Osteopontin: A novel marker of pre‐symptomatic sporadic Alzheimer's disease

Abstract

INTRODUCTION

We investigate the role of osteopontin (OPN) in participants with Pre‐symptomatic Alzheimer's disease (AD), mild cognitive impairment (MCI), and in AD brains.

METHODS

Cerebrospinal fluid (CSF) OPN, AD, and synaptic biomarker levels were measured in 109 cognitively unimpaired (CU), parental‐history positive Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT‐AD) participants, and in 167 CU and 399 participants with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. OPN levels were examined as a function of amyloid beta (Aβ) and tau positivity. Survival analyses investigated the link between OPN and rate of conversion to AD.

RESULTS

In PREVENT‐AD, CSF OPN was positively correlated with synaptic biomarkers. In PREVENT‐AD and ADNI, OPN was elevated in CSF Aβ_42/40(+)/total tau(+) and CSF Aβ_42/40(+)/phosphorylated tau181(+) individuals. In ADNI, OPN was increased in Aβ(+) positron emission tomography (PET) and tau(+) PET individuals, and associated with an accelerated rate of conversion to AD. OPN was elevated in autopsy‐confirmed AD brains.

DISCUSSION

Strong associations between CSF OPN and key markers of AD pathophysiology suggest a significant role for OPN in tau neurobiology, particularly in the early stages of the disease.

Highlights

• In the Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease cohort, we discovered that cerebrospinal fluid (CSF) osteopontin (OPN) levels can indicate early synaptic dysfunction, tau deposition, and neuronal loss in cognitively unimpaired elderly with a parental history.

• CSF OPN is elevated in amyloid beta(+) positron emission tomography (PET) and tau(+) PET individuals.

• Elevated CSF OPN is associated with an accelerated rate of conversion to Alzheimer's disease (AD).

• Elevated CSF OPN is associated with an accelerated rate of cognitive decline on the Alzheimer's Disease Assessment Scale‐Cognitive subscale 13, Montreal Cognitive Assessment, Mini‐Mental State Examination, and Clinical Dementia Rating Scale Sum of Boxes.

• OPN mRNA and protein levels are significantly upregulated in the frontal cortex of autopsy‐confirmed AD brains.

1. BACKGROUND

Genome‐wide association studies have identified several genetic risk factors that are associated with immune and inflammatory‐related processes. ¹ , ² Furthermore, the recent emergence of immunotherapies targeting amyloid beta (Aβ) highlights the need to further investigate the role of microglia and neuroinflammation in Alzheimer's disease (AD). ³ , ⁴ , ⁵

Osteopontin (OPN), also known as secreted phosphoprotein 1 (SPP1), is a multifunctional proinflammatory protein that is secreted by a diverse collection of cells, ranging from immune cells to non‐immune cells. ⁶ OPN binds to multiple cell‐surface receptors, such as integrin molecules (αv [β1, β3, β5, β6], [α4, α5, α8, or α9] β1, α4[β7]), and CD44. ⁷ Upon binding to its cell‐surface receptors, OPN regulates the recruitment of immune cells, cytokine secretion (interleukin [IL]‐10, IL‐12, IL‐17, interferon [IFN]‐α, IFN‐γ, tumor necrosis factor α), phagocytosis, cell adhesion, cell survival, and tissue repair after injury. ⁷

In the context of AD, OPN has been found to be elevated in CA1 pyramidal neurons of the AD brain ⁸ as well as in the cerebrospinal fluid (CSF) ⁹ , ¹⁰ , ¹¹ , ¹² and plasma ¹⁰ , ¹³ of AD patients. Several studies suggest that OPN may be a valuable biomarker of disease progression in AD. ¹⁴ For instance, OPN is elevated in the CSF of Pre‐symptomatic amyloid precursor protein and presenilin 1 mutation carriers that are ≥ 10 years away from expected disease onset, compared to non‐mutation carriers. ¹⁵ Similarly, compared to individuals with stable mild cognitive impairment (MCI), MCI patients that progressed to AD over the course of 4 to 6 years displayed elevated baseline CSF levels of a phosphorylated c‐terminal fragment of OPN. ¹⁶ Furthermore, in a longitudinal study, individuals with MCI that developed AD over a 3‐year clinical follow‐up exhibited a significant increase in plasma and CSF OPN relative to their baseline visit. ¹⁰ Although OPN is elevated in numerous inflammatory and autoimmune diseases afflicting the central nervous system (CNS), ¹⁷ there is evidence that suggests that OPN may be a potential marker for differential diagnoses. ⁹ , ¹² For instance, CSF OPN levels have been demonstrated to be significantly increased in AD patients, compared to non‐AD cognitive impairment cases, ¹² such as frontotemporal dementia. ⁹ Furthermore, plasma OPN levels have been found to be associated with neuroimaging markers of neurodegeneration and cerebrovascular disease, such as medial temporal lobe atrophy and cortical infarcts, as well as with cognitive impairment, in individuals with AD or vascular cognitive impairment. ¹³

RESEARCH IN CONTEXT

1. Systematic review: Osteopontin (OPN) has been shown to be elevated in the cerebrospinal fluid (CSF) of Pre‐symptomatic amyloid precursor protein and presenilin 1 mutation carriers, that will develop familial Alzheimer's disease (AD). However, OPN has never been examined in cognitively unimpaired (CU) individuals with Pre‐symptomatic sporadic AD. Furthermore, OPN has never been contrasted with rates of cognitive decline. Finally, brain tissue OPN levels have only been examined in pilot studies.

2. Interpretation: CSF OPN levels were significantly correlated with CSF AD and synaptic biomarkers, tau positron emission tomography (PET) burden and cerebral ventricle volumes in CU individuals with a parental history of AD. In an independent cohort, CSF OPN was elevated in individuals with positive amyloid beta and tau PET scans; was shown to predict the conversion to AD, over 0.5 to 16 years; and was associated with an accelerated rate of cognitive decline. Frontal cortex OPN levels were significantly elevated in AD brains—confirming findings from pilot studies. CSF OPN is a valuable biomarker for identifying individuals that possess a significantly greater risk of developing AD.

3. Future directions: Longitudinal studies are under way to investigate the link between OPN and future changes in various brain structures, in at‐risk CU participants.

OPN has been found to be elevated in the brains of several mouse models of AD. ¹⁸ , ¹⁹ , ²⁰ , ²¹ Single‐cell RNA sequencing experiments conducted in mouse models of AD and in human brains, have demonstrated SPP1 expression is upregulated by disease‐associated microglia, which are believed to respond to CNS damage and promote neuroprotection through both a distinct transcriptional profile and the internalization of Aβ. ²² Indeed, OPN has been found to enhance macrophage‐mediated Aβ clearance in cell culture experiments. ¹⁹ Furthermore, a recent study found that OPN is secreted by hippocampal perivascular macrophages and facilitates the engulfment of synapses by microglia via a transforming growth factor‐beta 1–mediated process. ²⁰

The aim of the current study is to examine the role of OPN in the CSF of cognitively unimpaired (CU) at‐risk adults with a parental history of sporadic AD, and in the CSF of an independent cohort of individuals with MCI. In the present study, we seek to confirm previous observations from Pre‐symptomatic familial AD studies. ¹⁵ Ultimately, our objective is to assess the potential application of CSF OPN as a biomarker to identify individuals with prodromal sporadic AD. More specifically, we seek to fill the gaps in the literature by quantifying the associations between OPN and rates of longitudinal cognitive decline and conversion to AD, in the sporadic form of the disease. Furthermore, through post mortem studies we assess the potential application of OPN to facilitate the differential diagnosis of AD. Thus, we characterize OPN in the frontal and temporal cortex of two independent cohorts of autopsy‐confirmed AD brains.

2. METHODS

2.1. PREVENT‐AD cohort

2.1.1. Study participants

The Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT‐AD) cohort consists of CU individuals that are at risk of developing AD due to their parental or multi‐sibling history of sporadic AD. The majority of participants were over the age of 60; however, individuals aged 55 to 59 years were included if they were within 15 years of the onset of their youngest affected relative's symptoms. The PREVENT‐AD cohort has been extensively described elsewhere. ²³ Each participant and their study partner provided written informed consent. All procedures were approved by the McGill University Faculty of Medicine Institutional Review Board and complied with the ethical principles of the Declaration of Helsinki. Data pertaining to the PREVENT‐AD cohort can be downloaded from https://openpreventad.loris.ca/.

2.1.2. CSF measurements

After overnight fast, lumbar punctures (LPs) were performed using a Sprotte 24‐gauge needle. Within 4 hours, CSF samples were centrifuged (≈ 2000 × g) for 10 minutes at room temperature, aliquoted, and stored at −80°C. OPN protein levels in the CSF were measured in a subset of PREVENT‐AD participants (n = 109) using the Olink proximity extension assay. Protein concentrations are expressed in arbitrary normalized protein expression (NPX) units, which are on a log₂ scale. The validated Innotest enzyme‐linked immunosorbent assay (ELISA) kit was used to measure the core AD biomarkers, Aβ₄₂, phosphorylated tau 181 (p‐tau181), and total tau (t‐tau), following the standardized protocols established by the Biomarkers for Alzheimer's Disease and Parkinson's Disease consortium (n = 101). CSF Aβ₄₀ was measured using a Meso Scale Discovery assay (n = 95), as previously described. ²⁴ The concentrations of synaptic biomarkers in the CSF, notably synaptosomal‐associated protein 25 (SNAP25) and synaptotagmin‐1 (SYT1), were determined using immunoprecipitation followed by mass spectrometry, as previously described (n = 106). ²⁵ , ²⁶ , ²⁷ CSF levels of synaptic markers growth‐associated protein 43 (GAP43) and neurogranin (NRGN) were assessed in a subset of PREVENT‐AD participants by validated ELISAs (n = 46). ²⁸ , ²⁹

2.1.3. CSF staging of participants

Following the recommendations of biological frameworks for defining AD, ³⁰ we used the core AD CSF biomarkers to stage PREVENT‐AD participants according to the progression of AD pathology. As previously reported by our group, ³¹ we originally defined a CSF Aβ₄₂ abnormality threshold at the 25th percentile value (< 989 pg/mL). Furthermore, we specified a CSF t‐tau abnormality threshold at the 75th percentile (> 336 pg/mL). ³¹ These values were generated from the subset of PREVENT‐AD participants that had data for both the core AD CSF biomarkers and CSF OPN (n = 101). However, to control for overall CSF biomarker dynamics and variability in CSF Aβ₄₂, ³² we finally staged participants based on their CSF Aβ_42/40 ratios (25th percentile cut‐off, < 0.157). We recognize that this CSF Aβ_42/40 cut‐off value is slightly larger than the values that are typically recommended in the literature. However, considering that these asymptomatic PREVENT‐AD participants are only at the beginning of the disease process, few of them would meet the recommended thresholds.

Individuals were considered CSF Aβ_42/40(–)/t‐tau(–), if they had normal CSF Aβ_42/40 ratios and normal levels of CSF t‐tau (n = 52). CSF Aβ_42/40(+)/t‐tau(–) participants exhibited early amyloid pathology, as reflected by reduced CSF Aβ_42/40 ratios (n = 11). However, these individuals did not display significant levels of neuronal loss, as reflected by low levels of CSF t‐tau. CSF Aβ_42/40(+)/t‐tau(+) participants exhibited decreased CSF Aβ_42/40 ratios, and elevated levels of CSF t‐tau, thus reflecting the final stage of pathology (n = 8). Finally, the Suspected Non‐Alzheimer Pathology (SNAP) group, Aβ_42/40(–)/t‐tau(+), exhibited normal CSF Aβ_42/40 ratios, but elevated levels of CSF t‐tau, thus suggesting other causes of neurodegeneration and/or dementia (n = 10).

To confirm our findings with another core AD CSF biomarker, we further staged PREVENT‐AD participants according to CSF p‐tau181 levels and CSF Aβ_42/40 ratios, using a CSF p‐tau181 abnormality threshold at the 75th percentile value (> 57 pg/mL). Because our thresholds were slightly arbitrary in both cases, and we desired to further differentiate these stages from one another, if possible, we decided a priori to exclude data from 14 individuals whose CSF Aβ_42/40 ratios, CSF t‐tau, and/or CSF p‐tau181 concentrations were within ± 5% of either threshold.

2.1.4. Neuroimaging acquisition and processing

Cortical Aβ and tau pathologies were assessed using ¹⁸F‐NAV4694 (Navidea Biopharmaceuticals) and flortaucipir (¹⁸F‐AV1451; Eli Lilly & Company), in a subset of PREVENT‐AD participants that also had CSF OPN measurements (n = 46, n = 49, respectively). The positron emission tomography (PET) protocol and image preprocessing pipeline have been previously described. ³³ Standardized uptake value ratios (SUVRs) were generated by dividing the signal in the regions of interest (ROIs) by the signal in the reference region. ³³ Thus, cerebellar gray matter was used as a reference region for ¹⁸F‐NAV4694, while the inferior cerebellar gray matter was used for flortaucipir. ³³ A global cortical ROI was computed to evaluate Aβ deposition, while tau deposition was assessed by averaging flortaucipir SUVRs in the entorhinal, fusiform, and lingual brain regions. ³³ The subset of PREVENT‐AD participants that had both CSF OPN and PET measurements was staged as Aβ(+) PET based on a previously established Aβ SUVR threshold of 1.37. ³³ Finally, PREVENT‐AD participants were staged as tau(+) PET based on an established tau SUVR threshold of 1.23. ³³

Considering OPN has been previously associated with cortical and medial temporal lobe atrophy, as defined by ventricular enlargement and sulcal dilation, ¹³ we were prompted to examine the relationship between OPN and a marker of global neurodegeneration, namely cerebral ventricle volume. Thus, lateral, third, and fourth ventricle volumes were computed from T1‐weighted magnetic resonance imaging (MRI) images, using a volumetric pipeline that has been previously described (n = 104). ³⁴

2.1.5. Apolipoprotein E genotyping

The protocol for apolipoprotein E (APOE) genotyping has been previously described. ²³ Briefly, the PyroMark Q96 pyrosequencer (Qiagen) was used to determine APOE genotype in PREVENT‐AD. Quantitative polymerase chain reaction (qPCR) was used to amplify DNA, with primers rs429358 amplification forward 5′‐ACGGCTGTCCAAGGAGCTG‐3′, rs429358 amplification reverse biotinylated 5′‐CACCTCGCCGCGGTACTG‐3′, rs429358 sequencing 5′CGGACATGGAGGACG‐3′, rs7412 amplification forward 5′‐CTCCGCGATGCCGATGAC‐3′, rs7412 amplification reverse biotinylated 5′‐CCCCGGCCTGGTACACTG‐3′, and rs7412 sequencing 5′‐CGA TGACCTGCAGAAG‐3′.

2.2. ADNI cohort

2.2.1. Study participants

Led by Principal Investigator Michael W. Weiner, MD, the primary objective of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to examine the earliest changes associated with AD, and to track the progression of AD pathology. In the current study, we restricted our analyses to 706 ADNI participants with CSF data available. However, three individuals with ambiguous diagnoses were excluded from subsequent analyses. Furthermore, considering our interest in the prodromal stage of AD, we focused our analyses on 167 CU participants and 399 individuals with MCI. At each ADNI study center, written informed consent was obtained from all research participants. Each ADNI study center received approval from its institutional review board. All research complied with the ethical principles of the Declaration of Helsinki. CSF, genetic, and clinical data from the ADNI cohort were downloaded from the ADNI website (http://adni.loni.usc.edu/).

2.2.2. CSF measurements

After an overnight fast, LPs were performed using a 20‐ or 24‐gauge spinal needle. Within 1 hour after collection, the CSF samples were frozen and shipped on dry ice to the ADNI Biomarker Core laboratory. The samples were thawed for 1 hour with gentle mixing at room temperature, aliquoted, and stored at −80°C. The core AD CSF biomarkers Aβ₄₂, p‐tau181, and t‐tau were measured in ADNI samples using the INNO‐BIA AlzBio3 immunoassay kits (Fujirebio) and the xMap Luminex platform. Baseline CSF Aβ_42/40 ratios were measured in a subset of ADNI participants using the Lumipulse G β‐Amyloid 1‐42 and Lumipulse G β‐Amyloid 1‐40 assays, on the Lumipulse G1200 system. Baseline CSF OPN levels were determined using the SomaScan 7K platform (SomaLogic). In supplementary analyses, we analyzed both multiplex immunoassay (Human Discovery Map panel, Rules‐Based Medicine) and mass spectrometry measurements of CSF OPN (AIPVAQDLNAPSDWDSR peptide), only in CU individuals and in participants with MCI.

2.2.3. CSF staging of participants

Given our interest in the predementia period, we staged 166 CU individuals and 399 individuals with MCI according to the progression of AD pathology, similar to the analyses performed in the PREVENT‐AD cohort. We originally used the recommended thresholds of 192 pg/mL for CSF Aβ₄₂ and 93 pg/mL for CSF t‐tau (INNO‐BIA AlzBio3 data). ³⁵ To replicate our findings, we further staged ADNI participants based on the combination of CSF p‐tau181 (recommended threshold > 23 pg/mL) and CSF Aβ₄₂. ³⁵

In a subsequent set of analyses, we also controlled for overall CSF biomarker dynamics and variability in CSF Aβ₄₂, by staging participants according to the CSF Aβ_42/40 ratio. ³² For these analyses, we used the established cut‐off CSF Aβ_42/40 value of 0.058, which has been generated from the same Lumipulse G β‐Amyloid assays (Fujirebio), and is consistent with a positive amyloid PET scan. ³⁶ However, individuals were also included as CSF Aβ_42/40 (+) if they had a ratio between 0.059 and 0.072, which is more likely consistent with a positive amyloid PET scan. Finally, similar to our original analyses, we used the INNO‐BIA AlzBio3 data to determine CSF t‐tau and CSF p‐tau181 positivity.

2.2.4. PET imaging and staging of participants

For a subset of healthy controls (n = 124) and participants with MCI (n = 273) that had both baseline CSF OPN SomaScan measurements and Aβ PET data, we established Aβ PET positivity at baseline. Aβ tracers [18F] florbetaben (FBB) and [18F] florbetapir (FBP), smoothed to a uniform 6 mm resolution, were used to determine tracer uptake in a cortical summary SUVR using the Desikan–Killiany atlas. The cortical summary ROI consisted of frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions. These SUVR values were normalized by the whole cerebellum for cross‐sectional analyses. The thresholds for Aβ PET positivity for each tracer and reference region, as informed by prior literature, were FBP (whole cerebellum reference): 1.11 SUVR; FBB (whole cerebellum reference): 1.08 SUVR. ³⁷ To compare the Aβ burden across subjects who were given different tracers, SUVR values were converted to Centiloid values, thereby permitting direct comparisons. The SUVR‐to‐Centiloid conversion equations based on the cortical summary SUVRs normalized by whole cerebellum, per ADNI, are listed below:

$$\mathrm{FBP}:\mathrm{CL}=188.22\times \mathrm{SUMMARY}\_\mathrm{SUVR}-189.16$$

$$\mathrm{FBB}:\mathrm{CL}=157.15\times \mathrm{SUMMARY}\_\mathrm{SUVR}-151.87$$

We also staged a subset of ADNI participants that had both baseline CSF OPN SomaScan data and tau PET data (flortaucipir, n = 60 healthy controls, n = 59 MCI). A temporal meta‐ROI was used as the primary measure of tau PET positivity. This comprised the average SUVR of the bilateral entorhinal cortex, amygdala, parahippocampal gyrus, fusiform gyrus, inferior temporal gyrus, and middle temporal gyrus. Tau PET positivity was defined as a meta‐ROI uptake surpassing two standard deviations from the mean of amyloid PET negative participants who were cognitively unimpaired at baseline (SUVR cutoff = 1.37). ³⁸ Details regarding PET protocol can be found at http://adni.loni.usc.edu/methods/documents/.

2.2.5. Cognitive assessments

All participants underwent extensive neuropsychological testing, which included the Alzheimer's Disease Assessment Scale–Cognitive subscale (ADAS‐Cog), the Clinical Dementia Rating Scale Sum of Boxes (CDR‐SB), the Montreal Cognitive Assessment (MoCA), and the Mini‐Mental State Examination (MMSE). Higher scores on the ADAS‐Cog and CDR‐SB indicate greater cognitive impairment, while lower scores on the MoCA and MMSE indicate greater cognitive impairment. All assessment scores (baseline to the most recent visit) were analyzed in the present study.

2.2.6. APOE genotyping

The ABI 7900 real‐time thermo‐cycler (Applied Biosystems) was used to determine the APOE genotype of ADNI participants from DNA prepared from ethylenediaminetetraacetic acid whole blood. ³⁵ TaqMan qPCR assays were used to determine APOE nucleotides. ³⁵

2.3. Quebec Founder Population cohort

2.3.1. Study participants

The Quebec Founder Population (QFP) is a unique population isolate from eastern Canada, which has been previously described in detail. ³⁹ We examined the brains of 55 autopsy‐confirmed AD cases and 31 autopsy‐confirmed age‐matched controls, which were obtained from the Douglas Bell Canada Brain Bank. According to medical record reviews, neuropsychological examinations, and caregiver interviews, there was no evidence of memory problems, or neurological or neuropsychiatric diseases in the elderly control group. Controls exhibited neuropathology that is associated with healthy aging (plaque and tangle densities < 10/mm³ and < 20/mm³ in at least one hippocampal and neocortical section). AD cases had to fulfill the histopathological National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer's Disease and Related Disorders Association (NINCDS‐ADRDA) criteria for definite AD. ⁴⁰ A single neuropathologist quantified plaque and tangle densities in six brain regions (CA1, subiculum, parasubiculum, fusiform, frontal, and parietal), as previously described. ⁴¹ This study conformed to the Code of Ethics of the World Medical Association and was approved by the ethics board of the Douglas Mental Health University Institute. This study complied with the ethical principles of the Declaration of Helsinki. Each participant provided written informed consent. Data collected from the QFP cohort are not publicly available; however, the data are available from the corresponding author upon reasonable request.

2.3.2. OPN mRNA levels in the frontal cortex

Transcriptome‐wide gene expression was measured using the human Clariom D Assay, by Génome Québec. Briefly, the Nanodrop Spectrophotometer ND‐1000 was used to measure total RNA (NanoDrop Technologies, Inc.). RNA integrity was evaluated with the Agilent 2100 Bioanalyzer. Ten nanograms of total RNA was used to synthesize sense‐strand cDNA. The GeneChip WT Terminal Labeling Kit was used to fragment and label single‐stranded cDNA, following the manufacturer's instructions. Five micrograms cDNA was hybridized on the GeneChip cartridge array and incubated at 45°C for 17 hours in the GeneChip Hybridization Oven 640, at 60 rpm. The microarrays were washed in the GeneChip Fluidics Station 450, using the GeneChip Hybridization, Wash, and Stain Kit, according to the manufacturer's instructions. Finally, microarrays were scanned in a GeneChip Scanner 3000. OPN mRNA levels are presented on a log₂ scale.

2.3.3. OPN protein levels in the frontal cortex

Of the 86 brains that had OPN mRNA levels measured, OPN protein levels were measured in 75 brains (n = 23 controls, n = 52 AD). Frontal cortex brain samples were placed in prefilled tubes containing 2.8 mm ceramic beads (Omni International). One tablet of protease inhibitor was dissolved in 50 mL of cold phosphate‐buffered saline. One milliliter of protease inhibitor solution was added to each tube. The Bead Ruptor 24 (Omni International) was used to mechanically homogenize brain samples. The Bead Ruptor 24 was set twice at 5.65 m/s for 30 seconds, with a 15 second pause between runs. After homogenization, the samples were stored overnight at −20°C. Two freeze–thaw cycles were performed. Finally, the homogenates were centrifuged for 5 minutes at 2400 g and 4°C. The supernatant was collected and stored for future use at −80°C.

Frontal cortex OPN protein levels were determined using a commercially available ELISA kit (Cat.# ELH‐OPN) developed by RayBiotech. Protocols were performed according to the manufacturer's instructions and results were obtained using the BioTek Synergy H1 microplate reader. Sample replicates had a coefficient of variability (CV) of < 20%. However, three samples with a CV > 20% were excluded from analyses. Finally, to normalize cortical OPN protein levels, total protein concentration was measured using a commercially available bicinchoninic acid assay developed by Pierce (Cat.# 23225). Finally, to meet model assumptions, normalized OPN protein ratios were log₂ transformed.

2.3.4. APOE genotyping

DNA was extracted from brain tissue with the DNeasy Tissue Kit (Qiagen). As previously described in PREVENT‐AD, ²³ APOE genotype was determined using the PyroMark Q96 pyrosequencer.

2.4. Statistical analyses

Across all cohorts, linear regression models examined the relationships between CSF OPN and confounding variables such as age, sex, and APOE ε4 carrier status.

In the PREVENT‐AD cohort, linear regression models adjusted for age, sex, and APOE ε4 carrier status were used to examine the associations between baseline CSF OPN and baseline measurements of CSF AD biomarkers (Aβ₄₂, p‐tau181, t‐tau), CSF synaptic proteins (SNAP25, SYT1, GAP43, NRGN), as well as PET and volumetric neuroimaging data. Across all analyses, OPN was assigned as a dependent variable, except for the association between OPN, CSF Aβ₄₂, and ventricular volume. p‐values ≤ 0.05 were considered statistically significant.

In both the PREVENT‐AD and ADNI cohorts, analysis of covariance (ANCOVA) was used to examine mean differences in CSF OPN levels across pathological stages, as determined by CSF Aβ_42/40, CSF t‐tau, CSF p‐tau181, and PET positivity. These analyses were adjusted for age, sex, and APOE ε4 carrier status. In PREVENT‐AD, we did not combine Aβ and tau PET in a single staging analysis, given that we were severely limited by sample size, as the vast majority of PREVENT‐AD participants were only at the beginning of the disease process. Furthermore, considering the significant lack of overlap between individuals that underwent both Aβ PET (n = 397) and tau PET (n = 119) scans in ADNI, we examined these factors separately in our initial set of analyses. However, a subsequent analysis combined Aβ and tau PET staging, in ADNI. When multiple comparisons were made between pathological stages in a single analysis, a Bonferroni correction was used on the p‐values (p _adj). p _adj ≤ 0.05 was considered statistically significant.

In the ADNI cohort alone, linear regression models examined the associations among SomaScan, mass spectrometry, and multiplex immunoassay CSF OPN measurements. Furthermore, in the ADNI cohort, Cox proportional hazards models examined the association between baseline CSF OPN levels (tertiles and continuous scale) and rate of conversion to AD. Given our interest in the prodromal stage of AD, individuals with MCI were followed from the baseline visit to the time of a clinical diagnosis (of AD), or to the time the participant was last confirmed to be free of AD. Participants with < 6 months of follow‐up were excluded from these analyses. Considering our primary goal was not to determine whether CSF OPN levels could outperform established AD biomarkers and risk factors, the initial Cox model did not include age, sex, or APOE ε4 carrier status. The second Cox model was adjusted for age, sex, and APOE ε4 carrier status. Finally, in a third Cox model, we also adjusted for baseline CSF Aβ₄₂, p‐tau181, and t‐tau levels. p‐values for multiple comparisons between tertiles were adjusted using a Bonferroni correction (p _adj). p _adj ≤ 0.05 was considered statistically significant.

In the ADNI cohort, random slope and random intercept linear mixed models examined the relationship between baseline CSF OPN levels (tertiles and continuous scale) and longitudinal cognitive decline on the ADAS‐Cog13, CDR‐SB, MoCA, and MMSE. Linear mixed models were adjusted for baseline age, sex, APOE ε4 carrier status, and years of education. In a second model, we also adjusted for baseline assessment scores. p‐values for multiple comparisons between tertiles were adjusted using a Bonferroni correction (p _adj). p _adj ≤ 0.05 was considered statistically significant.

In the QFP cohort, the relationships among frontal cortex OPN mRNA, protein levels, and diagnosis were evaluated with ANCOVA, adjusted for age at death, sex, APOE ε4 carrier status, and post mortem interval. Finally, linear regression models adjusted for age at death, sex, and APOE ε4 carrier status examined the associations between OPN (independent variable), neuritic plaque densities, and neurofibrillary tangle densities (dependent variables). Statistical significance was considered at p ≤ 0.05. All R ² values are presented as adjusted R ². All analyses were two‐tailed, and performed in SPSS 23 (IBM) and JMP Pro 16 (SAS).

3. RESULTS

3.1. Demographics

Table 1 summarizes the demographic characteristics of the three cohorts that were used to analyze the role of OPN in the CSF of asymptomatic (PREVENT‐AD) and symptomatic individuals (ADNI), as well as in the frontal cortex of autopsy‐confirmed AD and age‐matched control brains (QFP).

TABLE 1.

Baseline participant demographics.

	PREVENT‐AD	ADNI			QFP
	CU	CU	MCI	AD	CU	AD
N sample	109	167	399	137	31	55
Mean age, Years (SD)	62.6 (5.4)	74.4 (5.9)	72.2 (7.5)	75.3 (8.5)	77.4 (11.4)	80.7 (6.4)
N female (%)	76 (69.7)	78 (46.7)	170 (42.6)	54 (39.4)	11 (35.5)	23 (41.8)
N APOE ε4+ (%)	43 (39.4)	41 (24.6)	209 (52.4)	94 (68.6)	9 (29.0)	32 (58.2)
N amyloid positive (%)	24 (25.3) a	43 (34.7) b	153 (56.0) b	58 (89.2) b	0 (0) c	55 (100) c
Mean CSF Aβ42, pg/mL (SD)	1145.73 (277.62) d , e	200.7 (50.8) e	169.5(51.9) e	135.1 (35.9) e	–	–
Mean CSF p181tau, pg/mL (SD)	46.83 (18.00) d , e	29.9 (15.8) e	39.6 (23.1) e	47.2 (24.8) e	–	–
Mean CSF t‐tau, pg/mL (SD)	273.09 (129.97) d , e	68.6 (31.7) e	93.8 (58.8) e	126.8 (62.1) e	–	–
Mean CSF OPN, NPX (SD)	10.2 (0.3)	–	–		–	–
Mean CSF OPN, RFU (SD)	–	28 115 (5785)	29 145 (6461)	31 222 (6466)	–	–
Mean Cortical OPN mRNA, log2 (SD)	–	–	–		13.3 (1.5)	15.2 (1.2)
Mean cortical OPN protein, log2 (SD)	–	–	–		1.4 (1.0) f	2.2 (1.0) f
Mean post mortem interval, h (SD)	–	–	–		30.0 (19.9)	21.1 (10.4)

Abbreviations: AD, Alzheimer's disease; ADNI, Alzheimer's Disease Neuroimaging Initiative; APOE, apolipoprotein E; Aβ, amyloid beta; CSF, cerebrospinal fluid; CU, cognitively unimpaired; h, hours; MCI, mild cognitive impairment; N, sample size; ng/mL, nanograms per millilitre; NPX, normalized protein expression; OPN, osteopontin; PET, positron emission tomography; p‐tau, phosphorylated tau; pg/mL, picograms per milliliter; PREVENT‐AD, Pre‐symptomatic Evaluation of Experiment or Novel Treatments for Alzheimer's disease; QFP, Quebec Founder Population; RFU, relative fluorescence units; SD, standard deviation; SUVR, standardized uptake value ratio; t‐tau, total tau.

^a Considering the few PREVENT‐AD participants with PET scans, amyloid positivity in PREVENT‐AD (n = 95) was determined using a threshold of CSF Aβ_42/40 ratio  < 0.157 pg/mL.

^b Amyloid positivity in ADNI was determined using normalized florbetaben and florbetapir SUVRs of 1.08 and 1.11, respectively; 124 CU, 273 MCI, and 65 AD underwent Aβ PET scans.

^c Amyloid positivity in QFP was determined using a post mortem plaque density > 10/mm³.

^d 101 PREVENT‐AD participants had CSF Aβ₄₂, p‐tau181, and t‐tau (pg/mL) values available.

^e PREVENT‐AD (Fujirebio Innotest ELISA) and ADNI (INNO‐BIA AlzBio3 Immunoassay) used different assays to measure the core AD CSF biomarkers, which explains the differences.

^f 23 CU and 52 AD QFP participants had cortical OPN protein values available.

3.2. PREVENT‐AD cohort

3.2.1. Associations among CSF OPN, age, sex, and APOE ε4 carrier status

CSF OPN was positively correlated with participant age, at a trend level (n = 109, R ² = 0.019, β = 0.009, p = 0.080). However, CSF OPN levels did not differ between females and males (p = 0.977), or APOE ε4 carriers and non‐carriers (p = 0.599). Despite these findings, we still controlled for these covariates/confounders, considering their theoretical and practical implications on AD pathophysiology.

3.2.2. CSF OPN is associated with core AD CSF biomarkers and synaptic biomarkers in asymptomatic individuals

In CU PREVENT‐AD participants (n = 101), CSF OPN levels were positively correlated with CSF Aβ₄₂ (R ² = 0.200, β = 266.462, p = 0.004; Figure 1A), CSF p‐tau181 (R ² = 0.430, β = 0.010, p = 1.69 × 10⁻¹³; Figure 1B), and CSF t‐tau (R ² = 0.338, β = 0.001, p = 2.45 × 10⁻¹⁰; Figure 1C). Furthermore, CSF OPN was associated with synaptic markers in the CSF, notably SNAP25 (R ² = 0.104, β = 0.011, p = 0.001, n = 106; Figure 1D), SYT1 (R ² = 0.181, β = 0.008, p = 8.00 × 10⁻⁶, n = 106; Figure 1E), GAP43 (R ² = 0.427, β = 1.31 × 10⁻⁴, p = 4.00 × 10⁻⁶, n = 46; Figure 1F), and NRGN (R ² = 0.215, β = 0.001, p = 0.004, n = 46; Figure 1G).

FIGURE 1.

CSF OPN is associated with CSF AD biomarkers and synaptic biomarkers in asymptomatic PREVENT‐AD participants. CSF OPN levels were measured using the Olink Proximity Extension Assay (n = 109); (A) Aβ₄₂, (B) p‐tau181, and (C) t‐tau were measured using validated Innotest ELISA kits, following the standardized protocols established by the BIOMARKAPD consortium (n = 101). The synaptic markers (D) SNAP‐25 (n = 106), (E) SYT1 (n = 106), (F) GAP43 (n = 46), and (G) NRGN (n = 46) were quantified using immunoprecipitation followed by mass spectrometry. Significant linear regressions are represented with a blue confidence region of the fitted line. R ², β, and p‐values are located in the top left corners of each panel. Analyses were adjusted for age, sex, and APOE ε4 carrier status. Aβ, amyloid beta; AD, Alzheimer's disease; APOE, apolipoprotein E; a.u., arbitrary units; BIOMARKAPD, Biomarkers for Alzheimer's Disease and Parkinson's Disease; CSF, cerebrospinal fluid; ELISA, enzyme‐linked immunosorbent assay; GAP43, growth‐associated protein 43; NPX, normalized protein expression; NRGN, neurogranin; OPN, osteopontin; p‐tau, phosphorylated tau; PREVENT‐AD, Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease; SNAP‐25, synaptosomal‐associated protein 23; SYT1, synaptotagmin‐1; t‐tau, total tau.

3.2.3. CSF OPN is elevated in asymptomatic CSF Aβ_42/40(+)/t‐tau(+) and Aβ_42/40(+)/p‐tau181(+) individuals

We originally staged CU PREVENT‐AD participants based on CSF Aβ₄₂ and CSF t‐tau thresholds of 989 pg/mL and 336 pg/mL (Figure S1 in supporting information). Individuals that were within 5% of either threshold were excluded a priori. However, to account for variability in CSF Aβ₄₂ and overall CSF biomarker dynamics, ³² we staged participants according to their CSF Aβ_42/40 ratios, using a cut‐off value of 0.157 (Figure 2A). Despite originally finding a significant reduction in CSF OPN in CSF Aβ₄₂(+)/t‐tau(–) individuals, compared to CSF Aβ₄₂(–)/t‐tau(–) individuals (p _adj = 0.004; Figure S1), this difference was not statistically significant when the CSF Aβ_42/40 ratio was taken into consideration (p _adj = 0.999; Figure 2B). Nevertheless, CSF OPN was increased in CSF Aβ_42/40 (+)/t‐tau(+) individuals (n = 8) relative to CSF Aβ_42/40(–)/t‐tau(–) individuals (n = 52, trend‐level, P _adj = 0.100; Figure 2B). However, CSF OPN did not differ between CSF Aβ_42/40(+)/t‐tau(–) individuals (n = 11) and CSF Aβ_42/40(+)/t‐tau(+) individuals (p _adj = 0.420; Figure 2B). Finally, CSF OPN was significantly elevated in CSF Aβ_42/40(–)/t‐tau(+) individuals (n = 10) relative to CSF Aβ_42/40(–)/t‐tau(–) individuals (p _adj = 0.02; Figure 2B). In a subsequent set of analyses, we included participants that were within ± 5% of the CSF Aβ_42/40 and/or t‐tau cut‐off values. The results were very similar, as CSF OPN was significantly elevated in CSF Aβ_42/40(+)/t‐tau(+) individuals (n = 10, p _adj = 0.040) and CSF Aβ_42/40(–)/t‐tau(+) individuals (n = 14, p _adj = 0.004) compared to CSF Aβ_42/40(–)/t‐tau(–) individuals (n = 57).

FIGURE 2.

CSF OPN is elevated in asymptomatic CSF Aβ_42/40(+)/t‐tau(+) and CSF Aβ_42/40(+)/p‐tau181(+) PREVENT‐AD participants. CSF OPN levels were measured using the Olink Proximity Extension Assay (n = 109). Aβ₄₂, p‐tau181, and t‐tau were measured using validated Innotest ELISA kits, following the standardized protocols established by the BIOMARKAPD consortium (n = 101). CSF Aβ₄₀ was measured using a previously described Meso Scale Discovery assay (n = 95). ²⁴ (A) 95 CU PREVENT‐AD participants were staged as CSF Aβ_42/40 and/or CSF t‐tau positive according to the thresholds of 0.157 and 336 pg/mL, respectively. Fourteen participants that were within ± 5% of either threshold were excluded a priori. Linear models, adjusted for age, sex, and APOE ε4 carrier status were used to examine mean differences in OPN protein levels across stages. (B) Mean differences in CSF OPN were contrasted across CSF Aβ_42/40 and t‐tau stages. (C) 95 CU PREVENT‐AD participants were staged as CSF Aβ_42/40 and/or CSF p‐tau181 positive according to the thresholds of 0.157 and 57 pg/mL, respectively. Thirteen participants that were within ± 5% of either threshold were excluded a priori. (D) Mean differences in CSF OPN were contrasted across CSF Aβ_42/40 and p‐tau181 stages. The data are represented as mean ± SEM. Aβ, amyloid beta; AD, Alzheimer's disease; APOE, apolipoprotein E; BIOMARKAPD, Biomarkers for Alzheimer's Disease and Parkinson's Disease; CSF, cerebrospinal fluid; CU, cognitively unimpaired; ELISA, enzyme‐linked immunosorbent assay; NPX, normalized protein expression; OPN, osteopontin; p‐tau, phosphorylated tau; PREVENT‐AD, Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease; SEM, standard error of the mean; t‐tau, total tau.

To reproduce our findings, we further staged PREVENT‐AD participants (n = 82) according to CSF Aβ_42/40 and CSF p‐tau181 thresholds of 0.157 and 57 pg/mL, respectively (Figure 2C). Individuals that were within 5% of either threshold were excluded a priori. Once more, despite observing a significant reduction in CSF OPN in CSF Aβ₄₂(+)/p‐tau181(–) individuals, compared to CSF Aβ₄₂(–)/p‐tau181(–) individuals (p _adj = 0.004; Figure S1), this difference was not statistically significant when the CSF Aβ_42/40 ratio was taken into consideration (p _adj = 0.999; Figure 2D). However, CSF OPN was increased in CSF Aβ_42/40(+)/p‐tau181(+) individuals (n = 6) relative to CSF Aβ_42/40(–)/p‐tau181(–) individuals (n = 52, p _adj = 0.012; Figure 2D) and CSF Aβ_42/40(+)/p‐tau181(–) individuals (n = 12, trend‐level, p _adj = 0.052; Figure 2D). Finally, CSF OPN was significantly elevated in CSF Aβ_42/40(–)/p‐tau181(+) individuals (n = 12) relative to CSF Aβ_42/40(–)/p‐tau181(–) individuals (p _adj = 0.004; Figure 2D). In a final set of analyses, we included participants that were within ± 5% of the CSF Aβ_42/40 and/or p‐tau181 cut‐off values. Once again, the results were very similar, as CSF OPN was significantly elevated in CSF Aβ_42/40(+)/p‐tau181(+) individuals (n = 9) compared to CSF Aβ_42/40(–)/p‐tau181(–) individuals (n = 56, p _adj = 0.012) and CSF Aβ_42/40(+)/p‐tau181(–) individuals (n = 15, p _adj = 0.048). Finally, CSF OPN was markedly upregulated in CSF Aβ_42/40(–)/p‐tau181(+) individuals (n = 15) relative to CSF Aβ_42/40(–)/p‐tau181(–) individuals (p _adj = 3.04 × 10⁻⁴).

3.2.4. CSF OPN is associated with tau PET burden in Braak stages II to III in asymptomatic individuals

A subset of CU PREVENT‐AD participants that had CSF OPN measurements also underwent NAV4696 (n = 46) and flortaucipir (n = 49) PET imaging to assess early Aβ and tau deposition. We staged participants as Aβ(+) PET (n = 9) or Aβ(–) PET (n = 37) based on a previously established Aβ SUVR threshold of 1.37. ³³ CSF OPN levels did not differ between Aβ(+) PET and Aβ(–) PET individuals (p = 0.466; Figure 3A). However, due to severe limitations in sample size, we conducted exploratory analyses and omitted correction for confounding variables. When we omitted correction for age, sex, and APOE ε4 carrier status, Aβ(+) PET individuals exhibited elevated CSF OPN levels (trend‐level, p = 0.077). Next, we staged participants as tau(+) PET (n = 7) or tau(–) PET (n = 42), based on a previously established tau SUVR threshold of 1.23. ³³ Tau(+) PET individuals exhibited greater CSF OPN levels (trend‐level, p = 0.076; Figure 3B). In an exploratory analysis unadjusted for covariates, tau(+) PET individuals exhibited significantly greater CSF OPN levels (p = 0.016). Although we were very limited in sample size, in an exploratory analysis, Aβ(+)tau(+) PET individuals (n = 5) exhibited elevated CSF OPN levels, relative to Aβ(–)tau(–) PET individuals (n = 35, trend level, p = 0.073), which did not remain statistically significant after controlling for age, sex, and APOE ε4 carrier status. Finally, given our interest in the earliest stages of the disease, we examined CSF OPN in relation to tau deposition in brain regions corresponding to Braak stages II and III. ⁴² We found CSF OPN was positively correlated with significant tau deposition in the entorhinal cortex (R ² = 0.159, β = 0.814, p = 0.009; Figure 3C), fusiform gyrus (R ² = 0.118, β = 1.205, p = 0.030; Figure 3D), and lingual gyrus (R ² = 0.204, β = 1.320, p = 0.002; Figure 3E).

FIGURE 3.

CSF OPN is associated with tau PET burden in Braak stages II to III in asymptomatic PREVENT‐AD participants. CSF OPN levels were measured using the Olink Proximity Extension Assay (n = 109). (A) Global cortical Aβ deposition was measured using ¹⁸F‐NAV4694 (n = 46). Participants were staged as Aβ(+) PET (n = 9) based on a previously established SUVR threshold of 1.37. ³³ (B) Tau deposition was quantified using flortaucipir (n = 49). Participants were staged as tau(+) PET (n = 7) based on a previously established entorhinal cortex SUVR threshold of 1.23. ³³ Tau deposition was assessed in Braak stages II to III, most notably, (C) the entorhinal cortex, (D) fusiform gyrus, and (E) lingual gyrus. Significant linear regressions are represented with a blue confidence region of the fitted line. R ², β, and p‐values are located in the top left corners of each panel. Analyses were adjusted for age, sex, and APOE ε4 carrier status. Aβ, amyloid beta; APOE, apolipoprotein E; CSF, cerebrospinal fluid; NPX, normalized protein expression; OPN, osteopontin; PET, positron emission tomography; PREVENT‐AD, Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease; SUVR, standardized uptake value ratio.

3.2.5. CSF OPN is associated with reduced cerebral ventricular volume in asymptomatic individuals

Because OPN has been previously associated with central, cortical, and medial temporal lobe atrophy, we were prompted to examine the relationship between OPN and a marker of global neurodegeneration, namely cerebral ventricle volume. ¹³ Thus, we analyzed baseline structural neuroimaging data collected from a subset of PREVENT‐AD individuals (n = 104) in a cross‐sectional fashion. Four individuals were omitted from analyses due to failed quality control regarding subject‐specific stereotaxic registration and/or brain masking. After adjusting for total intracranial volume, baseline CSF OPN was negatively correlated with the volume of the lateral ventricle (n = 100, R ² = 0.356, β = −0.956, p = 0.001; Figure 4A) and with the volume of the third ventricle (R ² = 0.313, β = −0.033, p = 0.022; Figure 4B). However, CSF OPN levels were not correlated with the volume of the fourth ventricle (p = 0.10; Figure 4C).

FIGURE 4.

CSF OPN is associated with reductions in cerebral ventricle volume in asymptomatic PREVENT‐AD participants. CSF OPN levels were measured using the Olink Proximity Extension Assay (n = 109); (A) Lateral ventricle, (B) third ventricle, and (C) fourth ventricle volumes were computed from T1‐weighted MRI images, using a volumetric pipeline that has been previously described (n = 104). ³⁴ Significant linear regressions are represented with a blue confidence region of the fitted line. R ², β, and p‐values are located in the top right corners of each panel. Analyses were adjusted for total ICV, age, sex, and APOE ε4 carrier status. APOE, apolipoprotein E; CSF, cerebrospinal fluid; ICV, intracranial volume; MRI, magnetic resonance imaging; NPX, normalized protein expression; OPN, osteopontin; PREVENT‐AD, Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease.

3.3. ADNI cohort

3.3.1. Associations among CSF OPN, age, sex, and APOE ε4 carrier status

When combining data from healthy controls, individuals with MCI, and participants with AD, CSF OPN was positively correlated with participant age (n = 706, R ² = 0.004, β = 64.018, p = 0.046). Furthermore, CSF OPN was elevated in females compared to males (p = 0.008) and in APOE ε4 carriers relative to non‐carriers (p = 6.676 × 10⁻⁷). Finally, in supplementary analyses, all three assays that were used to measure CSF OPN in ADNI correlated moderately well with each other (Figure S2 in supporting information).

3.3.2. CSF OPN is elevated in CSF Aβ_42/40(+)/t‐tau(+) and CSF Aβ_42/40(+)/p‐tau181(+) individuals

We staged 166 CU individuals and 399 ADNI participants with MCI as amyloid and/or tau positive according to the recommended CSF Aβ₄₂ and CSF t‐tau thresholds of 192 pg/mL and 93 pg/mL, respectively. ³⁵ The results from these original analyses are available in Figure S3 in supporting information. However, in subsequent analyses, we controlled for overall CSF biomarker dynamics and variability in CSF Aβ₄₂ by incorporating the CSF Aβ_42/40 ratio, ³² in a subset of ADNI participants that had both baseline SomaScan CSF OPN and baseline CSF Aβ₄₀ data (n = 44 CU, n = 118 MCI). In this set of analyses, we used the recommended CSF Aβ_42/40 ratio cut‐off value of 0.058, ³⁶ for the Lumipulse G β‐Amyloid 1‐42 and Lumipulse G β‐Amyloid 1‐40 assays (Fujirebio). However, as recommended by the US Food and Drug Administration (FDA), CSF ratios between 0.059 and 0.072 were considered amyloid positive. Once again, we used the original INNO‐BIA AlzBio3 thresholds of 93 pg/mL and 23 pg/mL for CSF t‐tau and CSF p‐tau181, respectively. ³⁵

The CSF Aβ_42/40 and t‐tau staging analysis (Figure 5A) revealed  that CSF OPN levels did not significantly differ between CSF Aβ_42/40(–) /t‐tau(–) individuals (n = 70) and CSF Aβ_42/40(+)/t‐tau(–) individuals (n = 46, p _adj = 0.716). However, CSF OPN was significantly elevated in CSF Aβ_42/40(+)/t‐tau(+) individuals (n = 41) compared to CSF Aβ_42/40(–) /t‐tau(–) and CSF Aβ_42/40(+)/t‐tau(–) individuals (p _adj = 0.0001 and p _adj = 0.004, respectively). Finally, CSF OPN did not differ between CSF Aβ_42/40(–)/t‐tau(+) individuals (n = 5) and CSF Aβ_42/40(–)/t‐tau(–) individuals (p _adj = 0.148). The results from the original analyses that were unadjusted for CSF Aβ₄₀ were very similar; however, CSF Aβ₄₂(–)/t‐tau(+) individuals did exhibit a marked increase in CSF OPN, relative to CSF Aβ₄₂(–)/t‐tau(–) individuals (Figure S3).

FIGURE 5.

CSF OPN is elevated in CSF Aβ_42/40(+)/t‐tau(+), CSF Aβ_42/40(+)/p‐tau181(+), Aβ(+) PET, and tau(+) PET individuals from the ADNI cohort. CSF OPN levels were measured using the SomaScan proteomics assay. CSF Aβ_42/40 ratios were measured using the Lumipulse G β‐Amyloid assays from Fujirebio. CSF t‐tau and p‐tau181 were measured using the INNO‐BIA AlzBio3 immunoassay kits and the xMap Luminex platform. (A) Forty‐four CU participants and 118 participants with MCI from the ADNI cohort were staged as CSF Aβ_42/40 and/or CSF t‐tau positive according to the recommended thresholds of 0.058 and 93 pg/mL, respectively. ³⁵ , ³⁶ However, as recommended by the FDA, CSF Aβ_42/40 ratios 0.059 to 0.072 were considered amyloid positive. Linear models, adjusted for age, sex, and APOE ε4 carrier status, were used to examine mean differences in OPN protein levels across stages. (B) Forty‐four CU participants and 118 participants with MCI from the ADNI cohort were staged as CSF Aβ_42/40 and/or CSF p‐tau181 positive according to the recommended thresholds of 0.058 and 23 pg/mL, respectively. ³⁵ , ³⁶ However, as recommended by the FDA, CSF Aβ_42/40 ratios 0.059 to 0.072 were considered amyloid positive. (C) Aβ deposition in a cortical summary region of interest was quantified using florbetaben or florbetapir. One hundred twenty‐four CU participants and 273 participants with MCI from the ADNI cohort were staged as Aβ(+) PET based off of recommended SUVR thresholds of 1.08 and 1.11, respectively. ³⁷ (D) Tau deposition in a temporal meta‐region of interest (ROI) was determined using flortaucipir. Sixty CU participants and 59 participants with MCI from the ADNI cohort were staged as tau(+) PET (n = 21) if their meta‐ROI uptake surpassed two standard deviations from the mean of Aβ(–) PET participants that were CU at baseline (SUVR cut‐off = 1.37). ³⁸ (E) Aβ and tau PET staging combined was limited by the number of individuals that underwent tau PET scans (n = 119). The data are represented as mean ± SEM. Aβ, amyloid beta; ADNI, Alzheimer's Disease Neuroimaging Initiative; APOE, apolipoprotein E; CSF, cerebrospinal fluid; CU, cognitively unimpaired; FDA, US Food and Drug Administration; MCI, mild cognitive impairment; OPN, osteopontin; PET, positron emission tomography; p‐tau, phosphorylated tau; SEM, standard error of the mean; SUVR, standardized uptake value ratio; t‐tau, total tau.

FIGURE 6.

Elevated CSF OPN is associated with greater rates of cognitive decline and conversion to AD in the ADNI cohort. CSF OPN levels were measured using the SomaScan proteomics assay in 399 participants with MCI. Cox proportional hazards models examined the association between baseline CSF OPN levels and rate of conversion to AD. Participants were classified into tertiles based on their CSF OPN measurements. Fifteen individuals with < 6 months of follow‐up were excluded, and 14 individuals with ambiguous conversion dates were excluded from analyses. Of the 370 individuals that were followed longitudinally, 154 individuals eventually received a clinical diagnosis of AD. (A) The initial Cox model was unadjusted for confounding variables, such as age, sex, and APOE ε4 carrier status. Individuals with CSF OPN values in the top tertile exhibited a significantly greater rate of conversion to AD, compared to the first tertile. Furthermore, participants underwent extensive neuropsychological assessments, including the (B) ADAS‐Cog13, (C) CDR‐SB, (D) MoCA, and (E) MMSE. Linear mixed models demonstrated that individuals in the top tertile exhibited greater rates of cognitive decline on all cognitive assessments compared to the middle and bottom tertiles. Linear mixed models were adjusted for age, sex, APOE ε4 carrier status, and years of education. AD, Alzheimer's disease; ADAS‐Cog, Alzheimer's Disease Assessment Scale‐Cognitive Subscale; ADNI, Alzheimer's Disease Neuroimaging Initiative; APOE, apolipoprotein E; CDR‐SB, Clinical Dementia Rating Scale Sum of Boxes; CSF, cerebrospinal fluid; MCI, mild cognitive impairment; MMSE, Mini‐Mental State Examination; MoCA, Montreal Cognitive Assessment; OPN, osteopontin.

To verify our findings with p‐tau pathology, we further staged the same ADNI participants based on the recommended CSF Aβ_42/40 and CSF p‐tau181 thresholds of 0.058 (0.059–0.072 amyloid positive, as per FDA) and 23 pg/mL, respectively. ³⁵ , ³⁶ The CSF Aβ_42/40 and p‐tau181 staging analysis (Figure 5B) revealed that CSF OPN levels did not differ between CSF Aβ_42/40(–)/p‐tau181(–) individuals (n = 38) and CSF Aβ_42/40(+)/p‐tau181(–) individuals (n = 8, p _adj = 0.776). However, CSF OPN was significantly increased in CSF Aβ_42/40(+)/p‐tau181(+) individuals (n = 79) compared to CSF Aβ_42/40(–)/p‐tau181(–) individuals (p _adj = 0.008), but not CSF Aβ_42/40(+)/p‐tau181(–) individuals (p _adj = 0.999). Finally, CSF OPN did not differ between CSF Aβ_42/40(–)/p‐tau181(+) individuals (n = 37) and CSF Aβ_42/40(–)/p‐tau181(–) individuals (p _adj = 0.340). Once again, the results from the original analyses that were unadjusted for CSF Aβ₄₀ were very similar; however, CSF Aβ₄₂(+) /p‐tau181(+) individuals did exhibit a marked increase in CSF OPN, relative to CSF Aβ₄₂(+)/p‐tau181(–) individuals (Figure S3).

To reproduce our findings across different methodologies, we performed supplementary analyses using the CSF OPN multiplex immunoassay data (Figure S4 in supporting information) as well as the CSF OPN mass spectrometry data (AIPVAQDLNAPSDWDSR peptide (Figure S5 in supporting information), which both yielded very similar results to the CSF SomaScan analyses. These analyses involved a subset of ADNI participants that did not have baseline CSF Aβ₄₀ data.

3.3.3. CSF OPN is elevated in Aβ(+) PET and tau(+) PET individuals

Given our interest in the prodromal stage of AD, we staged 124 CU individuals and 273 participants with MCI as Aβ(+) PET (n = 196) or Aβ(–) PET (n = 201) based on florbetaben and florbetapir SUVR thresholds of 1.08 and 1.11, respectively. ³⁷ Aβ(+) PET individuals exhibited elevated CSF OPN levels compared to Aβ(–) PET individuals (p = 1.60 × 10⁻⁵; Figure 5C). Furthermore, CSF OPN was positively correlated with Aβ Centiloid values (R ² = 0.105, β = 35.735, p = 2.00 × 10⁻⁶). Of these individuals that underwent Aβ PET scans, only a subset underwent tau PET scans. Thus, we staged these participants as tau(+) PET (n = 21) or tau(–) PET (n = 98), based on a flortaucipir SUVR threshold of 1.37. ³⁸ Tau(+) PET individuals exhibited elevated CSF OPN levels relative to tau(–) PET individuals (p = 0.033; Figure 5D). Combining Aβ and tau PET, Aβ(+)tau(+) PET (n = 19) individuals displayed increased CSF OPN levels compared to Aβ(–)tau(–) PET individuals (n = 69); however, this finding only approached a trend level, after accounting for multiple comparisons (p _adj = 0.120, Figure 5E).

3.3.4. Elevated CSF OPN is associated with a faster rate of conversion to AD

To determine whether CSF OPN is associated with rates of conversion to AD in participants with MCI from the ADNI cohort (n = 399), we established CSF OPN threshold values at the bottom tertile (≤ 26,009 relative fluorescence units [RFU], T1), middle tertile (26,010–31,269 RFU, T2), and top tertile (≥ 31,270 RFU, T3). Fifteen participants with < 6 months of follow‐up were excluded from analyses, and 14 participants were excluded due to ambiguous conversion dates. Of these dementia‐free participants (n = 370), 154 individuals eventually met the clinical criteria for a diagnosis of AD (mean follow‐up, 3.80 years; range, 0.5–14.5 years). In our initial Cox proportional hazards model that examined CSF OPN alone (unadjusted for age, sex, and APOE ε4 carrier status), the top tertile exhibited a significantly greater rate of conversion to AD, compared to the bottom tertile (hazard ratio [HR] 1.966, 95% confidence interval [CI] 1.315–2.939, p _adj = 0.003; Figure 6A). Furthermore, at a trend level, the rate of conversion to AD was greater in the top tertile, compared to the middle tertile (HR 1.406, 95% CI 0.970–2.039, p = 0.072). However, this difference was not statistically significant after controlling for multiple comparisons (p _adj = 0.216). Finally, the rate of conversion to AD did not significantly differ between the middle and bottom tertiles (HR 1.398, 95% CI 0.915–2.137, p _adj = 0.366). Considering the magnitude of the SomaScan RFU measurements, and to facilitate the interpretation of the effect size of the HR, continuous CSF OPN levels were log₂ transformed. Thus, we found continuous OPN levels were associated with a faster rate of conversion to AD (for every 2‐fold increase in OPN; HR 2.170, 95% CI 1.289–3.653, p = 0.004).

To assess the performance of OPN in predicting the conversion to AD relative to established AD risk factors, we added age, sex, and APOE ε4 carrier status to subsequent Cox models. In these analyses, the top tertile exhibited a significantly greater rate of conversion to AD compared to the bottom tertile (HR 1.520, 95% CI 1.004–2.301, p = 0.048). However, this difference did not remain statistically significant when controlling for multiple comparisons (p _adj = 0.144). Finally, the rates of conversion to AD did not differ between the top and middle tertiles (p _adj = 0.663) nor between the middle and bottom tertiles (p _adj = 0.999). Finally, at a trend level, continuous CSF OPN levels were associated with a greater rate of conversion to AD, after controlling for competing risk factors (trend level, for every 2‐fold increase in OPN; HR 1.654, 95% CI 0.959–2.851, p = 0.070).

Finally, we examined the predictive performance of OPN compared to the core AD CSF biomarkers. Thus, when we combined CSF OPN tertiles and CSF Aβ₄₂ or CSF p‐tau181, at a trend level, the top CSF OPN tertile exhibited a greater rate of conversion to AD, compared to the bottom tertile. However, these trend‐level observations were attenuated after controlling for multiple comparisons. Furthermore, the addition of CSF Aβ₄₂ and/or CSF p‐tau181 completely attenuated the effect of continuous OPN on the rate of conversion to AD. When age, sex, APOE ε4 carrier status, CSF Aβ₄₂, and/or CSF p‐tau181 were added to the Cox models together, OPN was no longer associated with the rate of conversion to AD.

3.3.5. Elevated CSF OPN is associated with a greater rate of longitudinal cognitive decline

Given our interest in the prodromal stage of AD, we examined the relationship between CSF OPN and cognition, only in ADNI participants with MCI (n = 399). Once again, 15 participants with < 6 months of follow‐up were excluded from analyses. Although arbitrary, we staged ADNI participants (n = 384) in tertiles, based on their SomaScan CSF OPN measurements. Random slope and intercept linear mixed models revealed that the top tertile exhibited the greatest rate of cognitive decline on the ADAS‐Cog13 (Figure 6B) compared to the bottom tertile (time‐by‐group‐interaction, β = 0.11 points/month, p _adj = 8.40 × 10⁻⁴) and the middle tertile (time‐by‐group interaction, β = 0.08 points/month, p _adj = 0.036). Identical results were obtained when analyzing ADAS‐Cog11 scores. Furthermore, the top tertile demonstrated a greater rate of disease progression, as demonstrated through CDR‐SB scores (Figure 6C, time‐by‐group interaction versus bottom tertile β = 0.02 points/month, p _adj = 0.027; time‐by‐group interaction versus middle tertile β = 0.02 points/month, trend‐level, p _adj = 0.054).

Similar results were found with cognitive trajectories on the MoCA, as the top tertile displayed a greater rate of cognitive decline on the MoCA (Figure 6D) compared to the bottom tertile (time‐by‐group interaction β = −0.04 points/month, p _adj = 0.009) and the middle tertile (time‐by‐group interaction β = −0.04 points/month, p _adj = 0.012). Finally, the top tertile demonstrated a greater rate of cognitive decline on the MMSE (Figure 6E) compared to the bottom tertile (time‐by‐group interaction β = −0.04 points/month, p _adj = 0.003) and the middle tertile (time‐by‐group interaction β = −0.04 points/month, p _adj = 0.009). However, for each cognitive assessment, the rates of decline for the middle tertile did not significantly differ from the rates of decline for the bottom tertile (p _adj = 0.744, p _adj = 0.999, p _adj = 0.999, and p _adj = 0.999, respectively). Finally, using continuous OPN levels and controlling for baseline assessment scores did not change the results.

3.4. QFP cohort

3.4.1. Associations among brain tissue OPN, age at death, sex, APOE ε4 carrier status, and post mortem interval

Frontal cortex OPN mRNA and protein levels were not correlated with age at death (p = 0.166, p = 0.689, respectively) or sex (p = 0.646, p = 0.827, respectively). OPN mRNA levels were greater in APOE ε4 carriers, compared to non‐carriers (trend level, p = 0.064). However, OPN protein levels did not significantly differ between APOE ε4 carriers and non‐carriers (p = 0.987). Finally, OPN mRNA levels were negatively correlated with post mortem intervals (trend level, R ² = 0.020, β = −0.018, p = 0.103), but OPN protein levels were not (p = 0.111). Despite these findings, we still controlled for these covariates in subsequent analyses—considering their theoretical and practical implications on AD pathophysiology.

3.4.2. OPN mRNA and protein levels are increased in the frontal cortex of autopsy‐confirmed AD brains

OPN gene expression was assessed by DNA microarray in the QFP cohort, and demonstrated to be significantly increased in the frontal cortex of autopsied AD brains (n = 55), compared to age‐matched controls (n = 31; p = 1.00 × 10⁻⁶; Figure 7A). Furthermore, as demonstrated through ELISA, after controlling for total protein levels, OPN protein levels were also elevated in the frontal cortex of AD cases, relative to controls (p = 0.006; Figure 7B).

FIGURE 7.

OPN mRNA and protein levels are elevated in the frontal cortex of autopsy‐confirmed AD brains. (A) Microarray technology was used to measure OPN mRNA levels in the frontal cortex of autopsy‐confirmed AD brains (n = 55) and age‐matched elderly controls (n = 31) from the QFP cohort. (B) OPN protein levels were measured in the frontal cortex of AD brains (n = 52) and control brains (n = 23) using a commercially available ELISA kit. p‐values are located in the top left corner of each panel. Analyses were adjusted for age, sex, APOE ε4 carrier status, and post mortem interval. The data are represented as mean ± SEM. AD, Alzheimer's disease; APOE, apolipoprotein E; CTL, control; ELISA, enzyme‐linked immunosorbent assay; mRNA, messenger RNA; OPN, osteopontin; QFP, Quebec Founder Population; SEM, standard error of the mean.

3.4.3. Frontal cortex OPN protein levels are positively correlated with neuritic plaque and neurofibrillary tangle densities

Considering that AD cases had to fulfill the histopathological NINCDS‐ADRDA criteria for definite AD, ⁴⁰ we examined the associations among frontal cortex OPN protein levels, plaque densities, and tangle densities—only in AD brains. Our results suggest that frontal cortex OPN protein levels are positively correlated with plaque densities (Figure S6 in supporting information) in the parietal cortex (R ² = 0.435, β = 8.192, p = 0.014) and at a trend level, the frontal cortex (R ² = 0.247, β = 6.409, p = 0.075).

Furthermore, frontal cortex OPN protein levels were positively correlated with tangle densities (Figure S7 in supporting information) in parasubiculum (R ² = 0.222, β = 8.380, p = 0.003), fusiform (R ² = 0.162, β = 5.781, p = 0.049), and frontal (trend level, R ² = 0.169, β = 4.576, p = 0.052) brain regions.

4. DISCUSSION

Recent genome‐wide association studies have identified several genetic risk factors that are associated with immune and inflammatory‐related processes. ¹ , ² Furthermore, understanding microglia‐related activity is essential, especially considering their potential involvement in recent Aβ immunotherapies. ³ , ⁴ , ⁵ Therefore, in the present study, we investigated the potential role of the inflammatory protein OPN as a biomarker of prodromal AD.

First, we observed a positive relationship between CSF OPN and CSF Aβ₄₂, in asymptomatic PREVENT‐AD participants (Figure 1A). This finding suggests that elevated OPN levels may contribute to Aβ clearance in these Pre‐symptomatic individuals. This notion is consistent with the fact that amyloid‐clearing immunotherapies (lecanemab, donanemab) that reduce cognitive decline ultimately produce significant increases in CSF Aβ₄₂ (relative to baseline). ³ , ⁴ , ⁵ Furthermore, cell culture studies have demonstrated that OPN directly enhances the phagocytosis of Aβ by macrophages, ¹⁹ which further supports the potential role of OPN in AD pathophysiology.

We subsequently examined the relationship between p‐tau pathology and CSF OPN in asymptomatic PREVENT‐AD participants. Our results suggest that OPN may be significantly upregulated as a result of early CSF p‐tau181 production (Figure 1B) and deposition in brain regions associated with early Braak stages II and III (Figure 3C, Figure 3D, Figure 3E). ⁴² These findings are consistent with a longitudinal PET study which demonstrated that elevated CSF OPN was associated with an accelerated rate of tau accumulation (Braak III–IV and Braak V–VI) and concomitant cognitive decline, in Aβ(+)/tau(+) PET individuals. ⁴³ In this context, it is important to acknowledge that OPN has been shown to promote PI3K/AKT signalling, ⁴⁴ which attenuates the activity of glycogen synthase kinase‐3, a major producer of p‐tau in the CNS. ⁴⁵ Hence, we speculate that OPN, alongside other key factors, may play a regulatory role in the production and/or release of p‐tau. Furthermore, considering the established role of OPN in enhancing phagocytosis, we postulate that OPN may potentially regulate the microglia‐mediated internalization and/or degradation of secreted exosomes containing pathological tau species. ⁴⁶ , ⁴⁷ However, the data we have presented in the present study are limited, and further studies are required to support these hypotheses.

Considering the extensive loss of neurons in AD, we analyzed the relationship between CSF OPN and CSF t‐tau, in asymptomatic PREVENT‐AD participants (Figure 1C). We observed a positive relationship between CSF OPN and CSF t‐tau, which is certainly consistent with the fact that OPN and its cell‐surface receptors are upregulated after brain injury in rodents. ⁴⁸ Furthermore, in cell culture experiments and in a mouse model of stroke, the administration of OPN has been demonstrated to directly protect neurons from cell death induced by oxygen and glucose deprivation, and reduce infarct size. ⁴⁹ One possible interpretation of these results is that under certain conditions OPN may offer neuroprotection by promoting the survival of neurons, potentially during the Pre‐symptomatic stage of AD. ⁴⁸ , ⁴⁹

The prominent loss of synapses in AD, which correlates most strongly with cognitive decline, ⁵⁰ prompted us to examine the synaptic markers SNAP25 (Figure 1D), SYT1 (Figure 1E), GAP43 (Figure 1F), and NRGN (Figure 1G) in the CSF. ²⁵ , ²⁶ , ²⁷ , ²⁸ , ²⁹ Taken together, our results suggest that CSF OPN may be upregulated in response to early synaptic dysfunction and/or loss in Pre‐symptomatic individuals with a parental history of AD. Thus, considering these PREVENT‐AD participants have remained cognitively unimpaired despite significant increases in CSF p‐tau181 and t‐tau, it is tempting to postulate that OPN may promote or facilitate the remodeling of axons and synapses during the earliest stages of AD. Indeed, in combination therapies, OPN has been shown to promote the regeneration of axons and synapses in rodent models of spinal cord injury, ⁵¹ stroke, ⁵² and nerve transections. ⁵³ We have presented some data that support this hypothesis, as elevated CSF OPN levels accompanied reductions in cerebral ventricle volumes, which is often used to indirectly assess neuronal and synaptic integrity, as well as the progression of the disease in vivo. ⁵⁴

Following the recommendations of biological frameworks that are used for defining AD, ³⁰ , ⁵⁵ we staged participants according to CSF and PET pathology. The present study's results suggest that CSF OPN is significantly elevated in CSF Aβ_42/40(+)/t‐tau(+) and CSF Aβ_42/40(+)/ptau181(+) individuals from both the Pre‐symptomatic PREVENT‐AD cohort (Figure 2B, Figure 2D) as well as the clinically progressing ADNI cohort (Figure 5A, Figure 5B). Furthermore, although our PET analyses were rather exploratory in PREVENT‐AD (Figure 3A, Figure 3B), CSF OPN was markedly increased in Aβ(+) PET (Figure 5C) and tau(+) PET participants (Figure 5D) from the ADNI cohort. These findings suggest that, alongside established risk factors and AD biomarkers, CSF OPN may help identify suitable at‐risk individuals for clinical trials or early preventative therapies. More specifically, the CSF staging results suggest that CSF OPN is initially upregulated in response to early alterations in soluble amyloid and tau (Figure 2), which precede the accumulation of insoluble AD pathology, which is captured later using PET imaging (Figure 3). ⁵⁶

To examine the potential application of baseline CSF OPN levels as a predictor of conversion to AD, we conducted survival analyses in participants with MCI from the ADNI cohort. Overall, our results (Figure 6A) suggest that elevated CSF OPN is associated with a greater rate of conversion to AD over a mean follow‐up of 3.80 years (range, 0.5–14.5 years), which is consistent with the existing literature. ¹⁴ , ¹⁵ , ¹⁶ However, it is important to recognize this association was attenuated by the addition of age, sex, APOE ε4 carrier status, and the core AD CSF biomarkers into Cox models. Thus, although our data suggest that OPN may not outperform established genetic risk factors and AD biomarkers (i.e., APOE ε4, CSF Aβ₄₂), it is possible that the incorporation of CSF OPN into prediction models may help improve their accuracy to identify at‐risk individuals with compromised synaptic networks. For instance, while reductions in CSF Aβ₄₂ are a hallmark of AD, reductions in CSF Aβ₄₂ have also been seen in other neurodegenerative diseases in which plaques are absent, such as in frontotemporal dementia, Lewy body dementia, vascular dementia, Creutzfeldt–Jakob disease, and amyotrophic lateral sclerosis. ⁵⁷ Thus, to examine the specificity of OPN to AD, we conducted pilot analyses using RNA‐Seq data from the Mayo Brain Bank ⁵⁸ and found that OPN gene expression (isoforms; OPN‐a, OPN‐b, OPN‐c) was elevated in the temporal cortex of autopsy‐confirmed AD cases compared to progressive supranuclear palsy (PSP) cases, pathological aging cases, and age‐matched controls (Figure S8 in supporting information).

We found that elevated CSF OPN levels were associated with greater rates of cognitive and functional decline on the ADAS‐Cog13 (Figure 6B), CDR‐SB (Figure 6C), MoCA (Figure 6D), and MMSE (Figure 6E) in participants with MCI—over the course of several years.

Although a few pilot studies have reported that OPN is elevated in the frontal cortex of autopsied AD brains, ¹⁹ , ²¹ , ⁵⁹ , ⁶⁰ we verified these observations in a larger sample of autopsy‐confirmed AD brains from the QFP cohort. Consistent with previous studies, OPN mRNA (Figure 7A) and protein levels (Figure 7B) were markedly increased in the frontal cortex of AD brains, compared to age‐matched elderly control brains. Furthermore, our results suggest that frontal cortex OPN protein levels are positively correlated with AD‐specific neuropathological hallmarks, such as neuritic plaque densities (Figure S6) in the parietal and frontal cortices, and with neurofibrillary tangle densities (Figure S7) in brain regions corresponding to Braak stages III to IV (parasubiculum, fusiform gyrus). ⁴²

It cannot be dismissed that chronic neuroinflammation mediated by OPN ⁷ may be harmful to the brain during the later stages of the disease. ¹⁷ For instance, preclinical research has shown that the genetic ablation of OPN and the administration of anti‐OPN antibodies reduce plaque formation, the number of dystrophic neurites, and ultimately improve cognition in 5XFAD mice. ²¹ Furthermore, siRNA and antibodies targeted against CD44V10, a splice variant of the OPN receptor, have been found to protect primary neuronal cultures from Aβ‐induced cell death. ⁶¹ Thus, we propose that OPN may significantly contribute to synaptic pathology, especially during the deposition of tau.

5. LIMITATIONS

First, it is important for our findings to be replicated in diverse populations, as the PREVENT‐AD and ADNI cohorts are mainly composed of White individuals (98.9% and 79.3%, respectively), ²³ , ⁶² which does not entirely reflect the prevalence of AD across different racial/ethnic populations. ⁶² Another limitation of the present study is that some of the CSF and PET staging analyses involved slightly arbitrary cut‐off values and small group sizes in PREVENT‐AD. We recognize that these results must be interpreted with caution, and warrant further investigation in Pre‐symptomatic cohorts similar to PREVENT‐AD. However, rather than generating cohort‐specific threshold values, our main objective was to examine the extreme values of CSF AD biomarkers, such that our cut‐off percentiles could be applied to various cohorts and across various methodologies that are used to assess amyloid and tau deposition, as well as OPN levels. Furthermore, the small group sizes in PREVENT‐AD highlight the fact that we are essentially capturing the very beginning of the disease in asymptomatic participants. Finally, in the ADNI cohort, it is important to recognize that baseline Aβ PET scans were acquired much earlier, and closer to the CSF draw date, compared to tau PET scans.

6. CONCLUSION

The strong associations observed among CSF OPN, tau pathology, and synaptic pathology support a significant role for OPN in the early phase of the AD spectrum. However, additional studies are required to examine the clinical implications of spliced isoforms, cleaved isoforms, and phosphorylation states of OPN, which are all believed to influence OPN function. ⁶³

CONFLICT OF INTEREST STATEMENT

JP serves as a scientific advisor to the Alzheimer Society of France. HZ has served on scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave; has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche; and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and on advisory boards for Acumen, ALZPath, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served on data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials, and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai, and Roche Diagnostics; 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 work presented in this paper. MJQ, AL, CP, DCB, AB, and SV have nothing to disclose. Author disclosures are available in the supporting information.

CONSENT STATEMENT

Each PREVENT‐AD participant and their study partner provided written informed consent. All procedures were approved by the McGill University Faculty of Medicine Institutional Review Board and complied with the ethical principles of the Declaration of Helsinki. At each ADNI study center, written informed consent was obtained from all research participants. Each ADNI study center received approval from its institutional review board. All research complied with the ethical principles of the Declaration of Helsinki. The QFP study was conformed to the Code of Ethics of the World Medical Association and was approved by the Ethics Board of the Douglas Mental Health University Institute. This study complied with the ethical principles of the Declaration of Helsinki. Each QFP participant provided written informed consent.

Supporting information

Supporting Information

Supporting Information

PREVENT‐AD Collaborators

The members of the Pre‐symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease (PREVENT‐AD) Research Group are:

Paul Aisen (Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA); Elena Anthal, RN, Melissa Appleby, BA, Gülebru Ayranci, Pierre Bellec, Véronique Bohbot, John C.S. Breitner, Leopoldina Carmo, Mallar Chakravarty, Laksanun Cheewakriengkrai, Louis Collins, Leslie‐Ann Daoust, Marina Dauar‐Tedeschi, Doris Dea, Clément Debacker, Guerda Duclair, Marianne Dufour, Rana El‐Khoury, Pierre Etienne, Alan Evans, Fabiola Ferdinand, David Fontaine, Josée Frappier, Joanne Frenette, Guylaine Gagné, Serge Gauthier, Valérie Gervais, Renuka Giles, Julie Gonneaud, Renee Gordon, Rick Hoge, Yasser Ituria‐Medina, Justin Kat, Christina Kazazian, Theresa Köbe, Anne Labonté, Marie‐Elyse Lafaille‐Magnan, Tanya Lee, Illana Leppert, MEng, Cécile Madjar, Laura Mahar, Jean‐Robert Maltais, Ginette Mayrand, Pierre‐François Meyer, Justin Miron, Nathalie Nilsson, Pierre Orban, Tharick A. Pascoal, Mirela Petkova, Cynthia Picard, Alexa Pichet Binette, Morteza Pishnamazi, Galina Pogossova, Judes Poirier, Alexandra Poirier, Jens Pruessner, Natasha Rajah, Pedro Rosa‐Neto, Mélissa Savard, Jiarui Ao, Marc Quesnel, Shirin Tabrizi, Angela Tam, Christine Tardif, Eduard Teigner, Louise Théroux, Jennifer Tremblay‐Mercier, Miranda Tuwaig, Isabelle Vallée, Vinod Venugopalan, Sander C.J. Verfaillie, Sylvia Villeneuve, and Karen Wan (Studies on Prevention of Alzheimer's Disease Centre, Douglas Mental Health University Institute, Montreal, QC, Canada); Alan Barkun, Claudio Cuello, Mahsa Dadar, Samir Das, Mark Eisenberg, Vladimir Fonov, Penelope Kostopoulos, Claude Lepage, Gerhard Maultaup, Melissa McSweeney, MSc, Lisa‐Marie Münter, Pierre Rioux, Paule‐Joanne Toussaint, and Jacob Vogel (McGill University, Montreal, QC, Canada); Thomas Beaudry, Christophe Bedetti, Fatiha Benbouhoud, Charles Edouard Carrier, Blandine Courcot, Doris Couture, René Desautels, Sylvie Dubuc, Sarah Farzin, Anne‐Marie Faubert, David Maillet, Axel Mathieu, Sulantha Mathotaarachchi, Diane Michaud, Vasavan Nair, Jamie Near, Holly Newbold‐Fox, Véronique Pagé, Eunice Farah Saint‐Fort, Alyssa Salaciak, Stephanie Tullo, Seqian Wang, and Elsa Yu (Douglas Mental Health University Institute, affiliated with McGill University, Montreal, QC, Canada); Jason Brandt (John Hopkins University, Baltimore, MD, USA); Suzanne Craft (Wake Forest School of Medicine, Winston‐Salem, NC, USA); Christian Dansereau (Center de recherche de l'Institut Universitaire de Gériatrie de Montreal, Montreal, QC, Canada; Université de Montréal, Montreal, QC, Canada); Clifford R. Jack and David S. Knopman (Mayo Clinic, Rochester, MN, USA); Zaven S. Khachaturian (Khachaturian and Associates, Potomac, MD, USA); Jeannie‐Marie Leoutsakos (Johns Hopkins University, Baltimore, MD, USA); Thomas J. Montine (Washington University, Seattle, WA, USA); John C. Morris (Washington University School of Medicine at St. Louis, St. Louis, MO, USA); Mark A. Sager (Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA); Reisa A. Sperling (Center for Alzheimer's Research and Treatment Harvard Medical School, Boston, MA, USA); Nathan Spreng (Montreal Neurological Institute and Hospital, Montreal, QC, Canada); Pierre N. Tariot (Banner Alzheimer Institute, Phoenix, AZ, USA); Ronald G. Thomas (University of California School of Medicine, San Diego, CA, USA); and Etienne Vachon‐Presseau (Department of Anesthesiology, McGill University, Montreal, QC, Canada), Gabriel Aumont, Marina Tedeshi Dauar.

ADNI Collaborators

The collaborators associated with ADNI are:

Michael Weiner, MD (UC San Francisco, Principal Investigator, Executive Committee); Paul Aisen, MD (UC San Diego, ADCS PI and Director of Coordinating Center Clinical Core, Executive Committee, Clinical Core Leaders); Ronald Petersen, MD, PhD (Mayo Clinic, Rochester, Executive Committee, Clinical Core Leader); Clifford R. Jack, Jr., MD (Mayo Clinic, Rochester, Executive Committee, MRI Core Leader); William Jagust, MD (UC Berkeley, Executive Committee; PET Core Leader); John Q. Trojanowki, MD, PhD (U Pennsylvania, Executive Committee, Biomarkers Core Leader); Arthur W. Toga, PhD (USC, Executive Committee, Informatics Core Leader); Laurel Beckett, PhD (UC Davis, Executive Committee, Biostatistics Core Leader); Robert C. Green, MD, MPH (Brigham and Women's Hospital, Harvard Medical School, Executive Committee and Chair of Data and Publication Committee); Andrew J. Saykin, PsyD (Indiana University, Executive Committee, Genetics Core Leader); John Morris, MD (Washington University St. Louis, Executive Committee, Neuropathology Core Leader); Leslie M. Shaw (University of Pennsylvania, Executive Committee, Biomarkers Core Leader); Enchi Liu, PhD (Janssen Alzheimer Immunotherapy, ADNI 2 Private Partner Scientific Board Chair); Tom Montine, MD, PhD (University of Washington); Ronald G. Thomas, PhD (UC San Diego); Michael Donohue, PhD (UC San Diego); Sarah Walter, MSc (UC San Diego); Devon Gessert (UC San Diego); Tamie Sather, MS (UC San Diego); Gus Jiminez, MBS (UC San Diego); Danielle Harvey, PhD (UC Davis); Michael Donohue, PhD (UC San Diego); Matthew Bernstein, PhD (Mayo Clinic, Rochester); Nick Fox, MD (University of London); Paul Thompson, PhD (USC School of Medicine); Norbert Schuff, PhD (UCSF MRI); Charles DeCArli, MD (UC Davis); Bret Borowski, RT (Mayo Clinic); Jeff Gunter, PhD (Mayo Clinic); Matt Senjem, MS (Mayo Clinic); Prashanthi Vemuri, PhD (Mayo Clinic); David Jones, MD (Mayo Clinic); Kejal Kantarci (Mayo Clinic); Chad Ward (Mayo Clinic); Robert A. Koeppe, PhD (University of Michigan, PET Core Leader); Norm Foster, MD (University of Utah); Eric M. Reiman, MD (Banner Alzheimer's Institute); Kewei Chen, PhD (Banner Alzheimer's Institute); Chet Mathis, MD (University of Pittsburgh); Susan Landau, PhD (UC Berkeley); Nigel J. Cairns, PhD, MRCPath (Washington University St. Louis); Erin Householder (Washington University St. Louis); Lisa Taylor Reinwald, BA, HTL (Washington University St. Louis); Virginia Lee, PhD, MBA (UPenn School of Medicine); Magdalena Korecka, PhD (UPenn School of Medicine); Michal Figurski, PhD (UPenn School of Medicine); Karen Crawford (USC); Scott Neu, PhD (USC); Tatiana M. Foroud, PhD (Indiana University); Steven Potkin, MD UC (UC Irvine); Li Shen, PhD (Indiana University); Faber Kelley, MS, CCRC (Indiana University); Sungeun Kim, PhD (Indiana University); Kwangsik Nho, PhD (Indiana University); Zaven Kachaturian, PhD (Khachaturian, Radebaugh & Associates, Inc and Alzheimer's Association's Ronald and Nancy Reagan's Research Institute); Richard Frank, MD, PhD (General Electric); Peter J. Snyder, PhD (Brown University); Susan Molchan, PhD (National Institute on Aging/ National Institutes of Health); Jeffrey Kaye, MD (Oregon Health and Science University); Joseph Quinn, MD (Oregon Health and Science University); Betty Lind, BS (Oregon Health and Science University); Raina Carter, BA (Oregon Health and Science University); Sara Dolen, BS (Oregon Health and Science University); Lon S. Schneider, MD (University of Southern California); Sonia Pawluczyk, MD (University of Southern California); Mauricio Beccera, BS (University of Southern California); Liberty Teodoro, RN (University of Southern California); Bryan M. Spann, DO, PhD (University of Southern California); James Brewer, MD, PhD (University of California San Diego); Helen Vanderswag, RN (University of California San Diego); Adam Fleisher, MD (University of California San Diego); Judith L. Heidebrink, MD, MS (University of Michigan); Joanne L. Lord, LPN, BA, CCRC (University of Michigan); Ronald Petersen, MD, PhD (Mayo Clinic, Rochester); Sara S. Mason, RN (Mayo Clinic, Rochester); Colleen S. Albers, RN (Mayo Clinic, Rochester); David Knopman, MD (Mayo Clinic, Rochester); Kris Johnson, RN (Mayo Clinic, Rochester); Rachelle S. Doody, MD, PhD (Baylor College of Medicine); Javier Villanueva Meyer, MD (Baylor College of Medicine); Munir Chowdhury, MBBS, MS (Baylor College of Medicine); Susan Rountree, MD (Baylor College of Medicine); Mimi Dang, MD (Baylor College of Medicine); Yaakov Stern, PhD (Columbia University Medical Center); Lawrence S. Honig, MD, PhD (Columbia University Medical Center); Karen L. Bell, MD (Columbia University Medical Center); Beau Ances, MD (Washington University, St. Louis); John C. Morris, MD (Washington University, St. Louis); Maria Carroll, RN, MSN (Washington University, St. Louis); Sue Leon, RN, MSN (Washington University, St. Louis); Erin Householder, MS, CCRP (Washington University, St. Louis); Mark A. Mintun, MD (Washington University, St. Louis); Stacy Schneider, APRN, BC, GNP (Washington University, St. Louis); Angela Oliver, RN, BSN, MSG; Daniel Marson, JD, PhD (University of Alabama Birmingham); Randall Griffith, PhD, ABPP (University of Alabama Birmingham); David Clark, MD (University of Alabama Birmingham); David Geldmacher, MD (University of Alabama Birmingham); John Brockington, MD (University of Alabama Birmingham); Erik Roberson, MD (University of Alabama Birmingham); Hillel Grossman, MD (Mount Sinai School of Medicine); Effie Mitsis, PhD (Mount Sinai School of Medicine); Leyla deToledo‐Morrell, PhD (Rush University Medical Center); Raj C. Shah, MD (Rush University Medical Center); Ranjan Duara, MD (Wien Center); Daniel Varon, MD (Wien Center); Maria T. Greig, HP (Wien Center); Peggy Roberts, CNA (Wien Center); Marilyn Albert, PhD (Johns Hopkins University); Chiadi Onyike, MD (Johns Hopkins University); Daniel D'Agostino II, BS (Johns Hopkins University); Stephanie Kielb, BS (Johns Hopkins University); James E. Galvin, MD, MPH (New York University); Dana M. Pogorelec (New York University); Brittany Cerbone (New York University); Christina A. Michel (New York University); Henry Rusinek, PhD (New York University); Mony J. de Leon, EdD (New York University); Lidia Glodzik, MD, PhD (New York University); Susan De Santi, PhD (New York University); P. Murali Doraiswamy, MD (Duke University Medical Center); Jeffrey R. Petrella, MD (Duke University Medical Center); Terence Z. Wong, MD (Duke University Medical Center); Steven E. Arnold, MD (University of Pennsylvania); Jason H. Karlawish, MD (University of Pennsylvania); David Wolk, MD (University of Pennsylvania); Charles D. Smith, MD (University of Kentucky); Greg Jicha, MD (University of Kentucky); Peter Hardy, PhD (University of Kentucky); Partha Sinha, PhD (University of Kentucky); Elizabeth Oates, MD (University of Kentucky); Gary Conrad, MD (University of Kentucky); Oscar L. Lopez, MD (University of Pittsburgh); MaryAnn Oakley, MA (University of Pittsburgh); Donna M. Simpson, CRNP, MPH (University of Pittsburgh); Anton P. Porsteinsson, MD (University of Rochester Medical Center); Bonnie S. Goldstein, MS, NP (University of Rochester Medical Center); Kim Martin, RN (University of Rochester Medical Center); Kelly M. Makino, BS (University of Rochester Medical Center); M. Saleem Ismail, MD (University of Rochester Medical Center); Connie Brand, RN (University of Rochester Medical Center); Ruth A. Mulnard, DNSc, RN, FAAN (University of California, Irvine); Gaby Thai, MD (University of California, Irvine); Catherine Mc Adams Ortiz, MSN, RN, A/GNP (University of California, Irvine); Kyle Womack, MD (University of Texas Southwestern Medical School); Dana Mathews, MD, PhD (University of Texas Southwestern Medical School); Mary Quiceno, MD (University of Texas Southwestern Medical School); Ramon Diaz Arrastia, MD, PhD (University of Texas Southwestern Medical School); Richard King, MD (University of Texas Southwestern Medical School); Myron Weiner, MD (University of Texas Southwestern Medical School); Kristen Martin Cook, MA (University of Texas Southwestern Medical School); Michael DeVous, PhD (University of Texas Southwestern Medical School); Allan I. Levey, MD, PhD (Emory University); James J. Lah, MD, PhD (Emory University); Janet S. Cellar, DNP, PMHCNS BC (Emory University); Jeffrey M. Burns, MD (University of Kansas, Medical Center); Heather S. Anderson, MD (University of Kansas, Medical Center); Russell H. Swerdlow, MD (University of Kansas, Medical Center); Liana Apostolova, MD (University of California, Los Angeles); Kathleen Tingus, PhD (University of California, Los Angeles); Ellen Woo, PhD (University of California, Los Angeles); Daniel H.S. Silverman, MD, PhD (University of California, Los Angeles); Po H. Lu, PsyD (University of California, Los Angeles); George Bartzokis, MD (University of California, Los Angeles); Neill R. Graff Radford, MBBCH, FRCP (London) (Mayo Clinic, Jacksonville); Francine Parfitt, MSH, CCRC (Mayo Clinic, Jacksonville); Tracy Kendall, BA, CCRP (Mayo Clinic, Jacksonville); Heather Johnson, MLS, CCRP (Mayo Clinic, Jacksonville); Martin R. Farlow, MD (Indiana University); Ann Marie Hake, MD (Indiana University); Brandy R. Matthews, MD (Indiana University); Scott Herring, RN, CCRC (Indiana University); Cynthia Hunt, BS, CCRP (Indiana University); Christopher H. van Dyck, MD (Yale University School of Medicine); Richard E. Carson, PhD (Yale University School of Medicine); Martha G. MacAvoy, PhD (Yale University School of Medicine); Howard Chertkow, MD (McGill Univ., Montreal Jewish General Hospital); Howard Bergman, MD (McGill Univ., Montreal Jewish General Hospital); Chris Hosein, Med (McGill Univ., Montreal Jewish General Hospital); Sandra Black, MD, FRCPC (Sunnybrook Health Sciences, Ontario); Bojana Stefanovic (Sunnybrook Health Sciences, Ontario); Curtis Caldwell, PhD (Sunnybrook Health Sciences, Ontario); Ging Yuek Robin Hsiung, MD, MHSc, FRCPC (UBC Clinic for AD & Related Disorders); Howard Feldman, MD, FRCPC (UBC Clinic for AD & Related Disorders); Benita Mudge, BS (UBC Clinic for AD & Related Disorders); Michele Assaly, MA Past (UBC Clinic for AD & Related Disorders); Andrew Kertesz, MD (Cognitive Neurology St. Joseph's, Ontario); John Rogers, MD (Cognitive Neurology St. Joseph's, Ontario); Dick Trost, PhD (Cognitive Neurology St. Joseph's, Ontario); Charles Bernick, MD (Cleveland Clinic Lou Ruvo Center for Brain Health); Donna Munic, PhD (Cleveland Clinic Lou Ruvo Center for Brain Health); Diana Kerwin, MD (Northwestern University); Marek Marsel Mesulam, MD (Northwestern University); Kristine Lipowski, BA (Northwestern University); Chuang Kuo Wu, MD, PhD (Northwestern University); Nancy Johnson, PhD (Northwestern University); Carl Sadowsky, MD (Premiere Research Inst [Palm Beach Neurology]); Walter Martinez, MD (Premiere Research Inst [Palm Beach Neurology]); Teresa Villena, MD (Premiere Research Inst [Palm Beach Neurology]); Raymond Scott Turner, MD, PhD (Georgetown University Medical Center); Kathleen Johnson, NP (Georgetown University Medical Center); Brigid Reynolds, NP (Georgetown University Medical Center); Reisa A. Sperling, MD (Brigham and Women's Hospital); Keith A. Johnson, MD (Brigham and Women's Hospital); Gad Marshall, MD (Brigham and Women's Hospital); Meghan Frey (Brigham and Women's Hospital); Jerome Yesavage, MD (Stanford University); Joy L. Taylor, PhD (Stanford University); Barton Lane, MD (Stanford University); Allyson Rosen, PhD (Stanford University); Jared Tinklenberg, MD (Stanford University); Marwan N. Sabbagh, MD (Banner Sun Health Research Institute); Christine M. Belden, PsyD (Banner Sun Health Research Institute); Sandra A. Jacobson, MD (Banner Sun Health Research Institute); Sherye A. Sirrel, MS (Banner Sun Health Research Institute); Neil Kowall, MD (Boston University); Ronald Killiany, PhD (Boston University); Andrew E. Budson, MD (Boston University); Alexander Norbash, MD (Boston University); Patricia Lynn Johnson, BA (Boston University); Thomas O. Obisesan, MD, MPH (Howard University); Saba Wolday, MSc (Howard University); Joanne Allard, PhD (Howard University); Alan Lerner, MD (Case Western Reserve University); Paula Ogrocki, PhD (Case Western Reserve University); Leon Hudson, MPH (Case Western Reserve University); Evan Fletcher, PhD (University of California, Davis Sacramento); Owen Carmichael, PhD (University of California, Davis Sacramento); John Olichney, MD (University of California, Davis Sacramento); Charles DeCarli, MD (University of California, Davis Sacramento); Smita Kittur, MD (Neurological Care of CNY); Michael Borrie, MB ChB (Parkwood Hospital); T. Y. Lee, PhD (Parkwood Hospital); Rob Bartha, PhD (Parkwood Hospital); Sterling Johnson, PhD (University of Wisconsin); Sanjay Asthana, MD (University of Wisconsin); Cynthia M. Carlsson, MD (University of Wisconsin); Steven G. Potkin, MD (University of California, Irvine BIC); Adrian Preda, MD (University of California, Irvine BIC); Dana Nguyen, PhD (University of California, Irvine BIC); Pierre Tariot, MD (Banner Alzheimer's Institute); Adam Fleisher, MD (Banner Alzheimer's Institute); Stephanie Reeder, BA (Banner Alzheimer's Institute); Vernice Bates, MD (Dent Neurologic Institute); Horacio Capote, MD (Dent Neurologic Institute); Michelle Rainka, PharmD, CCRP (Dent Neurologic Institute); Douglas W. Scharre, MD (Ohio State University); Maria Kataki, MD, PhD (Ohio State University); Anahita Adeli, MD (Ohio State University); Earl A. Zimmerman, MD (Albany Medical College); Dzintra Celmins, MD (Albany Medical College); Alice D. Brown, FNP (Albany Medical College); Godfrey D. Pearlson, MD (Hartford Hosp, Olin Neuropsychiatry Research Center); Karen Blank, MD (Hartford Hosp, Olin Neuropsychiatry Research Center); Karen Anderson, RN (Hartford Hosp, Olin Neuropsychiatry Research Center); Robert B. Santulli, MD (Dartmouth Hitchcock Medical Center); Tamar J. Kitzmiller (Dartmouth Hitchcock Medical Center); Eben S. Schwartz, PhD (Dartmouth Hitchcock Medical Center); Kaycee M. Sink, MD, MAS (Wake Forest University Health Sciences); Jeff D. Williamson, MD, MHS (Wake Forest University Health Sciences); Pradeep Garg, PhD (Wake Forest University Health Sciences); Franklin Watkins, MD (Wake Forest University Health Sciences); Brian R. Ott, MD (Rhode Island Hospital); Henry Querfurth, MD (Rhode Island Hospital); Geoffrey Tremont, PhD (Rhode Island Hospital); Stephen Salloway, MD, MS (Butler Hospital); Paul Malloy, PhD (Butler Hospital); Stephen Correia, PhD (Butler Hospital); Howard J. Rosen, MD (UC San Francisco); Bruce L. Miller, MD (UC San Francisco); Jacobo Mintzer, MD, MBA (Medical University South Carolina); Kenneth Spicer, MD, PhD (Medical University South Carolina); David Bachman, MD (Medical University South Carolina); Elizabether Finger, MD (St. Joseph's Health Care); Stephen Pasternak, MD (St. Joseph's Health Care); Irina Rachinsky, MD (St. Joseph's Health Care); John Rogers, MD (St. Joseph's Health Care); Andrew Kertesz, MD (St. Joseph's Health Care); Dick Drost, MD (St. Joseph's Health Care); Nunzio Pomara, MD (Nathan Kline Institute); Raymundo Hernando, MD (Nathan Kline Institute); Antero Sarrael, MD (Nathan Kline Institute); Susan K. Schultz, MD (University of Iowa College of Medicine, Iowa City); Laura L. Boles Ponto, PhD (University of Iowa College of Medicine, Iowa City); Hyungsub Shim, MD (University of Iowa College of Medicine, Iowa City); Karen Elizabeth Smith, RN (University of Iowa College of Medicine, Iowa City); Norman Relkin, MD, PhD (Cornell University); Gloria Chaing, MD (Cornell University); Lisa Raudin, PhD (Cornell University); Amanda Smith, MD (University of South Florida: USF Health Byrd Alzheimer's Institute); Kristin Fargher, MD (University of South Florida: USF Health Byrd Alzheimer's Institute); Balebail Ashok Raj, MD (University of South Florida: USF Health Byrd Alzheimer's Institute).

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