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. Author manuscript; available in PMC: 2024 Oct 1.
Published in final edited form as: Alzheimers Dement. 2022 Feb 9;19(10):4498–4506. doi: 10.1002/alz.12557

Association between CSF biomarkers of Alzheimer’s disease and neuropsychiatric symptoms: Mayo Clinic Study of Aging

Janina Krell-Roesch a,b,*, Martin Rakusa a,c,*, Jeremy A Syrjanen a, Argonde C van Harten d, Val J Lowe e, Clifford R Jack Jr e, Walter K Kremers a, David S Knopman f, Gorazd B Stokin g, Ronald C Petersen a,f, Maria Vassilaki a, Yonas E Geda h
PMCID: PMC10433790  NIHMSID: NIHMS1921089  PMID: 35142047

Abstract

INTRODUCTION:

We examined the association between cerebrospinal fluid (CSF)-derived biomarkers of Alzheimer’s disease and neuropsychiatric symptoms (NPS) in older non-demented adults.

METHODS:

We included 784 persons (699 cognitively unimpaired, 85 with mild cognitive impairment) aged ≥ 50 years who underwent CSF amyloid (Aβ42), hyperphosphorylated Tau 181 (p-tau) and total Tau (t-tau) as well as NPS assessment using Beck Depression and Anxiety Inventories (BDI-II, BAI), and Neuropsychiatric Inventory-Questionnaire (NPI-Q).

RESULTS:

Lower CSF Aβ42, and higher t-tau/Aβ42 and p-tau/Aβ42 ratios were associated with BDI-II and BAI total scores, clinical depression (BDI-II ≥ 13) and clinical anxiety (BAI ≥ 10), as well as NPI-Q assessed anxiety, apathy and nighttime behavior.

DISCUSSION:

CSF Aβ42, t-tau/Aβ42 and p-tau/Aβ42 ratios were associated with NPS in community-dwelling individuals free of dementia. If confirmed by a longitudinal cohort study, the findings have clinical relevance of taking into account the NPS status of individuals with abnormal CSF biomarkers.

Keywords: Alzheimer’s disease, cerebrospinal fluid biomarkers, neuropsychiatric symptoms, non-demented, CSF Aβ42, CSF t-tau, CSF p-tau

BACKGROUND

Neuropsychiatric symptoms (NPS) are common in individuals with dementia due to Alzheimer’s disease (AD)1,2, and are also prevalent in mild cognitive impairment (MCI)3,4. However, the association between AD pathophysiology and NPS remains unclear5, particularly in older adults free of dementia. For example, it has been hypothesized that NPS could be a non-cognitive manifestation of AD pathology or a consequence of cognitive decline6.

A growing body of research has examined the associations between NPS and neuroimaging7–9 or plasma biomarkers of AD10. However, to date, only few studies have been published on the association between CSF-derived biomarkers of AD and NPS in MCI patients11–14; and a limited number of studies have examined the association between CSF-derived biomarkers and NPS in samples including cognitively unimpaired (CU) persons and MCI or AD patients15–19. In addition, little is known on the association between several CSF-biomarkers of AD (i.e., Aβ42, t-tau, p-tau, t-tau/Aβ42 ratio, p-tau/Aβ42 ratio) with various NPS in a large sample of community-dwelling older adults free of dementia.

Therefore, the aim of the present study was to examine the association between CSF biomarkers of AD with different self- and informant-reported NPS (e.g., depression, anxiety, apathy) in non-demented older adults. We hypothesized that higher neuropathological burden as indicated by lower levels of CSF Aβ42, higher levels of t-tau and p-tau 181, and higher t-tau/Aβ42 and p-tau/Aβ42 ratios would be associated with higher neuropsychiatric burden, i.e. increased levels of depression or anxiety, and presence of other NPS such as apathy or agitation.

METHODS

Design and Sample

We conducted a cross-sectional study derived from the population-based Mayo Clinic Study of Aging (MCSA) in Olmsted County, Minnesota, USA. We included community-dwelling non-demented individuals aged ≥ 50 years, i.e., they were either CU or had MCI. The final sample included 784 participants who had undergone a neurological evaluation, risk factor ascertainment, and neuropsychological testing, and also had valid data on CSF Aβ42, p-tau and t-tau as well as self- and informant-reported NPS. The MCSA was approved by the Institutional Review Boards of the Mayo Clinic and Olmsted Medical Center and the study also followed Health Insurance Portability and Accountability Act (HIPAA) guidelines. Written informed consent was obtained from all participants.

Neurocognitive Evaluation

All participants underwent an extensive evaluation including a neurological examination performed by a physician, a risk factor ascertainment conducted by a study coordinator, and standardized neuropsychological testing administered by a psychometrist and supervised by a board-certified neuropsychologist. Details on the procedures of the MCSA have previously been published20. Briefly, the neuropsychological test battery assessed performance in four cognitive domains: 1) Memory (delayed recall trials from Auditory Verbal Learning Test21, logical memory and visual reproduction subtests from Wechsler Memory Scale-Revised22); 2) language (Boston Naming Test23), category fluency24); 3) visuospatial skills (Picture completion and block design subtests from Wechsler Adult Intelligence Scale-Revised25); and 4) attention/ executive function (Trail-Making Test Part B26, Digit symbol substitution subtest from Wechsler Adult Intelligent Scale-Revised25). An expert consensus panel consisting of physicians, study coordinators, and neuropsychologists reviewed the results for each participant. The panel then determined whether a participant was CU (based on normative data developed in this community as part of the Mayo Older American Normative Studies27) or had MCI based on published criteria28,29.

CSF Measurements

We performed fasting lumbar punctures early in the morning in the lateral decubitus position using a 20- or 22-gauge needle (Quincke). Two cubic centimetres of CSF were used to determine routine markers (i.e., glucose level, protein level, cell count). The remainder was divided into 0.5-cm3 aliquots and stored at −80°C for future analyses avoiding multiple freeze-thaw cycles before the current analyses30. CSF amyloid-beta1-42 (Aβ42), total Tau (t-tau) and hyperphosphorylated Tau (p-tau) were analyzed using Elecsys® β-Amyloid(1–42) CSF, Elecsys® Total-Tau CSF, and Elecsys® Phospho-Tau (181P) CSF electrochemiluminescence immunoassays (Roche Diagnostics)30. Prior to performing the analysis of the samples included in this manuscript a thorough quality control procedure was performed to determine precision and accuracy of these analyses in our laboratory30. For the statistical analysis, we also created t-tau/ Aβ42 and p-tau/ Aβ42 ratios, respectively. Values above and below the technical limits of the assays were handled as described previously30.

Neuropsychiatric Assessment

For this study, we used data from the Beck Depression Inventory (BDI-II;31), Beck Anxiety Inventory (BAI;32), and Neuropsychiatric Inventory Questionnaire (NPI-Q;33). The BDI-II is a self-reported tool that measures common depressive symptoms such as feeling guilty or loss of interest over the past two weeks. Similarly, the BAI measures self-reported common anxiety symptoms such as nervousness or fear of losing control over the past one week. Both inventories are validated and consist of 21 items. The severity of each item is rated on a scale ranging from 0 to 3; thus, the total score ranges from 0 to 63. A higher score indicates a higher severity of depressive and anxiety symptoms, respectively. For our analyses, we used BDI-II and BAI total scores as continuous measures, as well as BDI-II score ≥ 13 (indicating clinical depression) and BAI score ≥ 10 (indicating clinical anxiety) as categorical measures. The NPI-Q was administered by a study coordinator as a structured interview to an informant, usually the spouse. The NPI-Q assesses the presence or absence of 12 emotional behaviors (i.e., depression, anxiety, apathy, agitation, delusion, hallucination, euphoria, disinhibition, irritability, aberrant motor behavior, sleep, and eating/appetite).

Statistical Analysis

Descriptive statistics were calculated and differences in median values or frequencies of variables were compared with Wilcoxon Rank Sum, Chi-Square or Fisher’s Exact tests depending on whether the variable was continuous or categorical. As the direction of causality is unknown in cross-sectional studies, we arbitrarily assigned CSF biomarkers as numeric predictors, and considered NPS as the outcome of interest of this study. CSF-derived variables (i.e., Aβ42, p-tau, t-tau, t-tau/ Aβ42, p-tau/ Aβ42) were log-transformed; continuous BDI and BAI measures had 1 added and were also log-transformed. Log-transformation was done because of right skewed distributions of these variables. 1 needed to be added to continuous BDI-II and BAI measures before log-transformation because a log of 0 is mathematically undefined and many participants have a value of 0 for these variables. We conducted linear regression analyses to examine the association between CSF-derived biomarkers and continuous BDI-II and BAI total scores. In addition, we also ran logistic regression analyses to examine the association between CSF-derived biomarkers with BDI-II score ≥ 13 (indicating clinical depression), BAI score ≥ 10 (indicating clinical anxiety) and presence of various NPS as assessed by the NPI-Q (categorical measures). Due to the expected low prevalence of NPS in our population-based sample, we only focused on 7 of the 12 items of the NPI-Q, namely agitation, anxiety, apathy, appetite change, nighttime behavior, depression, and irritability. For the linear models, we computed β-estimates, 95% confidence intervals (CI), and p-values. For the logistic models, we computed odds ratios (OR), 95% CI, and p-values. All models were adjusted for age, sex, education, and APOE ε4 carrier status. The linear models were conducted for the overall sample as well as stratified by CU and MCI. The logistic models on clinical depression and anxiety were conducted for the overall sample and the CU subsample where possible as some of the outcomes did not meet the requirement of 10 people in the smallest category for each predictor included in the model. The logistic models on NPI-Q derived NPS were only conducted for the overall sample. We have not applied a Bonferroni correction to our analyses. However, all models are presented with p-values in this manuscript which allows the reader to apply a correction, if so desired, when interpreting our results. All analyses were done using the conventional two-tailed alpha level of 0.05 and performed with SAS 9.4 (SAS Institute, Inc., Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

The whole sample included 784 participants with a median (range) age of 72.8 (50.7, 95.3) years. 699 participants were CU and 85 had MCI. 446 persons (57%) were males, and 212 (27%) were APOE ε4 carriers (Table 1). CSF Aβ42 levels were lower, and CSF levels of t-tau, p-tau, t-tau/Aβ42 ratio and p-tau/Aβ42 ratio were higher in participants with MCI than in CU participants. Participants with MCI had higher BDI-II and BAI total scores than CU persons. The frequency of persons with clinically relevant depression (BDI-II score ≥ 13) and anxiety (BAI score ≥ 10) was also higher in the MCI versus CU subsample (Table 1). Finally, frequencies of NPIQ-assessed anxiety, apathy, appetite change, nighttime behavior, disinhibition, and motor behavior were higher among MCI than CU persons (Table 1).

Table 1.

Participant demographics, CSF biomarkers levels and frequency of neuropsychiatric symptoms

Total sample (N = 784) CU (N = 699) MCI (N = 85) p-value
Age, median (range) 72.83 (50.72, 95.27) 72.31 (50.72, 95.27) 77.73 (56.39, 92.47) <0.001a
Male sex 446 (56.9) 396 (56.7) 50 (58.8) 0.703b
Education (yrs), median (range) 14 (0, 20) 14 (8, 20) 13 (0, 20) 0.002a
APOEɛ4 212 (27.0) 182 (26.0) 30 (35.3) 0.07b
Aβ42 (pg/mL), median (range) 1081.5 (222.4, 4255) 1105 (222.4, 3378) 911 (287.3, 4255) 0.004a
t-tau (pg/mL), median (range) 216.25 (79, 726.7) 213.7 (79, 726.7) 244.8 (92.5, 620) 0.002a
p-tau (pg/mL), median (range) 18.55 (7, 80.11) 17.99 (7, 80.11) 21.79 (8.89, 65.04) <0.001a
t-tau/ Aβ42, median (range) 0.18 (0.09, 1.19) 0.17 (0.09, 1.19) 0.24 (0.10, 1.19) <0.001a
p-tau/ Aβ42, median (range) 0.015 (0.012, 0.024) 0.015 (0.012, 0.022) 0.019 (0.013, 0.043) <0.001a
BDI-II total score, median (range) 3 (0, 51) 3 (0, 43){2} 5 (0, 51) <0.001a
BDI depression ≥ 13 points 68 (8.7) 51 (7.3){2} 17 (20.0) <0.001b
BAI total score, median (range) 1 (0, 40) 1 (0, 40){1} 2 (0, 40) <0.001a
BAI ≥ 10 points 63 (8.0) 45 (6.4){1} 18 (21.2) <0.001b
NPI-Q
 Agitation 37 (5.1) 30 (4.6){46} 7 (8.9){6} 0.105c
 Anxiety 41 (5.6) 29 (4.4){46} 12 (15.2){6} <0.001c
 Apathy/Indefference 41 (5.6) 32 (4.9){46} 9 (11.4){6} 0.033c
 Appetite/eating change 30 (4.1) 22 (3.4){46} 8 (10.1){6} 0.011c
 Nighttime behavior 53 (8.3) 39 (6.8){128} 14 (20.3){16} <0.001b
 Delusions 2 (0.3) 1 (0.2){46} 1 (1.3){6} 0.204c
 Depression/Dysphoria 87 (11.9) 76 (11.6){46} 11 (13.9){6} 0.553b
 Disinhibition 13 (1.8) 5 (0.8){46} 8 (10.1){6} <0.001c
 Euphoria/elation 7 (1.0) 5 (0.8){46} 2 (2.5){6} 0.169c
 Hallucinations  0 0{46} 0{6} /
 Irritability/lability 76 (10.4) 66 (10.1){46} 10 (12.7){6} 0.483b
 Motor Behavior 6 (0.8) 3 (0.5){46} 3 (3.8){6} 0.019c
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N (%) unless otherwise stated. Abbreviations: CSF, cerebrospinal fluid; CU, cognitively unimpaired persons; MCI, persons with mild cognitive impairment; APOEɛ4, apolipoprotein E ɛ4 homozygote or heterozygote; Aβ42, amyloid beta1–42; t-tau, total tau; p-tau, phosphorylated tau; BDI-II, Beck Depression Inventory (range 0–63); BAI, Beck Anxiety Inventory (range 0–63); NPI-Q; Neuropsychiatric Inventory-Questionnaire. {N} = number of participants with missing information.

P-values based on:

a

Wilcoxon Rank Sum test;

b

Chi-Square test;

c

Fisher Exact test

Associations of CSF biomarkers with BDI-II and BAI total scores

CSF levels of Aβ42 (estimate −0.244; 95% CI [−0.379, −0.109]; p<0.001), t-tau/Aβ42 (est. 0.242; 95% CI [0.108, 0.376]; p<0.001), and p-tau/Aβ42 ratios (est. 0.219; 95% CI [0.094, 0.343]; p=0.001) were associated with BDI-II total scores in the overall sample (Table 2). Thus, interpreting in terms of percentages, which log-transforming the predictor and outcome allows, a 1% decrease in CSF levels of Aβ42 was associated with about a 0.24% increase in BDI-II total score; and a 1% increase in t-tau/ Aβ42 and p-tau/ Aβ42 ratios was associated with about a 0.24% and 0.22% increase in BDI-II total score, respectively. CSF-derived biomarkers of Aβ42, t-tau/Aβ42 and p-tau/Aβ42 were also associated with BDI-II total scores in the CU and MCI subsample (Table 2).

Table 2.

Association of CSF biomarkers with depressive (BDI-II score) and anxiety symptoms (BAI score) in participants without dementia in the MCSA

CU MCI Total sample
Estimate (95% CI) p-value Estimate (95% CI) p-value Estimate (95% CI) p-value
BDI-II total score
Aβ42 −0.188 (−0.333, −0.042) 0.011 −0.501 (−0.877, −0.125) 0.010 −0.244 (−0.379, −0.109) <0.001
t-tau −0.024 (−0.214, 0.166) 0.805 −0.033 (−0.585, 0.520) 0.907 0.006 (−0.174, 0.185) 0.952
p-tau −0.007 (−0.188, 0.175) 0.944 −0.051 (−0.554, 0.453) 0.841 0.022 (−0.148, 0.192) 0.801
t-tau/Aβ42 ratio 0.175 (0.029, 0.321) 0.019 0.433 (0.075, 0.792) 0.018 0.242 (0.108, 0.376) <0.001
p-tau/Aβ42 ratio 0.159 (0.024, 0.295) 0.021 0.373 (0.034, 0.713) 0.032 0.219 (0.094, 0.343) 0.001
BAI total score
Aβ42 −0.129 (−0.271, 0.013) 0.075 −0.583 (−0.998, −0.168) 0.006 −0.211 (−0.346, −0.075) 0.002
t-tau 0.004 (−0.182, 0.189) 0.970 −0.416 (−1.022, 0.189) 0.175 −0.012 (−0.191, 0.167) 0.895
p-tau 0.019 (−0.158, 0.196) 0.832 −0.369 (−0.921, 0.183) 0.187 0.009 (−0.160, 0.179) 0.914
t-tau/Aβ42 ratio 0.132 (−0.011, 0.274) 0.070 0.334 (−0.071, 0.738) 0.104 0.199 (0.065, 0.333) 0.004
p-tau/Aβ42 ratio 0.123 (−0.010, 0.255) 0.069 0.285 (−0.097, 0.667) 0.142 0.184 (0.059, 0.308) 0.004
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Abbreviations: CSF, cerebrospinal fluid; MCSA, Mayo Clinic Study of Aging; CU, cognitively unimpaired persons; MCI, persons with mild cognitive impairment; CI, confidence interval; Aβ42, amyloid beta1–42; t-tau, total tau; p-tau, phosphorylated tau; BDI-II, Beck Depression Inventory; BAI, Beck Anxiety Inventory. Linear regression models were adjusted for age, sex, education, and APOE ε4 carrier status. Significant p-values appear bold.

CSF-derived Aβ42 (est. −0.211; 95% CI [−0.346, −0.075]; p=0.002), t-tau/Aβ42 (est. 0.199; 95% CI [0.065, 0.333]; p=0.004), and p-tau/Aβ42 ratio (est. 0.184; 95% CI [0.059, 0.308]; p=0.004) were associated with total BAI score in the overall sample. In addition, CSF-Aβ42 was associated with BAI total score in the MCI subsample (est. −0.583; 95% CI [−0.998, −0.168]; p=0.006; Table 2).

Associations of CSF biomarkers with categorical BDI-II and BAI

In the total sample, CSF Aβ42 (OR 0.508; 95% CI [0.289, 0.887]; p=0.018), t-tau/Aβ42 (OR 2.090; 95% CI [1.212, 3.579]; p=0.007), and p-tau/Aβ42 ratio (OR 1.895; 95% CI [1.138, 3.125]; p=0.013) were associated with clinical depression (BDI ≥ 13). Similarly, Aβ42 (OR 0.526; 95% CI [0.297, 0.925]; p=0.026), t-tau/Aβ42 (OR 2.069; 95% CI [1.207, 3.527]; p=0.008), and p-tau/Aβ42 (OR 1.969; 95% CI [1.193, 3.227]; p=0.007) were associated with clinical anxiety (BAI ≥ 10; Table 3). In the CU subsample, only t-tau/Aβ42 ratio was associated with clinical depression, and p-tau/Aβ42 ratio was associated with clinical anxiety (Table 3).

Table 3.

Association of CSF biomarkers with depressive (BDI-II ≥ 13) and anxiety (BAI ≥ 10) symptoms in participants without dementia in the MCSA

CU Total sample
OR (95% CI) p-value OR (95% CI) p-value
BDI-II ≥ 13
Aβ42 0.607 (0.312, 1.181) 0.140 0.508 (0.289, 0.887) 0.018
t-tau 1.318 (0.571, 3.046) 0.517 1.123 (0.547, 2.294) 0.752
p-tau 1.223 (0.542, 2.732) 0.626 1.101 (0.552, 2.174) 0.783
t-tau/Aβ42 ratio 2.090 (1.052, 4.077) 0.032 2.090 (1.212, 3.579) 0.007
p-tau/Aβ42 ratio 1.839 (0.965, 3.421) 0.058 1.895 (1.138, 3.125) 0.013
BAI ≥ 10
Aβ42 0.580 (0.290, 1.159) 0.122 0.526 (0.297, 0.925) 0.026
t-tau 1.203 (0.498, 2.897) 0.680 1.251 (0.600, 2.600) 0.549
p-tau 1.302 (0.557, 3.009) 0.539 1.339 (0.666, 2.666) 0.408
t-tau/Aβ42 ratio 1.974 (0.984, 3.882) 0.051 2.069 (1.207, 3.527) 0.008
p-tau/Aβ42 ratio 1.904 (0.998, 3.555) 0.046 1.969 (1.193, 3.227) 0.007
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Abbreviations: CSF, cerebrospinal fluid; MCSA, Mayo Clinic Study of Aging; CU, cognitively unimpaired persons; OR, odds ratio; CI, confidence interval; Aβ42, amyloid beta1–42; t-tau, total tau; p-tau, phosphorylated tau; BDI-II, Beck Depression Inventory; BAI, Beck Anxiety Inventory. Logistic regression models were adjusted for age, sex, education, and APOE ε4 carrier status. Significant p-values appear bold.

Associations of CSF biomarkers with NPS as assessed by NPI-Q

CSF Aβ42 (OR 0.293; 95% CI [0.144, 0.586]; p=0.001), t-tau/Aβ42 ratio (OR 3.366; 95% CI [1.798, 6.338]; p<0.001) and p-tau/Aβ42 ratio (OR 2.909; 95% CI [1.629,5.208]; p<0.001) were associated with NPI-Q-assessed presence of anxiety in the total sample (Table 4). Furthermore, CSF Aβ42 was associated with apathy and nighttime behavior, p-tau was associated with nighttime behavior, t-tau/Aβ42 ratio was associated with apathy and nighttime behavior, and p-tau/Aβ42 ratio was associated with apathy and nighttime behavior (Table 4).

Table 4.

Association between CSF biomarkers and NPI-Q assessed neuropsychiatric symptoms in participants without dementia in the MCSA

Aβ42 t-tau p-tau t-tau/Aβ42 p-tau/Aβ42
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Total sample
Agitation 0.716 (0.348, 1.479) 1.102 (0.427, 2.810) 1.106 (0.449, 2.656) 1.456 (0.713, 2.864) 1.395 (0.717, 2.612)
Anxiety 0.293 (0.144, 0.586)*** 1.321 (0.547, 3.170) 1.339 (0.581, 3.032) 3.366 (1.798, 6.338)*** 2.909 (1.629, 5.208)***
Apathy/Indifference 0.485 (0.245, 0.954)* 1.437 (0.593, 3.460) 1.329 (0.576, 3.006) 2.283 (1.223, 4.219)** 2.001 (1.121, 3.528)*
Appetite/eating change 1.009 (0.457, 2.254) 2.712 (0.982, 7.552) 2.104 (0.817, 5.353) 1.677 (0.803, 3.430) 1.465 (0.736, 2.838)
Nighttime behavior 0.460 (0.251, 0.834)* 2.181 (0.990, 4.812) 2.092 (1.015, 4.291)* 2.689 (1.583, 4.594)*** 2.384 (1.468, 3.888)***
Depression/Dysphoria 0.766 (0.470, 1.248) 1.073 (0.569, 2.016) 1.020 (0.558, 1.848) 1.344 (0.833, 2.140) 1.262 (0.807, 1.945)
Irritability/lability 0.668 (0.400, 1.113) 0.729 (0.368, 1.428) 0.714 (0.372, 1.348) 1.232 (0.747, 1.998) 1.164 (0.730, 1.824)
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Abbreviations: CSF, cerebrospinal fluid; MCSA, Mayo Clinic Study of Aging; OR, odds ratio; CI, confidence interval; Aβ42, amyloid beta1–42; t-tau, total tau; p-tau, phosphorylated tau. For all non-reported NPI-Q categories, there were not enough data in the categories. Logistic regression models were adjusted for age, sex, education, and APOE ε4 carrier status.

*

0.01<p≤0.05,

**

0.001<p≤0.01,

***

p≤0.001

DISCUSSION

In this cross-sectional study among older adults free of dementia, lower levels of CSF Aβ42 were associated with higher scores in self-reported measures of depressive (BDI-II) and anxiety sympoms (BAI). As expected, this association was stronger in individuals with MCI as compared to CU persons. In addition, both increased CSF t-tau/Aβ42 and p-tau/Aβ42 ratios were associated with higher BDI-II and BAI scores in the total sample, as well as in both CU and MCI subsamples. Furthermore, the odds of having clinical symptoms of depression (BDI-II ≥ 13) were on average increased by two-fold for each unit decrease in log transformed CSF t-tau/Aβ42 and p-tau/Aβ42 ratios. Nearly identical associations exist for these CSF biomarkers with clinical anxiety (BAI ≥ 10). We also observed an association between CSF-derived biomarkers and several NPS as assessed by the informant-reported NPI-Q, i.e., lower CSF Aβ42, and higher t-tau/Aβ42 and p-tau/Aβ42 ratios were associated with significantly increased odds of presence of anxiety, apathy, and nighttime behavior. Finally, the odds of having altered nighttime behavior more than doubled on average for each unit increase in log transformed CSF p-tau 181.

With regard to anxiety, our study is partly in line with previous research. For example, one study reported an association between abnormal CSF levels of Aβ42 or t-tau with more frequent anxiety in persons with MCI12. Similarly, investigators from the Netherlands also reported an association between lower levels of CSF Aβ42, and higher levels of t-tau and p-tau with anxiety19. In line with this, we also observed associations between higher t-tau/Aβ42 ratio and p-tau/Aβ42 ratio with anxiety (assessed by both self-reported BAI and informant-observed NPI-Q).

Furthermore, in our study we only observed significant associations between CSF amyloid and tau with self-reported (BDI-II) but not informant-observed depression (NPI-Q). In the past, few studies have examined associations between CSF amyloid and tau biomarkers and depression11,34. For example, a recent study from the Netherlands did not observe an association between depression and CSF levels of Aβ42, t-tau or p-tau in persons with subjective cognitive impairment, MCI and AD19. In contrast, a study based on ADNI data reported lower CSF Aβ42 levels in MCI persons with as compared to without chronic depressive symptomatology assessed using the Neuropsychiatric Inventory, but did not observe a difference between groups with regard to CSF tau35. A recent review including studies of individuals with AD and MCI even concluded that depression may be associated with lower core AD CSF pathology36. More research is needed to examine whether self- or informant-reported depression is associated with CSF-derived biomarkers of AD in older adults free of dementia.

Aside from depression or anxiety, fewer studies have assessed the associations between CSF-biomarker concentrations with other NPS. For example, studies have reported associations between CSF Aβ42 with paranoid or delusional ideation and aggression17, apathy19 as well as agitation and irritability12. In addition, associations between CSF t-tau/Aβ42 ratio with irritability, sleep disturbances, and eating disturbances18 have been reported. On the other hand, several studies failed to establish associations between CSF-derived AD biomarkers with apathy and hallucinations16, agitation, irritability and sleep disturbances19, or sleep problems14. Our study contributes to this body of research by providing preliminary evidence that lower CSF Aβ42, and higher t-tau/Aβ42 and p-tau/Aβ42 ratios are associated with increased odds of presence of apathy and altered nighttime behavior in community-dwelling persons free of dementia. Of note, nighttime behavior entails behavioral disturbances that may occur at nighttime such as sleeplessness, sleep walking, nightmares or bedwetting, and is the third most common NPS in our dataset (8.3% of the total sample). Given the conflicting findings on potential associations between nighttime behavior and CSF biomarkers, more research is needed that also examines potential mechanisms that may underlie this association.

Overall, as outlined above, there are conflicting findings on the associations between CSF biomarkers of amyloid and tau with depression, anxiety or other NPS. These discrepancies may be attributed to numerous methodological differences such as sampling method, cognitive and/or biomarker status of participants, sample size, severity of NPS and instruments used to assess NPS. On average, there appears to be evidence of an association between CSF Aβ42 with NPS, but less so for CSF t-tau or p-tau with NPS.

One possible explanation may be that amyloid peptides change rapidly in the brain37, fluctuate diurnaly38 and precede many AD changes39. These characteristics of amyloid peptides are consistent with the observed association between amyloid peptides and NPS. In fact, characteristics of amyloid peptides contrast with the biology of tau, which is less abrupt and linked to neuronal death and cognitive decline40. Increased CSF p-tau and t-tau levels are related to AD. P-tau is associated with neurofibrillary tangles formation in the brain parenchyma41; whereas t-tau not only indicates the severity of neurodegeneration in AD but can also be elevated in other neurodegenerative diseases such as Creutzfeldt-Jakob disease or it can transiently be elevated in stroke or brain trauma. Thus, p-tau may be more AD specific than t-tau42. It has been reported that the sensitivity of progression from MCI to AD for CSF t-tau ranges from 51–90% and specificity values range from 48–88%; for CSF p-tau, sensitivities range between 40–100% and specificities between 22–86%. Whereas for CSF p-tau/ Aβ42 ratio, sensitivities are between 80–96% and specificities between 33–95%43. Furthermore, investigators reported that CSF t-tau/ Aβ42 and p-tau/ Aβ42 ratios were highly concordant with amyloid PET brain imaging classification in the Swedish BioFINDER study involving patients with mild cognitive symptoms (overall percent agreement: 90%; area under the curve: 94%) as well as the ADNI database involving individuals with significant memory concern, MCI or AD (overall percent agreement: 90%; area under the curve: 96%). In addition, both ratios accurately predicted future disease progression of persons with MCI in the ADNI cohort, and the authors also observed a higher negative percent agreement of tau/ Aβ42 ratios which may indicate greater diagnostic utility compared to Aβ42 alone44. This is also supported by a literature review45 and several studies. For example, t-tau/ Aβ42 ratio increased concordance with PIB PET SUVR from 85.2% for CSF Aβ42 to 92.5%46; and CSF p-tau/ Aβ42 ratio in cognitively unimpaired individuals showed greater sensitivity for detection of PIB positivity compared with Aβ42 alone47. The authors also posit that reasons as to why tau/ Aβ42 ratios may be superior over the absolute value of Aβ42 alone, e.g., a combination of measures of two different pathologic processes into a single diagnostic biomarker may be more promising, or tau/ Aβ42 ratios may reduce random error or variance in Aβ42 measurements44.

Overall, more research is needed to untangle the association between CSF-derived biomarkers of amyloid and tau with NPS in presymptomatic AD, and to also investigate mechanisms that may underlie a potential association. Future research should also examine potential associations between CSF NfL and neurogranin with NPS.

Of note, our current study confirms observations on the associations between increased BDI and BAI scores and elevated cortical amyloid deposition as measured by PiB-PET that we previously reported based on analyses among CU participants from the same population48.

The strengths of our study are the large sample of community-dwelling older adults free of dementia, the detailed assessment of NPS using both self-reported BDI-II and BAI as well as informant-observed NPI-Q, and the large number of CSF-derived biomarkers (i.e., CSF Aβ42, t-tau, p-tau, t-tau/Aβ42 and p-tau/Aβ42 ratios). In addition, the study may also be directly relevant to the newly established construct of Mild Behavioral Impairment (MBI) which is considered a neurobehavioral syndrome and refers to the emergence of impactful NPS in late life49. MBI represents an at-risk state for new onset of dementia in persons with MCI and is also associated with AD biomarker abnormalities50. Furthermore, in line with our observations, a recent study by investigators from Sweden found that CSF p-tau levels were associated with higher scores on the MBI-checklist (MBI-C) in amyloid-positive cognitively unimpaired persons. The investigators also report that the MBI-C domains affective regulation and impulse dyscontrol but not drive and motivation, social inappropriateness, or perception and thought were associated with CSF p-tau51.

Our study has the following limitations: 1) The cross-sectional study design does not allow for making any conclusions about causality. 2) We did not account for psychotropic medications, and future studies may want to adjust for medication intake. 3) We did not run the analysis stratified for CSF amyloid status (high vs. low CSF Aβ42). It is possible that among participants with elevated t- or p-tau, there may be many individuals without underlying AD pathology (i.e., having high CSF AB42) and a potentially existing association between NPS and tau only in participants with AD may be obliterated. 4) We did not additionally control our analyses for global cognitive function because we stratified the analyses by CU and MCI subsamples. However, since we observed that the association between lower levels of CSF Aβ42 with higher BDI-II and BAI was stronger in individuals with MCI as compared to CU persons, future prospective cohort studies may need to control for cognition. This would further clarify whether the observed association between CSF biomarkers and depression and anxiety in the overall sample is simply a reflection of the underlying association between cognitive performance and biomarkers. Thus, a potential explanation of our current finding may be that there is no direct association between biomarkers and NPS, but that individuals with cognitive symptoms are more likely to present with depressive or anxiety symptoms and may also have more abnormal biomarkers. 5) Another limitation is that our analyses on the association between CSF biomarkers and NPI-Q assessed NPS were not stratified by cognitive status (CU and MCI). This was not possible as the number of participants with each NPS is a limiting factor for the logistic models by stratification. Depression and irritability are the only NPS in our dataset for which a stratification may be considered. When we applied stratification, we observed no difference between the results for the overall sample and the CU subset (data not shown). 6) We did not adjust our analyses for multiple comparisons which may increase Type I error. However, when considering a Bonferroni correction for our analyses, the alpha significance level would be 0.01 (i.e., 0.05/5 since we have a total of 5 models per predictor and subsample in tables 2 and 3) and 0.007 (i.e., 0.05/7 since we have a total of 7 models per outcome in table 4). Thus, when taking Table 2 as an example, 8 out of the 13 significant p-values for the association between NPS and CSF biomarkers would still remain significant and none of our major conclusions would be affected by the correction.

In conclusion, this cross-sectional study among non-demented community-dwelling older adults suggests an association between CSF biomarkers of Aβ42, t-tau/Aβ42 ratio and p-tau/Aβ42 ratio with depression, anxiety, apathy, and nighttime behavior. More research, particularly utilizing longitudinal study designs, is needed to confirm this preliminary observation.

Highlights.

  • We studied 784 community-dwelling, non-demented older persons

  • Lower CSF Aβ42 was associated with neuropsychiatric symptoms

  • Higher t-tau/Aβ42 and p-tau/Aβ42 were associated with neuropsychiatric symptoms

  • Clinicians should consider emotional health of adults with abnormal CSF biomarkers

ACKNOWLEDGEMENTS AND FUNDING SOURCES

The authors thank the participants and staff at the Mayo Clinic Study of Aging. This research was supported by the National Institute on Aging (R01 AG057708, U01 AG006786, P50 AG016574, R01 AG034676, R01 AG011378, and R01 AG041851), National Institute of Mental Health (K01 MH068351), and the National Institute of Neurological Disorders and Stroke (R01 NS097495), as well as the Arizona Alzheimer’s Consortium, the Edli Foundation, the GHR Foundation, the Mayo Foundation for Medical Education and Research, Project LQ1605 from the National Program of Sustainability II (MEYS CR), the Robert Wood Johnson Foundation, and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer’s Disease Research Program. The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

CONFLICTS

Dr. Rakusa reports a research grant that was partially funded by the Fulbright Program. Dr. van Harten served as a consultant for Roche Diagnostics. Dr. Lowe consults for Bayer Schering Pharma, Eisai Pharma, Piramal Life Sciences, AVID Radiopharmaceuticals and Merck Research and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals and the NIH (NIA, NCI). Dr. Jack serves as a consultant to Lily and serves on an independent data monitoring board for Roche. Dr. Kremers receives research funding from Astra Zeneca, Biogen, the Department of Defense, NIH, and Roche. Dr. Knopman serves on a data safety monitoring board for the Dominantly Inherited Alzheimer Network study and is an investigator in clinical trials sponsored by Biogen, Lilly Pharmaceuticals, and the University of Southern California. Dr. Petersen serves as a consultant to Biogen, GE Healthcare, Genentech, Merck, and Roche and receives royalties from Oxford University Press. Dr. Vassilaki has received in the past research funding from Roche and Biogen; she currently consults for Roche, receives research funding from NIH and has equity ownership in Abbott Laboratories, Johnson and Johnson, Medtronic and Amgen. Dr. Geda receives research funding from NIH and Roche and serves served on the advisory board of Lundbeck. The other authors report no financial relationships with commercial interests.

Footnotes

AUTHOR CONTRIBUTIONS

JKR: design and conceptualization of the study, interpretation of the data, drafting the manuscript, revising the manuscript. MR: design and conceptualization of the study, interpretation of the data, drafting the manuscript, revising the manuscript. JAS: analysis and interpretation of the data, revising the manuscript. AvH: revising the manuscript. VL: data collection, study funding, revising the manuscript. CRJ: Data collection, study funding, revising the manuscript. WKK: analysis and interpretation of the data, revising the manuscript. DSK: data collection, study funding, revising the manuscript. GBS: study funding, revising the manuscript. RCP: data collection, study funding, revising the manuscript. MV: data collection, analysis and interpretation of the data, revising the manuscript. YEG: data collection, analysis and interpretation of the data, study funding, revising the manuscript.

REFERENCES

  • 1.Banning LCP, Ramakers I, Deckers K, Verhey FRJ, Aalten P. Affective symptoms and AT(N) biomarkers in mild cognitive impairment and Alzheimer’s disease: A systematic literature review. Neurosci Biobehav Rev. 2019;107:346–359. [DOI] [PubMed] [Google Scholar]
  • 2.Geda YE, Schneider LS, Gitlin LN, et al. Neuropsychiatric symptoms in Alzheimer’s disease: past progress and anticipation of the future. Alzheimers Dement. 2013;9(5):602–608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Apostolova LG, Cummings JL. Neuropsychiatric manifestations in mild cognitive impairment: a systematic review of the literature. Dementia & Geriatric Cognitive Disorders. 2008;25(2):115–126. [DOI] [PubMed] [Google Scholar]
  • 4.Geda YE, Roberts RO, Knopman DS, et al. Prevalence of neuropsychiatric symptoms in mild cognitive impairment and normal cognitive aging: population-based study. Archives of General Psychiatry. 2008;65(10):1193–1198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Geda YE, Krell-Roesch J, Sambuchi N, Michel BF. Neuropsychiatric Symptoms and Neuroimaging Biomarkers in Alzheimer Disease: “Which is the Cart and Which is the Horse?”. Am J Geriatr Psychiatry. 2017;25(7):694–696. [DOI] [PubMed] [Google Scholar]
  • 6.Peters ME, Lyketsos CG. Beyond memory: a focus on the other neuropsychiatric symptoms of dementia. Am J Geriatr Psychiatry. 2015;23(2):115–118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Tommasi NS, Gonzalez C, Briggs D, et al. Affective symptoms and regional cerebral tau burden in early-stage Alzheimer’s disease. Int J Geriatr Psychiatry. 2021;36(7):1050–1058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Yasuno F, Minami H, Hattori H, Alzheimer’s Disease Neuroimaging I. Relationship between neuropsychiatric symptoms and Alzheimer’s disease pathology: An in vivo positron emission tomography study. Int J Geriatr Psychiatry. 2021;36(4):598–605. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Tissot C, Therriault J, Pascoal TA, et al. Association between regional tau pathology and neuropsychiatric symptoms in aging and dementia due to Alzheimer’s disease. Alzheimers Dement (N Y). 2021;7(1):e12154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Miao R, Chen HY, Gill S, et al. Plasma beta-Amyloid in Mild Behavioural Impairment - Neuropsychiatric Symptoms on the Alzheimer’s Continuum. J Geriatr Psychiatry Neurol. 2021:8919887211016068. [DOI] [PubMed] [Google Scholar]
  • 11.Lee GJ, Lu PH, Hua X, et al. Depressive symptoms in mild cognitive impairment predict greater atrophy in Alzheimer’s disease-related regions. Biol Psychiatry. 2012;71(9):814–821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Ramakers IH, Verhey FR, Scheltens P, et al. Anxiety is related to Alzheimer cerebrospinal fluid markers in subjects with mild cognitive impairment. Psychol Med. 2013;43(5):911–920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Nathan PJ, Lim YY, Abbott R, et al. Association between CSF biomarkers, hippocampal volume and cognitive function in patients with amnestic mild cognitive impairment (MCI). Neurobiol Aging. 2017;53:1–10. [DOI] [PubMed] [Google Scholar]
  • 14.Reijs BLR, Vos SJB, Soininen H, et al. Association Between Later Life Lifestyle Factors and Alzheimer’s Disease Biomarkers in Non-Demented Individuals: A Longitudinal Descriptive Cohort Study. J Alzheimers Dis. 2017;60(4):1387–1395. [DOI] [PubMed] [Google Scholar]
  • 15.Arnold SE, Xie SX, Leung YY, et al. Plasma biomarkers of depressive symptoms in older adults. Transl Psychiatry. 2012;2:e65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Donovan NJ, Wadsworth LP, Lorius N, et al. Regional cortical thinning predicts worsening apathy and hallucinations across the Alzheimer disease spectrum. Am J Geriatr Psychiatry. 2014;22(11):1168–1179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Kuo HC, Yen HC, Huang CC, Hsu WC, Wei HJ, Lin CL. Cerebrospinal fluid biomarkers for neuropsychological symptoms in early stage of late-onset Alzheimer’s disease. Int J Neurosci. 2015;125(10):747–754. [DOI] [PubMed] [Google Scholar]
  • 18.Scaricamazza E, Colonna I, Sancesario GM, et al. Neuropsychiatric symptoms differently affect mild cognitive impairment and Alzheimer’s disease patients: a retrospective observational study. Neurol Sci. 2019;40(7):1377–1382. [DOI] [PubMed] [Google Scholar]
  • 19.Banning LCP, Ramakers I, Kohler S, et al. The Association Between Biomarkers and Neuropsychiatric Symptoms Across the Alzheimer’s Disease Spectrum. Am J Geriatr Psychiatry. 2020; 28(7):735–744. [DOI] [PubMed] [Google Scholar]
  • 20.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(1):58–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Rey A L’examen clinique en psychologie. Paris: Presses Universitaires de France; 1964. [Google Scholar]
  • 22.Wechsler D Wechsler Memory Scale-Revised. New York: The Psychological Corporation; 1987. [Google Scholar]
  • 23.Kaplan E, Goodglass H, Weintraub S. Boston Naming Test. 2nd ed. Philadelphia: Lippincott Williams & Wilkins; 2001. [Google Scholar]
  • 24.Lucas JA, Ivnik RJ, Smith GE, et al. Mayo’s Older Americans Normative Studies: category fluency norms. J Clin Exp Neuropsychol. 1998;20(2):194–200. [DOI] [PubMed] [Google Scholar]
  • 25.Wechsler D Wechsler Adult Intelligence Scale-Revised. New York: Psychological Corporation; 1981. [Google Scholar]
  • 26.Reitan RM. Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills. 1958;8(3):271–276. [Google Scholar]
  • 27.Ivnik R, Malec J, Smith G, et al. Mayo’s Older Americans Normative Studies: WAIS-R, WMS-R, and AVLT norms for ages 56 through 97. Clin Neuropsychol. 1992;6(Supplement):1–104. [Google Scholar]
  • 28.Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256(3):183–194. [DOI] [PubMed] [Google Scholar]
  • 29.Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine. 2004;256(3):240–246. [DOI] [PubMed] [Google Scholar]
  • 30.Van Harten AC, Wiste HJ, Weigand SD, et al. CSF biomarkers in Olmsted County: Evidence of 2 subclasses and associations with demographics. Neurology. 2020;95(3):e256–e267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Beck AT, Steer RA, Brown GK. Manual for Beck Depression Inventory-II (BDI-II). San Antonio: Psychology Corporation; 2001. [Google Scholar]
  • 32.Beck AT, Steer RA. BAI, Beck Anxiety Inventory: Manual. San Antonio: Psychological Corp. : Harcourt Brace Jovanovich; 1990. [Google Scholar]
  • 33.Kaufer DI, Cummings JL, Ketchel P, et al. Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. Journal of Neuropsychiatry and Clinical Neurosciences. 2000;12(2):233–239. [DOI] [PubMed] [Google Scholar]
  • 34.Harrington KD, Lim YY, Gould E, Maruff P. Amyloid-beta and depression in healthy older adults: A systematic review. Aust N Z J Psychiatry. 2015;49(1):36–46. [DOI] [PubMed] [Google Scholar]
  • 35.Gonzales MM, Insel PS, Nelson C, et al. Chronic depressive symptomatology and CSF amyloid beta and tau levels in mild cognitive impairment. Int J Geriatr Psychiatry. 2018;33(10):1305–1311. [DOI] [PubMed] [Google Scholar]
  • 36.Showraki A, Murari G, Ismail Z, et al. Cerebrospinal Fluid Correlates of Neuropsychiatric Symptoms in Patients with Alzheimer’s Disease/Mild Cognitive Impairment: A Systematic Review. J Alzheimers Dis. 2019;71(2):477–501. [DOI] [PubMed] [Google Scholar]
  • 37.Meyer-Luehmann M, Spires-Jones TL, Prada C, et al. Rapid appearance and local toxicity of amyloid-beta plaques in a mouse model of Alzheimer’s disease. Nature. 2008;451(7179):720–724. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Kang JE, Lim MM, Bateman RJ, et al. Amyloid-beta dynamics are regulated by orexin and the sleep-wake cycle. Science. 2009;326(5955):1005–1007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Oddo S, Caccamo A, Shepherd JD, et al. Triple-transgenic model of Alzheimer’s disease with plaques and tangles: intracellular Abeta and synaptic dysfunction. Neuron. 2003;39(3):409–421. [DOI] [PubMed] [Google Scholar]
  • 40.Bierer LM, Hof PR, Purohit DP, et al. Neocortical neurofibrillary tangles correlate with dementia severity in Alzheimer’s disease. Arch Neurol. 1995;52(1):81–88. [DOI] [PubMed] [Google Scholar]
  • 41.Buerger K, Ewers M, Pirttila T, et al. CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer’s disease. Brain. 2006;129(Pt 11):3035–3041. [DOI] [PubMed] [Google Scholar]
  • 42.Lee JC, Kim SJ, Hong S, Kim Y. Diagnosis of Alzheimer’s disease utilizing amyloid and tau as fluid biomarkers. Exp Mol Med. 2019;51(5):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Ritchie C, Smailagic N, Noel-Storr AH, Ukoumunne O, Ladds EC, Martin S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2017;3:CD010803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Hansson O, Seibyl J, Stomrud E, et al. CSF biomarkers of Alzheimer’s disease concord with amyloid-beta PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 2018;14(11):1470–1481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Mattsson N, Lonneborg A, Boccardi M, Blennow K, Hansson O, Geneva Task Force for the Roadmap of Alzheimer’s B. Clinical validity of cerebrospinal fluid Abeta42, tau, and phospho-tau as biomarkers for Alzheimer’s disease in the context of a structured 5-phase development framework. Neurobiol Aging. 2017;52:196–213. [DOI] [PubMed] [Google Scholar]
  • 46.Wang MJ, Yi S, Han JY, et al. Analysis of Cerebrospinal Fluid and [11C]PIB PET Biomarkers for Alzheimer’s Disease with Updated Protocols. J Alzheimers Dis. 2016;52(4):1403–1413. [DOI] [PubMed] [Google Scholar]
  • 47.Fagan AM, Shaw LM, Xiong C, et al. Comparison of analytical platforms for cerebrospinal fluid measures of beta-amyloid 1–42, total tau, and p-tau181 for identifying Alzheimer disease amyloid plaque pathology. Arch Neurol. 2011;68(9):1137–1144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Krell-Roesch J, Lowe VJ, Neureiter J, et al. Depressive and anxiety symptoms and cortical amyloid deposition among cognitively normal elderly persons: the Mayo Clinic Study of Aging. Int Psychogeriatr. 2018;30(2):245–251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Ismail Z, Aguera-Ortiz L, Brodaty H, et al. The Mild Behavioral Impairment Checklist (MBI-C): A Rating Scale for Neuropsychiatric Symptoms in Pre-Dementia Populations. J Alzheimers Dis. 2017;56(3):929–938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Lussier FZ, Pascoal TA, Chamoun M, et al. Mild behavioral impairment is associated with beta-amyloid but not tau or neurodegeneration in cognitively intact elderly individuals. Alzheimers Dement. 2020;16(1):192–199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Johansson M, Stomrud E, Insel PS, et al. Mild behavioral impairment and its relation to tau pathology in preclinical Alzheimer’s disease. Transl Psychiatry. 2021;11(1):76. [DOI] [PMC free article] [PubMed] [Google Scholar]

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