A Novel Pre-Screening Kit for Cerebral Amyloid Deposition Using QPLEXTM-Alz Plus Assay Kit: Application to Cognitively Normal Individuals

Background The deposition of beta-amyloid (Ab) in the brain precedes the onset of symptoms such as cognitive abnormality in Alzheimer’s disease (AD); therefore, it is best to detect Ab accumulation early as possible. Methods We previously reported that QPLEX TM Alz plus assay could be used for pre-screening of Ab accumulation in the brain. In this study, we applied QPLEX TM Alz plus assay kit to the cognitively normal (CN) group for the brain Ab positivity. Total 221 CN participants with or without brain Ab were included. Receiver operating characteristics (ROC) curve analysis showed that the discrimination power reached 0.878 area under curve (AUC) with 69.7% sensitivity and 98.4% specicity in the CN group over 65 years.


Background
Alzheimer's disease (AD) is the most prevalent form of the disease that causes dementia, a icting more than 40 million individuals worldwide (1). There are many causes of Alzheimer's disease (2)(3)(4), but undoubtedly the primary pathology of AD is severe amyloid deposition in the brain (5). Multiple methods in assessing brain amyloid levels have been developed over the past years, including the use of Pittsburgh B compound positron emission tomography (PiB-PET) (6). However, due to limited accessibility of PiB-PET, ongoing trials have been made to nd surrogates for PiB-PET, such as blood biomarkers that reliably indicate the brain amyloid level (7). Without the need for burdensome cerebrospinal uid (CSF) extraction or high-cost brain imaging, numerous advantages of blood-based diagnosis are drawing the attention of researchers, clinicians, and the elderly population.
Several studies have shown changes in blood biomarker levels during the onset and progression of Alzheimer's disease (8,9). Blood biomarkers show changes in level long before cognitive impairment, which means that overall systemic changes occur before brain malfunction (10). Moreover, the fact that brain amyloid deposition precedes cognitive abnormalities emphasizes the need for screening to be performed in cognitively normal (CN) individuals (8). Asymptomatic cerebral amyloidosis becoming a new criterion for preclinical AD, PiB-PET positive individuals, regardless of their cognitive state, are at risk of developing the symptoms (11). In this context, the prediction of AD progression based on blood biomarkers could be bene cial to those CN individuals prone to progress to mild cognitive impairment (MCI) stage by providing preventive measures and aiding in early intervention.
In our previous studies, we discovered a novel blood-based biomarker panel to predict cerebral amyloid deposition (12,13) and developed a pre-screening platform for PiB-PET positivity using this panel (14).
Here we show that QPLEX TM Alz plus assay kit can be utilized to distinguish CN individuals with or without amyloid burden. The results show that the kit can be used to pre-screen amyloid deposition in the brain when no apparent cognitive abnormalities are present to identify individuals susceptible to AD progression due to the amyloid burden in the brain.

Participants
In total, 221 participants were included in this study. All participants were cognitively normal. These individuals were recruited as part of the Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease (KBASE). All participants were given appropriate clinical and neuropsychological assessments according to the KBASE assessment protocol. Participant recruitment, clinical diagnosis criteria, and further information are detailed in our previous report (15).

Ethical approval
All participants and (where applicable) their legal representatives read and con rmed the informed consent documents. This study was approved by the Seoul National University Hospital Institutional Review Board (IRB).

PiB-PET
All participants underwent PiB-PET scans using a 3.0T PET-MR scanner (Siemens Healthineers, Erlangen, Germany). Each participant was intravenously injected with 555 MBq of [11C] PiB (450-610MBq) PET tracer, which enabled visualization of cerebral amyloid deposition. The automatic anatomic algorithm determined the degree of amyloid accumulation, which was calculated by the standardized uptake value ratio (SUVR). Four regions of interest (ROIs) in the brain were the lateral temporal, lateral parietal, posterior cingulate-precuneus, and frontal regions. If the SUVR value was 1.4 or higher for at least one of the four ROIs, the individual was de ned as PiB-positive (PiB+). Additional information on the imaging protocols is provided in our previous paper (15).

Blood sampling
All fasting blood samples were collected at 9:00 AM. Whole-blood samples were gathered in K2 EDTA tubes (BD Vacutainer Systems, Plymouth, UK) and centrifuged at 700 g for 5 minutes at room temperature (RT). Then the supernatant was collected, the centrifugation was repeated, and the tubes were stored at −80 °C. QPLEX TM Alz plus assay All quanti cation method is detailed in our previous paper (14). Brie y, the QPLEX TM kit utilized Quantamatrix's multiplex diagnostics platform (QMAP; Quantamatrix Inc., Seoul, Republic of Korea) (16). First, human plasma samples were diluted in the diluent buffer and incubated with the coded beads and biotin-conjugated detection antibodies. The immunocomplexes were washed twice with washing buffer on a Biotek-510 magnetic wash station (Biotek, VT, USA). Diluted R-phycoerythrin-conjugated streptavidin was added to each well. After three washes, the immunocomplexes were resuspended in 100 μl of washing buffer by tapping and were analyzed automatically by the QMAP TM system.

Monotone regression spline analysis
Analyses for monotone penalized regression splines were performed to identify the relationship between each biomarker response and the imaging biomarkers (17). Monotone curves were generated by the smoothing spline method with the number of four knots. To effectively show comparisons between different QPLEX TM biomarkers, their levels have been transformed to z-scores. Participant's age acted as a proxy for progression time.

Statistical analyses
GraphPad Prism 8 (San Diego, CA, USA) and Medcalc 17.2 software (Ostend, Belgium) were used. Comparison analyses between two variables were conducted by independent t-test or analysis of covariance (ANCOVA) with correction for age and sex. Pearson's correlation analysis method was used for correlation analyses. Logistic regression, followed by receiver operating characteristic (ROC) curve analysis was performed to calculate the discriminatory power, sensitivity, and speci city for the biomarker panels. The formulas, coe cients, and constants could be optimized since there were appropriate outliers and various logistic regression models. By using the values of variance in ation factors (VIF), multicollinearities were checked. All statistical outliers were excluded from the cohort according to Grubb's double-side outlier test (p < 0.05).

Categorization of participants
The cognitively normal (CN) participants were classi ed as CN-(PiB-PET negative, n = 185) and CN+ (PiB-PET positive, n = 36) groups. The details were shown in Table 1. There were no signi cant differences in sex, education level, mini-mental state examination (MMSE) score, apolipoprotein E 4 (ApoE4) positivity, and clinical dementia rating (CDR) scores. In addition, the demographic data according to their agegroups (young, mid-age, and old-age) were shown in Supplementary Table 1.

Characterization of QPLEX TM biomarkers without any intervention of AD pathology
We had previously reported that our QPLEX TM biomarkers (beta-amyloid1-40, Ab1-40; periostin, POSTN; galectin-3 binding protein, LGALS3BP; angiotensin-converting enzyme, ACE) showed signi cant differences between age-matched (age > 55 years old) PiB − and PiB + participants (14). In the current study, our purpose was to utilize the biomarkers to distinguish whether amyloid accumulates within the CN group (CN-vs CN+; cognitively normal participants of all ages). Before the start, we tested the effects of age and sex, which are generally known as confounders of AD, on our biomarkers ( Figure 1A and 1B).
Note that we only used CN-samples for Figure 1 to exclude the intervention of AD pathology from testing the confounders (age, sex). Interestingly, our monotone regression spline analysis found three different curve patterns of biomarkers according to aging ( Figure 1A). The cut-off dividing lines (42 years old, 65 years old) separating age-groups were determined by the vertexes of each biomarker curve (POSTN and ACE showed the rst vertex at the point of 42 years old; POSTN, ACE, and LGALS3BP showed the second vertex at the point of 65 years old; Ab1-40 showed unclear vertex). According to this cut-off criterion, we de ned three age-groups as Young, Mid-age, and Old-age groups. Interestingly, the second cut-off point (65 years old) is exactly matched with the standard age for late-onset AD ( Figure 1A) (18). Next, we identi ed that there were no differences in the biomarkers between male and female ( Figure 1B). Furthermore, all of the biomarkers showed signi cant correlations between them ( Figure 1C) but with low variance in ation factors (VIF < 2), which means they are in the most suitable situation to be used as variables for the logistic regression.
Age-dependent approaches for identifying the correlation between the QPLEX TM markers and cerebral amyloid deposition Since the QPLEX TM biomarkers showed different patterns between the age groups, we checked relationships between the biomarkers and cerebral amyloid deposition (standard uptake value ratio; SUVR) for each age-group ( Figure 2). The correlations between cerebral amyloid deposition and the QPLEX TM biomarkers were not signi cant in Young and Mid-age groups. In the Old-age group, we con rmed that periostin (POSTN) and galectin-3 binding protein (LGALS3BP) levels in blood showed remarkably signi cant correlations with cerebral amyloid deposition ( Figure 2C). Thus, we decided to focus on the Old-age group and further investigated whether the QPLEX TM biomarkers can be utilized to discriminate PiB-PET positivity within this group. Young and Mid-age groups were not appropriate for this analysis because they had a too small number of PiB+ participants (Supplementary Table 1). However, since this model was only limited to old-age participants, we thought it was an insu cient model for satisfying our goal, which targets all ages of CN participants. Therefore, we decided to generate new models for all ages with the correction for covariates (age, sex) to compensate for the age-dependent difference in the level of biomarker pattern and the signi cant difference of age between CN-and CN+ (Supplementary Figure 1-2).
Relationship between the QPLEX TM biomarkers and cerebral amyloid deposition in Old-age group (>65 years) First, we performed a comparison analysis between CN-and CN+ in the Old-age group. Although the biomarkers did not show any differences between the sexes ( Figure 1B), we included this variable for a covariate because it has also been a well-known confounder of AD diagnosis (19). Interestingly, two biomarkers, periostin (POSTN) and galectin-3 binding protein (LGALS3BP) showed signi cant differences between CN-and CN+ ( Figure 3A-D, left). The overall level of POSTN in blood was signi cantly increased in individuals with cerebral amyloid deposition above the threshold, whereas LGALS3BP showed the opposite tendency. Partial correlation analysis with correction for age and sex also revealed a signi cant association between the blood biomarker levels and cerebral amyloid deposition ( Figure 3A-D, right). POSTN was positively correlated with cerebral amyloid deposition (Pearson's correlation coe cient R = 0.2186, P-value = 0.0333), whereas LGALS3BP was negatively correlated (Pearson's correlation coe cient R = -0.2626, P-value = 0.0094). When all age-groups were combined, all QPLEX TM biomarkers except for ACE showed signi cant differences and correlations, shown in Supplementary Figure 1. Furthermore, the results from the analysis of covariance (ANCOVA) showed that All CN + group had signi cantly different levels of Ab1-40, POSTN, and LGALS3BP than All CN − group (Supplementary Table 2). Our results showed the possibility of the QPLEX TM biomarkers as variables for the cerebral amyloid deposition, except for ACE.
Discriminative ability of the QPLEX TM Alz plus assay for CN-vs CN+ in Old-age group (>65 years) Before further analyses for the practical use, we had to determine whether Ab1-40 and ACE could be used as variables because they did not show the signi cant results from the previous analyses (Figure 2 Table 3). However, when we performed multiple regression analysis on all CN groups, we found that all QPLEX TM biomarkers, including Ab1-40 and ACE, showed a signi cant correlation with cerebral amyloid deposition (SUVR) in every combination of variables (Table 2). Thus, we decided to include Ab1-40 and ACE in our biomarker panel because they were veri ed to be useful variables in explaining cerebral amyloid deposition level, based on multiple regression analysis. Subsequently, we performed logistic regression and receiver operating characteristic (ROC) curve analysis to identify the discriminative ability of our QPLEX TM Alz plus assay ( Figure 4A). When three ROC curves were compared with each other, we found adding QPLEX TM biomarkers dramatically increased area under curve (AUC) (0.622 to 0.878, Curve I vs Curve III; 0.684 to 0.878, Curve II vs Curve III) ( Figure 4B). Each graph had high sensitivities or speci city (Curve I, 82.4% sensitivity and 42.9% speci city; Curve II, 52.9% sensitivity and 73.5% speci city; Curve III, 69.7% sensitivity and 98.4% speci city) ( Figure 4C). When all age-groups were combined, QPLEX TM Alz plus assay still retained its discriminative ability, which is shown in Supplementary Figure 2. Therefore, the results suggest that QPLEX TM Alz plus assay can also be used for pre-screening amyloid deposition in the brain even when there are no apparent symptoms of cognitive disorders.

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The lack of disease-modifying treatment is the major problem for AD, and the most e cient solution for this disease would be the early diagnosis in the initial stage of pathogenesis. Preclinical AD is a widely accepted disease stage that shows the initiation of brain pathology and silent symptoms (11). Advanced technology enables identifying preclinical AD, such as functional imaging of PET, MRI, and cerebrospinal uid (CSF) Aβ, tau, and p-tau (20). However, these modern technology diagnoses have di cult access due to high cost and invasive procedures (21,22). Easily accessible biomarkers for diagnosis are desperate in the stage of preclinical AD for early detection and prevention of disease progression.
In this study, cognitively normal PiB-PET negative individuals of a broad age range were included. We performed a monotone regression spline analysis from these samples in which participants' age acted as a proxy for time ( Figure 1A). This enabled us to observe the general trend of changes in QPLEX TM blood biomarkers with aging in the absence of AD pathology. With participants ensured to be normal under a speci c set of rules such as PiB-PET and clinical dementia rating, we could indirectly observe how our biomarkers undergo overall systemic changes under normal physiological conditions. Interestingly, the monotone regression spline curve's vertexes segmented the population into three age-groups with a distinct trend of QPLEX TM biomarkers. Further correlation analyses focusing on individual age-groups were performed, which revealed a signi cant correlation in the Old-age group ( Figure 2). Based on these results, along with the fact that 65-years is a clinical standard in sporadic AD and individuals in this age group are most vulnerable to be a icted by dementia, we primarily focused on the Old-age group but also performed the same analyses as a whole (including all age-groups) in parallel.
Comparison analyses revealed that blood levels of POSTN and LGALS3BP were signi cantly different according to PiB-PET status in Old-age and Ab1-40 was added to the list when analyzed as a whole (Figure 3, Supplementary Figure 1). These biomarkers also showed a correlation with the level of amyloid deposition in the brain, which supports these blood markers' appropriateness as indicators of brain status. Multiple regression analyses in which the QPLEX TM biomarkers serve as signi cant variables in the models further support this notion ( Table 2, Supplementary Table 3). ROC curve analyses show the biomarkers' discriminative ability and conclude that QPLEX TM Alz plus assay is apt to be used for prescreening purposes (Figure 4, Supplementary Figure 2).
Recently, we discovered the diagnostic blood biomarkers for AD (13,14) and reported the diagnostic accuracy from a clinical trial with AD patients and age control group using QPLEX TM Alz plus assay kit, which had been developed based on these biomarkers (14). In that study, we focused on the whole participants, including AD patients in old age and age-matched non-demented control groups (average age of CN-67.32 ± 0.8, CN+ 74.47 ± 1.0). Therefore, we were uncertain of the age at which individuals could apply the QPLEX TM Alz plus assay kit for early detection of AD. Based on the fact that the pathological process of the amyloid deposition begins many years prior to clinical symptoms (20), it is best to test cerebral amyloid deposition as early as possible, especially in the case of individuals with a family history or other risk factors. Early detection of cerebral amyloid deposition with QPLEX TM Alz plus assay kit during this preclinical or pre-symptomatic stage would be the best way to intervene to prevent or delay the clinical syndrome if possible. In this study, we tested whether this QPLEX TM Alz plus assay kit is suitable for non-demented individuals not only in old-age but also as a whole, including the young and mid-age population, to detect cerebral amyloid deposition. Thus, we included 47 young (average age 32.04 ± 0.9), 77 mid-aged (average age 56.25 ± 0.7) and 97 old-aged (average age 74.06 ± 0.5) nondemented individuals (Supplementary Table 1). We proved that the QPLEX TM Alz plus assay kit showed similar or even better e ciency of pre-screening in these groups (AUC 0.932), compared to the agematched, non-demented population reported in the previous publication (AUC 0.891) (14). Even though including age as a variable in ROC curve analysis showed a relatively high AUC value of 0.852 (Supplementary Figure 2) because many young PiB-PET negative individuals were included, signi cantly increased AUC value up to 0.932 was achieved in combination with QPLEX TM Alz plus assay. This suggests the possibility of early screening for cerebral amyloid accumulation in a non-demented population using the kit.

Limitations
Age is a prominent factor in neurodegenerative diseases such as AD, and consequently, it was challenging to recruit cognitively normal participants of young age with brain amyloid burden. Thus, we could not perform ROC curve analyses on the Young and Mid-age group individually to dismiss the age effect, although we tried to adjust the factor by various measures. In the future, we plan to apply the kit to a larger cohort, and if the cohort has enough CN+ individuals of young age, the applicability of our kit could be demonstrated more clearly.

Conclusions
This study demonstrates the ability of blood-based QPLEX TM Alz plus assay in detecting PiB-PET positivity with high sensitivity and speci city. It supports using the kit for pre-screening cerebral amyloid deposition in cognitively normal individuals, especially in the old age-group with individuals aged over 65 years.