Differences of longitudinal plasma biomarkers between single memory domain and multidomain subjective cognitive decline: Evidence from SILCODE

Abstract

Background

Plasma biomarkers demonstrated potential in identifying amyloid pathology in early Alzheimer's disease. Different subtypes of subjective cognitive decline (SCD) may lead to different cognitive impairment conversion risks.

Objective

To investigate the differences of plasma biomarkers in SCD subtypes individuals, which were unclear.

Methods

The 347 individuals were involved, including 93 normal controls (NC), 76 single memory domain SCD (sd-SCD), 79 multidomain SCD (md-SCD), 55 mild cognitive impairment and 44 dementia. We investigated plasma biomarkers (Aβ_42/40, p-tau181, p-tau217, NfL, and GFAP) and neuropsychological scales in the baseline and follow-up. The Kaplan-Meier survival analysis and Cox proportional hazards model were performed to investigate the risk of cognitive decline conversion. The t-test, Mann-Whitney U and multiple linear regression analysis were employed to evaluate the rate of change and correlation between PET-SUVR and plasma biomarker change.

Results

In cognitively normal subjects, md-SCD exhibited lower Aβ_42/40 and higher p-tau181 and p-tau217 levels. Kaplan-Meier survival analysis revealed that md-SCD group exhibited a higher risk of cognitive decline conversion compared to NC and sd-SCD. Within SCD subgroups, those with positive GFAP status showed higher conversion risk than negative. In the Cox model, the risk of conversion in the md-SCD group was 2.77 times that of sd-SCD. The md-SCD group demonstrated a faster rate of Aβ_42/40 decline than sd-SCD.

Conclusions

The study utilized plasma biomarkers to highlight the significance of staging in SCD. In cognitively normal subjects, md-SCD presents a higher risk of cognitive decline than sd-SCD, providing a valuable reference and convenient tool for early identification of individuals at risk for AD.

Keywords

Alzheimer's disease biomarker blood subjective cognitive decline

Introduction

Alzheimer's disease (AD), a serious neurodegenerative disorder, is the major cause of dementia, highlighting the vital significance of early recognition and diagnosis.^1,2 As clinical research delves deeper into the early stages of AD, subjective cognitive decline (SCD) emerges as a critical juncture for prevention and treatment.³ The deposition of amyloid-β (Aβ), as an early pathological marker, could be assessed by positron emission tomography (PET) or cerebrospinal fluid (CSF).⁴ Given considerations of accessibility, invasiveness and costs, concentrations of blood-based AD biomarkers, which correlate with pathological, diagnosis and prediction of future progression, have become the most promising markers.^5–7 Due to the rapid advancement of ultrasensitive quantitative technologies, single-molecule array (Simoa) has been widely adopted in the detection of brain specific proteins.^1,8

Blood measures of amyloid-β 42/40 (Aβ_42/40), tau phosphorylated at threonine181 (p-tau181), tau phosphorylated at threonine 217 (p-tau217), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) have revealed alterations in preclinical AD.^9,10 Earlier research has indicated associations between decreased plasma Aβ_42/40 levels and a heightened cognitive function decline.¹¹ As a marker of astrogliosis, GFAP has exhibited strong correlations with CSF biomarkers and the ability to determine AD.^12,13 For the inflammation (I) biomarker involved in revised criteria, GFAP plays a crucial role in the early staging of AD and parallel the progression of clinical syndrome.^14,15 As a crucial element of neuronal cytoskeleton, NfL might reflect the neuroaxonal degeneration, atrophy, hypometabolism and white matter integrity of brain regions.^16,17 Different variants of p-tau (p-tau181, p-tau217, p-tau231) are elevated in preclinical stage and demonstrate high performance in detecting AD pathology.^18,19 Longitudinal researches have revealed plasma p-tau181, p-tau217, NfL, and GFAP levels are related to the clinical progression of AD.^20–24

As a potentially early stage of the AD continuum, SCD is characterized by self-reported cognitive deterioration without objective cognitive impairment which accompanied by an elevated risk of cognitive decline and conversion.^25–27 Given the etiologically heterogeneous of SCD, distinguishing SCD subgroups and identifying individuals at high risk of conversion is essential.²⁸ According to the SCD interview (SCD-I), conducted by German Center for Neurodegenerative Diseases, SCD subjects could be classified into the single memory domain (sd-SCD) and multidomain SCD (md-SCD) subgroups.^28–30 Recent studies have found participants with md-SCD have increased Aβ accumulation than sd-SCD, indicating the increased risk in md-SCD and highlighting the necessity for subgroup analysis in SCD.²⁸ However, there remains a paucity of research exploring the risk assessment of conversion, longitudinal cognition decline, and the plasma rate of change among subgroups of SCD individuals.

This study focused on investigating the difference between sd-SCD and md-SCD groups in the risk of cognitive decline conversion and quantifying the rate of change in plasma biomarkers (Aβ_42/40, p-tau181, p-tau217, NfL, and GFAP) over time among SCD subjects by using longitudinal data.

Methods

Participants

The subjects in present study were from the Sino Longitudinal Study on Cognitive Decline (SILCODE), a registered continuous exploration in China. Approval for the study was granted by the ethics committee at Xuanwu Hospital of Capital Medical University (2017[046]) and the trial registration number was NCT03370744 in ClinicalTrials.gov. The study obtained written informed consent from each subject or their caregivers and was conducted in accordance with the Helsinki Declaration. And this study included participants who completed the comprehensive scale assessments at both baseline and follow-up visits. The sample size comprised 347 individuals, involving ninety-three normal controls (NC), Seventy-six sd-SCD subjects, seventy-nine md-SCD subjects, fifty-five MCI subjects and forty-four dementia subjects in this study. All participants were right-handed and aged sixty years or older.

The diagnosis of SCD derived from the concept proposed by Jessen and previous studies.^25,28 The inclusion criteria encompassed: (1) self-reported ongoing memory decline; (2) normal neuropsychological indicators; (3) not meet the criteria for MCI or AD. The NC group comprised cognitively normal individuals without persistent memory decline. The diagnoses of MCI and dementia referred to neuropsychological methods³¹ and workgroup criteria.^32,33 Exclusion criteria encompass subjects with diseases like stroke, abnormal thyroid function, severe psychiatric diseases, severe anemia, syphilis, traumatic brain injury and so on. Subjects were followed up longitudinally in this study.

Cognitive assessment

The assessment of subjective cognition utilized Chinese version SCD-I, a semi-structured interview.^29,34,35 It assesses SCD through different cognitive domains including memory, language, plan, attention and others. Professional clinical doctors administer the assessment, probing participants about specific changes in five cognitive domains over the past few years, as well as the timing and details of the onset of the disease (details are presented in the Supplemental Material).

All participants were administered the Mini-Mental State Examination test (MMSE), Hamilton depression rating scale (HAMD), Hamilton anxiety rating scale (HAMA), Auditory Verbal Learning Test long-delayed free recall (AVLT-N5), recognition (AVLT-N7), Shape Trail Test A (STT-A), Shape Trail Test B (STT-B), the verbal fluency test (VFT) as well as the Boston Naming Test (BNT) to assess cognition. At both baseline and follow-up, memory function was evaluated using the AVLT-N5 and AVLT-N7, language function was assessed via the VFT and BNT, and executive function was measured using the STT-A and STT-B.³⁶ Conversion to cognitive decline status was defined as compared to the baseline, if the number of impaired cognitive domains at follow-up had increased by at least one or already meet the diagnostic criteria for MCI.^31,36

Measurement of plasma biomarkers

Extracting blood samples from participants who underwent blood collection at both baseline and follow-up visits simultaneously. Following an overnight fasting period, venous blood was collected from each subject in the morning via EDTA tubes. Upon centrifugation at 4°C with 3000 × g, the supernatant was retrieved as plasma and preserved at −80°C for subsequent testing. Concentrations of plasma Aβ₄₀, Aβ₄₂, p-tau181, p-tau217, GFAP, and NfL had been measured by the Simoa from Quanterix Corporation. Concentration of p-tau181 was assessed by the Simoa^® pTau-181 Advantage V2 Kit (Cat #: 103714), p-tau217 was assessed using the ALZpath Simoa^® pTau-217 v2 Assay Kit (Cat #:104371), while Aβ₄₂, Aβ₄₀, NfL, and GFAP concentrations were measured in Simoa^TM Neurology 4-Plex E (N4PE) Advantage Kit (Cat #: 103670) assay. The assays were repeated, followed by the mean values were recorded. Each plate for every analyte involved seven or eight calibrators and two quality controls, both tested in duplicate. The lower limits of detection of the p-tau181, p-tau217, NfL, Aβ₄₀, Aβ₄₂, and GFAP, assays were 0.028, 0.006, 0.090, 0.384, 0.136, and 0.441 pg/ml, while the lower levels of quantification were 0.338, 0.020, 0.400, 1.020, 0.378, and 2.890 pg/ml, respectively. Intra-assay coefficients of variation (CV) for controls ranged from 1% to 10% for p-tau181, 2% to 11% for p-tau217, 2% to 12% for NfL, 1% to 5% for Aβ₄₀, 2% to 13% for Aβ₄₂, and 1% to 8% for GFAP. The cutoffs for all plasma biomarkers in SILCODE were calculated by the Shenzhen Bay Laboratory (longitudinal cohort Greater-BayArea Healthy Aging Brain Study, Shenzhen, China), and the thresholds of plasma GFAP divided the SCD individuals into two different plasma staging profiles: GFAP- and GFAP + groups.³⁷

Apolipoprotein E genotyping

The apolipoprotein E (APOE) genotype analysis was measured at the same time. Genotyping was conducted using genomic DNA purified from peripheral blood leukocytes, employing the TIANamp Genomic DNA Kit (Cat# 4992254, TIANGEN, China). Primers 5′-ACG CGG GCA CGG CTG TCC AAG G-3′ and 5′-GGC GCT CGC GGG ATG GCG CTG A-3′ were utilized to amplify the APOE gene fragment. Next, the amplicons were sequenced employing the standard Sanger method to identify single-nucleotide polymorphisms. Participants carrying an ε4 allele were categorized as APOE ε4 carriers, whereas those lacking ε4 allele were classified as noncarriers.

Image data acquisition and processing

The MRI and Aβ-PET images scans were performed on a hybrid 3.0 T PET/MR integrated simultaneous scanner (GE Healthcare, USA) at Xuanwu Hospital (Beijing, China). The PET imaging was performed using ¹⁸F-florbetapir (¹⁸F-AV-45). During imaging, headphones were provided to reduce noise and head movement. The T1-weighted images were obtained using following parameters: spoiled gradient-recalled sequence, field of view (FOV) = 256 × 256 mm², slice thickness = 1 mm, number of slices = 192, gap = 0, matrix size = 256 × 256, echo time (TE) = 2.98 ms, flip angle = 12°, inversion time (TI) = 450 ms, repetition time (TR) = 6.9 ms, voxel size = 1 × 1 × 1 mm³. For the Aβ-PET scanning, all participants received 7–10 mCi ¹⁸F-florbetapir tracer intravenously. The 20-min static PET scan were performed approximately 40 min after injection. The time-of-flight ordered subset expectation maximization (TOF-OSEM) algorithm was used to record PET data by following parameters: iterations = 8, FOV = 350 × 350 mm², 32 subset matrices = 192 × 192, and half-width height = 3.

The processing of Aβ-PET and T1 MRI images was performed in SPM12 toolbox (http://www.fil.ion.ucl.ac.uk/spm/software/spm12). Firstly, the original DICOM (Digital Imaging and Communications in Medicine) files were converted to NIfTI (Neuroimaging Informatics for Technology Initiation) files using the MRIcron toolbox (dcm2niigui.exe). Secondly, the origin node was corrected to anterior commissure - posterior commissure, followed by the co-registration of the Aβ-PET and T1-weighted images. Then, the T1-weighted images were segmented and performed spatial normalization. The PET individual space was converted into Montreal Neurological Institute (MNI) space using the transformation parameters of T1-weighted image standardization to MNI space. Subsequently, these images were smoothed using 8 mm full- width at half-maximum (FWHM). For the Aβ-PET, whole cerebral cortex was analyzed using the Anatomical Automatic Labeling (AAL) template, while the whole cerebellum serving as the reference brain region. The individuals were identified as amyloid positivity based on a cutoff of cortical standardized uptake value ratio (SUVR) > 1.18.^38,39

Kaplan-Meier survival analysis and cox proportional hazard models

In the cognitively normal group, Kaplan-Meier (K-M) survival analysis and cox proportional hazards model were applied among NC, sd-SCD and md-SCD groups. The conversion to cognitive decline we defined above was used as an endpoint event of conversion and analyzed for survival using the Log-rank test. Considering potential influencing factors (age, sex, education, and APOE ε4 carriers), we also employed cox proportional hazard models to investigate the risk of cognitive function conversion, which were performed in R version 4.3.2 (http://www.r-project.org/).

Statistical analysis

Demographic characteristics and plasma biomarkers among the five groups were analyzed using Chi-square test, one-way analysis of variance (ANOVA) and Kruskal-Wallis test with post hoc tests. After assessing the normal distribution of continuous variables, Chi-square test, two sample t-test, as well as Mann-Whitney U tests were performed for two SCD subgroups comparison on demographic informatics and continuous variables. Understanding longitudinal rate of plasma biomarker trajectories could help identify Alzheimer's progression and individuals at high risk. The rate of change in plasma biomarkers could be calculated by the following method: Plasma AD biomarkers Variation/Year*Baseline (%) = (Follow-up Plasma AD biomarker - Baseline Plasma AD biomarker) / (Follow-up Year*Baseline biomarker) (%). The association between Aβ accumulation and the rate of change in plasma biomarkers was evaluated through multiple linear regression analysis. The statistical analyses were performed in SPSS (version 26.0, IBM) for windows, with statistically significant was defined at p < 0.05.

Results

Participants assessment

The demographic and clinical characteristics of subjects has been listed in Table 1 and the twice concentrations of plasma biomarkers in SCD individuals have been presented in Table 2. Differences in neuropsychological evaluations (MMSE, AVLT-N5, AVLT-N7, VFT, BNT, STT-A, STT-B, MoCA-B) were found among NC, sd-SCD, md-SCD, MCI and dementia groups at baseline and follow up. The dementia group exhibited higher percentages of APOE ε4 carriers than NC, sd-SCD, and md-SCD groups. Additional detailed information on groups were available in Supplemental Tables 1 and 2. The average of follow-up time was 43.95 ± 19.20 months in cognitively unimpaired (CU) subjects. Between sd-SCD and md-SCD subgroups, no differences in age, sex, education time and APOE ε4 carriers were found to be significant at baseline and follow-up which have been listed in Table 2. Within the 79 participants comprising the md-SCD group, 100% showed decreased memory function, 64.56% showed decreased language function, 20.25% showed decreased planning ability and 55.70% showed decreased attention function, which indicating memory and language issues emerged as the predominant complaint.

Table 1.

General characteristics of the subjects.

	Baseline One (Two Complete Neuropsychological Assessments)					p	Follow-up (Two Complete Neuropsychological Assessments)					p
	NC (n = 93)	sd-SCD (n = 76)	md-SCD (n = 79)	MCI (n = 55)	Dementia (n = 44)		NC (n = 93)	sd-SCD (n = 76)	md-SCD (n = 79)	MCI (n = 55)	Dementia (n = 44)
Sex (M, n%)	34 (36.559%)	28 (35.443%)	27 (34.177%)	25 (45.455%)	16 (36.364%)	0.748	34 (36.559%)	28 (35.443%)	27 (34.177%)	25 (45.455%)	16 (36.364%)	0.748
Age (y)	65.118 (3.975)	66.421 (4.892)	66.835 (5.113)	71.582 (6.688)	72.818 (7.289)	<0.001	68.763 (4.063)	70.145 (4.906)	70.266 (5.341)	74.800 (7.074)	75.969 (7.416)	<0.001
Education	12.667 (3.314)	12.842 (2.912)	12.506 (3.104)	10.600 (4.263)	11.318 (3.463)	0.011	12.667 (3.314)	12.842 (2.912)	12.506 (3.104)	10.600 (4.263)	11.318 (3.463)	0.011
APOE ε4 (carrier, n, %)	15 (16.129%)	18 (23.684%)	26 (32.911%)	25 (45.455%)	27 (61.364%)	<0.001	15 (16.129%)	18 (23.684%)	26 (32.911%)	25 (45.455%)	27 (61.364%)	<0.001
Memory^d (Yes; n, %)	/	76 (100.000%)	79 (100.000%)	/	/	/	/	76 (100.00%)	79 (100.000%)	/	/	/
Language (Yes; n, %)	/	/	51 (64.557%)	/	/	/	/	/	51 (64.557%)	/	/	/
Plan (Yes; n, %)	/	/	16 (20.253%)	/	/	/	/	/	16 (20.253%)	/	/	/
Attention (Yes; n, %)	/	/	44 (55.696%)	/	/	/	/	/	44 (55.696%)	/	/	/
Others (Yes; n, %)	/	/	9 (11.392%)	/	/	/	/	/	9 (11.392%)	/	/	/
Plasma Aβ_42/40	0.062 (0.013)	0.060 (0.010)	0.055 (0.011)	0.051 (0.012)	0.047 (0.011)	<0.001	0.061 (0.013)	0.059 (0.010)	0.054 (0.011)	0.049 (0.012)	0.045 (0.011)	<0.001
Plasma p-tau 181	1.679 (0.547)	1.908 (0.817)	2.164 (0.966)	2.956 (1.086)	5.058 (2.300)	<0.001	1.988 (0.928)	2.312 (0.950)	2.665 (1.489)	3.866 (1.735)	5.286 (2.033)	<0.001
Plasma NfL	15.368 (8.493)	20.390 (15.331)	20.221 (13.098)	29.959 (17.606)	36.214 (20.196)	<0.001	15.176 (6.733)	17.656 (6.886)	21.029 (10.012)	32.975 (24.752)	32.763 (14.332)	<0.001
Plasma GFAP	86.841 (36.537)	98.573 (43.717)	101.084 (57.033)	165.792 (73.459)	301.678 (106.359)	<0.001	108.036 (43.299)	122.952 (50.360)	130.411 (66.853)	204.901 (109.759)	283.066 (83.900)	<0.001
Plasma p-tau 217	0.236 (0.096)	0.279 (0.102)	0.384 (0.252)	0.597 (0.397)	1.432 (0.543)	<0.001	0.292 (0.139)	0.340 (0.142)	0.454 (0.324)	0.755 (0.444)	1.378 (0.532)	<0.001
MMSE	28.956 (1.080)	28.768 (1.045)	28.689 (1.774)	24.981 (3.406)	16.195 (6.165)	<0.001	28.753 (1.281)	28.732 (1.195)	27.985 (1.665)	20.818 (5.288)	14.400 (6.916)	<0.001
HAMD	3.363 (3.650)	3.487 (3.572)	4.808 (4.215)	4.905 (4.471)	3.464 (3.766)	0.044	2.935 (2.769)	3.276 (3.058)	5.684 (5.197)	3.833 (4.423)	5.583 (5.143)	0.003
HAMA	3.791 (3.510)	4.434 (3.201)	5.372 (3.994)	4.951 (3.937)	6.533 (5.277)	0.010	4.856 (4.156)	5.747 (5.626)	8.051 (7.076)	6.682 (6.335)	10.867 (9.920)	0.005
AVLT-N5	7.283 (1.895)	7.158 (2.111)	6.897 (2.010)	2.477 (2.732)	0.154 (0.613)	<0.001	7.598 (2.174)	7.118 (2.257)	6.823 (2.510)	2.105 (2.685)	0.083 (0.289)	<0.001
AVLT-N7	22.423 (1.462)	22.276 (1.793)	22.090 (1.853)	18.227 (3.319)	12.500 (5.530)	<0.001	22.348 (1.627)	21.921 (2.250)	21.848 (1.888)	15.684 (5.588)	11.500 (5.649)	<0.001
VFT	19.225 (4.293)	19.395 (4.682)	17.861 (4.060)	14.659 (3.809)	9.577 (4.876)	<0.001	19.663 (4.012)	19.789 (3.817)	17.810 (3.853)	12.579 (0.959)	7.833 (3.589)	<0.001
BNT	25.461 (2.650)	24.789 (2.811)	24.646 (3.344)	21.386 (4.178)	17.154 (5.843)	<0.001	26.054 (2.447)	25.961 (2.441)	25.203 (3.031)	20.684 (4.820)	15.667 (5.314)	<0.001
STT-A	58.551 (16.708)	58.566 (19.288)	61.481 (15.154)	101.046 (38.308)	166.700 (70.302)	<0.001	54.620 (15.640)	57.474 (14.307)	69.316 (23.506)	113.222 (56.917)	199.222 (111.045)	<0.001
STT-B	132.955 (35.498)	134.592 (33.176)	147.519 (40.647)	240.091 (91.145)	268.211 (57.660)	<0.001	131.370 (38.335)	143.855 (46.502)	172.203 (66.812)	238.778 (72.569)	277.125 (45.855)	<0.001
MoCA-B	25.962 (3.131)	25.711 (2.550)	25.266 (2.368)	20.500 (3.580)	11.593 (4.693)	<0.001	26.174 (2.457)	25.987 (2.600)	25.000 (2.604)	17.316 (6.815)	9.692 (6.019)	<0.001

NC: normal control; sd-SCD: single memory domain SCD; md-SCD: multidomain SCD; MCI: mild cognitive impairment; Aβ_42/40: amyloid-β 42/40 ratio; p-tau 181: phosphorylated tau 181; p-tau 217: phosphorylated tau 217; NfL: neurofilament light chains; GFAP: glial fibrillary acidic protein; MMSE: Mini-Mental State Examination; HAMD: Hamilton depression rating scale; HAMA: Hamilton anxiety rating scale; AVLT-N5: long-term delayed recall; AVLT-N7: long-term delayed recognition; VFT: Verbal Fluency Task; BNT: Boston naming test; STT-A: shape trail test A; STT-B: shape trail test B; MoCA-B: Basic version of Montreal Cognitive Assessment; Continuous variables are presented as mean (SD).

Table 2.

General characteristics of the subjects.

	Baseline Two (Two Complete Plasma AD biomarkers)		p	Follow-up (Two Complete Plasma AD biomarkers)		p	Plasma AD biomarkers Variation/Year*Baseline (%)		p
	sd-SCD (n = 54)	md-SCD (n = 62)		sd-SCD (n = 54)	md-SCD (n = 62)		sd-SCD	md-SCD
Sex (M, n%)	18.000 (33.333%)	21.000 (33.871%)	0.951	18.000 (33.333%)	21.000 (33.871%)	0.951	/	/	/
Age (y)	66.389 (4.796)	67.161 (5.208)	0.491	70.333 (4.941)	70.468 (5.352)	0.874	/	/	/
Education	12.796 (2.910)	12.258 (2.953)	0.363	12.796 (2.910)	12.258 (2.953)	0.363	/	/	/
APOE ε4 (carrier, n, %)	12.000 (22.222%)	24.000 (38.710)	0.056	12.000 (22.222%)	24.000 (38.710)	0.056	/	/	/
Plasma Aβ_42/40	0.059 (0.009)	0.055 (0.010)	0.017	0.058 (0.010)	0.052 (0.011)	0.005	−0.076 (2.490)	−1.753 (3.717)	0.005
Plasma p-tau 181	1.855 (0.692)	2.223 (0.932)	0.033	2.298 (0.913)	2.643 (1.477)	0.345	6.507 (11.285)	6.816 (14.863)	0.666
Plasma NfL	18.671 (12.814)	21.102 (14.630)	0.603	17.312 (5.951)	20.945 (9.974)	0.059	0.288 (12.901)	3.155 (22.533)	0.207
Plasma GFAP	93.224 (38.545)	104.150 (58.957)	0.872	121.674 (56.153)	133.649 (69.337)	0.465	8.002 (8.161)	11.218 (19.221)	0.500
Plasma p-tau 217	0.275 (0.102)	0.389 (0.257)	0.026	0.337 (0.154)	0.471 (0.334)	0.047	5.891 (7.477)	6.476 (8.505)	0.718

sd-SCD: single memory domain SCD; md-SCD: multidomain SCD; Plasma AD biomarkers Variation/Year*Baseline (%): (Follow-up Plasma AD biomarker - Baseline Plasma AD biomarker) / (Follow-up Year*Baseline biomarker) (%); Continuous variables are presented as mean (SD).

The results revealed significant between-group differences in five plasma biomarkers across different phases in AD continuum. Figure 1(A)–(E) demonstrate the changes in plasma biomarkers across AD continuum at baseline. Plasma Aβ_42/40 levels were significantly higher at NC and sd-SCD groups than at the md-SCD and cognitive impairment groups (Figure 1(A)). Plasma p-tau181 levels were lower in individuals at the NC group than md-SCD, MCI, and dementia groups (Figure 1(B)). Similarly, we found that NC individuals showed significantly lower plasma p-tau217 levels than md-SCD, MCI, and dementia groups (Figure 1(C)). Additionally, plasma NfL and GFAP levels were significantly elevated in MCI and dementia groups compared to NC, sd-SCD, and md-SCD groups (Figure 1(D) and (E)).

Figure 1.

Comparisons of plasma biomarkers among different groups across AD continuum. Comparisons of (A) plasma Aβ_42/40, (B) plasma p-tau181, (C) plasma p-tau217, (D) plasma NfL, (E) plasma GFAP among NC, sd-SCD, md-SCD, MCI and Dementia groups. Aβ_42/40: amyloid-β 42/40 ratio; p-tau181: phosphorylated tau181; p-tau217: phosphorylated tau217; NfL: neurofilament light chain; GFAP: glial fibrillary acidic protein.

Survival analysis of SCD subtypes

As shown in Figure 2(A), the type of SCD subgroup has a good ability to distinguish high-risk and low-risk conversion to cognitive function decline. From all SCD (sd-SCD and md-SCD) participants who were at baseline, thirty-five participants converted to cognitive decline during follow-up resulting in an event frequency of 22.58%. The md-SCD group exhibited a higher risk of cognitive decline conversion (p = 0.002, Figure 2(A)) in comparison to the sd-SCD. The risk of conversion in md-SCD group was 3.176 times that of sd-SCD group and 3.332 times that of NC group. In addition, Table 3 presents the analyses of outcome evolution over time based on the cox proportional hazard. Age, sex, education, and APOE ε4 carriers were involved as covariates in models. The results were consistent with the K-M survival analyses, indicating that subjects in the md-SCD group exhibited a more pronounced decline risk than sd-SCD group. The risk of conversion in md-SCD group was 2.772 times that of that in sd-SCD group (the cox model adjusting for age, sex and education as covariates, p = 0.013). When APOE ε4 carriers was also considered as a covariate, the risk increased to 2.846-fold (p = 0.012). Given the role of GFAP, we further divided the two SCD subgroups into GFAP- and GFAP + subgroups. Survival analysis indicated that individuals in the sd-SCD GFAP + subgroup exhibited a higher risk of cognitive decline than those in GFAP- subgroup (p = 0.013, HR = 5.285, Figure 2(B)). The individuals in the md-SCD GFAP + subgroup might exhibit a higher risk of cognitive decline than those in GFAP- subgroup, but not reach statistical significance (p = 0.057, HR = 2.265, Figure 2(B)).

Figure 2.

The Kaplan-Meier survival curves among cognitively unimpaired groups. The cognitive decline conversion risk comparing via the Kaplan-Meier survival analysis among groups. (A) The Kaplan-Meier survival curves among NC, sd-SCD, and md-SCD groups. (B) The Kaplan-Meier survival curves among SCD subgroups.

Table 3.

Cox Proportional Hazard Models for conversion to cognitive decline according to SCD subtypes during follow-up period.

Group	Adjusted Model 1 (n = 248)^a HR (95% CI)	p	Adjusted Model 2 (n = 248)^b HR (95% CI)	p
md-SCD vs. NC	2.557 (1.165–5.611)	0.019	2.713 (1.223–6.019)	0.014
md-SCD vs. sd-SCD	2.772 (1.238–6.206)	0.013	2.846 (1.264–6.406)	0.012
NC vs. sd-SCD	1.076 (0.395–2.930)	0.886	1.064 (0.388–2.920)	0.903

HR: hazard ratio; NC: normal control; sd-SCD: single memory domain SCD; md-SCD: multidomain SCD.

Model 1 was adjusted by sex, age and education.

Model 2 was adjusted by sex, age, education and APOE.

Longitudinal rate of change in plasma biomarkers

The concentration changes of all plasma biomarkers (Aβ_42/40, p-tau181, p-tau217, NfL, GFAP) in SCD individuals from baseline to follow-up are illustrated in Figure 3(A)–(E). Among all SCD participants with twice available plasma in Table 2, compared to sd-SCD, md-SCD individuals had lower plasma Aβ_42/40 at baseline (p = 0.017, Figure 3(A)) and follow-up visit (p = 0.005, Figure 3(A)) and higher plasma p-tau217 at baseline (p = 0.026, Figure 3(C)) and follow-up visit (p = 0.047, Figure 3(C)) while plasma NfL and GFAP showed no obvious difference (p > 0.05, Figure 3(D) and (E)). The md-SCD individuals showed higher p-tau181 at baseline (p = 0.033, Figure 3(B)). The individuals with positive Aβ-PET SCD group (SCD+) exhibited higher GFAP concentrations compared to those with negative Aβ-PET group SCD (SCD-) at baseline (p = 0.046, Figure 3(F)) and follow-up (p = 0.013, Figure 3(F)). The APOE ε4 carriers demonstrated lower Aβ_42/40 (Figure 3(G)) and higher plasma p-tau217 (Figure 3(H)) concentrations than APOE ε4 non-carriers group at baseline and follow-up. In addition, two SCD subgroups exhibited different rates of longitudinal change in plasma biomarkers (Figure 4(A)–(E)). The md-SCD group showed significantly faster rate of plasma Aβ_42/40 ratio longitudinal decline per year compared with sd-SCD group (p = 0.005, Figure 4(A)). The rate of change in p-tau181, NfL, GFAP, and p-tau217 were not significantly different in SCD subgroups, but md-SCD group demonstrated a trend toward a faster increase (Figure 4(B)–(E)).

Figure 3.

The plasma AD biomarkers in baseline and follow-up between SCD subtypes. Plasma Aβ_42/40 (A), plasma p-tau181 (B), p-tau217 (C), NfL (D), and GFAP (E) at baseline and follow-up. Comparisons of plasma GFAP (F) between SCD- and SCD + group. Comparisons of plasma Aβ_42/40 (G) and plasma p-tau217 (H) between APOE- and APOE + groups. SCD-: SCD with negative Aβ-PET; SCD+: SCD with positive Aβ-PET; APOE-: APOE ε4 non-carrier; APOE+: APOE ε4 carrier.

Figure 4.

The rate of change in plasma AD biomarkers between sd-SCD and md-SCD group. Comparisons of the rate of change in plasma Aβ_42/40 (A), plasma p-tau 181 (B), p-tau217 (C), NfL (D), and GFAP (E) between sd-SCD and md-SCD groups. Plasma AD biomarkers Variation/Year*Baseline (%): (Follow-up Plasma AD biomarker - Baseline Plasma AD biomarker) / (Follow-up Year*Baseline biomarker) (%).

Relationship between SUVR and the rate of decline in plasma Aβ_42/40

According to the results of the rate of change of plasma Aβ_42/40 in two subgroups, global amyloid SUVR was included as an independent variable in the multiple linear regression model. Among the whole SCD individuals, elevated SUVR levels were correlated with the negative rate of change of plasma Aβ_42/40 (including covariates: β = 0.165, p = 0.040; Figure 5).

Figure 5.

Association of global SUVR and the rate of change in plasma Aβ_42/40 among SCD subjects. Age, sex, and years of education were adjusted. SUVR: standardized uptake value ratio; Plasma AD biomarkers Variation/Year*Baseline (%): (Follow-up Plasma AD biomarker - Baseline Plasma AD biomarker) / (Follow-up Year*Baseline biomarker) (%).

Discussion

In the present study, we investigated the plasma biomarkers to highlight the significance of staging in SCD and demonstrated that the md-SCD individuals presented a higher risk of cognitive decline compared to NC and sd-SCD during an average follow-up of 43.95 months. The md-SCD individuals demonstrated a higher level of plasma p-tau217 and lower Aβ_42/40 than sd-SCD individuals, while the rate of longitudinal decline per year in plasma Aβ_42/40 was significantly faster in the md-SCD group than sd-SCD. As measured by Aβ-PET, higher global SUVR, was strongly linked with the rate of decline in plasma Aβ_42/40. Moreover, SCD subtypes individuals with positive GFAP exhibited an elevated risk of cognitive decline. The results of the present study support the potential role of subgroup classification and plasma biomarkers Aβ_42/40, p-tau217, and GFAP in individuals with SCD. Importantly, the difference of SCD subtypes in plasma and survival analysis exhibited in the study serve as significant references for detecting high risk populations. These findings provided a valuable reference and convenient tool for the early identification of individuals at risk for AD.

The leading contribution of our study lies in the longitudinal assessment of changes in SCD subgroups. These notable findings are consistent with previous explorations on the topic to some extent. According to a cross-sectional study, participants with md-SCD displayed heightened amyloid accumulation in contrast to participants with sd-SCD, indicating a potential advancement of md-SCD in SCD cases.²⁸ Meanwhile, studies found that low Aβ₄₂ levels in CSF were excellent predictor of progression in SCD individuals and associated with declines in memory and language abilities.^29,40 Abnormalities in plasma Aβ_42/40 may precede abnormalities in CSF Aβ_42/40 and Aβ-PET.⁴¹ These findings suggest that the Aβ pathology may have a major impact influence on longitudinal changes in SCD subtypes. To be more specific, in recent years, researchers have delved into the association between plasma Aβ levels and clinical cognitive outcomes, employing advanced methodologies for plasma Aβ assessment.^42,43 Several studies have reported that plasma Aβ_42/40 might serve as a valuable premorbid biomarker in recognizing cognitively normal elderly participants who face an elevated risk of developing cognitive impairment, highlighting the significance of plasma biomarkers to comprehend the conversion of cognitive decline process.^24,44

Recent evidence has indicated that plasma Aβ_42/40 might detect the earliest cerebral Aβ changes, preceding brain amyloid accumulation.⁴⁵ Plasma Aβ_42/40 exhibited superior performance in comparison with other Simoa-based biomarkers in assessing Aβ-PET positivity visually.⁴⁶ Studies reported plasma Aβ_42/40 was correlated to longitudinal Aβ accumulation.³⁷ Consistent with our findings, the global Aβ accumulation was correlated with the rate of decline in plasma Aβ_42/40. The major AD tau pathologies, consisting of neurofibrillary tangles, neuropil threads, plaque-associated tau neurites, and the deposition of pathological tau correlates with the severity of disease.⁴⁷ Neurofilaments, as integral cytoskeletal elements within neurons, participate in the growth of axons, also in synaptic function within the central nervous system.⁴⁸ Previous research has shown an elevation in plasma NfL concentration during the prodromal stage of AD, with correlations established between this elevation and cognitive assessments as well as CSF biomarkers.⁴⁹ Meanwhile, studies have indicated that levels of NfL enhance in SCD.⁵⁰ A greater rate of NfL increase was observed in the md-SCD group in comparison with sd-SCD group, indicating an elevated trend in NfL and course of disease (Figure 4(D)) in our result.

Previous group reported that plasma p-tau181 might show strong ability to recognize tau tangles.⁵¹ The plasma p-tau217 showed high performance in identifying and predicting biological AD accurately, comparable to CSF.^52,53 Recent studies suggested plasma p-tau217 level at baseline was connected to subsequent Aβ and tau-PET pathology levels, and increased with AD severity.^54,55 As a core biomarker of revised criteria, plasma p-tau217 levels might be effective in predicting Aβ-PET positivity in CU individuals, and elevated p-tau217 was closely associated with future cognitive function worsen and brain atrophy in the follow-up.^56–58 In the present study, we found that md-SCD groups exhibited higher plasma p-tau217 at baseline and follow-up, which further demonstrated the progression of SCD phases, SCD subgroups stage across AD continuum and connections with the downstream events of AD. Meanwhile, md-SCD groups exhibited increasing cognitive function decline risk than sd-SCD and NC groups. The plasma p-tau181 level was higher in md-SCD individuals compared to sd-SCD, indicating a potential progression in SCD phase and the importance of risk stratification in SCD subgroups. Unfortunately, despite recent studies have demonstrated the good performance of p-tau217, our results did not show significant outcomes for the rate of change in p-tau217 among SCD subgroups, which may be attributed to the limited sample size, however, we found a trend of faster growth in md-SCD than sd-SCD groups. Given that the limitations might stem from the small sample size limitation, future researches of our group will focus on expanding the sample size for p-tau217 measurements and follow-up, and aim to improve subgroup analysis and a more comprehensive understanding of the role of p-tau217 in the early progression of SCD.

The fluid markers for detecting astrocyte reactivity were GFAP and chitinase-3-like protein 1.⁵⁹ GFAP, as a marker of astrocyte reactivity, generally found surrounding Aβ plaques, relating to morphological and functional remodeling of astrocytes, as well as contributing to the advance of dementia through immunological processes.^50,60 During the initial or asymptomatic stages of AD, elevated levels of plasma GFAP were observed, indicating potential variations in pathological proteins.¹³ Higher plasma GFAP in positive Aβ-PET CU older adults had been reported.⁶¹ Our work has suggested that an upregulation of GFAP levels was found in Aβ-positive SCD individuals at baseline and follow-up, further demonstrating the superior performance of plasma GFAP in detecting Aβ-positive SCD individuals. The SCD subgroups with positive GFAP shows higher risk of cognitive decline than negative, which further supports plasma GFAP might be closely connected with AD progression. Although the rate of GFAP increase did not exhibit significant difference between the two subgroups of SCD, the mean rate of GFAP increase in md-SCD group exceeded that of sd-SCD group, reflecting a trend in GFAP alterations (Figure 4(E)). In line with previous findings, the APOE ε4 carriers presented lower plasma Aβ_42/40 and higher p-tau217 than APOE ε4 non-carriers, which might indicate APOE ε4 take influence on Aβ and tau deposition.⁶²

The current study demonstrates the rate of change in the plasma Aβ_42/40 assay for detecting cognitive decline in SCD, filling up the features of SCD subtypes. The amyloid/tau/neurodegeneration (ATN) framework from the National Institute on Aging and Alzheimer's Association (NIA-AA) has highlighted that each plasma biomarker has distinct functions and is not interchangeable.³³ The recently revised criteria further emphasize the crucial role of core biomarkers in both diagnosis and staging.^58,63 However, given the varying sensitivities and specificities of each biomarker for different cases, screening the most valuable biomarker for large-scale identification of high-risk SCD individuals might consider realistic restrictions and cost condition. The assessment of plasma Aβ and p-tau217 levels helped performing the amyloid component and might serve as an initial marker replacing PET or CSF screening.⁶⁴ This aligns with our purpose that identifying high-risk SCD individuals, faster concentration changes and predicting cognitive conversion. Therefore, the convenient SCD-I questionnaire could be employed in primary health care to broadly identify md-SCD individuals at higher risk of cognitive decline, followed by more detailed objective evaluations such as plasma detection. An interesting phenomenon from our analyses is that the rate of decline in plasma Aβ_42/40, associated with amyloid pathology, could assist infer the risk difference of cognitive decline of SCD subtypes individuals to some extent. Regrettably, the rate of increase in p-tau217 did not demonstrate strong performance, which might be attributed to the limited sample size. Further researches are encouraged to elucidate the deeper mechanisms underlying the correlation between plasma biomarkers in SCD individuals and conversion outcome.

The strengths of present study involve the longitudinal design, comprehensive availability of biomarkers, cognition assessment and long follow-up period. As a single-site study, the longitudinal approach of the study is additional strength. However, there are still several limitations to be mentioned. Firstly, the sample size of this longitudinal assessment was small and plasma p-tau217 did not exhibited good performance. More plasma samples will be collected, and longer follow-up period will be extended in our cohort in the future. Secondly, biomarker trajectories have been characterized primarily from single center data, and the lack of data from multicenter prevents us from describing the complete scope of cross-cultural difference in biomarkers. Thirdly, given that the absence of CSF biomarkers in diagnosis, we hope to enhance CSF sample collection and detection in the future. In addition, increased frequency of plasma biomarkers and PET images, and immunoprecipitation mass spectrometry methods are needed to support the plasma biomarkers detection and the generalizability of this study in the future.⁶⁵ Furthermore, the duration of follow-up in our study could potentially restrict the applicability of our findings, given that the progression of AD could last for up to ten years. The results should be therefore interpreted with caution. Additional individuals and longer follow-up period would be necessary to further validate these findings in the future.

Conclusion

With increasing attention on the SCD stage of preclinical AD, the focus on identifying early biomarkers of cognitive decline had intensified, highlighting the potential of plasma biomarkers to predict cognitive impairment. This study suggests that the md-SCD group exhibits a higher risk of cognitive decline compared to the sd-SCD group over the longitudinal period. The md-SCD individuals demonstrated higher plasma p-tau217, p-tau 181 levels and faster rate of plasma Aβ_42/40 decline than sd-SCD. Our findings indicate that SCD subtypes could serve as indicators identifying individuals at higher risk of cognitive decline, offering a convenient and alternative tool to more complicated and costly approaches like PET scanning or CSF load measurements, especially in primary health care or broad screening.

Supplemental Material

sj-docx-1-alz-10.1177_13872877241309105 - Supplemental material for Differences of longitudinal plasma biomarkers between single memory domain and multidomain subject cognitive decline: Evidence from SILCODE

Supplemental material, sj-docx-1-alz-10.1177_13872877241309105 for Differences of longitudinal plasma biomarkers between single memory domain and multidomain subject cognitive decline: Evidence from SILCODE by Min Wei, Xianfeng Yu, Shimin Hu, Wenjing Hu, Rong Shi, Min Wang, Jiayi Zhong, Qi Zhang, Ying Zhang, Chenyang Li, Ziyan Song, Jiehui Jiang and Ying Han in Journal of Alzheimer's Disease

Footnotes

Acknowledgments

All the authors would like to express their most sincere gratitude to the SILCODE participants in the study.

ORCID iDs

Xianfeng Yu

Shimin Hu

Rong Shi

Ying Zhang

Chenyang Li

Ziyan Song

Jiehui Jiang

Ying Han

Author contributions

Min Wei (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Visualization; Writing – original draft); Xianfeng Yu (Conceptualization; Methodology; Writing – original draft; Writing – review & editing); Shimin Hu (Conceptualization; Methodology; Software; Supervision); Wenjing Hu (Conceptualization; Software; Supervision; Visualization; Writing – review & editing); Rong Shi (Conceptualization; Data curation; Methodology; Resources; Software; Validation; Visualization; Writing – original draft); Min Wang (Methodology; Supervision; Visualization; Writing – review & editing); Jiayi Zhong (Investigation; Software; Validation; Visualization; Writing – review & editing); Qi Zhang (Conceptualization; Investigation; Software; Visualization; Writing – review & editing); Ying Zhang (Methodology; Software; Validation; Visualization; Writing – review & editing); Chenyang Li (Data curation; Supervision; Validation; Writing – review & editing); Ziyan Song (Formal analysis; Project administration; Supervision; Validation); Jiehui Jiang (Conceptualization; Investigation; Methodology; Project administration; Software; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Ying Han (Conceptualization; Data curation; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Writing – review & editing).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by the National Natural Science Foundation of China (Grant: 82020108013, 82327809), STI2030-Major Projects (Grant: 2022ZD0211800), Sino-German Cooperation Grant (Grant: M-0759), Shenzhen Bay Scholars Program and Tianchi Scholars Program.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Ying Han and Jiehui Jiang are Editorial Board Members of this journal but were not involved in the peer-review process of this article nor had access to any information regarding its peer-review. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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Differences of longitudinal plasma biomarkers between single memory domain and multidomain subjective cognitive decline: Evidence from SILCODE

Abstract

Background

Objective

Methods

Results

Conclusions

Keywords

Introduction

Methods

Participants

Cognitive assessment

Measurement of plasma biomarkers

Apolipoprotein E genotyping

Image data acquisition and processing

Kaplan-Meier survival analysis and cox proportional hazard models

Statistical analysis

Results

Participants assessment

Survival analysis of SCD subtypes

Longitudinal rate of change in plasma biomarkers

Relationship between SUVR and the rate of decline in plasma Aβ42/40

Discussion

Conclusion

Supplemental Material

sj-docx-1-alz-10.1177_13872877241309105 - Supplemental material for Differences of longitudinal plasma biomarkers between single memory domain and multidomain subject cognitive decline: Evidence from SILCODE

Footnotes

Acknowledgments

ORCID iDs

Author contributions

Funding

Declaration of conflicting interests

Data availability

Supplemental material

References

Supplementary Material

Relationship between SUVR and the rate of decline in plasma Aβ_42/40