Abstract
Plasma biomarkers to aid the early diagnosis of Alzheimer’s disease (AD) or to monitor disease progression have long been sought and continue to be widely studied. Biomarkers that correlate with AD polygenic risk score, a measure of the polygenic architecture of the disease and highly predictive of AD status, would be excellent candidates. Therefore, we undertook a preliminary study to assess the association of plasma inflammatory biomarkers with an overall AD polygenic risk score as well as with an inflammation-specific AD polygenic risk score in a sample set of 93 AD cases. We measured five complement biomarkers [complement receptor 1 (CR1), clusterin, complement component 9 (C9), C1 inhibitor (C1inh), terminal complement complex (TCC)] and the benchmark inflammatory marker C-reactive protein (CRP). Plasma clusterin level showed an association with overall AD polygenic risk score, while clusterin, C1inh, and CRP levels each displayed some association with the inflammatory-specific AD polygenic risk score. The results suggest that elevated plasma levels of inflammatory biomarkers, including complement proteins, associate with polygenic risk scores in AD, further strengthening the link between genetic and biomarker disease predictors and indicating a potential role for these markers in disease prediction and patient stratification in AD.
INTRODUCTION
There are approximately 850,000 people with dementia in the UK, with numbers predicted to rise to over 2 million by 2050 [1]. Alzheimer’s disease (AD) is the most common type representing 62% of dementia cases [2]. There is substantial evidence supporting the involvement of inflammation in the pathogenesis of AD. History of serious head injury, which typically causes brain inflammation, is known to be a risk factor for AD [3–5], and systemic infections, another cause of inflammation, also accelerate the disease [6–8]. Epidemiological studies have suggested that anti-inflammatory drugs like indomethacin and ibuprofen reduce the risk of AD [9–13]. Evidence of inflammation, including activated microglia and astrocytes, as well as various cytokines and complement activation products, have been found around amyloid plaques and dystrophic neurites in AD brain [14, 15]. All these findings support the involvement of inflammation in AD but do not indicate whether it is a primary or secondary event. However, recent genetic evidence from genome wide association studies (GWAS), including pathway analysis, has highlighted a significant etiological role for immune-related processes and inflammation in AD [16–21].
The complement system is a pivotal part of the innate immune system and a key driver of inflammatory processes. Complement consists of more than 30 component proteins, regulators, and receptors, which work together to provide defense against infection and to clear toxic material [22]. Dysregulation of the balance between complement activation and inhibition may contribute to neuroinflammation and disease. Complement activation has been shown to occur in the AD brain, even at very early stages of the disease [23], and discovery/panel-based studies investigating blood protein markers have reported significant findings with complement proteins [24–28].
Genetic studies have identified AD-associated variants in complement pathway genes. Associations between disease status and common single nucleotide polymorphisms (SNPs) in clusterin (CLU) were identified in a two-stage study GWAS involving over 16,000 individuals [16]. A second GWAS study of over 2,000 AD and 5,000 control individuals replicated the CLU finding and also identified an association with a SNP in complement receptor 1 (CR1) [18]. The association of these loci has since been robustly replicated [20, 29–32]. GWAS results such as these, even though a huge success and of great importance to the field, still only explain a very small amount of the genetic risk in AD. The residual genetic risk is likely to reside both in rare genetic variation with larger effect sizes like that of TREM2 variants for example [33–25], and in multiple small effects implicated by polygenic risk score analyses [36]. A polygenic risk score (PS) encompasses more of the causal variance because it is calculated based not solely on genome-wide significant polymorphisms, but on all nominally associated variants at a defined significance threshold (typically thousands of variants). We have investigated the polygenic architecture of AD using the powerful International Genomics of Alzheimer’s Project (IGAP) GWAS dataset [20] and demonstrated that PS could predict AD status by over 78% [36]. Here we describe a pilot study to test whether plasma biomarkers correlate with PS. We analyzed a panel of complement and inflammatory biomarkers, selected based on literature and in-house evidence of relevance to AD (CR1, clusterin, C9, C1inh, TCC, and CRP), in a subset (n = 93) of the Genetic and Environmental Risk for Alzheimer’s disease (GERAD1) cohort [16]. PS profiles were calculated for these patients, using the full PS model [36] and an immune specific PS that includes only those genes relevant to immunity and inflammation. The plasma biomarker measurements were tested for correlation with the ‘full’ and immune-specific AD PS profiles.
METHODS
Samples
Blood for plasma separation was collected in 6 ml volume using EDTA anticoagulant tubes. Plasma samples were separated (1600 g/15 min) within 24 h of collection and stored in aliquots at –80°C until analysis.
This study utilized plasma samples from 93 AD cases (57 females/36 males) with data available for polygenic risk score calculation. The scores were calculated and normalized as previously described [36], with a p-value significance threshold of 0.5 and including APOE genotype, age, and gender. The full PS model included 87,605 single nucleotide polymorphisms (SNPs). The immune specific PS (IPS) was generated using 2,177 SNPs identified from the immune-specific AD enriched pathways described in the IGAP study [21].
Quantifying the levels of clusterin, C-reactive protein, complement receptor-1, C1 inhibitor, C9, and terminal complement complex in plasma
The plasma levels of clusterin and C-reactive protein (CRP) were determined using commercially available human clusterin and CRP DuoSet ELISAs (R&D systems) and the protocols followed as described by the manufacturer.
The plasma levels of complement receptor-1 (CR1), C1 inhibitor (C1inh), C9, and terminal complement complex (TCC) were determined using sandwich ELISAs with optimized antibody pairs developed in-house. Maxisorp (Nunc Life Technologies) plates were coated with 50 μl/well of capture antibody (1–5 μg/ml in 0.1 M carbonate buffer pH 9.6), and incubated for 1 h at 37°C. The plates were washed 3x in PBS + 0.1% Tween-20 (PBST) and then blocked with 100 μl/well of 2% BSA-PBST for 1 h at 37°C. After washing the plates 3x in PBST, 50 μl/well of an 8-point serial dilution of standard protein in 1% BSA-PBST was added in duplicate to individual wells followed by addition of plasma samples in duplicate to separate wells (diluted as necessary in 1% BSA-PBST). The plate was incubated for 1.5 h at 37°C. After washing 3x in PBST, 50 μl/well of 1-2 μg/ml HRP labeled detection antibody diluted in 1% BSA-PBST was added and the plates were incubated 1 h at 37°C. Wells were washed 3x in PBST and bound antibody was visualized with orthophenylenediamine (SIGMAFAST™ OPD). Color development was stopped by the addition of 10% sulphuric acid, and absorbance was measured at 492 nm. See Table 1 for individual details for each assay.
All standards and samples for all ELISAs were tested in duplicate. The intra-assay coefficients of variation (CV) % were 5.55 for Clusterin, 9.47 for CRP, 8.45 for CR1, 5.21 for C1inh, 15.77 for C9, and 12.05 for TCC. The inter-assay CVs were 5.16 for Clusterin, 7.88 for CRP, 21.95 for CR1, 21.79 for C1inh, 21.97 for C9, and 9.71 for TCC.
Statistical analysis
Protein concentrations were determined automatically from standard curves plotted using GraphPad Prism5 and data analysis was performed using statistical software R version 3.2.3. Spearman correlation tests were used to identify correlations between protein analyte levels and PS or IPS. Correlation coefficients were calculated for any analyte with a p value less than 0.1. The Mann-Whitney test was used to look for differences in protein levels between cases with high and low PS or IPS. Effect sizes were computed for the analytes that showed a significant difference.
RESULTS
The concentrations of the six different biomarkers measured in the AD samples are shown in Table 2. In the selected sample, the PS (normalized) ranged from –2.12 to 2.53 and the IPS (normalized) ranged from –2.34 to 3.11 (high scores are associated with increased AD risk). There was no correlation between PS and IPS (the Spearman correlation coefficient between the scores was 0.06, with a p-value of 0.56).
The cohort was tested for correlations between individual biomarker levels and PS (Fig. 1). Of the six analytes measured, only one, clusterin, was significantly positively correlated with PS (correlation coefficient 0.2, p = 0.05) in that as the level of clusterin increased so did the PS. None of the other five measured proteins were significantly correlated with PS. To further explore the relationship between PS and analyte concentrations, cases at the high and low extremes of PS (defined as those with a PS more than 1 standard deviation above or below the mean) were compared for individual biomarker levels (Fig. 2 and Table 3). Clusterin was the only biomarker to show a statistically significant difference between cases with a high and low PS (clusterin concentration: low PS, 264 μg/ml; high PS, 314 μg/ml; effect size 1.24, p = 0.03).
The data were also examined for correlations between biomarker levels and IPS (Fig. 3). Two of the analytes, C1inh and clusterin, were significantly positively correlated with IPS (C1inh correlation coefficient 0.22, p = 0.05; clusterin correlation coefficient 0.25, p = 0.02). C9 and CRP trended toward a positive correlation with IPS but the correlations were not statistically significant (C9 correlation coefficient 0.19, p = 0.08; CRP correlation coefficient 0.16, p = 0.13), and CR1 and TCC showed no correlation with IPS. Comparisons of individual biomarker levels between those with the highest IPS and those with the lowest IPS (Fig. 4 and Table 4) showed that C1inh levels were significantly higher in cases with a high IPS than in cases with a low IPS (212 μg/ml versus 154 μg/ml; effect size 1.55, p-value 0.008). CRP levels were also significantly different between high and low IPS (4.99 μg/ml versus 0.75 μg/ml; effect size 14.2; p-value 0.02). CR1, C9, clusterin, and TCC showed no significant difference in concentration between high and low IPS.
Note that as the study was a small-scale hypothesis-driven pilot study, multiple testing corrections were not applied and all p-values presented are uncorrected.
DISCUSSION
The AD field is pursuing the identification of plasma biomarkers, or biomarker sets, that are sensitive detectors of early disease and/or highly predictive of disease progression. Biomarkers that correlate with AD PS would be excellent candidates. Therefore, we undertook the described study to assess the association of plasma inflammatory biomarkers with an overall AD PS as well as with an inflammatory specific AD PS in a sample set of AD cases.
From the six analytes measured in this sample set we only observed a correlation with PS for one of the analytes— clusterin. Several published studies have reported elevated plasma levels of clusterin in AD compared to controls [28, 37–40]. Taken together with our findings, these data suggest that elevated plasma clusterin level is a valid marker for AD. We tested clusterin biomarker levels for association with individual CLU SNPs rs11136000 and rs9331896 but no association was observed (data not shown) indicating that the increase in plasma clusterin in AD patients with high PS is not attributable to CLU polymorphisms alone.
When focusing in on the IPS more of the markers were associated with this outcome. Clusterin and C1inh demonstrated a statistically significant correlation with IPS, and C1inh and CRP showed a statistically significant difference between those with high and low IPS. It is, perhaps, not surprising that more of the selected analytes were correlated with IPS than PS as these markers were specifically chosen for their roles in inflammation. Both clusterin and C1inh are inhibitors of complement activation; clusterin is a fluid-phase inhibitor of the membrane attack complex, while C1inh inhibits the C1 complex of the classical pathway of complement activation and the MBL/MASP complex of the lectin pathway. Both are suicide inhibitors, consumed in the act of inhibition and both are acute phase reactants; plasma levels in inflammatory disease thus represent a balance between consumption and increased synthesis. CRP is a major acute-phase reactant that can increase 1000-fold or more in plasma concentration in response to inflammation. The finding that CRP levels were only significantly increased when the extremes of the IPS were compared suggests that the observed changes in the complement biomarkers were not solely driven by the acute phase response but reflected other disease processes. The finding that more of the markers measured in this study correlated with IPS than PS is further evidence of their functional relevance and highlights the need for focused/targeted approaches to AD research by stratifying patients using biomarkers and other measurables in order to identify disease subtypes rather than looking at the disease as awhole.
We stress that the study reported here is preliminary and utilizes a relatively small sample set; we recognize that only one of the associations observed with these biomarkers survives adjustment for multiple testing (C1inhibitor levels in samples with low IPS versus high IPS) and the other reported findings could be false positives. However, the study presented here does flag the potential usefulness of testing the association of plasma inflammatory biomarkers with polygenic scores in AD.
This study cohort comprised patients with established AD; however, it might be more relevant and informative to test associations of PS and IPS with plasma biomarkers in patients with mild cognitive impairment or early AD, or even in cognitively normal controls.
To summarize, we have identified associations between plasma inflammatory biomarkers and polygenic scores in AD that further strengthens the link between genetic and biomarker disease predictors. While noting that replication in large, independent sample sets is essential, our data provide the first evidence that several inflammatory biomarkers, including complement proteins, associate with high polygenic risk scores, increasing confidence that these markers will help disease prediction and patient stratification in AD.
Footnotes
ACKNOWLEDGMENTS
This publication incorporates results from the research project entitled “Wellcome Trust Consortium for Neuroimmunology of Mood Disorders and Alzheimer’s Disease” which is funded by a grant from the Wellcome Trust.
Plasma samples, n = 93, from Alzheimer’s disease (AD) cases, were obtained from the Genetic and Environmental Risk for Alzheimer’s disease (GERAD1) cohort (Harold et al., Nat. Genet. 41, 1088–1093, 2009), recruited by the Medical Research Council (MRC) Genetic Resource for AD (Cardiff University; Kings College London; Cambridge University; Trinity College Dublin). Sample collection was supported by the Medical Research Council (MRC), Wellcome Trust, Alzheimer’s Research UK (ARUK), Welsh Assembly Government and Mercer’s Institute for Research on Ageing.
For the Polygenic Risk Score Calculations, we thank the International Genomics of Alzheimer’s Project (IGAP) for providing summary results data for the training data set. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i–Select chips were funded by the French National Foundation on Alzheimer’s disease and Related Disorders. EADI was supported by the LABEX (laboratory of Excellence Program Investment for the Future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. GERAD was supported by the Medical Research Council (Grant n° MR/K013041/1), Alzheimer’s Research UK (Grant n° ARUK-Network2011-4), the Wellcome Trust (Grant n° 082604/2/07/Z) and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant n° 01GI0102, 01GI0711, 01GI0420. CHARGE was partly supported by the NIH/NIA grant R01 AG033193 and the NIA AG081220 and AGES contract N01–AG–12100, the NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by the NIH/NIA grants: U01 AG032984, U24 AG021886, U01 AG016976, and the Alzheimer’s Association grant ADGC–10–196728.
Genetic and Environmental Risk for Alzheimer’s disease (GERAD1) Consortium data (Harold et al., Nat. Genet. 41, 1088–1093, 2009) was used as a test data set for polygenic score analysis. GERAD1 consists of 3,333 cases and 1,225 elderly screened controls genotyped at the Sanger Institute on the Illumina 610-quad chip. Study recruitment was via the Medical Research Council (MRC) Genetic Resource for AD, as described for plasma samples above, the Alzheimer’s Research UK (ARUK) Collaboration (University of Nottingham; University of Manchester; University of Southampton; University of Bristol; Queen’s University Belfast; the Oxford Project to Investigate Memory and Ageing (OPTIMA), Oxford University); Washington University, St Louis, United States; MRC PRION Unit, University College London; London and the South East Region AD project (LASER-AD), University College London; Competence Network of Dementia (CND) and Department of Psychiatry, University of Bonn, Germany and the National Institute of Mental Health (NIMH) AD Genetics Initiative. Data was combined with Illumina HumanHap300 BeadChip data from 608 AD cases and 853 elderly screened controls ascertained by the Mayo Clinics in Jacksonville, Florida and Rochester, Minnesota as well as the Mayo Brain Bank. All AD cases met criteria for either probable (NINCDS-ADRDA, DSM-IV) or definite (CERAD) AD. All elderly controls were screened for dementia using the MMSE or ADAS-cog, were determined to be free from dementia at neuropathological examination or had a Braak score of 2.5 or lower. A total of 5770 population controls were included in GERAD GWAS including the 1958 British Birth Cohort (1958BC) (http://www.b58cgene.sgul.ac.uk), NINDS funded neurogenetics collection at Coriell Cell Repositories (Coriell) (see 
), the KORA F4 Study, Heinz Nixdorf Recall Study and ALS Controls. The ALS Controls were genotyped using the Illumina HumanHap300 BeadChip. All other population controls were genotyped using the Illumina HumanHap550 Beadchip.
GERAD1 Acknowledgements: Cardiff University was supported by the Wellcome Trust, Medical Research Council (MRC), Alzheimer’s Research UK (ARUK), Welsh Assembly Government. Cambridge University and Kings College London acknowledge MRC support. ARUK supported sample collections at the South West Dementia Bank and Universities of Nottingham, Manchester and Belfast. The Belfast group acknowledges support from the Alzheimer’s Society, Ulster Garden Villages, N. Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The MRC and Mercer’s Institute for Research on Ageing supported the Trinity College group. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer’s and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by NIH grants, Barnes Jewish Foundation and the Charles and Joanne Knight Alzheimer’s Research Initiative. Patient recruitment for the MRC Prion Unit/UCL Department of Neurodegenerative Disease collection was supported by the UCLH/UCL Biomedical Centre and NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by Lundbeck SA. The Bonn group was supported by the German Federal Ministry of Education and Research (BMBF), Competence Network Dementia and Competence Network Degenerative Dementia, and by the Alfried Krupp von Bohlen und Halbach-Stiftung. Some samples were ascertained by the NIMH AD Genetics Initiative. The KORA F4 studies were financed by Helmholtz Zentrum München; German Research Center for Environmental Health; BMBF; German National Genome Research Network and the Munich Center of Health Sciences. The Heinz Nixdorf Recall cohort was funded by the Heinz Nixdorf Foundation (Dr. jur. G.Schmidt, Chairman) and BMBF. NINDS and the Intramural Research Program of the National Institute on Aging support Coriell Cell Repositories. We acknowledge use of 1958 British Birth Cohort, funded by the MRC and Wellcome Trust, genotyped by the Wellcome Trust Case Control Consortium and the Type-1 Diabetes Genetics Consortium, sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International.