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. Author manuscript; available in PMC: 2022 May 1.
Published in final edited form as: Alzheimers Dement. 2021 Feb 1;17(5):813–821. doi: 10.1002/alz.12248

Associations between plasma neurofilament light, in vivo brain pathology and cognition in non-demented individuals with autosomal- dominant Alzheimer’s disease

Edmarie Guzmán-Vélez 1, Henrik Zetterberg 2,3,4,5, Joshua T Fox-Fuller 1,6, Clara Vila-Castelar 1, Justin S Sanchez 1, Ana Baena 7, Gloria Garcia-Ospina 7, David Aguillon 7, Enmanuelle Pardilla-Delgado 1, Jennifer Gatchel 1,8, Reisa A Sperling 1, Keith Johnson 1, Eric M Reiman 9,10,11,12, Kaj Blennow 2,3, Francisco Lopera 7, Yakeel T Quiroz 1,7
PMCID: PMC8158654  NIHMSID: NIHMS1695008  PMID: 33527648

Abstract

Background:

Neurofilament light (NfL) is a promising biomarker of early neurodegeneration in Alzheimer’s disease (AD). We examined whether plasma NfL was associated with in vivo amyloid-β and tau, and cognitive performance in non-demented Presenilin-1 (PSEN1) E280A mutation carriers.

Methods:

Twenty-five mutation carriers and 19 non-carriers (age range: 28 to 49 years) were included in this study. Participants underwent 11C Pittsburgh compound B (PiB)-PET (positron emission tomography), flortaucipir–PET, blood sampling, and cognitive testing.

Results:

Mutation carriers exhibited higher plasma NfL levels than non-carriers. In carriers, higher NfL levels were related to greater regional tau burden and worse cognition, but not amyloid-β load. When adjusting for age, a proxy of disease progression, elevated plasma NfL levels were only correlated with worse memory recall.

Conclusions:

Findings support an association between plasma NfL, cognition, and tau pathology in non-demented individuals at genetic risk to develop AD-dementia. Plasma NfL may be useful for selecting individuals at increased risk and tracking disease progression in AD.

Keywords: Alzheimer’s disease, presenilin-1, preclinical, pathology, NfL, biomarkers

INTRODUCTION

There is an urgent need for widely available and inexpensive biomarkers of Alzheimer’s disease (AD) that can be used in clinical trials to evaluate the efficacy of disease-modifying drugs. One promising candidate is neurofilament light (NfL) chain, a sensitive marker of early neuronal injury and axonal degeneration that has been shown to be elevated in preclinical AD 1–4. We recently reported that plasma NfL levels were significantly elevated in individuals from a Colombian kindred with autosomal-dominant AD (ADAD) who are nearly certain to develop early-onset dementia 5. Plasma NfL levels subtly started diverging between ADAD mutation carriers and non-carrier family members approximately 22 years before the kindred’s estimated median age of clinical symptom onset 5. Our data also showed that higher baseline levels of plasma NfL predicted greater cognitive decline in the preclinical stage of the disease. These findings are in line with studies from the Dominantly Inherited Alzheimer’s Network (DIAN), which have reported that serum NfL levels can distinguish mutation carriers from non-carriers approximately 7 to 16 years before the expected age of clinical symptom onset 6,7. The disparity between cohorts in the estimated time in which NfL levels begin to differentiate carriers from non-carriers may be due to sample characteristics. Specifically, the DIAN studies a heterogeneous multi-center sample composed of families with distinct mutations in different genes, whereas the Colombian kindred is a homogenous sample of individuals with a single mutation (E280A) in the Presenilin-1 (PSEN1) gene who share a similar clinical profile and sociocultural factors. Notwithstanding, together these studies suggest that blood-based NfL levels are sensitive to early neuronal degeneration in ADAD.

These findings are further supported by recent studies examining the relationship between blood-based NfL levels and other brain imaging markers of neurodegeneration in individuals in the preclinical stage of AD. Specifically, cross-sectional studies have reported that greater plasma NfL levels are related to increased hippocampal atrophy and cortical thinning, precuneus cortical thickness and whole-brain volume, as well as reduced metabolism in those same regions 4,6–8. Similarly, longitudinal studies in both familial and sporadic AD have shown that higher baseline NfL concentrations in the blood are associated with greater subsequent rates of reduction in precuneus cortical thickness, faster white matter intensity changes, and greater decline in glucose metabolism and cognitive performance 6,9–11.

More recent studies have investigated the association between plasma NfL levels and markers of AD-related pathology in the cerebrospinal fluid (CSF), blood (serum and plasma), and post-mortem tissue. One of these studies showed that symptomatic ADAD mutation carriers with greater serum NfL levels had significantly higher total and phosphorylated tau levels in the CSF, compared to non-carriers 12. Consistent with these findings, higher plasma NfL levels have been related to increased neurofibrillary tangle accumulation in post-mortem tissue of older adults with a clinical diagnosis of AD dementia, and greater severity of the disease 13. Further, a population-based longitudinal study found that elevated plasma NfL and low plasma amyloid-β42 levels, individually and in combination at baseline, and not total tau, significantly predicted risk for progression to AD dementia in older adults 14. Altogether, the few available studies have provided some insight into the utility of blood-based NfL for tracking AD progression. However, very little is known about whether plasma NfL could be useful for tracking AD pathology accumulation in the living brain (especially neurofibrillary tau burden) in non-demented individuals at high risk for AD. Understanding how plasma NfL is related to AD pathology can inform future clinical trials, assist with participant selection and track disease progression, and treatment outcomes.

In this study, we sought to test whether baseline levels of plasma NfL were associated with brain markers of cortical amyloid-β and tau pathology burden, as well as cognition, in PSEN1 E280A cognitively-unimpaired carriers and carriers with mild cognitive impairment (MCI) from the world’s largest ADAD kindred. The disease in PSEN1 E280A carriers is estimated to progress to MCI at a median age of 44 years (95% confidence interval: 43-45) and dementia at the age of 49 (95% confidence interval: 49-50) 15,16. Mutation carriers also have a well-characterized disease trajectory with cortical amyloid-β accumulation beginning over a decade before the onset of MCI, elevated tau burden in medial temporal lobe regions (e.g. entorhinal cortex and inferior temporal gyrus) an average of six years before the onset of MCI as measured by positron-emission tomography (PET) 17, and cortical atrophy an average of six years before clinical symptom onset 17–19. We hypothesized that higher plasma NfL concentration would be associated with greater AD pathology burden, including mean cortical amyloid-β and tau burden in an aggregate of regions that are vulnerable to early pathology accumulation17,20,21. We also hypothesized that higher plasma NfL concentration would be related to worse cognitive performance in mutation carriers.

METHODS AND MATERIALS

Study design and participants

Baseline plasma NfL concentrations were characterized in 25 PSEN1 E280A mutation carriers (19 cognitively-unimpaired mutation carriers and 6 mutation carriers with MCI), and 19 age- and education-matched non-carriers from the same kindred who are enrolled in the Massachusetts General Hospital (MGH) COLBOS (Colombia-Boston) longitudinal biomarker study. Participants were recruited from the Alzheimer’s Prevention Initiative registry of familial AD, which currently includes more than 6,000 living members of the kindred and approximately 1,200 mutation carriers22. Those with a diagnosis of dementia or with a significant medical, psychiatric, or neurological disorder (e.g., stroke, seizures, substance abuse, and other disorders that affect motor, visuospatial or cognitive abilities) were excluded from this study. Participants and raters were not informed of the participants’ genetic test results.

The study was approved by both the institutional ethics review boards of the University of Antioquia in Medellín, Colombia and the MGH in Boston, MA. All participants provided written informed consent before participating in any procedures.

Clinical and Cognitive Assessments

Clinical assessments were performed at the University of Antioquia in Medellín, Colombia. Participants underwent a clinical interview and were administered the Mini Mental State Examination (MMSE) 23, a Spanish version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) word list test, which has been adapted for this Colombian population 15,24, and the functional assessment staging test (FAST) 25. In the CERAD word list delayed recall, participants were asked to recall as many words as they could remember from a previously learned list (10 items) after a 10-minute delay. Testing was conducted in Spanish by neuropsychologists or by psychologists trained in neuropsychological assessment. Neurological examinations were performed by a neurologist or by a physician trained in the assessment of neurodegenerative disorders.

Imaging acquisition and processing

All participants in this study travelled from Colombia to Boston (USA) and underwent amyloid and tau PET imaging, as well as MRI at the MGH.

11C Pittsburgh compound B and [F18] flortaucipir PET

As reported previously 17, 11C Pittsburgh compound B (PiB) PET was acquired with a 8.5 to 15 mCi bolus injection followed immediately by a 60-minute dynamic acquisition in 69 frames (12×15 seconds, 57×60 seconds). [F18] Flortaucipir (FTP) was acquired between 80 and 100 minutes after a 9.0 to 11.0 mCi bolus injection in 4 separate 5-minute frames.

11C PiB PET data were expressed as the distribution volume ratio (DVR) with cerebellar grey as reference tissue; regional time-activity curves were used to compute regional DVRs for each region of interest (ROI) using the Logan graphical method applied to data obtained between 40 and 60 minutes after injection 26. 11C PiB retention was assessed using a large cortical ROI aggregate that included frontal, lateral temporal and retrosplenial cortices as described previously 27.

[F18] FTP specific binding was expressed in FreeSurfer ROIs as the standardized uptake value ratio (SUVR) to cerebellum, similar to a previous report 28. The spatially transformed SUVR PET data was smoothed with an 8mm Gaussian kernel to account for individual anatomic differences 29. SUVR values were represented graphically on vertices at the pial surface. A tau summary metric was calculated by averaging regional SUVRs of the entorhinal cortex, amygdala, lateral occipital cortex, and inferior temporal cortex, as previously reported 17,20,21,30. PET data were not partial volume corrected.

Genotyping

Genomic DNA was extracted from blood by standard protocols, and PSEN1 E280A characterization was done at the University of Antioquia using methods previously described 31. Genomic DNA was amplified with the primers PSEN1-S 5′ AACAGCTCAGGAGAGGAATG 3′ and PSEN1-AS 5′ GATGAGACAAGTNCCNTGAA 3′. We used the restriction enzyme BsmI for restriction fragment length polymorphism analysis. Each participant was classified as a PSEN1 E280A carrier or non-carrier. Participants and investigators were blinded to the genetic status of the individual.

Plasma NfL assay

As previously reported32, plasma was collected in the morning (non-fasting collection). Three aliquots of 1ml were collected. Samples were stored at −80°C. For NfL analysis, one plasma aliquot was shipped on dry ice to the Clinical Neurochemistry Laboratory at Sahlgrenska University Hospital, Mölndal, Sweden. NfL concentration was measured using an in-house Single molecule array (Simoa) assay, as previously described in detail (manufacturer: Quanterix, Billerica, MA) 33. Measurements were performed by board-certified laboratory technicians who were blinded to clinical data, genetic status, and demographic characteristic.

Statistical Analyses

We used independent samples t-tests and Chi-square to test group differences in demographic and clinical variables. We conducted a univariate analysis of variance to examine group differences in NfL and cognitive performance with age as a covariate. We used Cohen’s d to calculate effect sizes and the Bonferroni correction to account for multiple comparisons. We then conducted linear regressions to examine the association between plasma NfL and markers of pathology, and cognition. Specifically, we tested the cross-sectional associations between plasma NfL with the following variables: mean cortical amyloid- β; tau summary measure; the MMSE; and the CERAD word list delayed recall score. Further, as age in PSEN1 E280A mutation carriers is predictive of disease progression, we also conducted multiple regressions, with age as a covariate. Analyses used a family-wise significance threshold of p<0.05 and were performed using statistical software (SPSS V.24.0; SPSS Inc, Chicago, Illinois, USA).

In follow-up analyses, we carried out an exploratory whole-brain analysis examining the relationship between plasma NfL level and amyloid and tau pathology burden in all mutation carriers. Regions were p < 0.01 after cluster-wise correction for multiple comparisons (minimum cluster extent k=100mm2).

RESULTS

Demographic and neuropsychological data

Demographic and neuropsychological data are presented in Table 1. Mutation carriers and non-carriers did not significantly differ in age, years of formal education, or sex. Compared to non-carriers, mutation carriers performed significantly worse than non-carriers in the CERAD word list delayed recall (F (1,41) =10.95, p=.002, d=1.14) and the MMSE (F (1,41)= 5.72, p=.021, d=0.90). Differences remained significant after correcting for multiple comparisons.

Table 1.

Demographic and neuropsychological data

Non-carriers Mutation Carriers
(n=19) (n = 25) p
M (SD)
Age (years) 36.36 (5.18) 38.76 (5.98) .535
Formal education (years) 11.40 (3.95) 9.25 (4.68) .200
Sex (f/m) 8/11 15/10 .361
MMSE 29 (1) 26.72 (3.43) .021
FAST
 Stages 1-2 19 19
 Stages >2 0 6
CERAD delayed recall 7.89 (1.20) 5.16 (3.15) .002
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Note. M = Mean; SD = Standard Deviation; MMSE=Mini Mental State Exam; FAST=Functional Analysis Screening Tool (stages 1 and 2=cognitively normal; stages >2=symptomatic); CERAD= Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery. Differences between cognitively-unimpaired mutation carriers and non-carriers were calculated using independent samples t-test (for age and years of formal education), and univariate analysis of variance controlling for age for the other variables.

Plasma NfL levels in carriers and non-carriers

As expected 32, we found that mutation carriers had greater levels of plasma NfL relative to non-carriers (F (1,41)=14.73, p<.001, d=0.81) (Table 2) (Figure 1A). Higher plasma NfL levels were associated with greater age in carriers (B=0.389, p=.003, CI [0.149,0.629]), such that those who had higher NfL levels were older and, hence, closer to the median age of onset of MCI in this cohort (Figure 1B). There were no significant relationships between age and plasma NfL in non-carriers. Further, plasma NfL levels and the relationship between plasma NfL levels and age, did not significantly differ between women and men.

Table 2.

Measures of neurodegeneration, amyloid and tau pathology

Non-carriers Mutation Carriers
(n=19) (n = 25) p
M (SD)
Neurofilament Light (pg/mL) 4.65 (2.10) 8.82 (4.01) <.001
11C PiB PET (DVR) 1.04 (0.02) 1.33 (0.17) <.001
Tau Summary Measure (SUVR) 1.04 (0.08) 1.25 (0.20) .001
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Note. M = Mean; SD = Standard Deviation; pg/mL = picograms per millilitre; DVR = distribution volume ratio; SUVR = standardized uptake value ratio; PiB = Pittsburgh Compound B. Differences between mutation carriers and non-carriers were calculated using univariate analysis of variance controlling for age for the other variables.

Figure 1. Plasma NfL levels and pathology in mutation carriers and non-carriers.

Figure 1.

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pg/mL = picograms per milliliter. Graph depicts the mean and standard deviation for each group. Black circles represent raw data for non-carriers, red circles represent cognitively-unimpaired carriers, and blue triangles represent carriers with MCI. Lines represent the best fit line for each group and shadowed areas the confidence intervals. (A) Mutation carriers had significantly higher levels of plasma NfL compared to non-carriers. (B) In mutation carriers only, higher plasma NfL levels were associated with higher age. (C, D) Plasma NfL levels were related to tau burden but not cortical amyloid-β without covarying for age.

Association between plasma NfL levels, mean cortical Aβ, and regional tau

Amyloid-β and tau burden for each group are presented in Table 2. Consistent with what we previously reported17, carriers had greater levels of amyloid-β (F (1,41)=69.04, p<.001, d=2.39) and tau pathology (F (1,41)=13.94, p=.001, d=1.38), compared to non-carriers. These differences remained significant after correcting for multiple comparisons.

Plasma NfL levels were not related to mean cortical amyloid-β (p=.137), even after controlling for age (p=.170) (Figure 1C). In contrast, NfL levels were significantly related to the tau summary measure (B=0.026, p=.017, CI [0.005,0.047]). This association with tau did not survive when adjusted by age (p=.966) (Figure 1D). No associations between NfL and PET markers were observed in the non-carrier group.

We also examined the relationship between plasma NfL levels and brain pathology in the whole brain within mutation carriers. Consistent with findings using the summary measures, greater plasma NfL levels were related to higher tau burden in the precuneus and temporal lobe regions, including the entorhinal cortex (Figure 2). There were no associations between NfL and amyloid-β burden in mutation carriers. We also did not find sex differences in the relationship between plasma NfL levels and pathology.

Figure 2. Whole-brain analysis of the relationship between plasma NfL levels and tau pathology.

Figure 2.

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Higher plasma NfL concentration was related to 18F FTP-binding in the precuneus and temporal regions, including the entorhinal cortex, in mutation carriers. Regions shown are p < 0.01 after cluster wise correction for multiple comparisons (minimum cluster extent k=100mm2).

Association between plasma NfL levels and cognition

In mutation carriers, higher plasma NfL levels were related to worse CERAD word list delayed recall scores (B=−0.607, p<.001, CI [−0.820,−0.394]) (Figure 3A) and MMSE scores (B=−0.475, p=.004, CI [−0.782,−0.169]) (Figure 3B). The association between plasma NfL levels and memory performance remained significant after controlling for age (B=−0.376, p=.001, CI [−0.574, −0.178]), amyloid-β (B=−0.527, p<.001, CI [−0.726, −0.329]), and tau burden (B=−0.408, p<.001, CI [−0.571, −0.245]). Plasma NfL levels were associated with MMSE scores when adjusting for amyloid-β (B=−0.373, p=.016, CI [−0.668, −0.078]), but not for age (p=.188) or tau burden (p=.111). We then examined age, NfL levels, amyloid-β and tau burden as predictors of CERAD word list delayed and MMSE scores in the same model. We found that NfL levels and tau were significantly associated with CERAD word list delayed recall scores (F(4,20)=29.183, p<.001, R2=.854), while tau was significantly related to MMSE scores (F(4,20)=12.520, p<.001, R2=.715). There were no sex differences in the relationship between plasma NfL levels and cognitive performance. Plasma NfL levels were also not associated with cognitive performance in non-carriers.

Figure 3. Plasma NfL levels and cognition in mutation carriers and non-carriers.

Figure 3.

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Black circles represent raw data for non-carriers, red circles represent cognitively-unimpaired carriers, and blue triangles represent carriers with MCI. Lines represent the best fit line for each group and shadowed areas the confidence intervals. Higher NfL levels in mutation carriers were related to lower scores on the CERAD word delayed recall and the MMSE. Only word delayed recall remained significant after adjusting for age.

DISCUSSION

This study examined the associations between plasma NfL levels, in vivo amyloid-β and tau pathology burden, and cognitive performance in non-demented ADAD mutation carriers who will develop dementia with virtually 100% certainty. As we previously reported, amyloid-β begins to accumulate in the brain of PSEN1 E280A carriers in their late 20s, 15 to 20 years before clinical onset, and regional tau pathology is evident 5 to 10 years before dementia onset 17. Whereas PET imaging has been proven to be valuable for the early identification of AD-related pathology, blood-based biomarkers of AD have gained increasing attention given their potential diagnostic value, accessibility, and utility for tracking disease progression and monitoring treatment response. NfL, in particular, has been proposed as a promising biomarker of early neuronal injury, axonal degeneration, and synapse loss, as it has been shown to distinguish individuals at risk for AD dementia many years before clinical onset. In fact, we recently reported that plasma NfL levels began to distinguish PSEN1 mutation carriers from non-carriers nearly 22 years before expected symptoms onset, and were strongly correlated with cognitive decline 5. Yet, very little is known about how plasma NfL levels relate to in vivo AD neuropathology and cognition in individuals with ADAD who will go on to develop dementia.

Consistent with our previous report, mutation carriers had significantly greater plasma NfL levels compared to non-carriers, supporting that there might be early axonal and neural injury in preclinical AD 32. Our results also showed that compared to age-matched non-carriers, non-demented carriers exhibited a significant relationship between higher plasma NfL levels and greater tau burden. However, plasma NfL levels were not associated with mean cortical amyloid-β burden or tau in an aggregate of regions of interest when covarying for age, a proxy of disease progression in this kindred. In contrast, we found that greater plasma NfL levels were related to worse verbal memory, even beyond the effects of age and other markers of disease progression. Contrary to what we previously reported 5, plasma NfL levels were not associated with age in non-carriers, which may be due to the limited age range in the current sample relative to the much larger dataset in our previous report with plasma NfL and cognitive data (but no PET imaging).

To date, the relationship between tau pathology and NfL in AD has only been reported in CSF, post-mortem tissue, and blood. Specifically, studies have found that elevated blood-based NfL levels are associated with greater CSF total and phosphorylated tau levels in symptomatic carriers of an ADAD mutation 12, and with greater neurofibrillary tangles in post-mortem tissue of older adults with AD dementia 13, but not plasma tau 34. While no study to our knowledge has reported on the relationship between plasma NfL levels and PET tau in AD, one recent study 35 showed that in five of ten veterans with blast injuries who displayed high levels of tau binding also exhibited elevated plasma NfL levels. This suggests a link between plasma NfL and aggregated neurofibrillary tangles measured by [F18] FTP PET. In our study, we found that plasma NfL did not relate to tau PET burden after controlling for age, suggesting that these two markers are asynchronous and that increases in plasma NfL are evident earlier than tau pathology measured by PET. It is also possible that plasma NfL is a good predictor of downstream tau pathology, a question that will be better addressed with longitudinal data.

While prior studies in ADAD have reported a relationship between serum NfL levels and PET amyloid-β in symptomatic mutation carriers, we did not find an association between plasma NfL and amyloid-β burden 6. The absence of a significant association between plasma NfL levels and amyloid-β burden may be related to the fact that NfL is a marker of neurodegeneration, and amyloid-β has been found to be a weaker predictor of neurodegeneration and cognitive decline compared to tau pathology21,36,37. However, we must also consider that findings may have been influenced by having only a few mutation carriers in the lower end of the amyloid-β range, which in turn may have dampened the relationship between amyloid-β and plasma NfL levels. Further, our data show that plasma NfL levels are associated with memory functioning beyond the effects of age, amyloid-β and tau pathology burden. As such, our findings raise the possibility that measuring plasma NfL could be very useful for recruiting participants for clinical trials who are at risk for dementia, and for tracking treatment response to disease-modifying drugs, in the absence of tau PET imaging. That is, that plasma NfL could be an accessible and less invasive measure of neurodegeneration and cognitive decline, that may also provide information about tau pathology in the brain.

The current study has multiple strengths. First, we did not rely on presenting symptoms or cognitive data to infer whether individuals will go on to develop dementia. Instead, we examined blood-based NfL levels in a group of individuals who have a well-characterized clinical trajectory with MCI starting at a median age of 44 years and dementia at 49 years 15–17. Studying ADAD provides a unique opportunity to study biomarkers of AD in the preclinical stage, as we can estimate how far mutation carriers are from the clinical symptom onset based on the mutation that they carry. In addition, we examined in vivo amyloid-β and tau pathology using PET imaging, which is considered the gold standard for quantifying and examining brain pathology in AD. Additionally, to our knowledge, this is the first study to assess how plasma NfL levels relates to the disease continuum by examining the two pathologies that characterize AD in vivo. Mutation carriers were also young and otherwise healthy, which minimizes potential confounding variables that are more common in advanced age and contribute to cognitive decline (e.g., cardiovascular disease). Finally, the nearly homogeneous clinical profile of mutation carriers allows us to infer how NfL levels may change as the disease progresses, supporting the utility of this blood-based biomarker for tracking disease progression.

The present study also has limitations which must be discussed. First, our sample size is relatively small compared to other studies of AD and cognitive aging, and is possible that some of the effects may be driven by the symptomatic MCI participants. However, individuals with these mutations are relatively rare and all our participants had a single mutation (PSEN1 E280A), which makes our sample highly homogeneous compared to other cohorts, and one of the larger single mutation ADAD samples with NfL and PET imaging. Further, there is a limited range of tau accumulation in mutation carriers, as many were over 6 years away from the estimated age at which significant tau burden is observed in PET. Future studies should consider recruiting individuals with a wider range of tau accumulation to better characterize the relationship between plasma NfL levels and tau pathology. Altogether, research with a larger sample and greater variability in pathology measured by PET is needed to better characterize the relationships between plasma NfL levels and AD-related pathology in the preclinical stage. More research is also needed to examine whether our findings in ADAD generalize to preclinical late-onset sporadic AD. Similarly, the utilization of plasma NfL as an early marker of brain pathology and risk for dementia needs to be validated in other cohorts before it is used in clinical settings, as learning about this risk may significantly impact the decisions that individuals make about their health and daily lives. We are currently conducting the first longitudinal biomarker study with this kindred, which will provide greater insight into how annual change in plasma NfL levels relates to in vivo pathology burden and cognitive decline over time.

Taken together, our findings suggest that higher plasma NfL is associated with, and may even be an earlier marker of, brain pathology as measured by PET and memory performance in PSEN1 E280A mutation carriers who are still years away from their estimated age of dementia onset. These results support the potential value of plasma NfL for tracking early disease progression and monitoring treatment response in clinical trials of disease-modifying drugs for AD.

HIGHLIGHTS.

  • Higher plasma neurofilament light (NfL) was related to greater in vivo tau pathology burden in preclinical Alzheimer’s disease (AD).

  • Plasma NfL levels did not predict amyloid beta positron emission tomography (PET) load in preclinical AD.

  • Higher NfL levels related to worse memory performance beyond the effects of age

  • More research is needed to assess if these findings may generalize to sporadic AD

RESEARCH IN CONTEXT.

Systemic review: We reviewed the literature on neurofilament light (NfL), pathology, neurodegeneration and cognition in Alzheimer’s disease (AD), using traditional sources (e.g., PubMed). Our search yielded that very little is known about whether plasma NfL predicts AD pathology burden in the living brain in individuals at high risk for AD.

Interpretation: We reported that elevated plasma NfL is an early marker of AD progression. Higher plasma NfL levels were associated with greater PET tau pathology and worse memory in non-demented Presenilin1 E280A carriers with ADAD, who will invariably develop dementia. Therefore, plasma NfL could be valuable for tracking early disease progression and monitoring treatment response in clinical trials.

Future directions: Findings set the stage for studies to investigate the relationship between plasma NfL and in vivo pathology in sporadic AD, as well as how change in plasma NfL levels relates to AD-related pathology burden and cognitive decline over time using longitudinal data.

ACKNOWLEDGEMENTS

The authors thank the Colombian families with autosomal dominant Alzheimer’s disease for contributing their valuable time and effort, without which this study would not have been possible. We also thank Francisco Piedrahita, Alex Navarro, and Claudia Ramos from Grupo de Neurociencias, Universidad de Antioquia in Medellín, Colombia, as well as Olivia Hampton, Heirangi Torrico-Teave, Jairo Martínez, and Diana Munera from the Massachusetts General Hospital in Boston, MA for helping coordinate visits to Boston and assisting with data collection and processing.

CONFLICTS OF INTEREST AND FUNDING SOURCES

Dr. Guzmán-Vélez was supported by funding from the National Institute on Aging (NIA) K23AG061276, the Massachusetts General Hospital ECOR Fund for Medical Discovery Clinical Fellowship Award and National Institutes of Health (NIH) Research Supplement for Diversity (DP5OD019833). Dr. Quiroz was supported by grants from the NIH Office of the Director (DP5OD019833), the NIA (R01 AG054671], the Alzheimer’s Association, and Massachusetts General Hospital ECOR (1200-228010 and 1200-228767). Mr. Fox-Fuller reports National Research Service Award (NRSA) support from the NIA (1F31AG06215801A1). Dr. Vila-Castelar is supported by a grant from the Alzheimer’s Association (2019A005859). Dr. Pardilla-Delgado is supported by the NHLBI 3T32HL007901-19S1. Dr. Zetterberg is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), the Swedish State Support for Clinical Research (#ALFGBG-720931), and the UK Dementia Research Institute at UCL. He has served at scientific advisory boards for Denali, Roche Diagnostics, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics, and CogRx; has given lectures in symposia sponsored by Fujirebio, Alzecure and Biogen; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside the submitted work). Dr. Lopera was supported by an Anonymous Foundation, and the Administrative Department of Science, Technology and Innovation (Colciencias Colombia;111565741185). Drs. Reiman and Lopera are principal investigators of the Alzheimer’s Prevention Initiative (API) Autosomal Dominant AD Trial, which is supported by NIA, philanthropy, Genentech, and Roche. Dr. Gatchel is supported by NIH/NIA (K23 AG058805-01), Alzheimer’s Association Clinical Fellowship (AACF_16-440965), and the Massachusetts General Hospital Rappaport Fellowship. Dr. Reiman reports grants from the NIA (R01 AG031581, P30 AG19610), Banner Alzheimer’s Foundation, and the NOMIS Foundation during the conduct of the study. He reports receiving personal fees as a Scientific Advisor to Roche Diagnostics (travel expenses only), MagQ, Avid Radiopharmaceuticals, and is a share-holding co-founder of ALZPath (outside the submitted work). In addition, he is the inventor of a patent issued to Banner Health, which involves the use of biomarker endpoints in at-risk persons to accelerate the evaluation of Alzheimer’s disease prevention therapies and is outside the submitted work. Dr. Blennow has served as a consultant or at advisory boards for Axon, Biogen, CogRx, Lilly, MagQu, Novartis, and Roche Diagnostics, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Venture-based platform company at the University of Gothenburg. Dr. Sperling receives research support for NIH grants P01AG036694, P50AG005134, 2009-2020, and U19 AG10483, as well as from Eli Lilly (clinical trial) and the Alzheimer’s Association. She is a site principal investigator or coinvestigator for Avid, Bristol-Myers Squibb, Pfizer, and Janssen Alzheimer Immunotherapy clinical trials. She receives travel funding and honoraria from AC Immune, Janssen, and Roche. She consults for Biogen, Roche, AC Immune, Eisai, Takeda, Neurocentria, and Janssen; Her spouse consults for Novartis, AC Immune, and Janssen. Dr. Johnson has provided consulting services for Novartis, Biogen, and Eli Lilly; received support from a joint NIH-Lilly-sponsored clinical trial (A4 Study–U19AG10483); and received research support from NIH grants R01 AG027435, P50 AG00513421, AG036694, R01 AG046396, R13 AG042201174210, U19AG10483, and U01AG024904, as well as the Alzheimer’s Association and Marr Foundation.

REFERENCES

  • 1.Bacioglu M, Maia LF, Preische O, et al. Neurofilament Light Chain in Blood and CSF as Marker of Disease Progression in Mouse Models and in Neurodegenerative Diseases. Neuron. 2016;91(1):56–66. [DOI] [PubMed] [Google Scholar]
  • 2.Bridel C, van Wieringen WN, Zetterberg H, et al. Diagnostic Value of Cerebrospinal Fluid Neurofilament Light Protein in Neurology: A Systematic Review and Meta-analysis. JAMA Neurol. 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Khalil M, Teunissen CE, Otto M, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol. 2018;14(10):577–589. [DOI] [PubMed] [Google Scholar]
  • 4.Hu H, Chen KL, Ou YN, et al. Neurofilament light chain plasma concentration predicts neurodegeneration and clinical progression in nondemented elderly adults. Aging (Albany NY). 2019;11(17):6904–6914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Quiroz YT, Zetterberg H, Reiman EM, et al. Plasma neurofilament light measurements in more than 2,100 Presenilin 1 E280A mutation carriers and non-carriers from the world’s largest autosomal dominant Alzheimer’s disease kindred: a cross-sectional and longitudinal cohort study. The Lancet Neurology. 2020;19(6). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Preische O, Schultz SA, Apel A, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med. 2019;25(2):277–283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Weston PSJ, Poole T, Ryan NS, et al. Serum neurofilament light in familial Alzheimer disease: A marker of early neurodegeneration. Neurology. 2017;89(21):2167–2175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Mayeli M, Mirshahvalad SM, Aghamollaii V, Tafakhori A, Abdolalizadeh A, Rahmani F. Plasma Neurofilament Light Chain Levels Are Associated With Cortical Hypometabolism in Alzheimer Disease Signature Regions. J Neuropathol Exp Neurol. 2019. [DOI] [PubMed] [Google Scholar]
  • 9.Zetterberg H, Skillback T, Mattsson N, et al. Association of Cerebrospinal Fluid Neurofilament Light Concentration With Alzheimer Disease Progression. JAMA Neurol. 2016;73(1):60–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kim WH, Racine AM, Adluru N, et al. Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis. Neuroimage Clin. 2019;21:101586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Benedet AL, Ashton NJ, Pascoal TA, et al. Plasma neurofilament light associates with Alzheimer’s disease metabolic decline in amyloid-positive individuals. Alzheimers Dement (Amst). 2019;11:679–689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Sanchez-Valle R, Heslegrave A, Foiani MS, et al. Serum neurofilament light levels correlate with severity measures and neurodegeneration markers in autosomal dominant Alzheimer’s disease. Alzheimers Res Ther. 2018;10(1):113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ashton NJ, Leuzy A, Lim YM, et al. Increased plasma neurofilament light chain concentration correlates with severity of post-mortem neurofibrillary tangle pathology and neurodegeneration. Acta Neuropathol Commun. 2019;7(1):5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.De Wolf F, Ghanbari M, Licher S, et al. Plasma tau, neurofilament light chain and amyloid-β levels and risk of dementia; a population-based cohort study. Brain. 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Aguirre-Acevedo DC, Lopera F, Henao E, et al. Cognitive Decline in a Colombian Kindred With Autosomal Dominant Alzheimer Disease: A Retrospective Cohort Study. JAMA Neurol. 2016;73(4):431–438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Acosta-Baena N, Sepulveda-Falla D, Lopera-Gomez CM, et al. Pre-dementia clinical stages in presenilin 1 E280A familial early-onset Alzheimer’s disease: a retrospective cohort study. Lancet Neurol. 2011;10(3):213–220. [DOI] [PubMed] [Google Scholar]
  • 17.Quiroz YT, Sperling RA, Norton DJ, et al. Association Between Amyloid and Tau Accumulation in Young Adults With Autosomal Dominant Alzheimer Disease. JAMA Neurology. 2018;75(5):548–556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Quiroz YT, Aguero C, Lopera F, et al. In vivo measurements of cortical thickness, amyloid and tau pathology, and episodic memory in preclinical autosomal dominant Alzheimer’s disease. In. Human Amyloid Imaging, Miami, FL2018. [Google Scholar]
  • 19.Fuller JT, Cronin-Golomb A, Gatchel JR, et al. Biological and Cognitive Markers of Presenilin1 E280A Autosomal Dominant Alzheimer’s Disease: A Comprehensive Review of the Colombian Kindred. J Prev Alzheimers Dis. 2019;6(2):112–120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Yokoi T, Watanabe H, Yamaguchi H, et al. Involvement of the Precuneus/Posterior Cingulate Cortex Is Significant for the Development of Alzheimer’s Disease: A PET (THK5351, PiB) and Resting fMRI Study. Front Aging Neurosci. 2018;10:304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Gordon BA, Blazey TM, Christensen J, et al. Tau PET in autosomal dominant Alzheimer’s disease: relationship with cognition, dementia and other biomarkers. Brain. 2019;142(4):1063–1076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Rios-Romenets S, Lopez H, Lopez L, et al. The Colombian Alzheimer’s Prevention Initiative (API) Registry. Alzheimer’s & Dementia. 2017;13(5):602–605. [Google Scholar]
  • 23.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198. [DOI] [PubMed] [Google Scholar]
  • 24.Aguirre-Acevedo DC, Gomez RD, Moreno S, et al. [Validity and reliability of the CERAD-Col neuropsychological battery]. Rev Neurol. 2007;45(11):655–660. [PubMed] [Google Scholar]
  • 25.Sclan SG, Reisberg B. Functional assessment staging (FAST) in Alzheimer’s disease: reliability, validity, and ordinality. Int Psychogeriatr. 1992;4 Suppl 1:55–69. [DOI] [PubMed] [Google Scholar]
  • 26.Logan J, Fowler JS, Volkow ND, et al. Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(-)-cocaine PET studies in human subjects. J Cereb Blood Flow Metab. 1990;10(5):740–747. [DOI] [PubMed] [Google Scholar]
  • 27.Amariglio RE, Mormino EC, Pietras AC, et al. Subjective cognitive concerns, amyloid-beta, and neurodegeneration in clinically normal elderly. Neurology. 2015;85(1):56–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Johnson KA, Schultz A, Betensky RA, et al. Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann Neurol. 2016;79(1):110–119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Chien DT, Szardenings AK, Bahri S, et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F18]-T808. J Alzheimers Dis. 2014;38(1):171–184. [DOI] [PubMed] [Google Scholar]
  • 30.Mishra S, Gordon BA, Su Y, et al. AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure. Neuroimage. 2017;161:171–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Lendon CL, Martinez A, Behrens IM, et al. E280A PS-1 mutation causes Alzheimer’s disease but age of onset is not modified by ApoE alleles. Hum Mutat. 1997;10(3):186–195. [DOI] [PubMed] [Google Scholar]
  • 32.Quiroz YT, Zetterberg H, Reiman EM, et al. Plasma neurofilament light chain in the presenilin 1 E280A autosomal dominant Alzheimer’s disease kindred: a cross-sectional and longitudinal cohort study. Lancet Neurol. 2020;19(6):513–521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Gisslén M, Price RW, Andreasson U, et al. Plasma Concentration of the Neurofilament Light Protein (NFL) is a Biomarker of CNS Injury in HIV Infection: A Cross-Sectional Study. EBioMedicine. 2016;3:135–140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.De Wolf F, Ghanbari M, Licher S, et al. Plasma tau, neurofilament light chain and amyloid-β levels and risk of dementia; a population-based cohort study. Brain. 2020;143(4):1220–1232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Dickstein DL, De Gasperi R, Gama Sosa MA, et al. Brain and blood biomarkers of tauopathy and neuronal injury in humans and rats with neurobehavioral syndromes following blast exposure. Mol Psychiatry. 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Hanseeuw BJ, Betensky RA, Jacobs HIL, et al. Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease. JAMA Neurology. 2019;76(8):915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Brier MR, Gordon B, Friedrichsen K, et al. Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease. Science Translational Medicine. 2016;8(338):338ra366–338ra366. [DOI] [PMC free article] [PubMed] [Google Scholar]

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