Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status

INTRODUCTION

Alzheimer’s disease (AD) is one of the most common type of dementia, and still lacks therapies that are definitive in lowering its morbidity [1, 2]. The discovery of AD biomarkers for early detection that is vital for successful disease regulation. For clinical trials, they can accelerate accurate screening and lower the burden of large, lengthy, and costly trials, and also overcome uncertainty in patient progression on treatment outcome measures [3, 4]. The two major pathogenic components of AD are plaques, composed of amyloid-β (Aβ), and neurofibrillary tangles, composed of hyperphosphorylated tau [5, 6]. Several metabolic processes like inflammation, oxidative damage, and lysosomal dysfunction interact with the aberrant protein deposition [2, 7]. The three biomarkers in cerebrospinal fluid (CSF) consistently studied and validated are Aβ₄₂, phosphorylated tau (p-tau), and total tau protein (t-tau). These biomarkers are good predictors for differentiating AD from cognitively normal (CN) patients with a mean sensitivity of 82% and specificity of 82% for Aβ₄₂, sensitivity of 80% and specificity of 90% for t-tau, and sensitivity of 80% and specificity of 83% for p-tau [8]. Combination of CSF biomarkers with structural or functional brain imaging markers may provide higher diagnostic accuracy than the CSF biomarkers or imaging biomarkers alone [9]. However, CSF biomarkers Aβ₄₂, p-tau, and t-tau are inadequate in distinguishing mild cognitive impairment (MCI) from CN subjects and AD from MCI subjects. Additionally, lumbar puncture to collect CSF is less accepted by the older adults, especially MCI patients [10]. Although there is an emerging pipeline of drugs for the treatment of MCI and early-stage AD, and the US Food and Drug Administration (FDA) has approved the anti-amyloid-beta protofibril antibody lecanemab (https://doi.org/10.1002/trc2.12295), the challenge remains that lack of effective biomarkers to diagnose and monitor MCI.

The National Institute on Aging and Alzheimer’s Association (NIA-AA) research framework for AD in 2018 defined AD biologically, by neuropathologic change or biomarkers, and treated cognitive impairment as a symptom/sign of the disease rather than the definition of the disease. This theory states that rather than relying solely on in vivo clinical diagnostic criteria, additional confirmatory evidence is required from positron emission tomography (PET) or fluid biomarkers of AD specific Aβ and tau pathology [8, 11–13]. Patients with AD, however, also have damage to their blood–brain barrier (BBB) which will led the identification of blood-based biomarkers possible, through the measurement of proteins that have passed through the disrupted BBB [14]. Recent studies suggest that the plasma Aβ₄₂, and p-tau biomarkers accurately predict the change in cognition and subsequent AD dementia in cognitively unimpaired patients [15–17]. Plasma p-tau₂₁₇ alone in BioFinder cohort was reported to accurately predict (area under the curve (AUC)  = 0.83) AD progression within 4 years in those with subjective cognitive decline and MCI subjects [16]. The AUC was improved if p-tau₂₁₇ combined with the test score of memory, executive function and APOE ϵ4 genotype [16, 18].

The above scientific evidence suggests that the ATN (amyloid/tau/neurodegeneration) biomarkers in CSF and blood have demonstrated usefulness in AD. Researchers has also identified other protein markers in blood distinct form Aβ and tau that also performed well in AD classification and prediction [19–23]. Improvement in MCI diagnosis may benefit from considering these other biomarkers under the umbrella of ATN biomarkers or in addition to them. As the population ages and more older adults develop AD dementia, we need to develop tests that are cost effective and can be widely used to aid accurate diagnosis of MCI and AD. In the present study, we aimed to compare commonly measured protein markers in plasma and CSF to make differential diagnoses of cognitive status at baseline and compared them with established AD biomarkers namely CSF Aβ₄₂ and p-tau.

MATERIAL AND METHODS

Data source

Data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) phase 1 (ADNI1) database (https://adni.loni.usc.edu/). ADNI was launched in 2003 as a public-private partnership led by principal investigator Michael W. Weiner, MD. The overall objective of ADNI was to develop and validate in vivo biomarkers for AD. Detailed methodology and description of ADNI can been found on the ADNI website. The 63 ADNI sites in the USA and Canada received approval from their local Institutional Review Boards, and each participant at each site provided written informed consent. The ADNI study consist of 4 phases: ADNI1, ADNIGO, ADNI2, and ADNI3 [24]. Plasma and CSF proteomics data, however, is only available in the ADNI1. Therefore, the ADNI1 database was the only source of participants for this study (Fig. 1).

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Fig. 1

Participant selection flow chart and study design.

RESULTS

Final model selection

Next, we applied the RF algorithm to check the performance of the final three plasma models based on 6 proteins (APOE, AMBP, C3, IL16, IGFBP2, APOD) as well as three CSF models based on 7 proteins (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN). These models were compared with typical AD biomarkers (CSF p-tau and Aβ₄₂) models (Model 1: CSF p-tau and Aβ₄₂ only (unadjusted), Model 2: CSF p-tau and Aβ₄₂ adjusted for age, sex, education, and Model 3: Model 2 plus APOE ϵ4). We calculated the average value of AUC, accuracy, sensitivity, and specificity over 500 resampling (Fig. 2 and Supplementary Figures 7–9). Plasma proteins had no strong correlation correlations; however, some CSF proteins are correlated (Fig. 3).

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Fig. 2

Mean AUC for both plasma and CSF (n = 257) over 500 resampling using Random Forest algorithm based on test data. CN, cognitive normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; CSF, cerebrospinal fluid; Model 1: unadjusted model, Model 2: adjusted for age, sex and education, Model 3: Model 2 plus APOE ϵ4.

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Fig. 3

Correlation plots of the selected proteins panels in plasma and cerebrospinal fluid (CSF) (n = 257) samples.

DISCUSSION

In this study, we used LASSO analyses and compared the performance of 75 commonly measured proteins in plasma and CSF for accuracy of disease classification at baseline. For the primary analyses it is observed that more plasma proteins were significantly associated with CN versus MCI, CN versus AD, and MCI versus AD classification (Table 3). Our present study results suggest that 6 protein candidates in plasma (APOE, AMBP, C3, IL16, IGFBP2, APOD) and 7 candidate protein in CSF (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) were important predictors. The diagnostic performance of these candidates was estimated and compared with conventional AD biomarkers namely CSF p-tau and Aβ₄₂ for MCI.

One of the key findings of our study is that the 6 plasma proteins panel performs better than CSF p-tau and Aβ₄₂ in classifying MCI versus AD subjects and resulted in good accuracy as CSF p-tau and Aβ₄₂ in classifying CN versus MCI subjects. Further, combining plasma proteins panel with age, sex, education, and APOE ϵ4 had no effect on the model’s performance (Fig. 2 and Supplementary Figure 7–9). CSF p-tau and Aβ₄₂ were measured by fully automated Roche Elecsys but we also consider CSF p-tau and Aβ₄₂ measured by AlzBio3 immunoassay to compare our results and we observed that still results are same (Supplementary Figure 12). ATN is the primary pathological trait that distinguishes AD from other diseases, and ATN biomarkers are increasingly used to support an AD continuum diagnosis. However, in clinical setting, the classification of pre-symptomatic subjects on the AD continuum is readily accepted due to the absence of disease-modifying therapies available to this disease stage. Further, elevated Aβ levels can be commonly found in cognitively intact older adults as well as in those with non-AD dementias or mixed dementias [8, 38]. Although Aβ and tau biomarkers have been reported to predict incident AD dementia in those with subjective cognitive decline, individualized risk modeling remains difficult [8]. To assess Aβ plaque deposition, typically CSF assays or amyloid PET imaging are used. Even though these tests provide valid results on Aβ deposition status and their use in measuring biomarkers is mentioned in the diagnostic criteria for AD [39], a person who tests positive for Aβ by PET may require additional tests to determine whether the amyloidosis is caused by AD or advanced age. In addition, the lumbar puncture procedure used to obtain CSF samples from patients is invasive and patients are frequently resistant to this procedure. However, due to low cost and easy measurements, these plasma proteins could be used as a screening test for MCI or AD. If a positive result is shown, more expensive and invasive tests like CSF and PET scan for AD will be suggested to patients.

We found that the top proteins in plasma were largely different from the top proteins in CSF and outperformed from CSF for determining MCI (Fig. 2 and Supplementary Figures 7–9), suggesting that peripheral proinflammatory factors increase AD risk at an early stage via different molecular pathways from the ones in CSF. Several studies demonstrated that combinations of plasma proteins were useful for AD classification and prediction. A recent study identified numerous pathway-specific plasma proteins that could be used as peripheral biomarkers in the early stages of AD and related dementias. In this study, 32 dementia-associated plasma proteins were revealed to be involved in proteostasis, immunology, synaptic function, and extracellular matrix architecture [40]. A combination of 18 plasma signaling and inflammatory proteins (ANG-2, CCL5, CCL7, CCL15, CCL18, CXCL8, EGF, G-CSF, GDNF, CAM-1, IGFBP-6, IL-1α, IL-3, L-11, M-CSF, PDGF-BB, TNF-α, and TRAIL-R4) can be used to differentiate CN versus AD subjects with an approximate accuracy of 90% and to predict the AD progression from MCI 2–6 years later [19, 20]. A model of ten plasma proteins (MTDH, ADIPOQ, APOB, TF, CA1, C9, APOA4, RBP4, F13A1, and FGA) derived by multiple reaction monitoring mass spectrometry was developed, which classified the CN versus AD and CN versus asymptomatic AD subjects with the AUCs of 81.7% and 77.7%, respectively, when combining with APOE ϵ4, model performance improved the AUCs of 87.3% and 82.6%, respectively [21]. Another study found that a set of eight plasma proteins (BDNF, AGT, IGFBP-2, OPN, cathepsin D, SAP, complement C4, and TTR) may function as a valuable diagnostic biomarker for AD in the Chinese population [22]. A combination of two plasma and four CSF proteins were used to predict mid-term AD dementia progression with sensitivity of 88% and a specificity of 70% [23]. Lower level of plasma C-reactive protein was associated with AD diagnosis and rapid progression to AD dementia [41–43].

In the selected plasma proteins in this study only IGFBP2 overlapped with the previous studies Ray et al. [20], O’Bryant et al. [44], and Cheng et al. [22]. IGFBP2 can affect the formation of DNA, cell growth, death, and cellular uptake of glucose and amino acids by preventing IGF functions [22, 45]. The mean level of plasma IGFBP2 was significantly high in MCI than the CN and AD p < 0.01 in both comparisons (Supplementary Figure 10). The significantly highly expressed plasma proteins in healthy population were APOE (p < 0.001) and IL16 (p < 0.01). APOE is a polymorphic lipoprotein that is a primary cholesterol transporter in the brain and it is generated and secreted from a number of tissues and cell types and is abundant in interstitial fluid, lymph, and plasma [46, 47]. IL16 is a chemoattractant for specific immune cells, and its overexpression has been reported in a variety of cerebral diseases, including AD [48]. Plasma AMBP differentially expressed in CN versus MCI (p < 0.01) and MCI versus AD (p < 0.001) and plasma C3 differentially expressed in CN versus MCI (p < 0.01) and MCI versus AD (p < 0.001). AMBP is a precursor for bikunin, a lipid transporter that has also been linked to inflammation control and neuroprotection and it is primarily synthesized in the liver and is upregulated in response to heme, reactive oxygen species and to the pro-inflammatory cytokines [49, 50]. C3, a large molecular weight protein (185 kDa), is a key component of the innate immune system, providing a primary host mechanism for the recognition and clearance of pathogenic microbes along with other complement proteins [51]. Plasma APOD is significantly decrease in MCI than CN and AD (p < 0.05). APOD is broadly expressed in humans, unlike to other apolipoproteins, which are mostly generated by the liver, implying fundamental cellular roles [52]. It is involved in oxidative stress, inflammation, and small hydrophobic molecule transport. APOD has been identified as the gene that is most upregulated with age, and its expression increases in a variety of illnesses, most notably Parkinson’s disease and AD [53].

Both plasma and CSF proteins analyzed by same technology as discussed above, and it is noticed that 45 proteins out of 75 were correlated (Supplementary Table 1) in both plasma and CSF. Four proteins in the selected 6 plasma proteins (APOE, AMBP, C3, IL16, IGFBP2, APOD) were significantly correlated with their CSF sample (Protein, correlation [p]: AMBP, 0.43 [5E-13], IL16, 0.2398 [1E-04], IGFBP2, 0.6609 [1.2E-33], APOD, 0.1474 [0.02]). However, these proteins were not found to be top CSF proteins for AD. Also, these proteins have significantly higher expression levels in plasma than CSF (Supplementary Figure 13). Interestingly, some of the plasma proteins including C3, IGFBP2, and APOD presented with a U shape when comparing CN, MCI, and AD (Supplementary Figure 10). Thus, these plasma proteins are not brain-specific and may have a specific influence from peripheral system on AD development and progression.

CSF protein models (Models 1, 2, and 3) performed satisfactorily only in distinct CN versus AD subjects, with AUCs of 88%, 88%, and 92% (Table 4) based on the number of proteins in each model (18, 18, and 15 (Table 3), respectively). These results were better than the previous results using the same data with model of 24 proteins have an AUC of 80% [54]. These models were not good classifier of the MCI versus AD groups AUC <70% (Table 4). Further, the performance of these models with 7 candidates protein models was also poor, with AUCs ≤60% in the MCI versus AD group. We observed that CSF p-tau and Aβ₄₂ were inadequate in differentiating MCI from AD subjects, with AUCs of 52%, 54%, and 56% (Fig. 2). However, 6 plasma candidates protein marker (Models) provide a much better AUC ≥75% for differentiating MCI from AD subjects. This implies that CSF biomarkers of either 7 candidate proteins or p-tau and Aβ₄₂ are good predictors in distinguishing the CN versus AD subjects. Mean level of 7 CSF proteins were significantly different in CN versus AD, four proteins VEGFA, HGF, FABP3 and FGF4 were significantly different in CN versus MCI and three CSF proteins VEGFA, CD40, and RETN were differentially expressed in MCI versus AD.

Although these are some promising results, our study carries a few limitations. Even though we identified plasma proteins that show potential in the classification of CN versus MCI and MCI versus AD subjects through training and test datasets, our findings are limited by the inability to validate these candidates in a separate cohort. Future research into the prognostic potential of the candidates identified here in other well-characterized prospective cohorts is required. Furthermore, we only chose 75 plasma proteins (out of 146) in order to compare the same proteins in CSF and plasma. As a result, future research may look at the entire set of plasma proteins to investigate the different set of candidate proteins in plasma for detecting AD. Finally, longitudinally monitoring these plasma proteins in the same subject along with cognitive function changes will help examine the causal role of these proteins. Using ADNI data set have lack of diversity and overall health of the cohort. Also, due to the insufficient sample size molecular PET imaging biomarkers were not included in this study. Thus, we were limited to compare the performance of the selected protein panels with CSF p-tau and Aβ₄₂.

In conclusion, we observed that plasma candidate proteins performed well in differentiating MCI from both CN and AD subjects. However, CSF candidate proteins and Aβ₄₂ and p-tau have a higher accuracy in distinguishing CN from AD subjects. As a result of these findings, plasma candidate marker can be considered an effective diagnostic tool for the early detection and monitoring progression of AD symptoms and later distinguishing AD from MCI. Our study also supports that peripheral inflammation increases AD risk at an early stage.