Skip to main content
Download PDF

Pathophysiology characterization of Alzheimer’s disease in South China’s aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS)

Alzheimer's Research & Therapy volume 16, Article number: 84 (2024) Cite this article

Abstract

Introduction

The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86 million population and faces a significant challenge of Alzheimer’s disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort.

Methods

Following the standard protocols of the Alzheimer’s Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), β-amyloid (Aβ) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aβ42/Aβ40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aβ PET, and tau PET, respectively. Plasma biomarkers, Aβ PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aβ PET positivity were also determined in different diagnostic groups.

Results

The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported.

Discussion

The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China’s older population.

Background

Alzheimer’s dementia patients suffer from memory loss, cognitive dysfunction, behavioral abnormalities, and social disorders [1]. Alzheimer’s disease (AD) is the leading cause of dementia, accounting for 60–80% of all cases [2]. Extracellular β-amyloid (Aβ) plaques and neurofibrillary tau tangles are the two key hallmarks of AD [3]. AD patients have reduced Aβ42 concentrations in cerebrospinal fluid (CSF) [4] or plasma [5], elevated cortical Aβ plaques [6,7,8], CSF or plasma phosphorylated Tau (p-Tau) concentrations [9], and cortical tau tangles [10], which eventually result in synaptic loss [11,12,13], hippocampal atrophy [14], Temporal-metaROI cortical thinning [15], hypometabolism and cognitive decline [4, 9, 16]. Such abnormal changes in Aβ and tau can be detected by biomarkers or positron emission tomography (PET) imaging 15–20 years before the earliest clinical symptoms of AD [17,18,19]. According to the research framework proposed by the National Institute on Aging and Alzheimer’s Association in 2018 [20], cognitively unimpaired (CU) older adults with the evidence of Aβ pathology measured by either CSF Aβ biomarker [4] or Aβ PET imaging [6] are defined as preclinical AD. Moreover, around 30% of the CU individuals aged 70 and over are at the preclinical AD stage [21] and have a high risk of cognitive decline in the future [22, 23].

The prevalence of dementia and mild cognitive impairment (MCI) among older adults aged 60 and above in China is 6.0% and 15.5%, respectively. Among those cases, the majority (65%) is dementia or MCI due to AD, followed by vascular dementia (26.7%) and other dementias (8.3%) [24]. Additionally, the prevalence of preclinical AD defined by Aβ PET imaging among adults aged 60 or older was around 18% [21]. Approximately 260 million individuals are 60 years or older in China [25]. Among them, around 15 million, 39 million, and 47 million individuals are at the stage of dementia, MCI, and preclinical AD, respectively. China’s total expenditure on dementia treatment and nursing services will be $1.89 trillion around 2050 [26]. AD has become the fifth leading cause of death disease in China in 2019 [27]. The age-standardized prevalence and the age-standardized death rate of AD and related dementias were 788.3/100 000 and 23.3/100 000, separately, which were slightly higher than that of the global levels (682.5/100 000 and 22.9/100 000, separately) [27]. The high prevalence and vast population of AD severely challenge China.

Recently, three anti-Aβ drugs, including Aducanumab [28], Lecanemab [29], and Donanemab [30] showed positive results in phase 3 clinical trials. Consequently, early AD diagnosis and intervention are critical for preventing AD progression. However, the standardization of biomarker measurements, magnetic resonance imaging (MRI), and PET image scanning and processing of AD are not fully established yet in China. Therefore, the neuroscientists, neurologists, pathologists, radio-pharmacists, biomedical engineers, and biochemists in Guangdong-Hong Kong-Macao Greater-Bay-Area are working together to initiate the Greater-Bay-Area Healthy Aging Brain Study (GHABS), aiming to investigate the pathological features and progression patterns of AD, especially in asymptomatic stage of AD. GHABS participants will undergo clinical neuropsychological assessments, biospecimen sample collection, MRI imaging, and PET imaging. The GHABS project aims to: (1) explore the risk factors of Aβ and tau aggregation in the early stage of AD among China’s aging population; (2) determine the effect of Aβ and tau pathologies upon downstream neurodegeneration and cognitive decline in both Aβ negative (Aβ-) and Aβ positive (Aβ+) elderly adults; (3) identify novel approaches and techniques for early detection of AD and provides significant reference for the target brain region and appropriate time window for anti-AD treatments.

In this study, we first summarized the aims, study design, data collection, and potential applications of GHABS. Second, we compared the plasma Aβ42/Aβ40, plasma p-Tau181, plasma GFAP, plasma NfL, Aβ PET, tau PET, hippocampal volume, and temporal-MetaROI cortical thickness between normal control (NC), subjective cognitive decline (SCD), MCI, and dementia groups. Third, we also reported the prevalence of plasma A/T staging and Aβ PET positivity of different diagnostic groups in the Guangdong-Hong Kong-Macao Greater-Bay-Area of South China.

Methods

Study design

Shenzhen Bay Laboratory launched the community-based longitudinal cohort study GHABS (ClinicalTrials.gov ID: NCT06183658) in May 2021. The GHABS project was approved by the Shenzhen Bay Laboratory’s and the collaborated hospitals’ Ethical Committees. The flow chart of participant engagement in the GHABS is delineated in Supplementary Fig. 1. The GHABS participants were recruited via posters and lectures in the community and nursing homes. Each participant signed the written informed consent of the GHABS project before enrollment. The participants who met the inclusion and exclusion requirements were informed about the baseline and follow-up examinations. From 2021 to 2026, the GHABS cohort will recruit 1400 individuals aged 55 and older, including 1100 CU older adults, 200 MCI patients, and 100 dementia patients. The scheme for recruiting GHABS participants is illustrated in Supplemental Fig. 1.

All the GHABS participants will undergo cognitive assessments, genetic screening, and blood sample collection. Some will have CSF collection, stool sample collection, MRI scanning, Aβ PET scanning, and tau PET scanning. All baseline examinations will be completed within three months. At follow-up, clinical assessments and blood sample collection will be conducted annually. CSF sample collection, MRI scan, Aβ PET scan, and tau PET scan are evaluated every two years. Six hundred twenty-seven participants have completed cognitive assessments and blood sample collection in the GHABS cohort by Sep 08, 2023 (Fig. 1). Among them, 26%, 44%, 19%, and 11% of the cohort were NC, SCD, MCI, and dementia, respectively. Additionally, 369 and 93 participants completed stool and CSF sample collection. So far, 469, 370, and 105 participants had MRI, Aβ PET, and tau PET imaging scans, respectively. This study identified 470 GHABS participants who simultaneously completed cognitive assessments, plasma Aβ42/Aβ40, p-Tau181, NfL, and GFAP data measured by the Simoa platform. Among them, 119, 207, 87, and 57 were NC, SCD, MCI, and dementia. Moreover, 301, 195, and 70 individuals had concurrent MRI, 18F-D3-FSP Aβ PET, and FTP tau PET scans.

Fig. 1
figure 1

The current sample sizes of neurocognitive assessments, blood samples, CSF samples, stool samples, multimodal MRI scans, Aβ PET scans, and tau PET scans of the Greater-Bay-Area Healthy Aging Brain Study (By Sep 2023)

Full size image

Inclusion and exclusion criteria

Briefly, the inclusion criteria of GHABS are as follows: (1) adults between the ages of 55 and 90 and speak Mandarin fluently [31]). Notably, individuals below 60 years old are required to have a family dementia history and meet the criteria of subjective cognitive decline (SCD), while people with family history of autosomal dominant or other familial AD are not limited by age; (2) the score on the Geriatric Depression Scale (GDS) is less than 6 points; (3) visual and auditory acuity is sufficient for neuropsychological testing (Including normal corrected vision and hearing); (4) female participants are not pregnant, lactating, or of childbearing potential (that is, women must be two years postmenopausal or surgically sterile); (5) a modified version of the Hachinski Ischemic scores less than or equal to 4; (6) have completed primary school (6 years of education) or have good work experience (sufficient to rule out mental retardation). Individuals with an infection, infarction, or other focal lesions or multiple lacunes or lacunes in critical memory structures and who do not meet the MRI scanning requirements are excluded from the GHABS study. More details of the inclusion and exclusion criteria can be found in the supplemental materials.

Cognitive assessments

The cognitive profiles are assessed via a series of cognitive ability tests, including the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog), Logical Memory Test I & II (the Chinese version), Mini-mental State Examination (MMSE, the Chinese version), Montreal Cognitive Assessment Basic (MoCA-Basic), Shape Trail Test (STT), Clock Drawing Test (CDT), Auditory Verbal Learning Test (AVLT), Symbol Digit Modalities Test (SDMT), Digit Span Test (DST), Animal Verbal Fluency Test (AFT), Cognitive Change Index (CCI), SCD. Besides, functional and behavioral tests were also executed, including the Hachinski Ischemic Score (HIS), Clinical Dementia Rating (CDR), Neuropsychiatric Inventory (NPI), the GDS, Functional Activities Questionnaire (FAQ), Activity of Daily Living Scale (ADL) [32,33,34], measurement of everyday cognition (Ecog), Pittsburgh sleep quality index (PSQI), REM sleep behavior disorder screening questionnaire (RBDSQ), Epworth Sleepiness Scale (ESS).

The participants were classified as NC, MCI, or dementia due to AD following the standard protocol of ADNI cohort [35]. We further define some CU individuals as SCD following the research criteria proposed by Jessen and colleagues in 2014 [36]. Several cognitive assessments were used for clinical diagnosis. The normal performance of these assessments was defined based on education or age. The MMSE used cutoff scores: >17 for participants without education, > 20 for 1–6 years of education, and > 24 for more than six years of education. The delayed recall of logical Memory test used cutoff scores as follows: ≥3 for 0–7 years, ≥5 for 8–15 years, and ≥9 for 16 years of education [37]. As for the ADL, < 23 is normal for participants under 75 years old, while < 25 is for 75 years and older.

CU participants were normal in MMSE, logical Memory recall, and ADL, and their CDR score was 0. Among CU individuals, the presence of the following symptoms as having SCD: (1) Self-experienced persistent decline in cognitive capacity in comparison with a previously normal status and unrelated to an acute event; (2) Normal age-adjusted, gender-adjusted and education-adjusted performance on standardized cognitive tests. CU individuals without SCD were defined as the NC group. Participants with MCI had unimpaired MMSE, while they showed impairment in the logical Memory test [38]. Their CDR score was 0.5, with a mandatory requirement of the memory box score being 0.5 or greater, but ADL was normal. Dementia due to AD was abnormal in MMSE, logical memory, and ADL. The CDR score was 0.5 or greater.

Biospecimen collection

Volunteers fasted for one night the day before (not less than 6 h), and blood was drawn in the morning of the next day. The venous blood of the volunteers is drawn into two 10 ml EDTA blood collection tubes and gently inverted and mixed 10–12 times to ensure that the blood and anticoagulant are thoroughly mixed. The mixed blood was placed in an incubator at 4 °C and shipped back to the laboratory within 4 h for subsequent analysis. The blood is centrifuged at 1600 g for 10 min in a refrigerated centrifuge at 4 °C. The upper plasma layer is transferred to several 2 ml centrifuge tubes using a sterile RNase-free pipette tip. To obtain more pure plasma, the separated plasma is centrifuged again at 16,000 g for 10 min at 4 °C, and then the supernatant is aliquoted into several 0.5 ml centrifuge tubes with labels, each with either 100–200 µl blood plasma. The aliquots are stored in a -80 °C refrigerator for subsequent analysis. After the whole blood is centrifuged in the first step, the buffy coat in the middle layer is gently transferred to the 2640 medium. Then, after density gradient centrifugation, erythrocyte lysis, and centrifugation steps, the isolated peripheral blood mononuclear cell sample is transferred to a 2 ml RNase-free centrifuge tubes and stored in a gradient-cooled freezer box at -80 °C for subsequent analysis. Samples will be used for genomic analysis (including whole genome sequencing and other analyses).

Before collecting the CSF sample, the volunteer must fast for one night (at least 6 h), and the lumbar spinal fluid is performed on an empty stomach the following day. Lumbar puncture is performed strictly with clinical standards, and about 5 ml of CSF is collected. The collected CSF is sent to Shenzhen Bay Laboratory within 2 h for biomarker analysis. CSF sample is quickly divided into 1.5 ml low protein adsorption centrifuge tubes. Afterward, they will be stored in a -80 °C refrigerator for subsequent analysis.

Fecal samples are collected on-site or at home after the volunteers’ consent is obtained. The volunteer stool samples are labeled, sub-packaged, and frozen in a -80 °C refrigerator. Fecal samples are used for 16 S rDNA, metagenomic, metagenome, and metabolome detection of intestinal microorganisms.

CSF and plasma biomarkers measurement

The concentrations of Aβ40, Aβ42, NfL, GFAP, SNAP25, p-Tau181, and p-Tau217 in CSF and plasma are detected using commercial Simoa® NEUROLOGY 4-PLEX E (N4PE, cat: 103,670), SNAP-25 (cat: 103,575), pTau-181 (cat: 104,111), and pTau-217 kit in Simoa HD-X Analyzer™ (Quanterix Corp.). The concentrations of YKL40 in CSF and plasma are measured using a commercial Human YKL-40 Assay (cat: K151VLK) in MESO SECTOR S 600MM (Meso Scale Diagnostics, LLC.). The concentrations of sTREM2 in CSF and plasma are also measured by the MSD platform using an in-house immunoassay as previously described [39]. Briefly, streptavidin-coated plates are blocked overnight in PBS containing 3% bovine serum albumin (BSA) and 0.05% Tween-20 before incubating with the biotinylated capture antibody (0.25 µg/mL, cat: BAF1828) for 1 h. After washing with PBS containing 0.05% Tween-20, the plates are incubated with CSF, and plasma samples diluted in PBS containing 0.25% BSA, 0.05% Tween-20, and Protease Inhibitor Cocktail (cat: P8340) for 2 h. Recombinant human TREM2 protein (SinoBiological, cat. 11,084-H08H) is used as a standard (62.5 to 8000 pg/mL). The plates are rewashed and incubated with the detector antibody (1 µg/mL, cat: sc373828) for 1 h, followed by incubation with the MSD SULFO-tag conjugated secondary antibody (0.5 µg/mL, cat: R32AC). Finally, the electrochemical signal is developed by adding 2x MSD Read buffer T (cat: R-92TC), and the MSD SECTOR S 600MM measures the electro chemiluminescent signal.

The concentrations of PDGFR-β and GAP43 in CSF and plasma are measured by commercial Human PDGFR-β (R&D, Catalog Number: DYC385) and GAP43 (Abbexa) ELISA kits. For neurogranin, an in-house sandwich ELISA is developed combining the mouse monoclonal antibodies Ng22 and Ng2 (Abcam). Nunc maxisorp 96-well microliter plates (Thermo Fisher Scientific) are coated with the mouse anti-human neurogranin (Ng22, 3 µg/mL) in bicarbonate buffer pH 9.6 overnight. After washing with PBS containing 0.05% Tween-20, the plates are blocked in PBS containing 1% BSA and 0.05% Tween-20 for 1 h, followed by incubation with CSF and plasma samples overnight. Recombinant full-length human neurogranin protein is used as a standard (25 to 3200 pg/ml for CSF, 55 to 40,000 pg/mL for plasma). After additional washes, the plates are incubated with the biotinylated mouse anti-human neurogranin (Ng2, 2.7 µg/mL) for 1 h, followed by incubation with a Streptavidin-HRP (R&D Systems) for 30 min. After additional washes, the plates are incubated with Substrate Reagent (TMB, R&D Systems) for 10 min in the dark. The color reaction is stopped by adding 0.2 M H2SO4, and the absorbance is read at 450 nm (650 nm as a reference value).

All samples were analyzed randomly and double-blindly to avoid bias because of the effect of inter-assay variability on specific patient groups. For all biomarker measurements, the samples in the first plate were tested in duplicate, and subsequent samples were tested in single. A pooled sample was generated as a reference, aliquoted and stored at -80 °C. In each measurement, the reference sample was tested in duplicate to compare the detection variability. The mean intra-assay and inter-assay coefficient of variation (CV) was controlled within 20%.

MRI image acquisition

All the MRI scanning sequences will be conducted following the standard ADNI protocol here. The MRI image data is collected on 3.0T scanners, and the scanning parameters vary slightly depending on the specifics of scanners from various clinical centers. A series of sequences are applied for head imaging of each volunteer, including3D T1 MPRAGE, 3D T2 FLAIR (Fluid-Attenuated Inversion Recovery), High Res Hippo (High Resolution Hippocampus), SWI (Susceptibility Weighted Imaging), ASL (Arterial spin labeling), Diffusion (Axial DTI), Field Mapping, EPI-BOLD. Each sequence and parameters of MRI scanning are described as follows:

3D MPRAGE scan parameters: field of view (FOV) = 208 × 240 × 256 mm3; matrix = 208 × 240 × 256 (resolution 1 × 1 × 1 mm3); imaging plane: sagittal; TR = 2300 ms; TE = minimum TE value of the system; TI = 900 ms; FA = 7°; 2-fold acceleration in the phase-encoding direction; scan time 6 min 11 s. Purpose: (1) To provide high-resolution anatomical structure templates for low-resolution functional imaging (PET, EPI images, etc.); (2) To calculate the brain tissue volume and cortical thickness of the subjects, and to measure brain atrophy. Note that the whole brain was covered to avoid aliasing artifacts.

3D FLAIR scanning parameters: FOV = 256 × 256 × 260 mm; matrix = 214 × 256 × 160 (resolution 1.2 × 1 × 1 mm); imaging plane: sagittal; TR = 4800 ms; TE = 119 ms; TI = 1650 ms; 3-fold acceleration in the phase-encoding direction; scan time 6 min 48 s. Purpose: To perform white matter hyperintensity (WMH) segmentation and to evaluate white matter lesions, including infarction and other pathological features.

High Res Hippo scanning parameters: FOV = 175 × 60 × 175 mm3; matrix = 449 × 30 × 449 (resolution 0.39 × 2 × 0.39 mm3); Oblique scan; layer direction perpendicular to the long axis of the hippocampus; TR = 8020 ms; TE = 50 ms; 2-fold acceleration in the phase-encoding direction; scan time 5 min 36s. Purpose: To perform hippocampal segmentation. Note: (1) the layer direction is perpendicular to the long axis of the hippocampus; (2) the imaging FOV should cover the skull, the hippocampal head, and the hippocampus tail.

SWI scanning parameters: FOV = 220 × 220 × 130 mm3; matrix = 368 × 368 × 130 (resolution 0.6 × 0.6 × 1 mm3); imaging plane: transverse; TR = 55 ms; 6 echoes, with the TE of 1st echo = 7.7 ms and the delta TE between echoes = 7.0 ms; 2-fold acceleration in the phase-encoding direction; scan time 6 min 10 s. Purpose: (1) To evaluate cerebral microbleeds; (2) To obtain the quantitative susceptibility map (QSM).

ASL scanning parameters: 3D pseudo-continuous ASL (pCASL) series; FOV = 240 × 240 × 160 mm3; matrix = 96 × 96 × 40 (resolution: 2.5 × 2.5 × 4 mm3); transverse positioning; TR = 4250 ms; TE = 9.0 ms; Label duration = 1800 ms; Post label delay (PLD) = 2000 ms; 2-fold acceleration in the phase-encoding direction; axial acquisition direction: fat chemical shift toward the posterior direction; a proton density image was also acquired within the same sequence to quantify cerebral blood flow (CBF) from the ASL series. The scanning time is 6 min 68 s. Purpose: To measure whole brain perfusion and to calculate CBF. Notes: (1) the labeling plane was placed 20 mm inferior to the lower edge of the imaging volume; (2) cover the cerebellum; (3) keep eyes open, not to think of anything in particular, and remain still during the scan.

Diffusion scanning parameters: FOV = 232 × 232 × 176 mm3; matrix = 116 × 116 × 88 (resolution: 2 × 2 × 2 mm3); transverse position; TR = 3300 ms; TE = 71 ms; multi-b value acquisition: b = 0, 1000, 2000 s/mm2 (in total 112 diffusion directions); 2-fold acceleration in the phase-encoding direction and multiband factor = 3; axial acquisition direction: fat chemical shift toward the posterior direction; scan time 11 min 16 s. 8 averages of b = 0 DTI images with fat chemical shift toward the anterior direction of the axial acquisition direction were also scanned for the geometry distortion correction. Purpose: (1) To evaluate the diffusion parameters of white matter fibers in the brain, such as anisotropy FA, AD, MD, and other parameters; (2) To track white matter fibers in the brain and evaluate the structural connection of white matter fibers. Note to cover the whole brain.

Field mapping scanning parameters: FOV = 220 × 220 × 160 mm3; matrix = 88 × 88 × 64 (resolution: 2.5 × 2.5 × 2.5 mm3); imaging plane: transverse; TR = 400 ms; TE1 = 4.92 ms, TE2 = 7.38 ms. Purpose: Used for geometry distortion correction for EPI series.

Echo-planar imaging (EPI) - Blood oxygen level-dependent (BOLD) scanning parameters: FOV = 220 × 220 × 160 mm3; matrix = 88 × 88 × 64 (resolution: 2.5 × 2.5 × 2.5 mm3); imaging plane: transverse; TR = 600 ms; TE = 30 ms; FA = 53° (Ernst angle with best BOLD contrast); Echo spacing = 0.49 ms; 2-fold acceleration in the phase-encoding direction and multiband factor = 4; axial acquisition direction: fat chemical shift toward the posterior direction; scan time 10 min. Purpose: to assess the functional connectivity between any pair of brain regions. Notes that participants were instructed to keep their eyes open, not to think of anything in particular, and to remain still during the scan.

PET image acquisition

The Aβ PET radiotracer [18F]-florbetapir (FBP) [40] or [18F]D3FSP (FSP) [41] and tau PET radiotracer [18F]-flortaucipir (FTP) [42] are used for PET imaging. The data acquisition is performed on either a GE Discovery™ MI Gen 2 PET/CT scanner or a Siemens Biograph™ TruePoint™ TrueV PET/CT scanner. The spatial resolution of each PET scanner is quantified with PET imaging of a Hoffman phantom. For the Aβ PET imaging, the subjects are injected with either [18F]-florbetapir or [18F]-D3FSP intravenously at 370 MBq (10 mCi ± 10%), rested for 45 min and prepared for the scanning. PET/CT imaging is performed 50 min after injection, and the PET acquisition time is 20 min. For the tau PET imaging, the participants are injected with [18F]-flortaucipir intravenously at 370 MBq (10 mCi ± 10%), rested for 75 min, and prepared for imaging. The dynamic acquisition of [18F]-flortaucipir tau PET data is completed 80–100 min after the radiotracer administration.

A dedicated head scanning procedure covering the whole brain from vertex to cerebellum is used for imaging. A diagnostic dose CT scan of the brain is acquired beforehand for attenuation correction and fusion localization of PET images. The PET scans are acquired using 3D list mode on the GE Discovery MI and Siemens BioGraph TruePoint scanners in two sites. For the GE scanner, the FOV is 256 mm×256 mm×220 mm, the scanning matrix is 336 × 336 × 109, and the voxel size is 1.02 mm×1.02 mm×2.03 mm. For the Siemens scanner, FOV is 256 mm×256 mm×198 mm, the scanning matrix is 192 × 192 × 71, and the voxel size is 1.33 mm×1.33 mm×2.79 mm. All the correction options were selected for both scanners, and no filter or smooth was used during the reconstruction. A reconstruction offset is applied to ensure that the head is entirely in the field of view within the plane. Finally, 4 frames of dynamic images are generated according to 5 min/frame segmentation, with each PET scan corresponding to a 20-minute PET image.

MRI and PET imaging analysis

The structural MRI images are segmented into different cortical and subcortical regions of interest (ROI) in Freesurfer (V7.2.0). The residual hippocampal volume (rHCV) is calculated using the hippocampal volume of both hemispheres and adjusted using the estimated total intracranial volume as we described previously [14]. In addition, the cortical thickness of AD-signature atrophy brain regions is obtained by calculating the surface area-weighted average thickness of the bilateral entorhinal, fusiform, inferior temporal, and middle temporal cortices [43].

As shown in Fig. 2, the PET images are preprocessed with the following steps before further analysis: (1) co-registering the 2nd, 3rd, and 4th frames to the 1st frame, respectively; (2) averaging the four frames into one averaged frame; (3) the averaged frame resliced into a standard AC-PC space (anterior commissure-posterior commissure) with image size = 160 × 160 × 128, voxel dimension = 1.5 mm×1.5 mm×1.5 mm; (4) this standardized image then served as a reference imaging, and each original individual frame was resliced to this reference imaging; (5) smoothing to a uniform 6 mm resolution using the Gaussian kernel function, and the full-width at half maximum (FWHM) of each site was determined from the Hoffman phantom [18F]FDG-PET imaging (As a reference for mitigating between-scanner differences in multi-center PET scans). The PET and MRI images are processed using in-house Matlab algorithms, as shown in Fig. 2. The PET images are co-registered with their corresponding structural MRI images in SPM12 (Statistical Parametric Mapping). Sixty-eight Freesurfer-defined cortical ROIs obtained from MRI segmentation extract regional FSP, FBP, and FTP measurements from the co-registered PET images.

Fig. 2
figure 2

The image processing pipelines of PET, structural MRI, functional MRI (fMRI), and diffusion MRI data of the Greater-Bay-Area Healthy Aging Brain Study

Full size image

The FSP and FBP standardized uptake value ratio (SUVR) of AD summary cortical regions (posterior cingulate cortex, precuneus, frontal lobe, parietal lobe, and lateral temporal) are obtained by dividing the radiotracer uptake value of AD typical brain regions by that in the whole cerebellum [44]. In the present study, we only presented FSP Aβ PET data. For the [18F]-flortaucipir images, FTP SUVR of 68 FreeSurfer-defined ROIs are calculated by normalizing the [18F]-flortaucipir value to the value of the inferior cerebellar cortex [45]. The FTP SUVR of the AD Temporal-MetaROI [43] (entorhinal cortex, parahippocampal gyrus, fusiform, amygdala, inferior temporal and middle temporal brain regions) is used to evaluate cortical tau deposition.

The general process of resting-state fMRI data preprocessing and brain functional connectivity construction are outlined in the third column of Fig. 2 and described in detail in the Supplemental Materials. The general process of diffusion MRI processing are outlined in the fourth column of Fig. 2 and described in detail in the Supplemental Materials. Diffusion-weighted data are denoised and corrected for Gibbs ring using Mrtrix3 (V3.0.3) [46], and then corrections were applied for head motion, eddy current, and EPI susceptibility distortion using FSL (V6.0.3) [47]. 3D pCASL imaging was employed to calculate the CBF map (Supplemental Fig. 3). The WMH segmentation was processed using a custom pipeline developed by our lab based on the T2 FLAIR images (Supplemental Fig. 4). More details of fMRI, dMRI, WMH, and pCASL can be found in Supplementary Material.

Cutoffs of plasma Aβ42/Aβ40, and plasma p-Tau181, and FSP Aβ PET

We used Gaussian mixed model analysis to estimate two Gaussian distributions of low Aβ and high Aβ (Supplemental Fig. 5) for COMPOSITE FSP SUVR to define an unsupervised threshold, which corresponds to a 90% probability of belonging to the high Aβ distribution. The thresholds of FSP SUVR were defined as COMPOSITE SUVR≥0.76 (Supplemental Fig. 5). The receiver operating characteristic (ROC) curve analysis using the Youden index classifying 154 Aβ- cognitively unimpaired (CU) participants and 69 Aβ + cognitively impaired (CI) individuals (MCI or dementia) as the endpoint to define the cutoff ≤ 0.0609 for plasma Aβ42/Aβ40 ratio (Supplemental Figs. 6–7). Similarly, the ROC analysis using the Youden index classifying 143 Aβ- CU participants and 67 Aβ + CI participants as the endpoint to define the cutoff ≥ 2.418 for plasma p-Tau181 (Supplemental Figs. 8–9). The cohort was divided into A-/T-, A-/T+, A+/T-, and A+/T + according to the plasma Aβ42/Aβ40 ratio and plasma p-Tau181 thresholds.

Statistical analysis

All the statistical analyses were conducted using R (v4.3.0, The R Foundation for Statistical Computing). The normal distribution of the data in this study was determined using the Shapiro-Wilk test. Plasma p-Tau181, plasma GFAP, and plasma NfL were log10 transferred before the following analysis to meet the normal distribution. We used a two-tailed Mann-Whitney U test and Fisher’s exact test to compare the continuous and categorical characteristics at baseline between different diagnosis groups, respectively. Data were presented as median (interquartile range, IQR) or No. (%) unless otherwise noted. Generalized linear models were used to compare plasma biomarkers, COMPOSITE FSP Aβ SUVR, temporal-metaROI FTP SUVR, rHCV, and temporal-metaROI cortical thickness between different clinical groups, controlling for age, sex, and APOE-ε4. Subsequently, we investigated the frequency of different A/T profiles defined by plasma biomarkers (A: plasma Aβ42/Aβ40, T: plasma p-Tau181) and Aβ PET positivity among NC, SCD, MCI, and dementia groups.

Results

Demographics of participants

The demographic characteristics of participants included in this study are summarized in Table 1. At baseline, MCI and dementia individuals had older ages, higher percentages of APOE-ε4 carriers, shorter duration of education, and lower MoCA and MMSE scores than NC and SCD individuals. The dementia group also had lower MoCA and MMSE scores than the MCI group. The SCD group had more females than the MCI and dementia groups. Additionally, 443 participants had risk factors records, including the medical history of hypertension, hyperlipidemia, and diabetes, as well as the assessments of PSQI and GDS. SCD individuals had higher percentage of hyperlipidemia compared to the NC and MCI groups. SCD and MCI individuals had worse sleep state than NC and dementia individuals, while dementia individuals showed better sleep state than NC individuals. MCI and dementia individuals had higher depression scores than SCD and NC individuals, whereas SCD individuals showed higher depression scores than NC individuals. The demographic data of the GHABS cohort by cities are summarized in Supplemental Tables 2 and have been compared with the corresponding demographcis from the literature, as presented in Supplemental Table 3 [48, 49].

Table 1 Demographics of Greater-Bay-Area Healthy Aging Brain Study (GHABS) participants included in this study
Full size table

Comparisons of plasma biomarkers and neuroimages among different clinical stages

Compared to the NC group, the SCD group had higher plasma p-Tau181 (standardized β (βstd) = 0.304[95% confidence interval (ci), 0.113, 0.494], p = 0.002) and plasma GFAP (βstd = 0.225[95% ci, 0.029, 0.420], p = 0.025), and the MCI group had lower plasma Aβ42/Aβ40 (βstd = -0.392[95% ci, -0.663, -0.121], p = 0.005), higher plasma p-Tau181 (βstd = 0.594[95% ci, 0.352, 0.836], p < 0.001), plasma GFAP (βstd = 0.363[95% ci, 0.114, 0.611], p = 0.004), plasma NfL (βstd = 0.388[95% ci, 0.161, 0.615], p < 0.001), COMPOSITE Aβ PET SUVR (βstd = 0.452[95% ci, 0.117, 0.788], p = 0.008), and temporal-metaROI FTP SUVR (βstd = 0.420[95% ci, -0.028, 0.867], p = 0.066), and more decreases in rHCV (βstd = -0.347[95% ci, -0.622, -0.072], p = 0.013) (Fig. 3).

The MCI group showed lower plasma Aβ42 (Supplemental Fig. 10, βstd = -0.365[95% ci, -0.619, -0.111], p = 0.014), plasma Aβ42/Aβ40 (Fig. 3A, βstd = -0.363[95% ci, -0.614, -0.112], p = 0.005), rHCV (Fig. 3G, βstd = -0.451[95% ci, -0.703, -0.200], p < 0.001), and temporal-metaROI cortical thickness (Fig. 3H, βstd = -0.315[95% ci, -0.600, -0.030], p = 0.030), and higher plasma p-Tau181 (Fig. 3B, βstd = 0.290[95% ci, 0.066, 0.514], p = 0.011), plasma NfL (Fig. 3D, βstd = 0.297[95% ci, 0.087, 0.508], p = 0.006), COMPOSITE Aβ PET SUVR (Fig. 3E, βstd = 0.373[95% ci, 0.074, 0.673], p = 0.014), temporal-metaROI FTP SUVR (Fig. 3F, βstd = 0.444[95% ci, 0.011, 0.878], p = 0.045) than the SCD group.

The dementia patients had significant abnormal alternations (p ≤ 0.011) in all the plasma biomarkers, and Aβ PET, tau PET, rHCV, and temporal-metaROI cortical thickness than the other three groups (Fig. 3). Besides, the dementia group had lower plasma Aβ42 compared to the NC group (βstd = -0.556[95% ci, -0.876, -0.237], p = 0.002) and SCD group (βstd = -0.597[95% ci, -0.897, -0.297], p < 0.001) as shown in Supplemental Fig. 9.

Fig. 3
figure 3

Comparisons of plasma biomarkers and neuroimaging between different clinical groups. Comparisons of (A) plasma Aβ42/Aβ40, (B) plasma p-Tau181, (C) plasma GFAP, (D) plasma NfL, (E) Aβ PET, (F) tau PET, (G) hippocampal volume and (H) temporal-MetaROI cortical thickness between NC, SCD, MCI, and dementia groups. Plasma p-Tau181, plasma GFAP, and plasma NfL were log10 transferred. The mean intra-assay and inter-assay coefficient of variation (CV)s were respectively 12% and 17% for plasma Aβ42, 9% and 17% for plasma Aβ40, 7% and 9% for plasma NfL, 5% and 14% for plasma GFAP, and 10% and 13% for plasma p-Tau181

Full size image

Frequency of plasma A/T and Aβ PET positivity among different clinical stages

The frequencies of A-/T- (NC; 53.8%, SCD: 55.6%, MCI: 26.4%, Dementia: 7.0%), A-/T+ (NC; 7.5%, SCD: 13.5%, MCI: 17.2%, Dementia: 14.0%), A+/T- (NC; 32.8%, SCD: 22.7%, MCI: 31.0%, Dementia: 14.0%), and A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, Dementia: 64.9%) profiles were illustrated in Fig. 4A. The frequencies of Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, Dementia: 89.3%) were illustrated in Fig. 4B.

Fig. 4
figure 4

The frequency of plasma A/T profiles and Aβ PET positivity between different clinical groups. Comparisons of (A) A/T profiles defined by plasma Aβ42/Aβ40 and plasma p-Tau181, and (B) Aβ PET positivity between NC, SCD, MCI, and Dementia groups

Full size image
Fig. 5
figure 5

The general design and goals of the Greater-Bay-Area Healthy Aging Brain Study cohort

Full size image

Discussion

There are approximately 47 million preclinical AD individuals, 39 million MCI patients, and 15 million dementia patients in China. In 2035, the older population aged 60 and above is expected to exceed 400 million, accounting for more than 30% of the total population. As long as there are no practical methods for the early detection and treatment of AD, the number of AD patients in China will continue to rise. To end this, the GHABS study was initiated in 2021. The GHABS study aims to investigate the prevalence and progression of AD in the Guangdong-Hong Kong-Macao Greater-Bay-Area of China. The ultimate goal is to develop novel biomarkers and neuroimaging approaches for early diagnosis of AD and support the early intervention of clinical trials in the Guangdong-Hong Kong-Macao Greater-Bay-Area (Fig. 5).

Following the standard protocol of ADNI [50], the GHABS is supposed to be a high-standard AD community cohort in the Guangdong-Hong Kong-Macao Greater-Bay-Area of China. GHABS referred to several well-established large-scale AD cohorts from Europe and the United States, such as the Mayo Clinical Study of Aging [51], the Harvard Aging Brain Study [52], the Swedish Biomarkers For Identifying Neurodegenerative Disorders Early and Reliably, the Amyloid Imaging to Prevent Alzheimer’s Disease [53], and the Australian Imaging, Biomarkers and Lifestyle Study [54]. Based on these databases, many breakthroughs have been made in AD-related gene loci, biomarker detection, PET molecular imaging, etc., which provide important support for screening target participants and therapeutic targets in AD clinical trials. Besides, GHABS also referred to several well-organized cohorts in China, such as the Beijing Aging Brain Rejuvenation Initiative [55], China Aging and Neurodegenerative Initiative [56], Chinese Alzheimer’s Biomarker and LifestylE [57], the Chongqing Ageing & Dementia Study (CADS) [58], Chinese Preclinical Alzheimer’s Disease Study (C-PAS) [59], the China Cognition and Ageing Study [60], the Taizhou Imaging Study [61], and Sino Longitudinal Study on Cognitive Decline [62]. Dr. Ying Han, the president of the “Pre-Alzheimer’s Disease Alliance of China,” is one of the principal investigators in GHABS. Dr. Han initiated the Pre-AD Alliance of China in 2017 [63] and led two AD cohorts focused on the population of SCD for early AD diagnosis and investigation, the SILCODE and Cross-Cultural Longitudinal Study on Cognitive Decline [64]. The design of GHABS will be updated by the researchers if necessary. GHABS closely follows the latest academic and industry developments in the field and tries to adapt and update protocols.

The primary goal of GHABS is to investigate the pathological characteristics, risk factors, protective indicators, and evolution of Aβ and tau pathologies in Guangdong-Hong Kong-Macao Greater-Bay-Area older adults. The specific studies are as follows: (1) summarize the incidence of preclinical AD (Aβ + CU) in the Guangdong-Hong Kong-Macao Greater-Bay-Area, and reveal the characteristics and progression of AD pathologies; (2) study the feasibility of plasma biomarkers for detecting early AD in older adults; (3) determine the risk factors related to abnormal changes in Aβ and tau proteins; (4) reveal the spatiotemporal patterns of cortical Aβ plaques, tau tangles aggregation, synapse loss, and neuroinflammation and their relations to brain atrophy and cognitive decline; (5) investigate the roles of neuroinflammation, synaptic loss, vascular diseases, myelination, metabolic dysfunction across the spectrum of AD. Hopefully, these studies based on the GHABS cohort may provide novel insights into the early diagnosis and intervention of AD in China and the AD community.

Compared to CSF biomarkers and PET imaging, plasma biomarkers are the most promising early screening technology for AD by considering the advantages of simple sampling, low traumatic, and cost [65]. Recently, plasma Aβ42/Aβ40 [66, 67], p-Tau181 [68], p-Tau217 [69], p-Tau231 [70], and GFAP [71] showed great potential in early diagnosis of AD. One of the primary goals of GHABS is to evaluate the performance of previously reported plasma biomarkers and further explore novel plasma biomarkers in China’s aging population. For example, the GHABS research group has investigated the characteristics of CSF GAP43 in different clinical and pathological stages of AD [11] based on the ADNI cohort and demonstrated that presynaptic dysfunction measured by CSF GAP43 occurs prior to AD typical neurodegeneration and predicts faster cognitive decline [12]. We are currently measuring GAP43 concentrations in plasma in the GHABS cohort and evaluating its suitability as a plasma synaptic biomarker of AD in the Chinese aging population.

The GHABS project also supports and fertilizes new diagnosis methods for early AD diagnosis from academics and pharmaceutical industries. GHABS has been committed to co-developing early diagnostic tools since its inception, including fluid biomarkers, new antibodies, brain PET instruments, and new PET tracers. In Shenzhen Bay Laboratory, Dr. Qiyu Peng’s group are dedicated to developing high-performance and low-cost brain PET/CT scanner and wearable brain PET/CT scanner [72]. The GHABS plans to support the clinical verification of the novel brain PET/CT instruments, as AD is one of the main neurodegenerative diseases that require a brain-dedicated PET/CT instrument. GHABS is also exploring adjusting the Aβ PET imaging protocols for clinical diagnosis by shortening the scanning time or reducing trace dose using brain PET/CT scanners with high spatial resolution and detection sensitivity. In the future, GHABS will also facilitate AD clinical trials.

In this study, we, for the first time, investigated the abnormal alterations of plasma Aβ42, plasma Aβ42/Aβ40, plasma p-Tau181, plasma GFAP, plasma NfL, Aβ PET, tau PET, hippocampal volume, and AD-signature cortical thickness across the different clinical stages of AD based on a Chinese aging cohort in Guangdong-Hong Kong-Macao Greater-Bay-Area. This showed that SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals, suggesting that SCD may be related to early increases in plasma p-Tau181 and plasma GFAP. In addition, CI individuals (MCI or dementia) had abnormal changes in plasma Aβ42/Aβ40, p-Tau181, and NfL, and Aβ PET, tau PET, hippocampal volume, and AD-signature cortical thickness compared to the CU (NC or SCD) individuals. Moreover, the frequencies of plasma Aβ42/Aβ40 positive and plasma p-Tau181 positive (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, Dementia: 64.9%) and Aβ PET positivity (NC; 25.6%, SCD: 22.5%, MCI: 47.7%, Dementia: 89.3%) were reported for the first time. The overall Aβ-PET positivity rates of NC, MCI, and dementia in C-PAS, an eastern China cohort, were 26.9%, 44.5%, and 85.8%, respectively [73], which was similar to the findings of the GHABS cohort. In general, the frequencies of Aβ PET positivity were in accordance with previous reports [74,75,76].

According to the latest NIA-AA research framework proposed by Jack and colleagues in AAIC 2023, it has been suggested to use a combination of plasma Aβ42/Aβ40 and plasma p-Tau rather than them alone to identify individuals with a high risk of AD. However, our findings indicate that using plasma Aβ42/Aβ40 positive and plasma p-Tau181 positive (A+/T+) may be only able to capture 23.0%, 36.4%, 53.4%, and 72.7% of Aβ PET positivity in NC, SCD, MCI, and dementia individuals respectively. Considering using A-/T + and A+/T- defined by plasma Aβ42/Aβ40 and plasma p-Tau181 in addition to A+/T + to identify individuals with a high risk of AD, the frequencies of abnormal plasma biomarkers increased by 46.2%, 44.4%, 73.6%, and 93.0% for NC, SCD, MCI, and Dementia respectively. Future investigation is required to determine whether A-/T + or A+/T- individuals defined by plasma Aβ42/Aβ40 and plasma p-Tau181 have evidence of Aβ plaques, tau tangles, brain atrophy, and cognitive decline.

In summary, we adapt the standard ADNI protocols to collect cognitive assessments, fluid biomarkers, and neuroimaging data to create a community-based observable AD cohort in the Guangdong-Hong Kong-Macao Greater Bay Area of China. The GHABS cohort is expected to identify novel biomarkers and neuroimaging techniques for early detection, determine the appropriate time window for AD intervention, and explore AD’s pathological features and progression patterns, especially during the asymptomatic stage of AD in South China’s aging population. We reported for the first time the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in a Chinese community aging cohort in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide important guidance towards designing standard AD community cohorts in South China and offer novel insights into understanding the characteristics of abnormal AD pathologies changes in South China’s older population.

Data availability

The data used in the current study were obtained from the GHABS cohort. Derived data is available from the corresponding author on request by any qualified investigator subject to a data use agreement.

Abbreviations

AD:

Alzheimer’s disease

ADAS-Cog:

Alzheimer’s Disease Assessment Scale–Cognitive Subscale

ADL:

Activity of Daily Living Scale

ADNI:

Alzheimer’s Disease Neuroimaging Initiative

AFT:

Animal Verbal Fluency Test

ASL:

Arterial Spin Label

AVLT:

Auditory Verbal Learning Test

Aβ:

β-amyloid

BOLD:

Blood Oxygen Level Dependent

CBF:

Cerebral Blood Flow

CCI:

Cognitive Change Index

CDR:

Clinical Dementia Rating

CDT:

Clock Drawing Test

CI:

Cognitive Impaired

ci:

confidence interval

CSF:

Cerebrospinal Fluid

CU:

Cognitively Unimpaired

CV:

coefficient of variation

DST:

Digit Span Test

DTI:

Diffusion Tensor Imaging

Ecog:

Everyday cognition

ELISA:

Enzyme-linked Immunosorbent Assay

EPI:

Echo-planar Imaging

ESS:

Epworth Sleepiness Scale

FAQ:

Function Activities Questionnaire

FBP:

[18F]-florbetapir

FLAIR:

Fluid-attenuated Inversion Recovery

fMRI:

functional MRI

FSP:

[18F]D3FSP

FTP:

[18F]-flortaucipir

FWHM:

Full-width at Half Maximum

GAP43:

Growth-associated Protein-43

GDS:

Geriatric Depression Scale

GFAP:

Glial Fibrillary Acidic Protein

GHABS:

Greater-Bay-Area Healthy Aging Brain Study

High Res Hippo:

High Resolution Hippocampus

IQR:

Interquartile Range

MCI:

Mild Cognitive Impairment

MMSE:

Mini-mental State Examination

MoCA-Basic:

Montreal Cognitive Assessment Basic

MRI:

Magnetic Resonance Imaging

NfL:

Neurofilament Light

NPI:

Neuropsychiatric Inventory

OR:

Odds Ratio

pCASL:

3D pseudo-continuous Arterial Spin Labeling

PDGFR-β:

Platelet-derived Growth Factor Receptorβ

PET:

Positron Emission Tomography

PSQI:

Pittsburgh Sleep Quality Index

p-Tau:

Phosphorylated Tau

RBDSQ:

REM Sleep Behavior Disorder Screening Questionnaire

rHCV:

residual Hippocampal Volume

ROC:

Receiver Operating Characteristic Curve

ROI:

Region of Interest

SCD:

Subjective Cognitive Decline

SDMT:

Symbol Digit Modalities Test

SILCODE:

Sino Longitudinal Study on Cognitive Decline

SNAP25:

Synaptosome Associated Protein 25

sTREM2:

Soluble Triggering Receptor Expressed on Myeloid Cells 2

STT:

Shape Trail Test

SUVR:

Standardized Uptake Value Ratio

SWI:

Susceptibility Weighted Imaging

WMH:

White-matter Hyperintensity

References

  1. Morris JC. The clinical dementia rating (CDR): current version and scoring rules. Neurology. 1993;43:2412–2412. https://doi.org/10.1212/WNL.43.11.2412-a.

    Article  CAS  PubMed  Google Scholar 

  2. Alzheimer Association. 2022 Alzheimer’s disease facts and figures. Alzheimer’s Dement 2022;18:700–89. https://doi.org/10.1002/alz.12638.

  3. Braak H, Braak E, Bohl J. Staging of alzheimer-related cortical destruction. Eur Neurol. 1993;33:403–8. https://doi.org/10.1159/000116984.

    Article  CAS  PubMed  Google Scholar 

  4. Guo T, Shaw LM, Trojanowski JQ, Jagust WJ, Landau SM. Association of CSF Aβ, amyloid PET, and cognition in cognitively unimpaired elderly adults. Neurology. 2020;95:e2075–85. https://doi.org/10.1212/WNL.0000000000010596.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Shi D, Xie S, Li A, Wang Q, Guo H, Han Y, et al. APOE-ε4 modulates the association among plasma Aβ42/Aβ40, vascular diseases, neurodegeneration and cognitive decline in non-demented elderly adults. Transl Psychiatry. 2022;12. https://doi.org/10.1038/s41398-022-01899-w.

  6. Guo T, Brendel M, Grimmer T, Rominger A, Yakushev I. Predicting Regional Pattern of Longitudinal β-Amyloid Accumulation by Baseline PET. J Nucl Med. 2017;58:639–45. https://doi.org/10.2967/jnumed.116.176115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Guo T, Dukart J, Brendel M, Rominger A, Grimmer T, Yakushev I. Rate of β-amyloid accumulation varies with baseline amyloid burden: implications for anti-amyloid drug trials. Alzheimer’s Dement. 2018;14:1387–96. https://doi.org/10.1016/j.jalz.2018.05.013.

    Article  Google Scholar 

  8. Du J, Li A, Shi D, Chen X, Wang Q, Liu Z, et al. Association of APOE-ε4, Osteoarthritis, β-Amyloid, and Tau Accumulation in Primary Motor and Somatosensory regions in Alzheimer Disease. Neurology. 2023;101:e40–9. https://doi.org/10.1212/WNL.0000000000207369.

    Article  CAS  PubMed  Google Scholar 

  9. Guo T, Korman D, La Joie R, Shaw LM, Trojanowski JQ, Jagust WJ, et al. Normalization of CSF pTau measurement by Aβ40 improves its performance as a biomarker of Alzheimer’s disease. Alzheimers Res Ther. 2020;12:97. https://doi.org/10.1186/s13195-020-00665-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cai Y, Du J, Li A, Zhu Y, Xu L, Sun K et al. Initial levels of β-amyloid and tau deposition have distinct effects on longitudinal tau accumulation in Alzheimer’s disease. Alzheimers Res Ther 2023:1–14. https://doi.org/10.1186/s13195-023-01178-w.

  11. Lan G, Cai Y, Li A, Liu Z, Ma S, Guo T. Association of Presynaptic Loss with Alzheimer’s Disease and Cognitive decline. Ann Neurol. 2022;92:1001–15. https://doi.org/10.1002/ana.26492.

    Article  CAS  PubMed  Google Scholar 

  12. Lan G, Li A, Liu Z, Ma S, Guo T. Presynaptic membrane protein dysfunction occurs prior to neurodegeneration and predicts faster cognitive decline. Alzheimer’s Dement. 2023;19:2408–19. https://doi.org/10.1002/alz.12890.

    Article  CAS  Google Scholar 

  13. Lan G, Du J, Chen X, Wang Q, Han Y, Guo T. Association of APOE-ε4 and GAP-43-related presynaptic loss with β-amyloid, tau, neurodegeneration, and cognitive decline. Neurobiol Aging. 2023;132:209–19. https://doi.org/10.1016/j.neurobiolaging.2023.09.012.

    Article  CAS  PubMed  Google Scholar 

  14. Guo T, Korman D, Baker SL, Landau SM, Jagust WJ. Longitudinal cognitive and biomarker measurements support a unidirectional pathway in Alzheimer’s Disease Pathophysiology. Biol Psychiatry. 2021;89:786–94. https://doi.org/10.1016/j.biopsych.2020.06.029.

    Article  CAS  PubMed  Google Scholar 

  15. Li A, Du J, Cai Y, Chen X, Sun K, Guo T. Body Mass Index decrease has a Distinct Association with Alzheimer’s Disease Pathophysiology in APOE ɛ4 carriers and non-carriers. J Alzheimer’s Dis. 2023;96:643–55. https://doi.org/10.3233/JAD-230446.

    Article  CAS  Google Scholar 

  16. Guo T, Landau SM, Jagust WJ. Detecting earlier stages of amyloid deposition using PET in cognitively normal elderly adults. Neurology. 2020;94:e1512–24. https://doi.org/10.1212/WNL.0000000000009216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bateman RJ, Xiong C, Benzinger TLS, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s Disease. N Engl J Med. 2012;367:795–804. https://doi.org/10.1056/NEJMoa1202753.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gordon BA, Blazey TM, Su Y, Hari-Raj A, Dincer A, Flores S, et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol. 2018;17:241–50. https://doi.org/10.1016/S1474-4422(18)30028-0.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Moscoso A, Grothe MJ, Ashton NJ, Karikari TK, Rodriguez JL, Snellman A, et al. Time course of phosphorylated-tau181 in blood across the Alzheimer’s disease spectrum. Brain. 2021;144:325–39. https://doi.org/10.1093/brain/awaa399.

    Article  PubMed  Google Scholar 

  20. Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement. 2018;14:535–62. https://doi.org/10.1016/j.jalz.2018.02.018.

    Article  Google Scholar 

  21. Roberts RO, Aakre JA, Kremers WK, Vassilaki M, Knopman DS, Mielke MM, et al. Prevalence and outcomes of amyloid positivity among persons without dementia in a Longitudinal, Population-based setting. JAMA Neurol. 2018;75:970. https://doi.org/10.1001/jamaneurol.2018.0629.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Donohue MC, Sperling RA, Petersen R, Sun C-K, Weiner MW, Aisen PS. Association between Elevated Brain Amyloid and subsequent cognitive decline among cognitively normal persons. JAMA. 2017;317:2305. https://doi.org/10.1001/jama.2017.6669.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ossenkoppele R, Pichet Binette A, Groot C, Smith R, Strandberg O, Palmqvist S et al. Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline. Nat Med 2022:1–19. https://doi.org/10.1038/s41591-022-02049-x.

  24. Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Heal. 2020;5:e661–71. https://doi.org/10.1016/S2468-2667(20)30185-7.

    Article  Google Scholar 

  25. National Bureau of Statistics. The seventh national census of China 2021. http://www.stats.gov.cn/zt_18555/zdtjgz/zgrkpc/dqcrkpc/ggl/202302/t20230215_1904001.html (accessed March 1, 2023).

  26. Jia L, Quan M, Fu Y, Zhao T, Li Y, Wei C, et al. Dementia in China: epidemiology, clinical management, and research advances. Lancet Neurol. 2020;19:81–92. https://doi.org/10.1016/S1474-4422(19)30290-X.

    Article  PubMed  Google Scholar 

  27. Ren R, Qi J, Lin S, Liu X, Yin P, Wang Z, et al. The China Alzheimer Report 2022. Gen Psychiatry. 2022;35:e100751. https://doi.org/10.1136/gpsych-2022-100751.

    Article  Google Scholar 

  28. Cummings J, Rabinovici GD, Atri A, Aisen P, Apostolova LG, Hendrix S, et al. Aducanumab: Appropriate Use recommendations Update. J Prev Alzheimer’s Dis. 2022;8:1–2. https://doi.org/10.14283/jpad.2022.34.

    Article  Google Scholar 

  29. van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, et al. Lecanemab in Early Alzheimer’s Disease. N Engl J Med. 2023;388:9–21. https://doi.org/10.1056/NEJMoa2212948.

    Article  PubMed  Google Scholar 

  30. Mintun MA, Lo AC, Duggan Evans C, Wessels AM, Ardayfio PA, Andersen SW, et al. Donanemab in Early Alzheimer’s Disease. N Engl J Med. 2021;384:1691–704. https://doi.org/10.1056/NEJMoa2100708.

    Article  CAS  PubMed  Google Scholar 

  31. Jessen F, Amariglio RE, Buckley RF, van der Flier WM, Han Y, Molinuevo JL, et al. The characterisation of subjective cognitive decline. Lancet Neurol. 2020;19:271–8. https://doi.org/10.1016/S1474-4422(19)30368-0.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lawton MP, Brody EM. Assessment of Older people: self-maintaining and instrumental activities of daily Living1. Gerontologist. 1969;9:179–86. https://doi.org/10.1093/geront/9.3_Part_1.179.

    Article  CAS  PubMed  Google Scholar 

  33. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged: the Index of ADL: a standardized measure of biological and psychosocial function. JAMA. 1963;185:914–9. https://doi.org/10.1001/jama.1963.03060120024016.

    Article  CAS  PubMed  Google Scholar 

  34. Katz S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J Am Geriatr Soc. 1983;31:721–7. https://doi.org/10.1111/j.1532-5415.1983.tb03391.x.

    Article  CAS  PubMed  Google Scholar 

  35. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology. 2010;74:201–9. https://doi.org/10.1212/WNL.0b013e3181cb3e25.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chételat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement. 2014;10:844–52. https://doi.org/10.1016/j.jalz.2014.01.001.

    Article  PubMed  Google Scholar 

  37. Bennett DA, Wilson RS, Schneider JA, Evans DA, Beckett LA, Aggarwal NT, et al. Natural history of mild cognitive impairment in older persons. Neurology. 2002;59:198–205. https://doi.org/10.1212/WNL.59.2.198.

    Article  CAS  PubMed  Google Scholar 

  38. Weiner M, Aisen P, Petersen R, Rafii M, Chow T, Shaw LM et al. Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3) Protocol. 2016;3:1.

  39. Gispert JD, Suárez-Calvet M, Monté GC, Tucholka A, Falcon C, Rojas S, et al. Cerebrospinal fluid sTREM2 levels are associated with gray matter volume increases and reduced diffusivity in early Alzheimer’s disease. Alzheimer’s Dement. 2016;12:1259–72. https://doi.org/10.1016/j.jalz.2016.06.005.

    Article  Google Scholar 

  40. Clark CM, Schneider JA, Bedell BJ, Beach TG, Bilker WB, Mintun MA, et al. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA. 2011;305:275–83. https://doi.org/10.1001/jama.2010.2008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zha Z, Ploessl K, Choi SR, Alexoff D, Kung HF. Preclinical evaluation of [18F]D3FSP, deuterated AV-45, for imaging of β-amyloid in the brain. Nucl Med Biol. 2021;92:97–106. https://doi.org/10.1016/j.nucmedbio.2020.03.003.

    Article  CAS  PubMed  Google Scholar 

  42. Fleisher AS, Pontecorvo MJ, Devous MD, Lu M, Arora AK, Truocchio SP, et al. Positron Emission Tomography Imaging with [ 18 F]flortaucipir and Postmortem Assessment of Alzheimer Disease Neuropathologic Changes. JAMA Neurol. 2020;77:829. https://doi.org/10.1001/jamaneurol.2020.0528.

    Article  PubMed  Google Scholar 

  43. Jack CR, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS, et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimer’s Dement. 2017;13:205–16. https://doi.org/10.1016/j.jalz.2016.08.005.

    Article  Google Scholar 

  44. Landau SM, Fero A, Baker SL, Koeppe R, Mintun M, Chen K, et al. Measurement of longitudinal β-Amyloid change with 18 F-Florbetapir PET and standardized Uptake Value Ratios. J Nucl Med. 2015;56:567–74. https://doi.org/10.2967/jnumed.114.148981.

    Article  CAS  PubMed  Google Scholar 

  45. Maass A, Landau S, Baker SL, Horng A, Lockhart SN, La Joie R, et al. Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer’s disease. NeuroImage. 2017;157:448–63. https://doi.org/10.1016/j.neuroimage.2017.05.058.

    Article  CAS  PubMed  Google Scholar 

  46. Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. NeuroImage. 2019;202:116137. https://doi.org/10.1016/j.neuroimage.2019.116137.

    Article  PubMed  Google Scholar 

  47. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL Neuroimage. 2012;62:782–90. https://doi.org/10.1016/j.neuroimage.2011.09.015.

    Article  PubMed  Google Scholar 

  48. Weekes B, Carthery-Goulart MT. Intervention and Prevention of Dementia in the Greater Bay Area (GBA) of China. Am J Alzheimer’s Dis Other Dementias®. 2023;38:15333175231211096. https://doi.org/10.1177/15333175231211097.

    Article  Google Scholar 

  49. Qi SG, Wang ZH, Wei CB, Yang Z, Zhu XQ. [Case-control study on the influencing factors related to cognitive impairment in the elderly population of China]. Zhonghua Yu Fang Yi Xue Za Zhi. 2018;52:926–31. https://doi.org/10.3760/cma.j.issn.0253-9624.2018.09.011.

    Article  CAS  PubMed  Google Scholar 

  50. Weiner MW, Veitch DP, Miller MJ, Aisen PS, Albala B, Beckett LA, et al. Increasing participant diversity in AD research: plans for digital screening, blood testing, and a community-engaged approach in the Alzheimer’s Disease Neuroimaging Initiative 4. Alzheimer’s Dement. 2023;19:307–17. https://doi.org/10.1002/alz.12797.

    Article  Google Scholar 

  51. Roberts RO, Geda YE, Knopman DS, Cha RH, Pankratz VS, Boeve BF, et al. The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology. 2008;30:58–69. https://doi.org/10.1159/000115751.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Dagley A, LaPoint M, Huijbers W, Hedden T, McLaren DG, Chatwal JP, et al. Harvard Aging Brain Study: dataset and accessibility. NeuroImage. 2017;144:255–8. https://doi.org/10.1016/j.neuroimage.2015.03.069.

    Article  PubMed  Google Scholar 

  53. Lopes Alves I, Collij LE, Altomare D, Frisoni GB, Saint-Aubert L, Payoux P, et al. Quantitative amyloid PET in Alzheimer’s disease: the AMYPAD prognostic and natural history study. Alzheimer’s Dement. 2020;16:750–8. https://doi.org/10.1002/alz.12069.

    Article  Google Scholar 

  54. Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, et al. The Australian imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatr. 2009;21:672–87. https://doi.org/10.1017/S1041610209009405.

    Article  PubMed  Google Scholar 

  55. Yang C, Li X, Zhang J, Chen Y, Li H, Wei D, et al. Early prevention of cognitive impairment in the community population: the Beijing Aging Brain Rejuvenation Initiative. Alzheimer’s Dement. 2021;17:1610–8. https://doi.org/10.1002/alz.12326.

    Article  Google Scholar 

  56. Gao F, Lv X, Dai L, Wang Q, Wang P, Cheng Z, et al. A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer’s dementia: China Aging and Neurodegenerative Initiative (CANDI) study. Alzheimer’s Dement. 2023;19:749–60. https://doi.org/10.1002/alz.12700.

    Article  CAS  Google Scholar 

  57. Hu H, Bi Y-L, Shen X-N, Ma Y-H, Ou Y-N, Zhang W, et al. Application of the amyloid/tau/neurodegeneration framework in cognitively intact adults: the CABLE study. Ann Neurol. 2022;97:455–68. https://doi.org/10.1002/ana.26439.

    Article  CAS  Google Scholar 

  58. Fan D-Y, Jian J-M, Huang S, Li W-W, Shen Y-Y, Wang Z, et al. Establishment of combined diagnostic models of Alzheimer’s disease in a Chinese cohort: the Chongqing Ageing & Dementia Study (CADS). Transl Psychiatry. 2022;12:252. https://doi.org/10.1038/s41398-022-02016-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Cui L, Huang L, Pan F-F, Wang Y, Huang Q, Guan Y-H, et al. Chinese preclinical Alzheimer’s Disease Study (C-PAS): design and challenge from PET Acceptance. J Prev Alzheimer’s Dis. 2023;10:571–80. https://doi.org/10.14283/jpad.2023.49.

    Article  CAS  Google Scholar 

  60. Jia J, Zhao T, Liu Z, Liang Y, Li F, Li Y et al. Association between healthy lifestyle and memory decline in older adults: 10 year, population based, prospective cohort study. BMJ 2023:1–11. https://doi.org/10.1136/bmj-2022-072691.

  61. Jiang Y, Cui M, Tian W, Zhu S, Chen J, Suo C, et al. Lifestyle, multi-omics features, and preclinical dementia among Chinese: the Taizhou Imaging Study. Alzheimer’s Dement. 2021;17:18–28. https://doi.org/10.1002/alz.12171.

    Article  Google Scholar 

  62. Li X, Wang X, Su L, Hu X, Han Y. Sino longitudinal study on Cognitive decline (SILCODE): protocol for a Chinese longitudinal observational study to develop risk prediction models of conversion to mild cognitive impairment in individuals with subjective cognitive decline. BMJ Open. 2019;9:e028188. https://doi.org/10.1136/bmjopen-2018-028188.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Li T-R, Han Y, Pre -AD. Alliance of China. Insights on amyloid-related imaging abnormalities from the Pre-alzheimer’s disease Alliance of China. Adv Neurol. 2022;1:1–9. https://doi.org/10.36922/an.v1i1.2.

    Article  Google Scholar 

  64. Sheng C, Yang K, He B, Li T, Wang X, Du W, et al. Cross-cultural longitudinal study on Cognitive decline (CLoCODE) for subjective cognitive decline in China and Germany: a protocol for Study Design. J Alzheimer’s Dis. 2022;87:1319–33. https://doi.org/10.3233/JAD-215452.

    Article  Google Scholar 

  65. Hansson O. Biomarkers for neurodegenerative diseases. Nat Med. 2021;27:954–63. https://doi.org/10.1038/s41591-021-01382-x.

    Article  CAS  PubMed  Google Scholar 

  66. Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93:e1647–59. https://doi.org/10.1212/WNL.0000000000008081.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V, et al. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature. 2018;554:249–54. https://doi.org/10.1038/nature25456.

    Article  CAS  PubMed  Google Scholar 

  68. Karikari TK, Pascoal TA, Ashton NJ, Janelidze S, Benedet AL, Rodriguez JL, et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. 2020;19:422–33. https://doi.org/10.1016/S1474-4422(20)30071-5.

    Article  CAS  PubMed  Google Scholar 

  69. Palmqvist S, Janelidze S, Quiroz YT, Zetterberg H, Lopera F, Stomrud E, et al. Discriminative accuracy of plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative disorders. JAMA. 2020;324:772. https://doi.org/10.1001/jama.2020.12134.

    Article  CAS  PubMed  Google Scholar 

  70. Ashton NJ, Janelidze S, Mattsson-Carlgren N, Binette AP, Strandberg O, Brum WS, et al. Differential roles of Aβ42/40, p-tau231 and p-tau217 for Alzheimer’s trial selection and disease monitoring. Nat Med. 2022;28:2555–62. https://doi.org/10.1038/s41591-022-02074-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Benedet AL, Milà-Alomà M, Vrillon A, Ashton NJ, Pascoal TA, Lussier F, et al. Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the Alzheimer Disease Continuum. JAMA Neurol. 2021;78:1471. https://doi.org/10.1001/jamaneurol.2021.3671.

    Article  PubMed  Google Scholar 

  72. Zhang X, Xie S, Yang J, Weng F, Xu J, Huang Q, et al. A depth encoding PET detector using four-crystals-to-one-SiPM coupling and light-sharing window method. Med Phys. 2019;46:3385–98. https://doi.org/10.1002/mp.13603.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. He K, Li B, Huang L, Zhao J, Hua F, Wang T, et al. Positive rate and quantification of amyloid pathology with [18F]florbetapir in the urban Chinese population. Eur Radiol. 2023. https://doi.org/10.1007/s00330-023-10366-z.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Ossenkoppele R, Jansen WJ, Rabinovici GD, Knol DL, van der Flier WM, van Berckel BNM, et al. Prevalence of amyloid PET positivity in dementia syndromes: a Meta-analysis. JAMA. 2015;313:1939–50.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, Scheltens P, Verhey FRJ, et al. Prevalence of cerebral amyloid Pathology in persons without Dementia: a Meta-analysis. JAMA. 2015;313:1924–38. https://doi.org/10.1001/jama.2015.4668.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Jansen WJ, Janssen O, Tijms BM, Vos SJB, Ossenkoppele R, Visser PJ, et al. Prevalence estimates of amyloid abnormality across the Alzheimer Disease Clinical Spectrum. JAMA Neurol. 2022;79:228–43.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We want to thank all the participants and staff of the GHABS research group for their immense contributions to data collection.

Funding

This study was funded by the Guangdong Basic and Applied Basic Science Foundation for Distinguished Young Scholars (Grant No. 2023B1515020113), the Shenzhen Science and Technology Program (Grant No. RCYX20221008092935096), the National Natural Science Foundation of China (Grant No. 82171197), and Shenzhen Bay Laboratory (Grant No. 21300061, Grant No. S211101002-2).

Author information

Author notes

  1. Zhen Liu and Dai Shi contributed equally to this work.

Authors and Affiliations

  1. Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China

    Zhen Liu, Yue Cai, Anqi Li, Guoyu Lan, Pan Sun, Lin Liu, Yalin Zhu, Jie Yang, Yajing Zhou, Lizhi Guo, Laihong Zhang, Shuqing Deng, Xuhui Chen, Qiyu Peng, Ying Han & Tengfei Guo

  2. Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China

    Dai Shi, Shuda Chen & Liemin Zhou

  3. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China

    Xianfeng Yu & Ying Han

  4. Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518000, China

    Xuhui Chen

  5. Department of Nuclear Medicine, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China

    Ruiyue Zhao & Xinlu Wang

  6. Department of Neurology, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, 518107, China

    Qingyong Wang

  7. Department of Nuclear Medicine, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, 510120, China

    Pengcheng Ran

  8. Department of Medical Imaging, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, 518106, China

    Linsen Xu

  9. Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China

    Kun Sun

  10. School of Biomedical Engineering, Hainan University, Haikou, 570228, China

    Ying Han

  11. Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China

    Ying Han

  12. National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China

    Ying Han

  13. Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China

    Tengfei Guo

Authors
  1. Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dai Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anqi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guoyu Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yalin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yajing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lizhi Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Laihong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Shuqing Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Shuda Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Xianfeng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Xuhui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ruiyue Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Qingyong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Pengcheng Ran
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Linsen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Liemin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Kun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Xinlu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Qiyu Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Ying Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Tengfei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.L and D.S, Conceptualization, Investigation, Methodology, Interpretation of results, Visualization, Writing – original draft, Writing – review & editing; Y.C, Aq.L, Gy.L, P.S and L.L Investigation, Methodology, Formal analysis, Visualization, Writing – original draft; J.Y and Yj.Z Investigation, Visualization, Writing – original draft; Lz.G and Lh.Z Investigation, Visualization; Sq. D Investigation, Writing – review & editing; Yl. Z, Xf.Y, Xh.C, and Ry.Z Investigation; Qy.W, Pc.R, Ls.X, and Lm.Z Investigation, Resources; K.S, Xl.W, Qy.P, and Y.H Conceptualization, Methodology, Investigation, Resources; T.G Conceptualization, Investigation, Methodology, Interpretation of results, Visualization, Funding acquisition, Supervision, Writing – original draft, Writing – review & editing.

Corresponding author

Correspondence to Tengfei Guo.

Ethics declarations

Ethics approval and consent to participate

All the GHABS participants provided informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Shi, D., Cai, Y. et al. Pathophysiology characterization of Alzheimer’s disease in South China’s aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS). Alz Res Therapy 16, 84 (2024). https://doi.org/10.1186/s13195-024-01458-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13195-024-01458-z

Keywords

Alzheimer's Research & Therapy

ISSN: 1758-9193