Age-Dependency of Total Tau in the Cerebrospinal Fluid Is Corrected by Amyloid-β 1–40: A Correlational Study in Healthy Adults

Abstract

Background:

Tau proteins are established biomarkers of neuroaxonal damage in a wide range of neurodegenerative conditions. Although measurement of total-Tau in the cerebrospinal fluid is widely used in research and clinical settings, the relationship between age and total-Tau in the cerebrospinal fluid is yet to be fully understood. While past studies reported a correlation between age and total-Tau in the cerebrospinal fluid of healthy adults, in clinical practice the same cut-off value is used independently of patient’s age.

Objective:

To further explore the relationship between age and total-Tau and to disentangle neurodegenerative from drainage-dependent effects.

Methods:

We analyzed cerebrospinal fluid samples of 76 carefully selected cognitively healthy adults and included amyloid-β 1–40 as a potential marker of drainage from the brain’s interstitial system.

Results:

We found a significant correlation of total-Tau and age, which was no longer present when correcting total-Tau for amyloid-β 1–40 concentrations. These findings were replicated under varied inclusion criteria.

Conclusion:

Results call into question the association of age and total-Tau in the cerebrospinal fluid. Furthermore, they suggest diagnostic utility of amyloid-β 1–40 as a possible proxy for drainage-mechanisms into the cerebrospinal fluid when interpreting biomarker concentrations for neurodegenerative diseases.

Keywords

Alzheimer’s disease amyloid-beta 1–40 tau proteins total-Tau

INTRODUCTION

Tau proteins in the human central nervous system (CNS) occur in six isoforms produced from a single gene through alternative mRNA splicing. These proteins play a crucial role in maintaining the stability of microtubules in axons and are established biomarkers for several neurological and neuropsychiatric conditions [1]. While tau phosphorylated at threonine 181 (p-Tau) is considered a specific biomarker of Alzheimer’s disease (AD), total-tau (t-Tau) is interpreted as a non-specific marker of neuronal and axonal damage [2]. t-Tau concentrations in the cerebrospinal fluid (CSF) are elevated in numerous neurological and psychiatric conditions, ranging from acute brain injury like stroke [3] or traumatic brain injury [4] to neurodegenerative diseases like AD [5] and corticobasal degeneration [6]. Most markedly increased levels of t-Tau are observed in rapidly progressing conditions like Creutzfeldt-Jakob Disease [7].

Accordingly, while elevated t-Tau concentrations in the CSF are thought to reflect neuroaxonal damage, it could be assumed that age-related neurotoxic actions like glial activation, mitochondrial dysfunction, and oxidative stress [8] lead to increased levels of t-Tau in the CSF. However, as data is scarce on this, in clinical practice the same cut-off value is used independently of age.

Few studies explicitly address the relationship between age and t-Tau in the CSF of healthy adults and those that do reported divergent results. Sjögren et al. [9] were the first to report a positive correlation in a sample of clinically healthy adults. Participants were required to score above 27 on Mini-Mental State Examination (MMSE) [10]. Similarly, in later studies a positive association between t-Tau and age was found [11, 12]. In contrast, another study found no correlation [13]. Note that in two of the studies [12, 13], no cognitive screening was performed, and inclusion was solely based on patient history and clinical impression. The Mattsson et al. [11] sample was part of a larger study comparing patients with AD, patients with mild cognitive impairment (MCI) and healthy controls; the latter were included if showing no signs of active neurological or psychiatric disease and a MMSE score above 25. Importantly, none of the above studies included additional data on CSF amyloid markers of AD to further rule out underlying AD pathology.

In conclusion, although most studies reported a positive correlation between age and t-Tau in the CSF of healthy adults, the quality of inclusion criteria is questionable and limits the generalizability of the findings.

Besides age-related changes in brain metabolism, other mechanisms could elevate t-Tau levels in the CSF of elderly subjects. Age-associated increased drainage from the cerebrum into the CSF might offer an alternate explanation, which would then result in altered levels of other peptides in the CSF as well. Drainage mechanisms of soluble peptides from the brain interstitial system into the CSF remain poorly understood [14], although recent studies suggest a central role of perivascular pathways called the glymphatic system [15]. The functionality of these mechanisms is suspected to be impaired in the aging brain [16, 17], thereby potentially leading to altered peptide levels in the CSF.

Even more than tau proteins, the role of amyloid-β (Aβ) peptides has been studied extensively in the context of AD and neurodegeneration [18]. Aβ peptides derive from the membrane-bound amyloid-β protein precursor (AβPP) and, when deposited in extracellular plaques, constitute a pathological hallmark of AD [19]. Cleaving of AβPP results in various Aβ-species of different length. Those consisting of 40 amino acids (Aβ₄₀) are the most abundant, followed by those consisting of 42 amino acids (Aβ₄₂). While accumulation of Aβ₄₂ is widely recognized as a key driver of AD pathophysiology [18], Aβ₄₀ has been traditionally interpreted as non-pathogenic for AD [20]. Contrary to Aβ₄₂, Aβ₄₀ CSF concentrations only marginally differ between patients with AD dementia and controls [5]. Hence, the Aβ_{1–42/1 –40}-ratio is widely used in clinical decision making and research to minimize pre-analytical Aβ₄₂ biases and account for between-patient amyloid variations [21]. In view of the above, Aβ₄₀ could be interpreted as a general marker of drainage from the brain’s interstitial system into the CSF, whereas this would not be the case for more specific biomarkers like Aβ₄₂ and tau proteins.

In this study, we aimed to provide further insight into the relationship between age and t-Tau in the CSF by using Aβ₄₀ as a proxy for drainage mechanisms of peptides into the CSF. Specifically, we explored relationships of age, t-Tau, and the ratio of t-Tau and Aβ₄₀ to disentangle putative age-related from putative drainage-related effects. To ensure the exclusion of patients with neurological or psychiatric conditions, we employed rigorous inclusion criteria for study participation and thereby present data from a unique and carefully selected sample of cognitively healthy adults.

MATERIALS AND METHODS

The study was conducted in accordance with the Declaration of Helsinki [22] and approved by the ethics committee of the Technical University of Munich (TUM), Munich, Germany (project code: 29/17 S). All study participants provided written informed consent.

Sample characteristics and study procedures

Detailed inclusion and exclusion criteria are summarized in Table 1. Key points were the absence of any neurological or psychiatric disease (as indicated by clinical examination, medical history, reviewing patient files, and scoring on psychopathology questionnaires) and an MMSE score above 26, a validated cut-off to distinguish healthy subjects from patients with cognitive impairment and dementia [23]. To rule out prodromal or undetected AD, only patients with a normal Aβ_42/40 ratio were included (> 0.05, established in-house using amyloid PET positivity as standard of truth in an independent cohort). Furthermore, a normal CSF cell count and lactate concentration were required to rule out inflammatory/infectious CNS processes.

Table 1

Inclusion and exclusion criteria of study participation

Inclusion	Exclusion
Age 30–90	CSF cell count > 4/mm³
MMSE score > 26	CSF lactate > 3.5 mmol/l
Aβ_42/40 ratio > 0.05	ASA score > 2
	Psychiatric/neurological diagnosis
	BDI-II score > 13 / MADRS score > 6
	History of brain surgery

CSF, cerebrospinal fluid; MMSE, Mini-Mental State Examination; ASA, American Society of Anesthesiologists [26]; BDI-II, Beck Depression Inventory II [27]; MADRS, Montgomery–Åsberg Depression Rating Scale [28].

Participants were recruited at the TUM School of Medicine hospital Klinikum Rechts der Isar in the years 2014 to 2019. CSF samples were obtained in the context of spinal anesthesia prior to orthopedic or urological surgery procedures.

As diagnostic validity of the MMSE in discriminating healthy participants from patients with subtle cognitive deficits has been criticized [24], a subset of participants underwent comprehensive neuropsychological testing covering the domains of memory, visuospatial, attentional, and executive functions for additional analyses. MMSE administration and neuropsychological testing was carried out during the week before surgery to avoid potentially confounding effects of post-operative pain medication [25].

CSF collection and analysis

For spinal anesthesia, lumbar puncture was performed between segments L3/4 or L4/5 and 5 to 10 ml of CSF were collected in polypropylene tubes prior to injection of the anesthetic. Samples were immediately stored on ice in upright position and processed within 1 h (i.e., centrifugated for 10 min and stored at –80°C for further analyses). Peptide (t-Tau, Aβ₄₀, Aβ ₄₂) concentrations were analyzed with established commercially available enzyme-linked immunosorbent assays (ELISA; INNOTEST hTAU Ag, Fujirebio Europe N.V.; Amyloid-Beta [1–40] CSF ELISA, IBL International GmbH; Amyloid-Beta [1–42] CSF ELISA, IBL International GmbH) following standard clinical procedures in our round robin test-certified laboratory for neurochemical analyses. Additionally, CSF was analyzed regarding cell count, lactate and total protein count following clinical standard procedures no more than one hour after the lumbar puncture.

Statistical analysis

Data was analyzed using R: A language and environment for statistical computing [29]. Normality was checked for employing Shapiro Wilk tests. As most continuous variables were distributed non-normally, non-parametric methods were applied. As more male than female participants were included in the study, we checked for differences between sexes regarding peptide concentrations and demographics using Mann-Whitney U-Tests. For associations between age and CSF peptides, Spearman rank correlations and partial correlation analyses were conducted. Testing for differences between correlation coefficients was calculated according to the procedures suggested by Diedenhofen and Musch [30]. Bonferroni corrections of alpha levels were applied to adjust for multiple comparisons in subsamples.

RESULTS

Sample characteristics

CSF samples were obtained from n = 112 participants, of which n = 105 had an MMSE score above 26. After applying the delineated further criteria (see Table 1), n = 76 participants were included in the primary analysis. Exclusion was mainly due to non-normal Aβ_42/40 ratios and MMSE scores. Patient characteristics and descriptive values of CSF peptide concentrations for the core sample (primary analyses) are shown in Table 2. As indicated by Mann-Whitney U-tests, neither t-Tau (p = 0.043) nor Aβ₄₀ concentrations (p = 0.099) differed between male (n = 52) and female (n = 24) participants after applying a Bonferroni correction for multiple comparisons (0.05/2 = 0.025).

Table 2

Core sample (n = 76): patient characteristics and CSF peptide concentrations

	Age [y]	t-Tau [pg/ml]	Aβ₄₀ [pg/ml]	t-Tau/Aβ₄₀
M	59.3	236	14314	0.0174
SD	14.2	89.5	5301	0.0055
Range	30–85	79–551	5023–25120	0.0077–0.0320
MD	59.5	224	13142	0.0174
25th pctl	50.8	172	10337	0.0134
75th pctl	71.3	289	18690	0.0208

CSF, cerebrospinal fluid; M, mean; SD, standard deviation; MD, median; pctl, percentile.

For secondary analyses, we included subjects with normal Aβ₄₂ concentrations (>650 pg/ml, cut-off established in-house using amyloid PET positivity as standard of truth in an independent cohort) instead of normal Aβ_42/40 ratios (n = 82). Furthermore, analyses were repeated in a subset of participants who underwent extensive cognitive testing (n = 53) and scored within normal limits (cut-off –1.5 standard deviations below the mean of the age-, sex-, and education-adjusted normative values) and were thus determined to not suffer from MCI (n = 43) in addition to having normal Aβ_42/40 ratios (n = 36).

Correlation analyses

Correlation analyses revealed a positive association of t-Tau and age (rho = 0.433, p < 0.001, significant after Bonferroni-correcting alpha-levels in the core sample; 0.05/4 = 0.0125) and age was similarly correlated with Aβ₄₀ (rho = 0.581, p < 0.001). In contrast, the t-Tau/Aβ₄₀ ratio was not correlated with age (rho = –0.026, p = 0.825), resulting in a significant difference between the correlation coefficients of t-Tau and age and the t-Tau/Aβ₄₀ ratio and age (p < 0.001). Corresponding scatterplots are depicted in Fig. 1. Furthermore, when controlling for Aβ₄₀ in a partial correlation analysis of t-Tau and age, no significant association was observed (rho = 0.190, p = 0.103).

Fig. 1

Scatterplots and correlation coefficients for age and t-Tau as well as age and t-Tau/Aβ₄₀ ratio in the core sample (n = 76, normal Aβ_42/40 ratio).

Fig. 2

Secondary analyses: Scatterplots and correlation coefficients for age and t-Tau as well as age and t-Tau/Aβ₄₀ ratio (A: n = 82 for participants with normal Aβ₄₂ concentrations, B: n = 36 for participants who underwent extensive neuropsychological evaluation with normal results and had normal Aβ_42/40 ratios).

Secondary analyses revealed similar coefficients when using normal Aβ₄₂ concentrations for inclusion regarding t-Tau and age (rho = 0.457, p < 0.001, significant after Bonferroni-correcting alpha-levels in this subsample; 0.05/3 = 0.0167) and the t-Tau/Aβ₄₀ ratio and age (rho = –0.019, p = 0.886). A partial correlation analysis of t-Tau and age controlled for Aβ₄₀ showed a non-significant association (rho = 0.218, p = 0.051) for this subset of patients. Comparable results were obtained when examining the subsample of neuropsychologically validated cognitive healthy subjects for t-Tau and age (rho = 0.405, p = 0.010, significant after Bonferroni-correcting alpha-levels in this subsample; 0.05/3 = 0.0167) and for the t-Tau/Aβ₄₀ ratio and age (rho = 0.083, p = 0.631), as well as in a partial correlation analysis of t-Tau and age controlled for Aβ₄₀ (rho = 0.151 p = 0.386). Testing for differences of correlation coefficients between primary and secondary analyses consistently revealed non-significant results for differences in correlations between t-Tau and age, as well as for differences in correlations between the t-Tau/ Aβ₄₀ ratio and age.

Note that in all subsamples age was correlated with Aβ₄₀ (rho = 0.439 in the core sample, rho = 0.403 for participants with normal Aβ₄₂ concentrations and rho = 0.429 for participants who underwent comprehensive neuropsychological testing, all ps < 0.01).

DISCUSSION

In this study, we found a positive correlation between t-Tau and age in a sample of carefully selected healthy adults ranging from 30 to 85 years of age, which is in line with previous work. Crucially, this association was no longer present when correcting t-Tau for Aβ₄₀ concentrations or controlling the correlation of t-Tau and age for Aβ₄₀. These findings were replicated applying varied inclusion criteria regarding amyloid biomarkers for AD in order to exclude underlying asymptomatic AD and applying rigorous criteria for extensive cognitive testing in order to rule out subtle cognitive deficits.

Our results question the validity of the reported association of age and t-Tau in the CSF of normally aging subjects [9 , 12] and raise issues regarding the role of Aβ₄₀ in the context of interpreting CSF biomarkers associated with AD and other neurodegenerative diseases. While Aβ₄₀ has traditionally been used to correct pre-analytical Aβ₄₂ biases and to account for interindividual amyloid variations [21], it might have additional diagnostic value in clinical decision making and research settings. Since clinicians are constantly presented with ambiguous constellations of CSF biomarkers when testing patients suspected of suffering from neurodegenerative diseases, there is a clear need for better understanding factors influencing CSF peptide levels. While drainage-mechanisms from the brain’s interstitial system into the CSF remain a matter of ongoing debate [14], considering Aβ₄₀ as a possible proxy for drainage-related interindividual variance in peptide concentrations could be of heuristic value. Note, however, that despite known diurnal fluctuations of amyloid peptide in CSF and plasma [31], our study does not provide direct in vivo evidence for Aβ₄₀ as a marker of peptide drainage into the CSF.

In line with this, the diagnostic utility of the t-Tau/Aβ₄₀ ratio should be explored in AD and other neurodegenerative diseases. For example, the diagnostic accuracy of CSF-t-Tau in distinguishing patients with AD at an asymptomatic or MCI stage from healthy aging might be underestimated when not taking drainage-related interindividual differences into account.

In contrast to previous studies examining t-Tau in the CSF of healthy subjects, we excluded subjects with abnormal amyloid profiles of CSF biomarkers for AD (as the most common underlying cause of dementia; [32]). Furthermore, we ruled out other infectious/inflammatory CNS processes by analyzing samples regarding cell count and lactate levels. In order to minimize influences of undetected psychopathology on the results of cognitive testing [33], we also included measures of affective symptoms and excluded depression. We strongly recommend following strict inclusion and exclusion criteria when examining biomarkers of neurodegeneration in healthy subjects to maximize validity of results.

Our study has some limitations, which should be addressed in further research. As our sample size is moderate and patients were recruited at a single hospital, results should be replicated with a larger number of patients in a multi-center approach. Additionally, the drainage mechanisms of other, non-amyloid proteins should be evaluated in further studies as Aβ₄₀ cannot be viewed as completely neutral to AD pathology. For instance, Aβ₄₀ has been studied as a CSF marker for cerebral amyloid angiopathy [34]. As analysis of AD-related peptides from more easily accessible body fluids than CSF is gaining attention [35], the relationship between age and individual biomarkers of neurodegeneration in blood and saliva should be examined in future studies.

In this study, we found a correlation of CSF t-Tau and age in cognitively healthy adults, which was no longer present when correcting t-Tau for Aβ₄₀ concentrations. The results call into question the reported association of age and t-Tau. Furthermore, they suggest diagnostic utility of Aβ₄₀ as a proxy for CSF drainage mechanisms when interpreting biomarker concentrations of neurodegeneration in research and clinical settings.

Footnotes

ACKNOWLEDGMENTS

The authors like to cordially thank Tamara Eisele for measuring the peptides in the CSF, and all patients for their donating CSF to this study. We would also like to give special thanks to Stephen Starck from the Language Center of the Technical University of Munich for his help proof reading and editing the manuscript.

Authors’ disclosures available online ().

References

Iqbal

, Liu

, Gong

C-X

(2016) Tau and neurodegenerative disease: The story so far. Nat Rev Neurol 12, 15–27.

Zetterberg

(2017) Review: Tau in biofluids - relation to pathology, imaging and clinical features. Neuropathol Appl Neurobiol 43, 194–199.

Hesse

, Rosengren

, Vanmechelen

, Vanderstichele

, Jensen

, Davidsson

, Blennow

(2000) Cerebrospinal fluid markers for Alzheimer’s disease evaluated after acute ischemic stroke. J Alzheimers Dis 2, 199–206.

Franz

, Beer

, Kampfl

, Engelhardt

, Schmutzhard

, Ulmer

, Deisenhammer

(2003) Amyloid beta 1-42 and tau in cerebrospinal fluid after severe traumatic brain injury. Neurology 60, 1457–1461.

Olsson

, Lautner

, Andreasson

, Öhrfelt

, Portelius

, Bjerke

, Hölttä

, Rosén

, Olsson

, Strobel

, Wu

, Dakin

, Petzold

, Blennow

, Zetterberg

(2016) CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: A systematic review and meta-analysis. Lancet Neurol 15, 673–684.

Mitani

, Furiya

, Uchihara

, Ishii

, Yamanouchi

, Mizusawa

, Mori

(1997) Increased CSF tau protein in corticobasal degeneration. J Neurol 245, 44–46.

Skillbäck

, Rosén

, Asztely

, Mattsson

, Blennow

, Zetterberg

(2014) Diagnostic performance of cerebrospinal fluid total tau and phosphorylated tau in Creutzfeldt-Jakob disease: Results from the Swedish Mortality Registry. JAMA Neurol 71, 476–483.

Mattson

, Arumugam

(2018) Hallmarks of brain aging: Adaptive and pathological modification by metabolic states. Cell Metab 27, 1176–1199.

Sjögren

, Vanderstichele

, Agren

, Zachrisson

, Edsbagge

, Wikkelsø

, Skoog

, Wallin

, Wahlund

, Marcusson

, Kägga

, Andreasen

, Davidsson

, Vanmechelen

, Blennow

(2001) Tau and Abeta42 in cerebrospinal fluid from healthy adults 21-93 years of age: Establishment of reference values. Clin Chem 47, 1776–1781.

10.

Folstein

, Folstein

, McHugh

(1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189–198.

11.

Mattsson

, Zetterberg

, Hansson

, Andreasen

, Parnetti

, Jonsson

, Herukka

, van der Flier

, Blankenstein

, Ewers

, Rich

, Kaiser

, Verbeek

, Tsolaki

, Mulugeta

, Rosén

, Aarsland

, Visser

, Schröder

, Marcusson

, de Leo

, Hampel

, Scheltens

, Pirttilä

, Wallin

, Jönhagen

, Minthon

, Windblad

, Blennow

(2009) CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302, 385–393.

12.

Shoji

, Matsubara

, Murakami

, Manabe

, Abe

, Kanai

, Ikeda

, Tomidokoro

, Shizuka

, Watanabe

, Amari

, Ishiguro

, Kawarabayashi

, Harigaya

, Okamoto

, Nishimura

, Nakamura

, Takeda

, Urakami

, Adachi

, Nakashima

, Arai

, Sasaki

, Kanemaru

, Yamanouchi

, Yoshida

, Ichise

, Tanaka

, Hamamoto

, Yamamoto

, Matsubayashi

, Yoshida

, Toji

, Nakamura

, Hirai

(2002) Cerebrospinal fluid tau in dementia disorders: A large scale multicenter study by a Japanese study group. Neurobiol Aging 23, 363–370.

13.

Burkhard

, Fournier

, Mermillod

, Krause

, Bouras

, Irminger

(2004) Cerebrospinal fluid tau and Abeta42 concentrations in healthy subjects: Delineation of reference intervals and their limitations. Clin Chem Lab Med 42, 396–407.

14.

Brinker

, Stopa

, Morrison

, Klinge

(2014) A new look at cerebrospinal fluid circulation. Fluids Barriers CNS 11, 10.

15.

Iliff

, Wang

, Liao

, Plogg

, Peng

, Gundersen

, Benveniste

, Vates

, Deane

, Goldman

, Nagelhus

, Needergard

(2012) A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci Transl Med 4, 147ra111.

16.

Benveniste

, Liu

, Koundal

, Sanggaard

, Lee

, Wardlaw

(2019) The glymphatic system and waste clearance with brain aging: A review. Gerontology 65, 106–119.

17.

Kress

, Iliff

, Xia

, Wang

, Wei

, Zeppenfeld

, Xie

, Kang

, Xu

, Liew

, Plog

, Ding

, Deane

, Needergard

(2014) Impairment of paravascular clearance pathways in the aging brain. Ann Neurol 76, 845–861.

18.

Selkoe

, Hardy

(2016) The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med 8, 595–608.

19.

Montine

, Phelps

, Beach

, Bigio

, Cairns

, Dickson

, Duyckaerts

, Frosch

, Masliah

, Mirra

, Nelson

, Schneider

, Thal

, Trojanowski

, Vinters

, Hyman

(2012) National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: A practical approach. Acta Neuropathol 123, 1–11.

20.

Molinuevo

, Ayton

, Batrla

, Bednar

, Bittner

, Cummings

, Fagan

, Hampel

, Mielke

, Mikulskis

, O’Bryant

, Scheltens

, Sevigny

, Shaw

, Soares

, Tong

, Trojanowski

, Zetterberg

, Blennow

(2018) Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol 136, 821–853.

21.

Hansson

, Lehmann

, Otto

, Zetterberg

, Lewczuk

(2019) Advantages and disadvantages of the use of the CSF Amyloid β (Aβ) 42/40 ratio in the diagnosis of Alzheimer’s Disease. Alzheimers Res Ther 11, 34.

22.

World Medical Association (2013) World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 310, 2191–2194.

23.

O’Bryant

, Humphreys

, Smith

, Ivnik

, Graff-Radford

, Petersen

, Lucas

(2008) Detecting dementia with the mini-mental state examination in highly educated individuals. Arch Neurol 65, 963–967.

24.

Mitchell

(2009) A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment. J Psychiatr Res 43, 411–431.

25.

Marco

, Mann

, Rasp

, Ballester

, Perkins

, Holbrook

, Rako

(2018) Effects of opioid medications on cognitive skills among emergency department patients. Am J Emerg Med 36, 1009–1013.

26.

Saklad

(1941) Grading of patients for surgical procedures. Anesthesiology 2, 281–284.

27.

Beck

, Steer

, Brown

(1996) Manual for the Beck Depression Inventory-II, Psychological Corporation, San Antonio, TX.

28.

Montgomery

, Asberg

(1979) A new depression scale designed to be sensitive to change. Br J Psychiatry 134, 382–389.

29.

R Core Team (2018) R: A Language and Environment for statistical computing.

30.

Diedenhofen

, Musch

(2015) cocor: A comprehensive solution for the statistical comparison of correlations. PLoS One 10, e0121945.

31.

Huang

, Potter

, Sigurdson

, Kasten

, Connors

, Morris

, Benzinger

, Mintun

, Ashwood

, Ferm

, Budd

, Bateman

(2012) β-amyloid dynamics in human plasma. Arch Neurol 69, 1591–1597.

32.

Barker

, Luis

, Kashuba

, Luis

, Harwood

, Loewenstein

, Waters

, Jimison

, Shepherd

, Sevush

, Graff-Radford

, Newland

, Todd

, Miller

, Gold

, Heilman

, Doty

, Goodman

, Robinson

, Pearl

, Dickson

, Duara

(2002) Relative frequencies of Alzheimer disease, Lewy body, vascular and frontotemporal dementia, and hippocampal sclerosis in the State of Florida Brain Bank. Alzheimer Dis Assoc Disord 16, 203–212.

33.

Rock

, Roiser

, Riedel

, Blackwell

(2014) Cognitive impairment in depression: A systematic review and meta-analysis. Psychol Med 44, 2029–2040.

34.

Charidimou

, Friedrich

, Greenberg

, Viswanathan

(2018) Core cerebrospinal fluid biomarker profile in cerebral amyloid angiopathy: A meta-analysis. Neurology 90, 754–762.

35.

Zetterberg

(2019) Blood-based biomarkers for Alzheimer’s disease-An update. J Neurosci Methods 319, 2–6.