Association of Plasma Neurofilament Light Chain with Neocortical Amyloid-β Load and Cognitive Performance in Cognitively Normal Elderly Participants

Abstract

Background:

The disruption of neurofilament, an axonal cytoskeletal protein, in neurodegenerative conditions may result in neuronal damage and its release into the cerebrospinal fluid and blood. In Alzheimer’s disease (AD), neurofilament light chain (NFL), a neurofilament subunit, is elevated in the cerebrospinal fluid and blood.

Objective:

Investigate the association of plasma NFL with preclinical-AD features, such as high neocortical amyloid-β load (NAL) and subjective memory complaints, and cognitive performance in cognitively normal older adults.

Methods:

Plasma NFL concentrations were measured employing the single molecule array platform in participants from the Kerr Anglican Retirement Village Initiative in Ageing Health cohort, aged 65– 90 years. Participants underwent a battery of neuropsychological testing to evaluate cognitive performance and were categorized as low NAL (NAL-, n = 65) and high NAL (NAL+, n = 35) assessed via PET, and further stratified into subjective memory complainers (SMC; nNAL- = 51, nNAL+ = 25) and non-SMC (nNAL- = 14, nNAL+ = 10) based on the Memory Assessment Clinic– Questionnaire.

Results:

Plasma NFL inversely correlated with cognitive performance. No significant difference in NFL was observed between NAL+ and NAL- participants; however, within APOE ɛ4 non-carriers, higher NAL was observed in individuals with NFL concentrations within quartiles 3 and 4 (versus quartile 1). Additionally, within the NAL+ participants, SMC had a trend of higher NFL compared to non-SMC.

Conclusion:

Plasma NFL is inversely associated with cognitive performance in elderly individuals. While plasma NFL may not reflect NAL in individuals with normal global cognition, the current observations indicate that onset of axonal injury, reflected by increased plasma NFL, within the preclinical phase of AD may contribute to the pathogenesis of AD.

Keywords

Alzheimer’s disease blood neurofilaments positron emission tomography cognitive function episodic memory executive function verbal memory visual memory

INTRODUCTION

Neurofilament (NF) is a crucial axonal cytoskeletal component comprising three subunits, namely, neurofilament light chain (NFL), neurofilament medium chain (NFM), and neurofilament heavy chain (NFH) [1]. The disruption of NF in neuronal damage occurring within neurodegenerative conditions, results in the release of NF into the cerebrospinal fluid (CSF) [2]; consequently giving rise to elevated NFL concentrations in the CSF and blood, as has been reported in Alzheimer’s disease (AD) [3] and other neurodegenerative diseases [4 –7].

With the invasive nature of CSF collection via lumbar puncture, the potential of employing NFL as a biomarker for disease diagnosis or progression, and possibly for screening, further drove the investigation of blood NFL alterations, in disease [2 , 8–10]. Employing the Alzheimer’s Disease Neurodegenerative Initiative (ADNI) cohort, Mattsson and colleagues reported significantly higher plasma NFL in AD and mild cognitively impaired (MCI) patients compared to controls [11]. Additionally, higher plasma NFL has been associated with compromised cognition and hippocampal atrophy both cross-sectionally and longitudinally. Furthermore, plasma NFL was inversely associated with brain glucose metabolism, longitudinally in the same highly characterized ADNI cohort [11]. More recently, a study conducted on autosomal dominant Alzheimer’s disease (ADAD) families found that plasma NFL was higher in the asymptomatic mutation carriers and symptomatic mutation carriers compared to their non-carrier relatives, wherein plasma NFL concentrations were reported to be about 1.3-fold higher in asymptomatic mutation carriers and 3.6-fold higher in symptomatic mutation carriers [12].

However, plasma NFL in individuals with preclinical AD prior to cognitive impairment, characterized by neocortical amyloid-β load (NAL) measured via positron emission tomography (PET) and subjective memory complaints, has not been investigated previously. Furthermore, the association between plasma NFL and cognitive performance in cognitively normal elderly individuals has also not been examined.

Therefore, the current study investigated the association between plasma NFL and NAL, employing a standard uptake value ratio (SUVR) cut-off value of 1.35, categorizing study participants as high NAL (SUVR≥1.35; NAL+) and low NAL (SUVR<1.35; NAL-), given that the aberrant build-up of NAL begins as early as two decades prior to the clinical manifestation of AD [13, 14]. Based on the Memory Assessment Clinic–Questionnaire (MAC-Q) [15], NAL+ participants were further categorized into subjective memory complainers (SMC, MAC-Q = 25–35) and non-complainers (non-SMC, MAC-Q≤24) to examine NFL alterations within the preclinical cohort subset at highest risk of AD (NAL+ SMC). The current study also examined whether plasma NFL levels inversely correlated with cognitive performance within cognitively normal elderly individuals. Our investigations were therefore aimed at gaining better insight into whether axonal injury, reflected by increased plasma NFL, is present in the early pathogenesis of AD.

METHODS

Participants

Study participants belonged to the Kerr Anglican Retirement Village Initiative in Ageing Health (KARVIAH) cohort, at baseline. All participants were residents of Anglicare, New South Wales, Australia.

Cohort volunteers (N = 206) were required to meet the set screening inclusion and exclusion criteria to be eligible for the KARVIAH cohort. Briefly, the inclusion criteria for the KARVIAH cohort comprised an age range of 65–90 years, good general health, no known significant cerebral vascular disease, fluent in English, adequate/corrected vision and hearing to enable testing, no objective cognitive impairment as screened by a Montreal Cognitive Assessment (MoCA) score ≥26. MoCA scores lying between 18–25 were assessed on a case by case basis by the study neuropsychologist following stratification of scores according to age and education [16]. The exclusion criteria comprised, the diagnosis of dementia based on the revised criteria from the National Institute on Aging - Alzheimer’s Association [17], presence of acute functional psychiatric disorder (including lifetime history of schizophrenia or bipolar disorder), history of stroke, severe or extremely severe depression (based on the depression, anxiety, stress scales; DASS) and uncontrolled hypertension (systolic BP >170 mm Hg or diastolic BP >100 mm Hg).

One hundred and five participants out of the 134 volunteers meeting the inclusion/exclusion criteria, underwent neuroimaging, neuropsychometric evaluation and blood collection, as the remaining either declined undergoing neuroimaging or withdrew from the study. Within these 105 participants, 100 participants were considered to have normal global cognition based on their Mini-Mental State Examination score [18] (MMSE ≥26), and were included in the current study. Plasma NFL concentrations were reported in all 100 participants considered to have normal global cognition. All volunteers provided written informed consent prior to participation, and the Bellberry Human Research Ethics Committee, Australia, provided approval for the study.

Neuroimaging

All study participants were imaged within three months of blood collection. Participants underwent PET using ligand ¹⁸F-Florbetaben (FBB) at Macquarie Medical Imaging in Sydney. Participants were administered an intravenous bolus of FBB slowly over 30 s, while in a rested position. Images were acquired over a 20 min scan, in 5 min acquisitions, beginning 50 min post injection. Neocortical amyloid-β load was calculated as the mean SUVR of the frontal, superior parietal, lateral temporal, lateral occipital, and anterior and posterior cingulate regions using image processing software, CapAIBL [19, 20].

Ninety-six of the 100 participants within the current study passed all standard MRI inclusion/exclusion criteria, and underwent MRI as described previously using a General Electric (GE) 3 Tesla scanner (Model 750 W) [13]. Hippocampal volume calculated from the images acquired was normalized with the total intracranial volume comprising the cerebrospinal fluid, grey matter, and white matter.

Blood collection, APOE genotyping, measurement of plasma NFL

All study participants fasted for a minimum of 10 h overnight prior to blood withdraw employing standard serological methods and processing [21]. Apolipoprotein E (APOE) genotype was determined from purified genomic DNA extracted from 0.5 ml whole blood as previously described [21].

Plasma NFL concentrations were measured employing the ultra-sensitive single-molecule array (Simoa) platform [10, 11]. Calibrators were run in duplicates and samples were run in singlicates with a 4-fold dilution. Two quality control (QC) levels were run in duplicates at the beginning and the end of each plate. For QC with concentration 12.1 pg/mL, repeatability and intermediate precision were 20.2%, while for QC with concentration 155.8 pg/mL, repeatability was 14.6% and intermediate precision was 14.9%. The lowest limit of quantification was 6 pg/mL.

Neuropsychological tests

All study participants underwent a comprehensive battery of neuropsychological testing. The full battery comprised the MoCA [16], MMSE [18], MAC-Q [15], Rey Auditory Verbal Learning Test (RAVLT) [22], Logical Memory (LM) I and II (WMS-III; Story A only) [23], Rey Complex Figure Test (RCFT)[24], Wechsler Adult Intelligence Scale – Third edition (WAIS-III) Digit Span [25], WAIS– III Digit Symbol Substitution Test (DSST) [26], D-KEFS Category Fluency (Boys Names) and Switching (Fruits and Furniture) Tasks [27], Controlled Oral Word Association Test [28], Stroop Test (Victoria version) [29], the Boston Naming Test [30], Wechsler Test of Adult Reading [31] and the DASS [32]. Composite scores were generated for verbal and visual episodic memory and for working memory and executive function. The verbal and visual episodic memory composite score was created from the mean of the z-scores of RAVLT List A, RAVLT short delay, RAVLT long delay, LM I, LMII, RCFT 3 min and RCFT 30 min while the working memory and executive function composite score was generated from the mean of the z-scores of Digit Span backward, DSST, D-KEFS Boys names and Fruits and Furniture Switching tasks. The global composite score was constructed from the mean of the z-score measures of RAVLT List A, RAVLT short delay, RAVLT long delay, LM I, LM II, RCFT 3 min, RCFT 30 min, Digit Span backward, DSST, D-KEFS Boys names and Fruits and Furniture Switching tasks and MMSE.

Statistical analyses

Descriptive statistics including means and standard deviations were calculated for NAL+ and NAL- groups. Chi-square tests were employed to compare the frequency of gender, APOE ɛ4 carrier status and SMC between NAL+ and NAL- groups. Additionally, linear models were employed to compare continuous variables examined in the study between groups of interest (e.g., NAL- versus NAL+, NAL+/non-SMC versus NAL+/SMC, NFL quartiles Q1 versus Q2, Q3, Q4, etc.) with and without adjusting for covariates age, gender and APOE ɛ4 carrier status. Continuous response variables were tested for approximate normality and variance homogeneity, and log transformed when required to satisfy test criteria. Pearson’s correlation coefficient was employed to investigate correlations between NFL and other continuous variables of interest, except for the correlation between NFL and MMSE where Spearman’s correlation was used. Partial correlations were used when associations investigated were adjusted for age. All analyses were carried out using IBM^® SPSS^® Version 23.

RESULTS

Demographic characteristics of study participants have been represented in Table 1, wherein no significant differences were observed between NAL- and NAL+ participants, except for the expected observation of a significantly higher APOE ɛ4 carrier frequency in the NAL+ group.

Table 1

Demographic characteristics of study participants

	NAL-	NAL+	p
Gender (M/F)	19/46	13/22	0.419
Age (years, mean±SD)	77.61±5.55	79.22±5.38	0.165
BMI (mean±SD)	27.38±4.47	28.05±4.73	0.486
Education (years, mean±SD)	14.84±3.37	13.64±2.91	0.078
n APOE ɛ 4 carriers (%)	5 (7.69)	16 (45.71)	0.00000848
MMSE (mean±SD)	28.50±1.16	28.80±1.10	0.225
n SMC (%)	51 (78.46)	25 (71.42)	0.432
HV% (left; right lobes, mean±SD)	0.195±0.020;	0.194±0.019;	0.805;
	0.199±0.021	0.199±0.018	0.890
FBB-PET SUVR (mean±SD)	1.15±0.08	1.71±0.26	–

Baseline characteristics including gender, age, body mass index (BMI), education, APOE ɛ 4 status, Mini-Mental State Examination (MMSE) scores, number of subjective memory complainers (SMC) based on the Memory Assessment Clinic-Questionnaire (MAC-Q), hippocampal volume (HV) normalized by the intracranial volume and neocortical amyloid-β load (NAL) represented by the standard uptake value ratio (SUVR) of ligand ¹⁸F-Florbetaben (FBB) in the neocortical region normalized with that in the cerebellum, have been compared between NAL- (SUVR<1.35) and NAL+ (SUVR≥1.35) study participants. Chi-square test or linear models were employed as appropriate.

Plasma NFL was observed to correlate with age (r = 0.533, p < 0.0001) while no significant association between plasma NFL was observed with education (r = –0.004, p = 0.965), gender (mean±SD: males, 39.26±20.55 pg/mL; females, 35.02±16.30 pg/mL; p = 0.268) or APOE ɛ4 carriage (mean±SD: non-carriers, 37.49±18.29 pg/mL; carriers, 32.18±15.39 pg/mL; p = 0.226).

Plasma NFL was not observed to be significantly elevated in NAL+ participants (or preclinical AD) compared to NAL- participants (mean±SD: NAL-, 35.23±17.27 pg/mL; NAL+, 38.50±18.75 pg/mL) with (p = 0.563) and without (p = 0.384) adjusting for age, gender, and APOE ɛ4; however, a trend of elevated plasma NFL concentrations was observed in NAL+ SMC compared to NAL+ non-SMC, adjusting for age, gender, and APOE ɛ4 carrier status (mean±SD, NAL+/non-SMC (n = 10): 31.50±12.57 pg/mL, NAL+/SMC (n = 25): 41.30±20.26 pg/mL; p = 0.069).

Additionally, after stratifying the cohort into APOE ɛ4 carriers (n = 21) and non-carriers (n = 79), within the APOE ɛ4 non-carrier subset, participants carrying NFL concentrations lying within quartile 1 were observed to have significantly lower NAL (1.15±0.13) compared to those in quartiles 3 (1.32±0.29, p = 0.031) and 4 (1.38±0.32, p = 0.006, Fig. 1). However, after adjusting for age, significance disappeared between quartile 1 and 3, and only a trend remained between quartile 1 and 4 (p = 0.068), likely due to the strong correlation between NFL and age, confounding associations of NFL with other age-related outcomes within the scope of this modest sample size.

Fig.1

Comparison of NAL between NFL quartiles in APOE non-ɛ4 carriers. Neocortical amyloid-β load (NAL) assessed via positron emission tomography using ligand ¹⁸F-florbetaben was observed to be elevated in plasma neurofilament light chain (NFL) quartile (Q) 3 and Q4 compared to Q1, using linear models within APOE non-ɛ4 carriers (n = 79); however, this was not the case in APOE ɛ4 carriers (n = 21, Supplementary Figure 1). NFL quartile cut-offs were 23.33 pg/ml, 34.05 pg/ml, and 45.41 pg/ml for Q1, Q2, and Q3 respectively, in the APOE non-ɛ4 carriers. ^* p < 0.05, ^** p < 0.01, error bars represent SE not adjusted for covariates age and gender. Corresponding analysis adjusted for covariates is available in the text. Percentages of NAL+ individuals in each quartile have been shown within the bar graphs.

Plasma NFL correlated inversely with verbal and visual episodic memory (r = –0.305, p = 0.002) and working memory and executive function (r = –0.357, p = 0.0002) in all participants (Fig. 2). Plasma NFL was also observed to correlate inversely with global cognition assessed via MMSE (r = –0.326, p = 0.001) and the global composite score (r = –0.407, p < 0.0001) (Fig. 2). On adjusting for age, plasma NFL continued to significantly correlate inversely with working memory and executive function (r = –0.200, p = 0.047) and the global composite score (r = –0.223, p = 0.026).

Fig.2

Association between plasma NFL and cognition in cognitively normal elderly participants. Plasma neurofilament light chain (NFL) concentrations (pg/ml) inversely correlated with verbal and visual episodic memory (A), working memory and executive function (B) and global cognition (C) using Pearson’s correlation coefficient. Z-scores were calculated for Rey Auditory Verbal Learning Test (RAVLT) List A, RAVLT short delay, RAVLT long delay, Logical Memory (LM) I, LMII, Rey Complex Figure Test (RCFT) 3 min, RCFT 30 min, Digit Span backward, Digit Symbol Substitution Test (DSST), D-KEFS Category Fluency Task (Boys names), D-KEFS Switching Task (Fruit and Furniture) and MMSE. Composite scores for Verbal and Visual Episodic Memory were calculated by summation of z-scores of RAVLT List A, RAVLT short delay, RAVLT long delay, LM I, LMII, RCFT 3 min, RCFT 30 min while composite scores for working memory and executive function were generated by summation of z-scores of Digit span backward, DSST, and D-KEFS Category Fluency and Switching Tasks. The global composite score was constructed by calculating the mean of z-score measures of RAVLT List A, RAVLT short delay, RAVLT long delay, LM I, LMII, RCFT 3 min, RCFT 30 min, Digit Span backward, DSST, D-KEFS tasks, and MMSE.

No significant association was observed between plasma NFL and hippocampal volume within the current study (left hemisphere, r = –0.095, p = 0.356; right hemisphere, r = –0.096, p = 0.352).

DISCUSSION

Plasma NFL concentrations were not significantly elevated in cognitively normal elderly NAL+ versus NAL- participants. Our findings are in line with those reported by Mattsson and colleagues wherein no significant difference in plasma NFL was observed between NAL- (based on CSF Aβ₄₂ ≥192 ng/L, n = 71) and NAL+ (based on CSF Aβ₄₂ <192 ng/L, n = 41) cognitively normal participants [11]. Furthermore, associations between brain atrophy and plasma (and CSF) NFL concentrations reported previously in MCI, AD (and cognitively normal) individuals with both high and low CSF Aβ load [33], indicate that NFL is a general neurodegeneration biomarker, with high levels in many disorders such as frontotemporal dementia and progressive supranuclear palsy [10, 34], which also is consistent with the observations of NFL between NAL+ and NAL- participants in the current study. However, we observed a trend of elevated plasma NFL in SMC compared to non-memory complainers within the NAL+ subset employed in the current study— the cohort subset likely to be the most advanced in the preclinical AD pathogenesis trajectory.

Interestingly, Mattsson and colleagues, also observed that MCI and AD APOE ɛ4 non-carriers had significantly higher plasma NFL concentrations compared to ɛ4 carriers. Within the current study as well, APOE ɛ4 non-carriers had a higher mean plasma NFL (37.49 pg/ml) compared to APOE ɛ4 carriers (32.18 pg/ml), although it did not reach significance presumably due to the relatively small numbers in the latter group. However, within the APOE ɛ4 non-carrier cohort subset, a trend of higher NAL was observed in participants with plasma NFL lying within Q3 and Q4 compared to Q1, suggesting that the onset of axonal cytoskeletal disruption may commence as early as this preclinical phase of AD.

Within the current study we did not observe a significant correlation between plasma NFL and hippocampal atrophy: an observation in line with findings reported by Pereira and colleagues, wherein plasma NFL was associated with brain atrophy only in symptomatic cases, while CSF NFL concentrations were associated with brain atrophy in AD, MCI, and cognitively normal subjects [33]. Similar observations have also been reported in the autosomal dominant form of AD by Weston and colleagues [12]. No association between plasma NFL and neocortical glucose metabolism (data not shown) was observed within the current cross-sectional study, which is in agreement with the findings reported by Mattsson and colleagues [11].

Plasma NFL was observed to correlate strongly with age in the present study. This correlation is consistent with previous studies reporting significant correlations between age and NFL in both, CSF and plasma (or serum) from healthy controls, individuals with pre-symptomatic neurodegenerative disease and within cohorts comprising healthy controls, MCI and AD patients [3 , 36].

Additionally, plasma and CSF NFL concentrations have previously been reported to be inversely associated with MMSE, the AD assessment scale–cognitive subscale, Clinical Dementia Rating Scale and the Trail Making Test-B scores in participants with MCI, sporadic AD, ADAD and bipolar disorder [3 , 37]. The current study also observed that plasma NFL correlated inversely with cognitive performance, particularly, verbal and visual episodic memory, executive function and working memory and global cognition suggesting that higher plasma NFL concentrations are associated with inferior cognitive performance in elderly individuals with normal global cognition as well.

We acknowledge that the current study has limitations with regard to its relatively modest sample size. However, the present study also has its strengths given that it utilizes a highly characterized, cognitively normal cohort with a representative proportion of preclinical AD individuals, in agreement with other established cohorts [38, 39], employing PET for NAL measurement, a stronger marker of AD neuropathology compared to CSF Aβ, as employed previously [11]. Additionally, the study also incorporates a comprehensive battery of neuropsychological tests and most importantly, a highly sensitive assay to measure plasma NFL; however, the current findings need to be replicated in other cohorts, also employing alternative methods.

To summarize, plasma NFL was not significantly higher in NAL+ versus NAL- cognitively normal elderly individuals, however a trend of elevated plasma NFL was observed within the NAL+ SMC (the cohort subset likely to be the farthest in the preclinical AD pathogenesis trajectory), indicating onset of axonal injury occurs well before the onset of clinical AD symptoms. Additionally, significant associations were observed between plasma NFL and neuropsychometric parameters representing visual and verbal memory, executive function and working memory and global cognition, in the current study. Our current plasma NFL observations along with those previously published indicate that plasma NFL alterations begin to manifest within the end stage of preclinical AD, and while NFL is not a sufficient stand-alone marker for preclinical and clinical AD, it has the potential to serve as an early marker of neurodegeneration. Moreover, given that plasma NFL correlated with cognition, it is a promising biomarker for disease progression and for monitoring disease modifying therapies, reaffirming the potential of elevated plasma NFL as a marker of progressive neurodegeneration. The current findings warrant investigation of plasma NFL levels and cognitive decline longitudinally, providing further insight on neuronal damage (characterized by plasma NFL) reflecting cognitive decline.

Footnotes

ACKNOWLEDGMENTS

This study was funded by the Foundation for Aged Care, Anglicare, Sydney, the Australian Alzheimer Research Foundation (AARF), Perth, the Swedish Research Council (grant #2017-00915 and #2013-2546), the Swedish Alzheimer Foundation (grant #AF-553101), the Torsten Söderberg Professorship in Medicine at the Royal Swedish Academy of Sciences, the Knut and Alice Wallenberg Foundation and LUA/ALF VGR project (grant #ALFGBG-715986 and 720931), and the KaRa Institute of Neurological Diseases (KaRa MINDS), Sydney. We thank the participants and their families for their participation and cooperation, and Anglicare, KaRa MINDS and AARF research and support staff for their contributions to this study. We specially thank Professor Ian James for his contributions to the study. We thank Ms. Emma Toovey, Ms. Kate Fredericks, Ms. Bethany Ball, and Ms. Catherine Brown for their contributions to the study. We also thank the staff of the Macquarie Medical Imaging centre in Macquarie University Hospital, Sydney, for their contributions. KG is a recipient of the Cooperative Research Centre for Mental Health top-up scholarship. Florbetaben is a proprietary PET radiopharmaceutical owned by Piramal Imaging SA. For this study, Florbetaben was manufactured and supplied under GMP conditions by Cyclotek (Aust) Pty Ltd.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

References

Petzold

(2005) Neurofilament phosphoforms: Surrogate markers for axonal injury, degeneration and loss. J Neurol Sci 233, 183–198.

Gaiottino

, Norgren

, Dobson

, Topping

, Nissim

, Malaspina

, Bestwick

, Monsch

, Regeniter

, Lindberg

, Kappos

, Leppert

, Petzold

, Giovannoni

, Kuhle

(2013) Increased neurofilament light chain blood levels in neurodegenerative neurological diseases. PLoS One 8, e75091.

Zetterberg

, Skillback

, Mattsson

, Trojanowski

, Portelius

, Shaw

, Weiner

, Blennow

, Alzheimer’s Disease Neuroimaging Initiative (2016) Association of cerebrospinal fluid neurofilament light concentration with Alzheimer disease progression. JAMA Neurol 73, 60–67.

Backstrom

, Eriksson Domellof

, Linder

, Olsson

, Ohrfelt

, Trupp

, Zetterberg

, Blennow

, Forsgren

(2015) Cerebrospinal fluid patterns and the risk of future dementia in early, incident Parkinson disease. JAMA Neurol 72, 1175–1182.

Zetterberg

, Jacobsson

, Rosengren

, Blennow

, Andersen

(2007) Cerebrospinal fluid neurofilament light levels in amyotrophic lateral sclerosis: Impact of SOD1 genotype. Eur J Neurol 14, 1329–1333.

Scherling

, Hall

, Berisha

, Klepac

, Karydas

, Coppola

, Kramer

, Rabinovici

, Ahlijanian

, Miller

, Seeley

, Grinberg

, Rosen

, Meredith

Jr , Boxer

(2014) Cerebrospinal fluid neurofilament concentration reflects disease severity in frontotemporal degeneration. Ann Neurol 75, 116–126.

van Eijk

, van Everbroeck

, Abdo

, Kremer

, Verbeek

(2010) CSF neurofilament proteins levels are elevated in sporadic Creutzfeldt-Jakob disease. J Alzheimers Dis 21, 569–576.

Zhou

, Zhang

, Ye

, Xu

, Su

, Zhang

, Alzheimer’s Disease Neuroimaging Initiative (2017) Plasma neurofilament light chain levels in Alzheimer’s disease. Neurosci Lett 650, 60–64.

Meeter

, Dopper

, Jiskoot

, Sanchez-Valle

, Graff

, Benussi

, Ghidoni

, Pijnenburg

, Borroni

, Galimberti

, Laforce

, Masellis

, Vandenberghe

, Ber

, Otto

, van Minkelen

, Papma

, Rombouts

, Balasa

, Oijerstedt

, Jelic

, Dick

, Cash

, Harding

, Jorge Cardoso

, Ourselin

, Rossor

, Padovani

, Scarpini

, Fenoglio

, Tartaglia

, Lamari

, Barro

, Kuhle

, Rohrer

, Teunissen

, van Swieten

(2016) Neurofilament light chain: A biomarker for genetic frontotemporal dementia. Ann Clin Transl Neurol 3, 623–636.

10.

Rohrer

, Woollacott

, Dick

, Brotherhood

, Gordon

, Fellows

, Toombs

, Druyeh

, Cardoso

, Ourselin

, Nicholas

, Norgren

, Mead

, Andreasson

, Blennow

, Schott

, Fox

, Warren

, Zetterberg

(2016) Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia. Neurology 87, 1329–1336.

11.

Mattsson

, Andreasson

, Zetterberg

, Blennow

, Alzheimer’s Disease Neuroimaging Initiative (2017) Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol 74, 557–566.

12.

Weston

PSJ

, Poole

, Ryan

, Nair

, Liang

, Macpherson

, Druyeh

, Malone

, Ahsan

, Pemberton

, Klimova

, Mead

, Blennow

, Rossor

, Schott

, Zetterberg

, Fox

(2017) Serum neurofilament light in familial Alzheimer disease: A marker of early neurodegeneration. Neurology 89, 2167–2175.

13.

Goozee

, Chatterjee

, James

, Shen

, Sohrabi

, Asih

, Dave

, Ball

, ManYan

, Taddei

, Chung

, Garg

, Martins

(2017) Alterations in erythrocyte fatty acid composition in preclinical Alzheimer’s disease. Sci Rep 7, 676.

14.

Dubois

, Hampel

, Feldman

, Scheltens

, Aisen

, Andrieu

, Bakardjian

, Benali

, Bertram

, Blennow

, Broich

, Cavedo

, Crutch

, Dartigues

, Duyckaerts

, Epelbaum

, Frisoni

, Gauthier

, Genthon

, Gouw

, Habert

, Holtzman

, Kivipelto

, Lista

, Molinuevo

, O’Bryant

, Rabinovici

, Rowe

, Salloway

, Schneider

, Sperling

, Teichmann

, Carrillo

, Cummings

, Jack

Jr , Proceedings of the Meeting of the InternationalWorking Group, the American Alzheimer’s Association on “The Preclinical State of AD, July, Washington DC USA (2016) Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement 12, 292–323.

15.

Crook

3rd , Feher

, Larrabee

(1992) Assessment of memory complaint in age-associated memory impairment: The MAC-Q. Int Psychogeriatr 4, 165–176.

16.

Rossetti

, Lacritz

, Cullum

, Weiner

(2011) Normative data for the Montreal Cognitive Assessment (MoCA) in a population-based sample. Neurology 77, 1272–1275.

17.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

Jr , Kawas

, Klunk

, Koroshetz

, Manly

, Mayeux

, Mohs

, Morris

, Rossor

, Scheltens

, Carrillo

, Thies

, Weintraub

, Phelps

(2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 263–269.

18.

Folstein

, Folstein

, McHugh

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

19.

Zhou

, Salvado

, Dore

, Bourgeat

, Raniga

, Macaulay

, Ames

, Masters

, Ellis

, Villemagne

, Rowe

, Fripp

, AIBL Research Group (2014) MR-less surface-based amyloid assessment based on 11C PiB PET. PLoS One 9, e84777.

20.

Bourgeat

, Villemagne

, Dore

, Brown

, Macaulay

, Martins

, Masters

, Ames

, Ellis

, Rowe

, Salvado

, Fripp

, AIBL Research Group (2015) Comparison of MR-less PiB SUVR quantification methods. Neurobiol Aging 36(Suppl 1), S159–166.

21.

Goozee

, Chatterjee

, James

, Shen

, Sohrabi

, Asih

, Dave

, ManYan

, Taddei

, Ayton

, Garg

, Kwok

, Bush

, Chung

, Magnussen

, Martins

(2017) Elevated plasma ferritin in elderly individuals with high neocortical amyloid-β load. Mol Psychiatry. doi:10.1038/mp.2017.146 .

22.

Estevez-Gonzalez

, Kulisevsky

, Boltes

, Otermin

, Garcia-Sanchez

(2003) Rey verbal learning test is a useful tool for differential diagnosis in the preclinical phase of Alzheimer’s disease: Comparison with mild cognitive impairment and normal aging. Int J Geriatr Psychiatry 18, 1021–1028.

23.

Elwood

(1991) The Wechsler Memory Scale-Revised: Psychometric characteristics and clinical application. Neuropsychol Rev 2, 179–201.

24.

Meyers

, Meyers

(1995) Rey Complex FigureTest and Recognition Trial. Professional Manual. Psychological Assessment Resource, Inc.

25.

Wechsler

(1987) Wechsler memory scale-revised. Psychological Corporation, New York.

26.

Wechsler

(1997) Wechsler Adult Intelligence Scale – III (WAIS-III) Administration and Scoring Manual. The Psychological Corporation, San Antonio, TX.

27.

Delis

, Kaplan

, Kramer

(2001) Delis-Kaplan Executive Function System (D-KEFS). The Psychological Corporation, San Antonio, TX.

28.

Patterson

(2011) Controlled oral word association test. In Encyclopedia of Clinical Neuropsychology, Kreutzer

, DeLuca

, Caplan

, eds. Springer, New York. 703–706.

29.

Strauss

, Sherman, Spreen

(2006) A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary (3rd edn). Oxford University Press, New York.

30.

Saxton

, Ratcliff

, Munro

, Coffey

, Becker

, Fried

, Kuller

(2000) Normative data on the Boston Naming Test and two equivalent 30-item short forms. Clin Neuropsychol 14, 526–534.

31.

Wechsler

(2001) Wechsler Test of Adult Reading: Examiner’s Manual. The Psychological Corporation, San Antonio, TX.

32.

Lovibond

, Lovibond

(1995) Manual for the Depression Anxiety Stress Scales. (2nd. Ed.) Psychology Foundation, Sydney.

33.

Pereira

, Westman

, Hansson

, Alzheimer’s Disease Neuroimaging Initiative (2017) Association between cerebrospinal fluid and plasma neurodegeneration biomarkers with brain atrophy in Alzheimer’s disease. Neurobiol Aging 58, 14–29.

34.

Hansson

, Janelidze

, Hall

, Magdalinou

, Lees

, Andreasson

, Norgren

, Linder

, Forsgren

, Constantinescu

, Zetterberg

, Blennow

, Swedish BioFINDER study (2017) Blood-based NfL: A biomarker for differential diagnosis of parkinsonian disorder. Neurology 88, 930–937.

35.

Jakobsson

, Bjerke

, Ekman

, Sellgren

, Johansson

, Zetterberg

, Blennow

, Landen

(2014) Elevated concentrations of neurofilament light chain in the cerebrospinal fluid of bipolar disorder patients. Neuropsychopharmacology 39, 2349–2356.

36.

Gisslen

, Price

, Andreasson

, Norgren

, Nilsson

, Hagberg

, Fuchs

, Spudich

, Blennow

, Zetterberg

(2016) Plasma concentration of the neurofilament light protein (NFL) is a biomarker of CNS injury in HIV infection: A cross-sectional study. EBioMedicine 3, 135–140.

37.

Rolstad

, Jakobsson

, Sellgren

, Ekman

, Blennow

, Zetterberg

, Palsson

, Landen

(2015) Cognitive performance and cerebrospinal fluid biomarkers of neurodegeneration: A study of patients with bipolar disorder and healthy controls. PLoS One 10, e0127100.

38.

Ellis

, Bush

, Darby

, De Fazio

, Foster

, Hudson

, Lautenschlager

, Lenzo

, Martins

, Maruff

, Masters

, Milner

, Pike

, Rowe

, Savage

, Szoeke

, Taddei

, Villemagne

, Woodward

, Ames

(2009) The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatr 21, 672–687.

39.

Mueller

, Weiner

, Thal

, Petersen

, Jack

, Jagust

, Trojanowski

, Toga

, Beckett

(2005) The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin N Am 15, 869–877, xi-xii.