Longitudinal Protein Changes in Blood Plasma Associated with the Rate of Cognitive Decline in Alzheimer’s Disease

Abstract

Biomarkers of Alzheimer’s disease (AD) progression are needed to support the development of urgently needed disease modifying drugs. We employed a SOMAscan assay for quantifying 1,001 proteins in blood samples from 90 AD subjects, 37 stable mild cognitive impaired (MCI) subjects, 39 MCI subjects converting to AD within a year and 69 controls at baseline and one year follow up. We used linear mixed effects models to identify proteins changing significantly over one year with the rate of cognitive decline, which was quantified as the reduction in Mini Mental State Examination (MMSE) scores. Additionally, we investigated proteins changing differently across disease groups and during the conversion from MCI to AD. We found that levels of proteins belonging to the complement cascade increase significantly in fast declining AD patients. Longitudinal changes in the complement cascade might be a surrogate biomarker for disease progression. We also found that members of the cytokine-cytokine receptor interaction pathway change during AD when compared to healthy aging subjects.

Keywords

Alzheimer’s disease cognitive decline complement cascade cytokine-cytokine receptor interaction plasma proteins

INTRODUCTION

Alzheimer’s disease (AD), the most common form of dementia, is a devastating illness characterized by progressive short-term memory loss, followed by the inability of patients to care for themselves and leading to eventual death 10–15 years after diagnosis. To date, there is no cure and available medication can only temporarily alleviate some symptoms or slow down progression in a subset of patients. Thus, new drugs are urgently needed.

To support the development of disease modifying drugs, biomarkers for early diagnosis and disease progression are required [1]. Blood based biomarkers have been the focus of much recent work as blood can be accessed for repeated measures relatively easily. Of particular note, is the growing body of work around changes in plasma proteins in relation to AD and MCI-related protein biomarkers using Mass Spectrometry and antibody capture technologies [2 –10].

The majority of studies have investigated the potential of blood proteins only by considering cross sectional measures. In this study, we investigated longitudinal protein changes associated with the rate of cognitive decline in AD patients. Additionally, we compared longitudinal protein changes between normal aging, AD, and those occurring during the transition from MCI to AD.

METHODS

Subjects

We obtained protein measures for 235 subjects (69 controls, 37 ‘stable’ MCI patients (MCIs), 39 patients with MCI converting to AD within a year (MCIc) and 90 AD patients) from the EU funded AddNeuroMed (ANM) biomarker study [11, 12]. Informed consent was obtained for all subjects according to the Declaration of Helsinki (1991), and protocols and procedures were approved by the relevant local ethical committees at each site. All subjects were assessed with a standardized assessment protocol including informant interview for diagnosis, a cognitive assessment, including the Mini Mental State Examination (MMSE), and an assessment of functional capacity, behavior and dementia severity as previously reported[3 , 10].

Blood samples

At the time of assessment, all blood samples were drawn by venipuncture and collected into EDTA glass tubes. Subjects were required to fast for at least 2 hours prior to collection. All samples were centrifuged at 2000 g for 10 min at 4°C within approximately 2 h of collection. Plasma supernatant was collected, divided into aliquots, and frozen at –80°C until furtheruse.

Protein measures

Proteins were measured using a Slow Off-rate Modified Aptamer (SOMAmer)-based capture array called ‘SOMAscan’ (SomaLogic, Inc, Boulder, Colorado). This approach uses chemically modified nucleotides to transform a protein signal to a nucleotide signal that can be quantified using relative florescence on microarrays. Therefore, all gathered SOMAscan measures are in relative fluorescence units (RFU). This assay has been shown to have a median intra- and inter-run coefficient of variation of ∼5% . The median lower and upper limits of quantification were ∼1 pM and ∼1.5 nM in buffer, and ∼2.95 pM and ∼1.5 nM for a subset of the somamers in plasma, full details are given in Goldet al. [13].

Quality control was performed at the sample and SOMAmer level, and involved the use of control SOMAmers on the microarray and calibration samples. At the sample level, hybridization controls on the microarray were used to monitor sample-by-sample variability in hybridization, while the median signal over all SOMAmers was used to monitor overall technical variability. The resulting hybridization scale factor and median scale factor were used to normalize data across samples. The acceptance criteria for these values was 0.4–2.5, based on historical trends in these values. SOMAmer-by-SOMAmer calibration occurred through the repeated measurement of calibration samples, these samples were of the same matrix as the study samples, and were used to monitor repeatability and batch to batch variability. Historical values for these calibrator samples for each SOMAmer were used to generate a calibration scale factor. The acceptance criteria for calibrator scale factors was 95% of SOMAmers having a calibration scale factor within ±0.4 of the median.

The assay required 8 μL of plasma from each sample. A single assay was used per plasma sample and, thus, no technical replicates were performed. All measurements were log2 transformed.

The assay measures the level of 1,001 human proteins representing different molecular pathways and gene families. The majority of proteins are involved in the following processes: signal transduction pathways, stress response, immune process and phosphorylation, but in addition, proteolysis, cell adhesion, cell differentiation and intracellular transport proteins are also represented.

Statistical analysis

All statistical analyses were performed using R and the library ‘nlme’ version 3.1 [14]. Firstly, we investigated the association between change in protein level (follow-up minus baseline) and the rate of cognitive decline in AD subjects at the single analyte level. Cognitive decline was calculated by fitting a multi-level linear mixed model to the longitudinal Mini Mental State Examination (MMSE) assessment scores, which were gathered over five visits during a one-year follow-up period. Subjects and center were included as random effects in the model. Further covariates including age at baseline, disease duration at baseline, gender, presence of apolipoprotein (APOE) ɛ4 allele, living in a nursing home and years of education were added as fixed effects. The associations between protein changes and the rate of MMSE decline was assessed by including a protein change by time interaction in the multi-level model.

Next, we aimed to identify proteins changing over time in the diagnostic groups relative to controls and therefore possibly associated with disease progression. For each individual protein we estimated the rate of change by using a multi-level linear model with random intercepts for subject and center level clustering. The slope was fixed as there were only protein measures at two time points, namely baseline and follow-up. Age at baseline, gender and apolipoprotein (APOE) ɛ4 allele presence were included as fixed covariates. In order to estimate and test the association between protein changes and disease status, we included a time by diagnosis interaction term in the model. Control subjects were set as the reference level to which all the disease groups were compared. Dummy coding was applied for the three disease groups (MCIs, MCIc and AD). We used the same approach to identify proteins, which were changing during the transition from MCI to AD, relative to unchanging MCI subjects. Stable MCI subjects were set as the reference to which MCI subjects converting to AD were compared (MCIs = 0, MCIc = 1).

All p-values were FDR (false discovery rate) adjusted and an association was considered significant if it passed a threshold of q-value <0.05. However, we also considered associations passing a threshold of p-value <0.01 as nominally significant.

Enrichment analysis

Enrichment analysis for pathways (KEGG) and Gene Ontologies (molecular function, cellular component and biological process) was performed on significantly associated proteins (q-value <0.05) using the DAVID knowledgebase [15, 16]. In the case that only a small number of proteins passed the threshold, a more exploratory threshold of p-value <0.01 was used. The background for enrichment was set to include the full list of proteins measured on the SomaLogicpanel.

Again, all p-values were FDR (false discovery rate) adjusted and enrichment was considered significant if it passed a threshold of q-value <0.05 and nominally significant at a p-value <0.01.

RESULTS

Demographics

Demographic characteristics stratified by diagnostic group are provided in Table 1. Differences in demographic characteristics according to clinical diagnosis were assessed using a one-way ANOVA. Significant differences were found for age, number of APOE ɛ4 alleles and MMSE score at baseline; no significant differences in gender were observed.

Protein changes associated with the rate of cognitive decline in AD patients

Twelve proteins were found to be significantly associated with the rate of progression on an exploratory level (p-value <0.01) (see Table 2): C2 (Complement C2), SAA (Serum amyloid A-1 protein), C9 (Complement C9), MBL (Mannose-binding protein C), SAP (Serum amyloid P-component), α2-Antiplasmin, CHK1 (Serine/threonine-protein kinase Chk1), IL-17 (Interleukin-17A), eIF-5A-1 (Eukaryotic translation initiation factor 5A–1), Hemopexin, CDC37 (C-C motif chemokine 19) and Complement factor H-related protein 5. C2 and SAA passed multiple testing corrections at a q-value threshold of <0.05. Ten out of 12 proteins showed a negative association with rate of cognitive decline. Increasing levels of these proteins were associated with a greater loss in MMSE scores per year, or in other words, with faster decline. Only eIF–5A–1 and CDC37 showed a positive association with the rate of cognitive decline. Thus, increasing levels of these proteins were associated with lower loss in MMSE scores per year, or in other words, with slower decline. Scatter plots of the four proteins showing the most significant change with rate of cognitive decline are shown in Fig. 1

The 12 proteins passing the threshold of p-value <0.01 (Table 2) were significantly enriched in members of the complement and coagulation cascades (q-value = 0.005, KEGG hsa04610). These proteins are: MBL, C9, C6, and α2-Antiplasmin. We also found significant enrichment (q-value <0.05) of proteins involved in 24 biological processes, including acute inflammatory response (q-value = 0.0003), inflammatory response (q-value = 0.002), defense response (q-value = 0.011) and complement activation (q-value = 0.015).

Protein changes across disease groups

Eleven proteins were found to change in MCIs and in AD over time relative to controls (see Table 3). HCC-1 (C-C motif chemokine 14) was the only protein showing significant changes when comparing the MCIs group with the control group (p-value < 0.01). Ten proteins showed a significant change in levels between the AD and control group (q-value < 0.05). Notably protein levels gradually decrease over time with more established disease and thus AD patients showed a generally larger decrease in protein levels than MCIs over a year. Raw differences in plasma levels are shown in Fig. 2 for the four strongestassociations.

The ten proteins changing in AD did not show significant enrichment, not even at the exploratory level (p-value <0.01), for KEGG pathways, GO molecular function, cellular components, or biological processes.

Protein level changes associated with conversion from MCI to AD

No proteins showed a significant enough change to pass multiple testing correction (q-value <0.05) during conversion from MCI to AD. However nine proteins, SDF-1α (Stromal cell-derived factor 1 alpha, p-value = 3.3E–5), SLPI (Antileukoproteinase, p-value = 8.5E–4), HCC–1 (C-C motif chemokine 14, p-value = 0.002), BCMA (Tumor necrosis factor receptor superfamily member 17, p-value = 0.005), LTA-4 hydrolase (Leukotriene A-4 hydrolase, p-value = 0.006), Endostatin (p-value = 0.006), Trypsin (p-value = 0.006), IL-6 sRα (Interleukin-6 receptor subunit alpha, p-value = 0.007), and Albumin (p-value = 0.010) passed the suggestive threshold of p-value <0.01 (Table 4).

The nine proteins were not significantly enriched for any pathway or Gene Ontologies at an uncorrected p-value.

Assay performance

In a previous study, we compared blood-based protein biomarkers identified in our SomaLogic data set with candidate biomarkers reported in the literature. We found that a total of nine proteins replicated. For instance, we replicated Pancreatic prohormone and Insulin-like growth factor-binding protein 2, which were identified as AD biomarkers by O’Bryant et al. [17] and Doecke et al. [18], both using Myriad RBM Luminex xMAP technology. Details on the other seven proteins can be found in Kiddle et al. [4].

In order to further assess the quantitative performance of the assay, we generated limit of quantification (LOQ), six-point standard curves, spanning six logs in concentration, from 10 nM to 10 fM, for four of our most significant findings (C2, C9, MBL, and SAA). The upper limit of quantification (ULOQ), the lower limit of quantification (LLOQ) and the dynamic range of quantification (ROQ) were measured. Standard curves and precision profiles for all four analytes are shown in the Supplementary Material.

DISCUSSION

In this study, we investigated the relationship between changes in 1,001 proteins and the rate of AD progression, as measured by change in MMSE scores. As we had longitudinal measures for only a subset of samples (69 controls, 37 ‘stable’ MCI patients (MCIs), 39 patients with MCI converting to AD within a year (MCIc), and 90 AD patients) cross-sectional results for the complete data set comprising protein measures for 691 subjects (211 controls, 106 MCI patients, 43 patients with MCI converting to AD within a year, and 331 AD patients) are reported elsewhere [4, 7].

We found that proteins exhibiting rate of cognitive decline associated changes were enriched with products from the complement cascade, namely MBL (Mannose-binding protein C), C2 (Complement C2), and C9 (Complement C9). All three were shown to increase more rapidly in AD patients with faster cognitive decline. Complement proteins are found at the earliest stages of amyloid deposition and their activation coincides with the clinical expression of AD [19, 20]. The complement cascade is activated through three pathways: the classical, alternative, and lectin pathway [21]. Target recognition of the three pathways varies but they all share the common step of activating the central component C3 [22]. Mannose-binding protein C (MBL) activates the lectin pathway, following the recognition and binding of pathogen-associated molecular patterns [22]. MBL activates the MBL-associated serine protease that leads to activation of Complement C4 and Complement C2 [23]. We found strong associations between changes in levels of C2 and MBL, which are both components of the lectin complement pathway, and the rate of cognitive decline. Therefore, our results allow us to speculate that changes in components of the lectin complement pathway may be associated with faster cognitive decline. To our knowledge, this is the first study to report a potential association between changes in the lectin complement pathway and the rate of cognitive decline in AD patients. However, C2 is also a member of the classic pathway, which has an established association with AD, as it was found to be directly activated in vitro by fibrillar Aβ₄₀ and Aβ₄₂ by binding to C3 and the globular heads of C1q [24].

We also found that serum amyloid P-component (SAP) levels increase more in individuals with a faster cognitive decline. SAP is reported to be co-localized with Aβ plaques in human AD brain [25, 26], exhibit upregulated synthesis in AD affected brain regions [27], induce neuronal apoptosis in vitro, and protect senile plaques from proteolysis [30]. Increasing levels of SAP could potentially create an increasingly neurotoxic environment and therefore result in a faster cognitive decline in AD patients. Interestingly, SAP can also activate the classical complement pathway via C1q [31]. Thus, greater increase of SAP, together with increased levels of Complement C2 and Complement C9 levels could also indicate an association between the classic complement pathway products and the fast cognitive decline in AD patients.

To date, no study reported a correlation between the levels of complement cascade members and the rate of cognitive decline in AD patients. However, our study is limited by the fact that we only observed protein changes over a year, which is a short timeframe for a slow developing disease such as AD. Thus, a longer follow-up of cognitive as well as protein changes in plasma is needed to further support our findings.

SAP has previously been reported as a candidate AD diagnostic biomarker in the literature [4], albeit with a conflicting direction of association [3 , 33]. Although we found changes in SAP to be associated with rates of cognitive decline, we did not observe significant changes of SAP when comparing diagnostic groups against controls.

The comparison of protein changes across clinical diagnostic groups showed that the most significant changes are in AD subjects when compared to controls. However, the sample number for stable MCI and converting MCI patients was smaller than the AD group, resulting in lower statistical power for these groups. We found that the levels of proteins that passed multiple testing corrections for the AD group, generally reduced over time. Among the proteins, four were identified as being involved in cytokine-cytokine receptor interactions: Ck-β-8-1, MIP-1α, VEGFA, BMP RII and HCC-1. A number of studies have previously identified cytokine proteins as being predictive of a clinical AD diagnosis and progression from MCI to AD [6 , 35]. Leung et al. [36] investigated plasma levels of cytokines changing with the rate of cognitive decline. That study investigated 27 cytokines, of which 14 were also measured on our panel. They found a significant increase (p-value <0.05) in the levels of IL-4, IL-10, and granulocyte-colony stimulating factor in AD patients with a fast cognitive decline compared to those with a slow cognitive decline. IL-4 was not measured on our panel and IL-10 and granulocyte-colony stimulating factor were not found to be significant in our study.

No significant enrichment for pathways or gene ontology terms were found in protein changes during the conversion from MCI to AD. Generally, associations were weak, with no proteins passing multiple testing corrections, possibly in part due to the low sample number. Protein levels of SDF-1α (Stromal cell-derived factor 1 alpha) showed the most significant increase and it has been suggested that SDF-1α could act in the central nervous system as a classical neuromodulator under normal conditions [37, 38]. Enhanced production of SDF-1α is suspected to affect neuronal and neuroendocrine activities, consequently altering brain function and leading to pathological behaviors and/or neurotoxicity [39]. Our findings could support the idea that increasing levels of SDF-1α occur during the transition from MCI to AD. We found a suggestive association between albumin and the conversion from MCI to AD (p-value = 0.010). Albumin has been previously associated with AD [3 , 40]. In a recent review, we found that albumin was associated with an AD-related phenotype in four independent research cohorts [4]. We also found that albumin was associated with Parkinson’s disease, schizophrenia, and depression [41]. The majority of associations between AD and albumin are positive, while associations with Parkinson’s disease [42 –45], schizophrenia [46 –48], and depression [49 –54] have been found to be mainly negative [41]. Albumin is an acute-phase protein, which can increase or decrease in plasma concentrations as part of an inflammatory response. In addition albumin is also generally used as a nutritional marker [50] and thus might only reflect nutritional status.

Another protein found to be associated, on a suggestive level, with conversion from MCI to AD was trypsin (p-value = 0.006). Literature suggests that Aβ may play a direct role in protease activities. For instance, it was shown that fibrillar Aβ, a key component of AD plaques, is resistant to proteolytic digestion [55]. Further, it was reported that fibrillar Aβ inhibits the proteolytic activity of trypsin and that soluble Aβ is a substrate for trypsin [56]. We found higher trypsin levels in subjects converting to AD than stable MCI subjects and therefore our findings may support the hypothesis that trypsin is involved in the Aβ catabolism.

We have presented the largest longitudinal study to date of changes in plasma protein levels associated with rates of disease progression and clinical diagnosis in AD. Our results suggest that levels of complement pathway products are elevated in faster declining AD patients. Thus, longitudinal changes in the complement cascade might be a surrogate biomarker for disease progression and also be a promising target for AD drug discovery. However, we only investigated protein plasma level changes over one year, therefore, studies with a longer follow-up time are needed for further validation.

Footnotes

ACKNOWLEDGMENTS

This work was supported by InnoMed (Innovative Medicines in Europe), an Integrated Project funded by the European Union of the Sixth Framework program priority [FP6-2004-LIFESCIHEALTH-5]; the Alzheimer’s Research Trust; The John and Lucille van Geest Foundation and the NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at the South London and Maudsley NHS Foundation Trust and Kings College London, and a joint infrastructure grant from Guy’s and St Thomas’ Charity and the Maudsley Charity; Kuopio University Hospital (HS) and funding from UEFBRAIN (HS). SOMAscantrademark and SOMAmertrademark are trademarks of SomaLogic, Inc.

Authors’ disclosures available online ().

Appendix

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