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Figure.
Cognitive Decline in 90 Cognitively Normal Individuals at Baseline as Predicted by Baseline Cerebrospinal Fluid (CSF) Factors Stratified by APOE-ε4 Allelic Status
Cognitive Decline in 90 Cognitively Normal Individuals at Baseline as Predicted by Baseline Cerebrospinal Fluid (CSF) Factors Stratified by APOE-ε4 Allelic Status

A and B, The association between the baseline CSF tau/Aβ1-42 ratio with annual change in (A) the Rey Auditory Visual Learning Test (RAVLT) score and (B) Alzheimer’s Disease Assessment Scale-cognition (ADAS-Cog13) score in 21 APOE-ε4 carriers and 69 noncarriers over 7 years. C and D, The association between baseline ferritin level with annual change in (C) RAVLT and (D) ADAS-Cog13 in APOE-ε4 carriers and noncarriers over 7 years. Means ± 95% CIs are shown. E, The receiver under the operating curve of baseline CSF ferritin level (indicated by the colors) for predicting stable or deteriorating (≥1 RAVLT unit decrease per year) cognition in cognitively normal ε4 carriers over 7 years. Area under the curve = 0.96.

Table.  
Patient Demographics and Statistical Modelsa
Patient Demographics and Statistical Modelsa
1.
Ayton  S, Faux  NG, Bush  AI; Alzheimer’s Disease Neuroimaging Initiative.  Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE.  Nat Commun. 2015;6:6760.PubMedGoogle ScholarCrossref
2.
Alzheimer Disease Neuroimaging Initiative (ADNI) database http://adni.loni.usc.edu. Accessed July 15, 2014.
3.
Weiner  MW, Veitch  DP, Aisen  PS,  et al; Alzheimer’s Disease Neuroimaging Initiative.  The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception.  Alzheimers Dement. 2012;8(1)(suppl):S1-S68.PubMedGoogle ScholarCrossref
4.
Estévez-González  A, Kulisevsky  J, Boltes  A, Otermín  P, García-Sánchez  C.  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. 2003;18(11):1021-1028.PubMedGoogle ScholarCrossref
5.
Iturria-Medina  Y, Sotero  RC, Toussaint  PJ, Mateos-Pérez  JM, Evans  AC; Alzheimer’s Disease Neuroimaging Initiative.  Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis.  Nat Commun. 2016;7:11934.PubMedGoogle ScholarCrossref
6.
Ali-Rahmani  F, Schengrund  CL, Connor  JR.  HFE gene variants, iron, and lipids: a novel connection in Alzheimer’s disease.  Front Pharmacol. 2014;5:165.PubMedGoogle ScholarCrossref
Research Letter
January 2017

Association of Cerebrospinal Fluid Ferritin Level With Preclinical Cognitive Decline in APOE-ε4 Carriers

Author Affiliations
  • 1The Florey Institute for Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
  • 2IBM Research–Australia, Melbourne, Australia
  • 3Cooperative Research Center for Mental Health, Melbourne, Australia
 

Copyright 2016 American Medical Association. All Rights Reserved.

JAMA Neurol. 2017;74(1):122-125. doi:10.1001/jamaneurol.2016.4406

The ε4 allele of APOE confers the greatest genetic risk for Alzheimer disease (AD), and recent data implicate brain-iron load as a pathogenic mechanism because ε4 carriage elevates the level of cerebrospinal fluid (CSF) ferritin.1 Controlled for potential confounders like inflammation and bleeding, CSF ferritin level, although not elevated in AD, was associated with longitudinal cognitive performance and the risk of developing AD.1 Herein, we investigate whether CSF ferritin level combines with established AD risk variables in predicting cognitive decline over 7 years in the preclinical phase.

Methods

This study used data obtained from the Alzheimer Disease Neuroimaging Initiative (ADNI) database.2 The ADNI study and patient inclusion criteria have been reported.3 The ADNI study was launched in 2003 as a public-private partnership, led by principal investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. The ADNI study was approved by institutional review boards of all participating institutions. Informed written consent was obtained from all participants at each site. There was no compensation.

Baseline CSF levels of Aβ1-42, tau, APOE, ferritin, factor H (an inflammation marker), and hemoglobin (a blood leakage marker) were analyzed as previously described.1 The scores for cognition using the longitudinal Rey Auditory-Visual Learning Task (RAVLT) (sensitive to early changes4) and the AD Assessment Scale–cognitive subset (ADAS-Cog13) were analyzed using linear mixed-effects models with R software (version 3.2.1). Normality and the absence of multicollinearity were confirmed. Data from individuals who left the study prematurely were included to the point of leaving. Statistical significance was set at P = .05 for all results.

Results

In participants who, at baseline, were cognitively normal or were categorized as having MCI, CSF ferritin level predicted cognition in a 4-way interaction with time, APOE-ε4, and diagnosis (RAVLT: β [SE] = −1.45 [0.60], P = .02; ADAS-Cog13: β [SE] = 0.14 [0.07], P = .03) (Table). In contrast, the tau/Aβ1-42 ratio interacted with time and not APOE-ε4, diagnosis, CSF ferritin levels, or APOE levels, to predict cognitive performance (RAVLT: β [SE] = −0.39 [0.15]; P = .01; ADAS-Cog13: β [SE] = 0.06 [0.02]; P = .001).

In separate modeling of cognitively normal individuals and those with MCI , the tau/Aβ1-42 ratio predicted cognitive deterioration for those with MCI (RAVLT: β [SE] = −0.30 [0.17], P = .07; ADAS-Cog13: β [SE] = 0.05 [0.02], P = .03) and those who are cognitively normal (RAVLT: β [SE] = −0.68 [0.33], P = .04; ADAS-Cog13: β [SE] = 0.07 [0.03], P = .06) (Figure, A and B), and this index did not interact with the other included variables. Ferritin level was associated with cognitive performance in those with MCI (RAVLT: β [SE] = −2.24 [0.95], P = .02; ADAS-Cog13: 0.17 [0.08], P = .04), but it did not interact with time or the other included variables. For cognitively normal individuals, however, ferritin level was associated with cognitive deterioration in a 3-way interaction with time and ε4 (RAVLT: β [SE] = −1.58 [0.54], P = .004; ADAS-Cog13: β [SE] = 0.11 [0.04], P = .01) (Figure, C and D). Categorization of cognitively normal individuals according to ε4 revealed that ferritin level was strongly associated with cognitive decline in ε4 carriers (RAVLT: β [SE] = −1.4 [0.4], P < .001; ADAS-Cog13: β [SE] = 0.09 [0.04], P = .02). For ε4-negative individuals, lower ferritin levels predicted a modest deterioration in cognition (ADAS-Cog13: β [S.E.] = −0.04 [0.016], P = .02 but not RAVLT: β [SE] = 0.16 [0.22], P = .48), which might signify that abnormally low brain-iron could impair performance.

Finally, baseline CSF ferritin level was used to discriminate stable from declining (≥1 point/year worsening on RAVLT) cognitively normal ε4-positive individuals. The area under the receiver operating characteristic (ROC) curve was 0.96, at a threshold predictive value of 6.6 ng/mL of ferritin per milliliter (Figure, E).

Discussion

These findings demonstrate the potential for CSF ferritin as a biomarker, especially for ε4 carriers, and also provide new insight into the pathophysiologic mechanisms of AD. Cerebrospinal ferritin level might potentially be affected by abnormal vascular permeability, which occurs early in AD.5 However, in our model of the relationship between paired CSF and plasma samples from this cohort, plasma ferritin levels accounted for only 4% of the variance of CSF ferritin levels, regardless of diagnosis.1 Thus, plasma ferritin permeating into CSF is unlikely to explain the adverse prognosis associated with higher CSF ferritin levels. Rather, CSF ferritin level probably reflects brain-iron burden (analogous to the periphery). Our observation that ferritin had a markedly divergent impact on ε4 carriers and noncarriers agrees with findings of prior genetic studies demonstrating an epistatic interaction between the APOE-ε4 allele and the iron-accumulating H63D variant of the hemochromatosis protein, HFE, leading to earlier onset of AD (by 5.5 years) (Ali-Rahmani et al6). Therefore, while CSF ferritin level is an indirect measure of ferritin level in the brain, our findings are consistent with a role for iron in the pathogenesis of AD.

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Article Information

Corresponding Author: Ashley I. Bush, MBBS, PhD, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, Victoria 3052, Australia (ashley.bush@florey.edu.au).

Published Online: November 28, 2016. doi:10.1001/jamaneurol.2016.4406

Author Contributions: Drs Ayton and Faux had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Faux and Ayton contributed equally to the manuscript.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: All authors.

Administrative, technical, or material support: Bush.

Study supervision: Faux, Bush.

Conflict of Interest Disclosures: Dr Bush is a shareholder in Prana Biotechnology Pty Ltd, Cogstate Pty Ltd, Eucalyptus Pty Ltd, Mesoblast Pty Ltd, Brighton Biotech LLC, Nextvet Ltd, Grunbiotics Pty Ltd, and Collaborative Medicinal Development LLC, and a paid consultant for Collaborative Medicinal Development, Pty Ltd. Drs Ayton, Faux, and Bush have filed a provisional patent encompassing findings from these data. Drs Ayton and Bush have received funding relevant to this study from the Australian National Health and Medical Research Council (NHMRC), the Alzheimer’s Association, Alzheimer’s Research UK, the Michael J. Fox Foundation for Parkinson’s Research, the Weston Brain Institute, and the Perpetual-Salteri Foundation. No other disclosures are reported.

Funding/Support: Data collection and sharing for this project were funded by the Alzheimer Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant U01 AG024904) and the Department of Defense ADNI (award No. W81XWH-12-2-0012). The ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica Inc; Biogen Idec Inc; Bristol-Myers Squibb Company; Eisai Inc; Elan Pharmaceuticals Inc; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech Inc; GE Healthcare; Innogenetics, NV; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace Inc; Merck & Co Inc; Meso Scale Diagnostics LLC; NeuroRx Research; Novartis Pharmaceuticals Corp; Pfizer Inc; Piramal Imaging; Servier; Synarc Inc; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Analysis was supported by funds from the Australian Research Council, the NHMRC, the Cooperative Research Centre for Mental Health (an Australian Government Initiative), Alzheimer’s Australia, the Yulgilbar Foundation, and Perpetual-Salteri Foundation. The Florey Institute of Neuroscience and Mental Health acknowledges support from the Victorian government, in particular funding from the Operational Infrastructure Support grant.

Role of the Funder/Sponsor: No funder of this study had any role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Information: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

References
1.
Ayton  S, Faux  NG, Bush  AI; Alzheimer’s Disease Neuroimaging Initiative.  Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE.  Nat Commun. 2015;6:6760.PubMedGoogle ScholarCrossref
2.
Alzheimer Disease Neuroimaging Initiative (ADNI) database http://adni.loni.usc.edu. Accessed July 15, 2014.
3.
Weiner  MW, Veitch  DP, Aisen  PS,  et al; Alzheimer’s Disease Neuroimaging Initiative.  The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception.  Alzheimers Dement. 2012;8(1)(suppl):S1-S68.PubMedGoogle ScholarCrossref
4.
Estévez-González  A, Kulisevsky  J, Boltes  A, Otermín  P, García-Sánchez  C.  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. 2003;18(11):1021-1028.PubMedGoogle ScholarCrossref
5.
Iturria-Medina  Y, Sotero  RC, Toussaint  PJ, Mateos-Pérez  JM, Evans  AC; Alzheimer’s Disease Neuroimaging Initiative.  Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis.  Nat Commun. 2016;7:11934.PubMedGoogle ScholarCrossref
6.
Ali-Rahmani  F, Schengrund  CL, Connor  JR.  HFE gene variants, iron, and lipids: a novel connection in Alzheimer’s disease.  Front Pharmacol. 2014;5:165.PubMedGoogle ScholarCrossref
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