and sociodemographic diversity, with participants predominantly identifying as White and very small cell counts for other ethnicity categories, further restricting generalizability and precluding meaningful subgroup analyses by ethnicity.

The present study was designed as a hypothesis-driven analysis focused specifically on MBI-apathy, based on prior findings showing an association with CSF p-tau₁₈₁.²⁷ However, the other individual MBI domains were not examined independently in relation to plasma p-tau₁₈₁. While this study supports an association between MBI-apathy and plasma p-tau₁₈₁, it does not imply that apathy is the only MBI domain associated with this biomarker. Examining all individual MBI domains would require adequately powered analyses with appropriate consideration of multiple comparisons. Future studies are needed to determine associations between persistent behavioral changes and plasma tau biomarkers across all MBI domains.

The MBI-apathy group is relatively small. While this was anticipated given the specificity of the MBI criteria, it limits our ability to examine more nuanced relationships. For instance, we were unable to assess interactions between NPS status and clinical cognitive diagnosis (i.e., CN versus MCI), as the number of CN individuals with MBI-apathy was too small for meaningful stratification (n = 5 at two years; n = 1 at three years). In addition, in a sensitivity analysis replacing MMSE with CDR Sum of Boxes (CDR-SB), the association between MBI-apathy and plasma p-tau₁₈₁ was attenuated and no longer statistically significant (Supplemental Table 2). Since CDR-SB captures cognitive and functional impairment, this finding suggests that the observed association may be partly influenced by clinical disease severity and highlights the need for replication in larger samples that can more fully account for cognitive and functional status. The limited sample also precluded analysis of plasma biomarkers beyond p-tau₁₈₁, including p-tau₂₁₇, despite growing evidence supporting p-tau₂₁₇ as a more sensitive and diagnostically specific marker of AD pathology, including improved differentiation of AD from other neurodegenerative conditions.^3,11–17 Moreover, while plasma p-tau₁₈₁ is an AD-relevant biomarker, elevated levels are not sufficient in isolation to establish AD-specific pathology or distinguish AD from other neurodegenerative conditions, including frontotemporal dementia (FTD). MBI is intended to identify increased dementia risk rather than to establish a specific underlying neurodegenerative etiology. This consideration is especially relevant when examining apathy, which can occur across neurodegenerative disorders, including behavioral variant FTD.³⁰ Accordingly, the observed association between MBI-apathy and plasma p-tau₁₈₁ should be interpreted as suggestive of alignment with AD-related tau pathology in this sample rather than as evidence that MBI-apathy specifically identifies AD. Indeed, our research group has recently found an association between MBI and plasma p-tau₂₁₇,⁴¹ highlighting the need for future studies incorporating more informative AD biomarkers, including plasma p-tau₂₁₇ and confirmatory amyloid or tau biomarkers, alongside biomarkers of other dementia etiologies, to better characterize the underlying neurodegenerative pathology in individuals with MBI-apathy.

In addition, apathy was operationalized using the NPI and NPI-Q, instruments originally developed for dementia populations. These tools may have limited sensitivity for detecting subtle or early motivational changes in non-dementia individuals, particularly when using the brief NPI-Q, potentially underestimating apathy severity or prevalence in preclinical stages. Future studies using the MBI-C might be informative, as the assessment explicitly identifies apathy and the three apathy domains of interest, initiative, and emotional reactivity, and may be better suited for examining variability in behavioral trajectories and their relationship to underlying AD pathology.^56–58

Additionally, plasma p-tau₁₈₁ was analyzed as a continuous outcome in this study, allowing detection of subtle group-level differences but not whether individuals with MBI-apathy were more likely to exceed clinical or pathological cutoffs. Future research incorporating binary classifications of biomarker positivity (e.g., normal versus abnormal plasma p-tau₁₈₁) and comparison with non-MBI apathy could further clarify the prognostic relevance of persistent, later-life emergent apathy for risk stratification and early detection. Finally, our longitudinal analyses relied on LME models, which assume linear trajectories. Although diagnostic checks supported model adequacy, it is possible that non-linear changes in plasma p-tau₁₈₁ were not fully captured. Future studies with larger samples and extended follow-up could examine flexible modeling approaches to better account for potential non-linearity.

Conclusions

MBI-apathy represents a clinically meaningful behavioral syndrome that aligns with biological and cognitive markers of AD risk. In this study, emergent and persistent apathy symptoms, defined using the MBI framework, were associated with elevated plasma p-tau₁₈₁, reinforcing the link between later-life behavioral change and early tau pathology. These findings build on a growing body of research supporting the utility of MBI for sample enrichment and risk stratification in preclinical and prodromal AD.

As blood-based biomarkers gain traction in both research and clinical settings, incorporating scalable behavioral indicators, such as MBI-apathy, offers a complementary and pragmatic approach to early detection. Relevance is heightened in contexts where biomarker access remains limited or cost-prohibitive. Screening for emergent and persistent apathy symptoms may help identify individuals who would benefit from closer monitoring, timely counselling, or preventative intervention. Future research should continue to explore how integrating behavioral and biological data can improve prognostic accuracy and inform targeted, stage-specific care strategies in AD.

Supplemental Material

sj-docx-1-alz-10.1177_13872877261470257 - Supplemental material for Mild behavioral impairment-apathy and Alzheimer's disease plasma phosphorylated tau biomarker levels

Supplemental material, sj-docx-1-alz-10.1177_13872877261470257 for Mild behavioral impairment-apathy and Alzheimer's disease plasma phosphorylated tau biomarker levels by Daniella Vellone, Rebeca Leon, Zahra Goodarzi, Nils D. Forkert, Eric E. Smith and Zahinoor Ismail in Journal of Alzheimer's Disease

Footnotes

Acknowledgements

Data used in preparation of this article were obtained from the ADNI database (https://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: Data collection and sharing for this project were supported by ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense Award Number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through the generous support of 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 affiliate Genentech, Inc., Fujirebio, GE Healthcare, IXICO Ltd, Janssen Alzheimer Immunotherapy Research & Development, LLC., Johnson & Johnson Pharmaceutical Research & Development LLC., Lumosity, Lundbeck, Merck & Co., Inc., Meso Scale Diagnostics, LLC., NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc., Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics.

The Canadian Institutes of Health Research also provide funding to support ADNI clinical sites in Canada. Contributions from the private sector are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.

ORCID iDs

Daniella Vellone

Zahra Goodarzi

Nils D. Forkert

Zahinoor Ismail

Ethical considerations

ADNI, which includes ADNI-1, ADNI-GO, ADNI-2, and ADNI-3, adheres to Good Clinical Practice guidelines, the ethical principles of the Declaration of Helsinki, US regulatory requirements (21 CFR Part 50: Protection of Human Subjects and Part 56: Institutional Review Boards), the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2), as well as Health Canada and International Committee on Harmonization of Good Clinical Practice guidelines. Study protocols were reviewed and approved by the Institutional Review Boards (IRBs) at all participating sites, including the IRB at the University of California, San Francisco, where the ADNI Coordinating Center is located. The full protocols and associated ethical procedures are publicly available through the ADNI website ().

Participant privacy and data confidentiality were protected through the use of coded research identifiers; no personally identifying information was linked to the datasets used in this analysis. De-identified data were made publicly available through the ADNI data repository in line with the National Institute of Health data sharing policies.

This study involved a secondary analysis of existing de-identified ADNI data. As such, additional ethics approval was not required by the authors’ institution.

Consent to participate

All ADNI participants provided written informed consent prior to the initiation of any study procedures, including consent for data collection, storage, and future use in research. Informed consent included permission to collect and store biospecimens (e.g., blood and CSF), neuroimaging data, and longitudinal clinical assessments for research purposes. Where required, consent was also obtained from legally authorized representatives. Separate consent was obtained for genetic analyses and autopsy, in accordance with site-specific IRB requirements.

Consent for publication

Participants provided written informed consent authorizing the use of de-identified data and biospecimens for future research dissemination, including scientific publication. Consent documents specified that only coded, non-identifiable data would be shared and used in publications. No identifying personal information or images are reported in this manuscript.

Author contribution(s)

Daniella Vellone: Conceptualization; Data curation; Formal analysis; Funding acquisition; Methodology; Project administration; Software; Visualization; Writing – original draft; Writing – review & editing.

Rebeca Leon: Data curation; Methodology; Writing – review & editing.

Zahra Goodarzi: Investigation; Writing – review & editing.

Nils D. Forkert: Investigation; Writing – review & editing.

Eric E. Smith: Investigation; Writing – review & editing.

Zahinoor Ismail: Conceptualization; Funding acquisition; Resources; Supervision; Writing – original draft; Writing – review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the William H. Davies Medical Research Scholarship; Alberta Graduate Excellence Scholarships (AGES) for Doctoral Research; the Canadian Institute of Health Research (ISU191479, SMP192995, BCA2633, BCA 527734), and UK National Institute for Health and Care Research Exeter Biomedical Research Centre.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: ZI has served as advisor/consultant for Eisai, Lilly, Lundbeck/Otsuka, Novo Nordisk, and Roche. ES has participated in advisory boards for Eisai and Eli Lilly. All other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Redistribution of ADNI data is prohibited as per the ADNI Data Sharing Publication Policy and Data Use Agreement (https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_DSP_Policy.pdf). Data from ADNI are only available by request and can be found here: . Authors can share data cleaning script with interested parties.

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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0.03 MB

Mild behavioral impairment-apathy and Alzheimer's disease plasma phosphorylated tau biomarker levels

Abstract

Background

Objective

Methods

Results

Conclusions

Keywords

Introduction

Methods

Study cohort and data source

Participant inclusion criteria and classification

Biomarker analysis: plasma p-tau181

Statistical analyses

Results

Participant demographics and characteristics

Cross-sectional and longitudinal associations between MBI-apathy and plasma p-Tau181

Discussion

Limitations

Conclusions

Supplemental Material

sj-docx-1-alz-10.1177_13872877261470257 - Supplemental material for Mild behavioral impairment-apathy and Alzheimer's disease plasma phosphorylated tau biomarker levels

Footnotes

Acknowledgements

ORCID iDs

Ethical considerations

Consent to participate

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Author contribution(s)

Funding

Declaration of conflicting interests

Data availability statement

Supplemental material

References

Supplementary Material

Biomarker analysis: plasma p-tau₁₈₁

Cross-sectional and longitudinal associations between MBI-apathy and plasma p-Tau₁₈₁