The Hippocampal Subfield Volume Reduction and Plasma Biomarker Changes in Mild Cognitive Impairment and Alzheimer’s Disease

Abstract

Background:

The hippocampus consists of histologically and functionally distinct subfields, which shows differential vulnerabilities to Alzheimer’s disease (AD)-associated pathological changes.

Objective:

To investigate the atrophy patterns of the main hippocampal subfields in patients with mild cognitive impairment (MCI) and AD and the relationships among the hippocampal subfield volumes, plasma biomarkers and cognitive performance.

Methods:

This cross-sectional study included 119 patients stratified into three categories: normal cognition (CN; N = 40), MCI (N = 39), and AD (N = 40). AD-related plasma biomarkers were measured, including amyloid-β (Aβ)₄₂, Aβ₄₀, Aβ₄₂/Aβ₄₀ ratio, p-tau181, and p-tau217, and the hippocampal subfield volumes were calculated using automated segmentation and volumetric procedures implemented in FreeSurfer.

Results:

The subiculum body, cornu ammonis (CA) 1-head, CA1-body, CA4-body, molecular_layer_HP-head, molecular_layer_HP-body, and GC-ML-DG-body volumes were smaller in the MCI group than in the CN group. The subiculum body and CA1-body volumes accurately distinguished MCI from CN (area under the curve [AUC] = 0.647–0.657). The subiculum-body, GC-ML-DG-body, CA4-body, and molecular_layer_HP-body volumes accurately distinguished AD from MCI (AUC = 0.822–0.833) and AD from CN (AUC = 0.903–0.905). The p-tau 217 level served as the best plasma indicator of AD and correlated with broader hippocampal subfield volumes. Moreover, mediation analysis demonstrated that the subiculum-body volume mediated the associations between the p-tau217 and p-tau181 levels, and the Montreal Cognitive Assessment and Auditory Verbal Learning Test recognition scores.

Conclusions:

Hippocampal subfields with distinctive atrophy patterns may mediate the effects of tau pathology on cognitive function. The subiculum-body may be the most clinically meaningful hippocampal subfield, which could be an effective target region for assessing disease progression.

Keywords

Alzheimer’s disease cognitive performance hippocampal subfield volumes mild cognitive impairment plasma biomarkers

INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative disease and the leading cause of dementia. The core neuropathological hallmarks of AD include amyloid-β (Aβ)-containing extracellular plaques and intracellular neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau proteins [1]. The accumulation of these pathological proteins begins years or even decades before the onset of the clinical features, ultimately leading to neuronal loss and cognitive decline [1]. Moreover, AD-associated neuropathological changes typically affect the hippocampal formation very early, which is the most established structural imaging biomarker for AD to date [2, 3].

The hippocampus plays a critical role in episodic memory through extensive afferent and efferent connections with cortical and subcortical structures [5]. Several studies have demonstrated that hippocampal atrophy is closely associated with the clinical severity and AD progression, which typically presents as progressive memory deficits [5]. However, the hippocampus is not a homogeneous structure but consists of multiple subfields with distinctive anatomies and functions. These subfields include the cornu ammonis (CA) 1–4, the dentate gyrus (DG) composed of a granule cell and molecular layer, the subiculum/presubiculum complex, and the hippocampal tail [7]. Previous studies using volumetric [8] and shape-based structural magnetic resonance imaging (MRI) [9] have shown that patients with AD have distinctive atrophy patterns across hippocampal subfields. For patients with mild cognitive impairment (MCI) or AD, most studies identified reduced volume predominantly in CA1 [10, 11], involving either the DG [12] or the subiculum [13]. In contrast, Carlesimo et al. found that the CA1 subfield was the least involved in AD-related atrophy, as its size did not differ between healthy control participants and AD patients [14]. Therefore, there is no consensus on which subfields atrophy the earliest during AD.

Several histopathological studies have reported different AD-associated pathological changes in various hippocampal subfields [15]. For instance, NFTs are considered to be initially deposited in CA1, followed by the subiculum, CA3, CA4, and DG [16]. Moreover, other studies have reported CA1 and presubiculum/subiculum complex atrophy in early AD [17], representing the earliest sites of amyloid deposits and tau aggregation [18]. Consistent with these findings, the pathology in the hippocampus appeared to have a very specific topography, with tau pathology affecting the CA1 and DG and Aβ affecting the CA1, subiculum and presubiculum prior to other subfields [19]. Therefore, investigating the relationship between the hippocampal subfield volume and pathological biomarkers of AD is of great research significance.

Previous studies have shown that both CSF Aβ and p-tau contribute to hippocampal atrophy [20 –22] and that tau 18F-AV-1451-PET uptake and elevated Aβ-PET binding have been associated with reduced hippocampal volume in patients with AD [23]. Further studies have indicated that the hippocampal volume may mediate the relationship between biomarkers and cognitive function. One study reported that the grey matter volume (GMV) of the hippocampus mediates the effect of Aβ on cognition in patients with MCI [24]. Our team’s previous study showed for the first time that the GMV in the right hippocampus mediates the association between plasma p-tau217 concentrations and Mini-Mental State Examination (MMSE) scores [25]. Subsequent studies have shown that different hippocampal subfields support distinct cognitive and memory functions [26]. For example, significant positive correlations have been found between the CA2/3, CA4-DG, and the subiculum complex volumes and immediate and delayed recall in patients with MCI and AD [14]. Furthermore, an association was reported between subiculum atrophy, poor cognition, and a higher risk of dementia in a non-demented community-dwelling cohort [27]. These studies suggest that hippocampal subfield atrophy exhibits differential vulnerabilities to AD-associated pathology, which seems to be a reliable indicator of cognitive performance. However, the biomarkers currently used to study the correlations between the hippocampal subfield volumes and the pathophysiological hallmarks of AD are obtained based on cerebrospinal fluid (CSF) sampling or imaging techniques that are invasive or expensive. The advent of blood-based biomarkers has substantially changed the AD landscape, and results from cross-sectional and longitudinal studies in well-characterized cohorts have shown a high potential for their use in identifying the core hallmarks of AD pathology (Aβ and p-tau) [28]. Interestingly, to date, no study has investigated the relationship between hippocampal subfield volumes, AD-related plasma biomarkers, and cognitive performance.

The aims of the present cross-sectional study were to delineate the atrophy progression in the main hippocampal subfields at different stages of cognitive decline (MCI and AD) and to investigate the nature of the associations among the subfield volumes, plasma biomarkers, and cognitive functions. Our hypotheses were: 1) that one or more specific hippocampal subfields could best predict cognitive impairment severity; 2) significant correlations exist among specific hippocampal subfield volumes, plasma biomarker levels, and cognitive test scores; and 3) the effects of plasma biomarkers on cognitive function are partially mediated by the hippocampal subfield volumes.

MATERIALS AND METHODS

Participants

This study included 119 right-handed participants (Chinese Han origin) aged 53–81 years from the First Affiliated Hospital of Anhui Medical University. The participants included 40 individuals with AD, 39 with MCI, and 40 cognitively normal controls (CN). All the participants underwent neuropsychological assessments, laboratory examinations, and MRI. The principal demographic and clinical characteristics of the study participants are presented in Table 1. Patients with AD were clinically diagnosed in accordance with the National Institute of Aging and Alzheimer’s Association (NIA-AA) criteria [29] as follows: 1) meeting the criteria of probable AD; 2) an MMSE score of ≤24 points [30]; and 3) a Clinical Dementia Rating (CDR) score of 1 or 2. Patients with MCI were also clinically diagnosed based on the NIA-AA criteria [31] as follows: 1) significant complaints about their cognition reported by themselves, an informant, or as assessed by a clinician; 2) significant deficits (>1.5 standard deviations below normal) in either memory or other cognitive domains without significant impairment in daily functioning; and 3) a CDR score of 0.5. The inclusion criteria for the CN group were as follows: 1) no complaints of memory loss; 2) an MMSE score of >26 points; and 3) a CDR score of 0.

The exclusion criteria for all groups were: 1) history of brain tumor, traumatic brain injury, stroke, epilepsy, psychiatric illness, or treatment with electroconvulsive therapy; 2) other neuropsychiatric disorders resulting in cognitive decline; 3) severe liver and kidney diseases, thyroid diseases, tumors, immune, and digestive diseases; 4) major depression, anxiety, severe cerebrovascular disease, and metabolic abnormalities; and 5) severe dementia with lack of cooperation.

The First Affiliated Hospital of Anhui Medical University ethics committee approved this study (ethical approval number: Quick-PJ 2023-08-39). All participants provided written informed consents per the Declaration of Helsinki.

Clinical data collection

A structured questionnaire was administered to collect demographic and lifestyle information. We recorded demographic and comorbid conditions, including age, gender, education level, body mass index (BMI), creatinine clearance (CrCl), smoking and drinking habits, and a history of hypertension, diabetes mellitus (DM), dyslipidemia, current medications. Current smoking were defined as those who smoked ≥1 cigarette(s) per day within in the past month. Current drinking (at least within the past 1 month) was defined as respondents who drank currently based on patients’ self-reporting. Hypertension was defined by the administration of antihypertensive drugs, or a systolic blood pressure ≥140 mm Hg and/or a diastolic blood pressure ≥90 mm Hg measured with a standard mercury sphygmomanometer. DM was defined as self-reported diabetes or currently taking antidiabetic drugs or fasting blood glucose ≥7.0 mmol/L or 2 h postload glucose ≥11.1 mmol/L or hemoglobin A1c≥6.5%. Hyperlipidemia was defined as self-reported hyperlipidemia, treatment with anti-hyperlipidemic medication, total cholesterol >5.2 mmol/L or low-density lipoprotein >3.36 mmol/L.

Neuropsychological assessment

All participants underwent the following cognitive assessments, which were administered by two trained neuropsychological technicians: the MMSE and Montreal Cognitive Assessment (MoCA) [32] for evaluating the global cognitive function; the Auditory Verbal Learning Test (AVLT) [33] for assessing the speech memory function of the participants, including immediate recall (IR), short-term delayed recall (5-min delay recall, SR), long-term delayed recall (20-min delay recall, LR), and recognition; the Geriatric Depression Scale (GDS) [34] for excluding emotional disorders; the Activities of Daily Living (ADL) scale [35]; and the CDR [36] for assessing the disease severity.

Blood sample collection and analyses

Blood samples were collected, processed, and stored per previously published international guidelines [72]. Blood was collected via venipuncture after overnight fasting. A 2 mL ethylenediaminetetraacetic acid (EDTA) tube was used to collect whole blood. First, 500μL of the whole blood sample was extracted into EP tubes and stored at –80°C for apolipoprotein E (APOE) genotyping. Centrifugation at 3,500 r/min for 8 min was performed within 1 h of blood collection. After centrifugation, the 500μL plasma sample was extracted into Eppendorf tubes and stored in a –80°C freezer within 2 h of collection until analysis. The samples in the EP tubes were sent to the Beijing Genomics Institution Gene Technology Co., Ltd. for testing. According to the protocols, genotype data of APOE ɛ4 status was obtained using a BigDyeTM Direct cycle sequencing kit (Applied BiosystemsTM, USA). Plasma Aβ₄₀, Aβ₄₂, p-tau181, and p-tau217 concentrations were measured using corresponding human enzyme-linked immunosorbent assay (ELISA) kits (Shanghai Lianshuo Biotechnology Co., Ltd. China). The measurements were performed in one round of experiments using one batch of reagents by board-certified laboratory technicians blinded to the clinical data. The inter- and intra-plate coefficients of variation for all plasma indicators were 5.2–7.5%. The ratio of Aβ₄₂ to Aβ₄₀ was considered as one of the primary biomarkers for amyloid.

MRI data acquisition

MRI scans were obtained using a 3.0-Tesla MR system (Discovery MR750 w; General Electric, Milwaukee, WI, USA) with a 24-channel head coil. Earplugs and foam padding were used to reduce scanner noise and minimize head motion, respectively. T1-weighted structural images were acquired by employing a brain volume (BRAVO) sequence with the following parameters: repetition time (TR) = 8.5 ms; echo time (TE) = 3.2 ms; inversion time = 450 ms; flip angle (FA) = 12°; field of view (FOV) = 256 mm×256 mm; matrix size = 256×256; slice thickness = 1 mm no gap; voxel size = 1×1× 1 mm³; 188 sagittal slices; and acquisition time = 296 s. T2 fluid-attenuated inversion recovery (FLAIR) images were acquired with the following parameters: TR = 9,000 ms, TE = 119.84 ms, FA = 160°, FOV = 225 mm×225 mm, matrix size = 512×512, number of layers = 19, layer thickness = 7 mm, and scan time = 118 s. Moreover, two experienced radiologists visually checked all images, identifying no obvious artefacts and lesions. The time interval between blood sample collection, neuropsychological assessment, and MRI was less than 1 week.

MRI data processing

The image processing pipeline from FreeSurfer 7.2 version (http://surfer.nmr.mgh.harvard.edu/) was used to preprocess the images and automatically segment the hippocampal subfields. The preprocessing steps involved bias correction, automated Talairach transformation, intensity normalization, and removal of non-brain tissue. This was followed by subcortical segmentation, white and grey matter boundary tessellation, automated topology correction, and surface deformation for optimal placement of boundaries for grey and white matter, grey matter, and cerebrospinal fluid. Subsequently, automated hippocampal subfield segmentation was performed using a Bayesian inference technique that employed a statistical atlas of hippocampal formations, which was created using ultra-high ex vivo MRI scans [38]. All segmentations were visually verified using a quality control protocol similar to the ENIGMA protocol (http://enigma.ini.usc.edu/). None of the subjects exhibited segmentation failures. Finally, the GMVs of the 19 subfields for each hemisphere were calculated. We focused on the mean volume of 14 hippocampal subfelds, the subiculum body, CA1 body, subiculum head, presubiculum head, CA1 head, presubiculum body, molecular layer hippocampal (molecular_layer_HP) head, molecular_layer_HP body, granule cell molecular layer of the dentate gyrus (GCML-DG) head, CA3 body, GC-ML-DG body, CA4 head, CA4 body, and CA3 head, which have previously been associated with the disease progression [39]. Left and right regions were combined. Total intracranial volumes (TICVs) were obtained as a covariate in the statistical analyses described below.

Statistical analyses

All the statistical analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY, USA). The Shapiro-Wilk test was used to confirm data normality. Clinical, demographic, and neuropsychological were compared using analysis of variance (ANOVA), the Kruskal-Wallis test, or Pearson’s chi-squared test, as appropriate. Analysis of covariance (ANCOVA) with controlled age, sex, APOE ɛ4 carriers, BMI, and creatinine clearance was used to compare the plasma biomarker data among the three groups. ANCOVA with controlled age, sex, and TICV was used to compare the hippocampal subfield volumes among the three groups, and the significance threshold was set at p < 0.0035 (Bonferroni-corrected for the number of subfields, 0.05/14). Post hoc comparisons between pairs of groups were performed for subfields with significantly different results in the ANCOVA, and the significance level was set at a Bonferroni-corrected P-value of < 0.05. To evaluate the accuracy of the hippocampal subfield volumes in assigning participants to their own groups, the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were calculated. The sensitivity and specificity were determined using Youden’s index. Across the study cohorts, we further investigated the associations between hippocampal subfield volumes, plasma biomarkers, and neuropsychological performance using partial correlation by controlling for sex, age, education, TICV, APOE ɛ4 carrier status, BMI, and CrCl. A false discovery rate (FDR) correction using the Benjamini-Hochberg criterion was used for the correlation analysis. P_FDR < 0.05 was deemed significant. To further test whether the association between plasma biomarkers and cognitive decline was mediated by hippocampal subfield volume, a mediation analysis was performed using the SPSS macro PROCESS. In the mediation model, all paths are reported as standardized ordinary least squares regression coefficients, namely, the total effect of X on Y (c) = the indirect effect of X on Y through M (a×b) + the direct effect of X on Y (c^′). The significance analysis was based on 5000 bootstrap realizations, and a significant indirect effect was indicated when the bootstrap 95% confidence interval (CI) did not include zero. Age, sex, education, TICV, APOE ɛ4 carrier status, BMI, and CrCl were considered as covariates.

RESULTS

Participant characteristics

Table 1 summarizes the participants’ demographics, plasma biomarkers, and neuropsychological assessments. There were no significant differences in sex, age, years of education, CrCl, or TICV among the CN, MCI, and AD groups (all p > 0.05). Regarding vascular risk factors, the prevalence of smoking, alcohol consumption, hypertension, diabetes mellitus, and hyperlipidemia was comparable among the three groups (all p > 0.05), while lower BMI was observed between the AD and other groups (AD versus CN, AD versus MCI, p < 0.001). However, the proportion of APOE ɛ4 carriers was higher in the AD group than in the CN and MCI groups (p < 0.05). In terms of neuropsychological test scores, the MMSE, MoCA, AVLT-IR, AVLT-SR, AVLT-LR, and AVLT-recognition scores significantly differed among the three groups, with the AD participants scoring worse than the MCI participants (all p < 0.001), who in turn, scored worse than the CN participants (all p < 0.001). As shown in Fig. 1, p-tau181 levels were significantly higher in the AD group than in the CN (p < 0.001) and MCI groups (p = 0.009); however, significant differences were not observed between the CN and MCI groups (p > 0.05) (Fig.1A). The p-tau217 levels significantly differed across all three groups (p < 0.01), with AD participants exhibiting the highest p-tau217 levels, followed by the MCI and CN groups (Fig. 1B). However, no group differences were observed for the Aβ₄₀ and Aβ₄₂ levels and the Aβ₄₂/Aβ₄₀ ratio (all p > 0.05) (Fig. 1C–E).

Table 1

Demographic, plasma biomarkers, and cognitive profiles of the participants

	CN (n = 40)	MCI (n = 39)	AD (n = 40)	γ²/F/Z	p
Demographics
Age ()	64.85±5.21	65.59±6.46	66.45±7.96	0.581^a	0.561
Gender (female, %)	17 (42.5%)	20 (51.3%)	26 (65%)	4.128^b	0.127
Education (y)	8 (5,12)	8 (6,10)	8 (3,9)	1.296^c	0.523
Hypertension (n, %)	19 (47.5%)	16 (41.0%)	13 (32.5%)	1.881^b	0.390
Diabetes mellitus (n, %)	5 (12.5%)	8 (20.5%)	4 (10.0%)	1.939^b	0.379
Hyperlipidemia (n, %)	7 (17.5%)	6 (15.4%)	3 (7.5%)	1.906^b	0.386
Current smoking (n, %)	11 (27.5%)	9 (23.1%)	6 (15.0%)	1.881^b	0.390
Current drinking (n, %)	14 (35.0%)	10 (25.6%)	7 (17.5%)	3.185^b	0.203
BMI (kg/m²)	24.12±2.52	23.27±2.76	21.01±2.81^*†	14.202^a	<0.001
CrCl (ml/min/1.73 m²)	99.7±9.28	98.51±9.71	97.96±7.31	0.407^a	0.666
Cognitive assessments
MMSE	29 (27,29)	26 (24,27)^*	15 (10,21)^*†	59.017^c	<0.001
MoCA	25 (23,27)	21 (17,22)^*	9 (5,13)^*†	61.322^c	<0.001
AVLT-IR	15 (13,18)	13 (10,17)^*	5 (3,9)^*†	58.331^c	<0.001
AVLT-SR	6 (4,8)	3 (2,5)^*	1 (0,2)^*†	63.669^c	<0.001
AVLT-LR	5 (3,7)	3 (2,5)^*	0 (0,1)^*†	69.371^c	<0.001
AVLT-recognition	22 (20,23)	19 (17,22)^*	12 (9,16)^*†	46.927^c	<0.001
Plasma biomarkers
Aβ₄₂ (pg/ml)	66.66±10.69	63.45±10.69	62.69±10.51	1.085^d	0.342
Aβ₄₀ (pg/ml)	314.83±81.38	307.11±55.45	296.45±56.47	0.486^d	0.726
Aβ₄₂/Aβ₄₀	0.223±0.06	0.213±0.05	0.219±0.06	0.227^d	0.798
p-tau181 (pg/ml)	58.75±12.30	61.34±13.37	69.65±10.75^*†	6.297^d	<0.001
p-tau217 (pg/ml)	32.93±6.00	37.52±7.53	42.11±5.41	15.361^d	<0.001
Others
APOE ɛ4 carriers (n, %)	4 (10.0%)	8 (20.5%)	20 (50.0%)^*†	17.477^b	<0.001
TICV (cm³)	1,482.35±131.82	1,471.32±160.85^*	1,409.20±136.23^*†	3.019^a	0.053

^aANOVA test, ^bchi-square test (χ ² test), ^cKruskal-Wallis test, ^dANCOVA with age, sex, APOE ɛ4 carriers, BMI, and CrCl as covariates. ^*compared to CN group p < 0.05, ^†compared to MCI group p < 0.05. CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; BMI, body mass index; CrCl, creatinine clearance; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; AVLT, Auditory Verbal Learning Test; IR, immediate recall; SR, 5-min delay recall; LR, 20-min delay recall; Aβ, amyloid-β; TICV, total Intracranial volume.

Fig. 1

Comparison of plasma biomarkers concentrations among three groups, Bonferroni correction. A) Comparison of plasma p-tau181 concentration among three groups. B) Comparison of plasma p-tau217 concentration among three groups. C) Comparison of plasma Aβ₄₂ concentration among three groups. D) Comparison of plasma Aβ₄₀ concentration among three groups. E) Comparison of the plasma Aβ₄₂/Aβ₄₀ ratio among three groups. P-values derived from ANCOVA with age, sex, APOE ɛ4 carriers, BMI and CrCl as covariates followed by post hoc Bonferroni test. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001, ns not statistically significant. ANCOVA, Analysis of covariance; BMI, body mass index; CrCl, creatinine clearance; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease.

Comparison of hippocampal subfield volume in subgroups

Figure 2 shows the main hippocampal subfield volumes in the three groups. The AD participants had significantly smaller volumes in all subfields than the MCI and CN participants (all p < 0.05, FDR-corrected). The MCI participants had smaller subiculum-body, CA1-head, CA1-body, CA4-body, molecular_layer_HP-head, molecular_layer_HP-body, and GC-ML-DG-body volumes than CN participants (all p < 0.05, FDR-corrected). Notably, the subicular head, CA3-head, CA3-body, CA4-head, and GC-ML-DG-head volumes did not differ between the CN and MCI groups (all p > 0.05). Supplementary Table 1 reports detailed information on the hippocampal subfield volumes in the three groups of participants with relative group comparisons.

Fig. 2

Comparison of hippocampal subfield volume in subgroups, Bonferroni correction. P-values derived from ANCOVA with controlled age, sex, and TICV followed by post hoc Bonferroni test. ns not statistically significant, ^*p < 0.05, ^**p < 0.01, ^***p < 0.001. ANCOVA, analysis of covariance; TICV, total intracranial volume; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; CA, cornu ammonis; GC-ML-DG, granule cell molecular layer of dentate gyrus; HP, hippocampal.

Discriminative power of hippocampal subfield volumes

To examine the discriminative power of the hippocampal subfields among the three groups, an ROC curve analysis was performed on the specific subfield volumes that consistently decreased in volume with cognitive decline (Fig. 3), including the subiculum body, CA1-head, CA1-body, CA3-head, CA4-body, molecular_layer_HP-body, and GC-ML-DG-body. For differentiating between CN and MCI, all subfield volumes exhibited comparable AUCs (greater than 0.63), but only the CA1-body and subiculum-body had sensitivities and specificities higher than 60% (all p < 0.05) (Fig. 3A). For differentiating between MCI and AD, the CA4-body and GC-ML-DG-body volumes had the highest AUCs (0.833), followed by the subiculum-body and molecular_layer_HP-body, which had AUCs higher than 0.8 (all p≤0.005). All these subfields had combined moderate (>70%) sensitivity and specificity (Fig. 3B). For differentiating between CN and AD, the hippocampal subfield volumes performed best, and the subiculum-body volume had the highest AUC value (0.905), followed by the CA4-body, molecular_layer_HP-body, and GC-ML-DG-body with AUCs greater than 0.9. All subfields had combined high (>80%) sensitivity and specificity (all p < 0.001) (Fig. 3C).

Fig. 3

Discriminative power of hippocampal subfields volume. A) ROC analyses for distinguishing CN and MCI participants. B) ROC analyses for distinguishing MCI and AD participants. C) ROC analyses for distinguishing CN and AD participants. D) Detailed information of ROC analyses. Sensitivity and specificity were calculated using the cutoff that produced the highest Youden index (sensitivity + specificity –1). ROC, receiver-operating characteristic; AUC, area under the receiver-operating characteristic curve; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; CA, cornu ammonis; GC-ML-DG, granule cell molecular layer of dentate gyrus; HP, hippocampal.

Correlation analysis

Table 2 presents the results of the partial correlation between the hippocampal subfield volumes and the plasma biomarkers across all groups. After adjusting for age, sex, APOE ɛ4 carrier status, BMI and CrCl (FDR correction), plasma p-tau217 levels were significantly negatively correlated with multiple hippocampal subfield volumes, including subiculum-body, molecular_layer_HP-body, presubiculum-body, GC-ML-DG-body, CA4-body, molecular_layer_HP-head and GC-ML-DG-head (presented in order based on their correlation coefficient). In addition, plasma p-tau181 levels were only negatively correlated with the subiculum-body and presubiculum-body volumes. However, the plasma Aβ₄₂/Aβ₄₀ ratio showed some emerging correlations with multiple hippocampal subfield volumes, although they did not survive multiple comparison corrections. Furthermore, no significant association was observed for any hippocampal subfield volume with the plasma Aβ₄₂ and Aβ₄₀ levels after FDR correction.

Table 2

Partial correlation between the hippocampal subfield volumes and the plasma biomarker concentrations in all participants

Hippocampal subfields	Aβ₄₂	Aβ₄₀	Aβ₄₂/Aβ₄₀	ptau217	ptau181
subiculum-head	r = –0.113	r = 0.139	r = –0.211	r = –0.184	r = –0.202
	p = 0.234	p = 0.143	p = 0.025	p = 0.030	p = 0.032
subiculum-body	r = –0.094	r = 0.112	r = –0.169	r = –0.293	r = –0.264
	p = 0.322	p = 0.237	p = 0.074	p = 0.002^*	p = 0.004^*
presubiculum-head	r = –0.020	r = 0.166	r = –0.160	r = –0.204	r = –0.169
	p = 0.830	p = 0.080	p = 0.091	p = 0.030	p = 0.073
presubiculum-body	r = –0.104	r = 0.124	r = –0.179	r = –0.278	r = –0.267
	p = 0.275	p = 0.190	p = 0.058	p = 0.002^*	p = 0.004^*
CA1-head	r = -0.080	r = 0.202	r = –0.230	r = –0.192	r = –0.177
	p = 0.400	p = 0.032	p = 0.014	p = 0.042	p = 0.048
CA1-body	r = –0.011	r = 0.109	r = –0.112	r = –0.147	r = –0.137
	p = 0.910	p = 0.252	p = 0.235	p = 0.121	p = 0.148
CA3-head	r = 0.028	r = 0.227	r = –0.162	r = –0.209	r = –0.121
	p = 0.769	p = 0.016	p = 0.086	p = 0.018	p = 0.105
CA3-body	r = –0.033	r = 0.117	r = –0.127	r = –0.201	r = –0.122
	p = 0.726	p = 0.216	p = 0.179	p = 0.022	p = 0.116
CA4-head	r = 0.001	r = 0.184	r = –0.160	r = –0.191	r = –0.138
	p = 0.989	p = 0.052	p = 0.091	p = 0.030	p = 0.073
CA4-body	r = –0.047	r = 0.140	r = –0.165	r = –0.251	r = –0.148
	p = 0.623	p = 0.139	p = 0.081	p = 0.003^*	p = 0.057
molecular_layer_HP-head	r = –0.056	r = 0.194	r = –0.210	r = –0.247	r = –0.188
	p = 0.557	p = 0.039	p = 0.026	p = 0.009^*	p = 0.040
molecular_layer_HP-body	r = –0.051	r = 0.144	r = –0.169	r = –0.302	r = –0.235
	p = 0.591	p = 0.129	p = 0.074	p = 0.003^*	p = 0.006^*
GC-ML-DG-head	r = –0.013	r = 0.188	r = –0.168	r = –0.234	r = –0.147
	p = 0.894	p = 0.046	p = 0.075	p = 0.008^*	p = 0.057
GC-ML-DG-body	r = –0.057	r = 0.154	r = –0.184	r = –0.268	r = –0.152
	p = 0.551	p = 0.104	p = 0.051	p = 0.004^*	p = 0.051

The correlation analyses were performed using partial Pearson’s correlation with age, gender, APOE ɛ4 status, BMI, CrCl and TICV as covariates. P values in asterisk survived FDR correction for multiple comparison at P_FDR < 0.05 using Benjamini-Hochberg criterion (α=0.05). TICV, total intracranial volume; CrCl, creatinine clearance; FDR, false-discovery rate; Aβ, amyloid-β; CA, cornu ammonis; GC-ML-DG, granule cell molecular layer of dentate gyrus; HP, hippocampal.

Table 3 presents correlation matrices of the hippocampal subfield volumes with the cognition and memory test scores in all groups. All the hippocampal subfield volumes were significantly positively correlated with all neuropsychological scores after adjusting for age, sex, education, and APOE ɛ4 carrier status (FDR correction). On one side, MoCA exhibited the most associations with all subfield volumes, followed by MMSE and AVLT. On the other side, the subiculum-body, GC-ML-DG-body, and molecular_layer_HP (head and body) had the high correlation coefficients with MMSE, MoCA, or AVLT scores. Based on the above results, the subiculum-body exhibited the most associations with plasma biomarkers levels and neuropsychological test scores in all cases.

Table 3

Partial correlation between the hippocampal subfield volumes and the neuropsychological test scores in all participants

Hippocampal subfields	MMSE	MoCA	AVLT-IR	AVLT-SR	AVLT-LR	AVLT-recognition
subiculum-head	r = 0.496	r = 0.528	r = 0.399	r = 0.424	r = 0.438	r = 0.431
	p = 1.99×10^–8	p = 1.58×10^–9	p = 1.10×10^–5	p = 3.00×10^–6	p = 1.00×10^–6	p = 2.00×10^–6
subiculum-body	r = 0.582	r = 0.615	r = 0.427	r = 0.452	r = 0.454	r = 0.477
	p = 1.09×10^–11	p = 3.47×10^–13	p = 2.00×10^–6	p = 4.51×10^–7	p = 3.87×10^–7	p = 8.29×10^–8
presubiculum-head	r = 0.556	r = 0.572	r = 0.449	r = 0.406	r = 0.431	r = 0.439
	p = 1.40×10^–10	p = 2.94×10^–11	p = 5.40×10^–7	p = 7.00×10^–6	p = 2.00×10^–6	p = 1.00×10^–6
presubiculum-body	r = 0.504	r = 0.490	r = 0.339	r = 0.283	r = 0.297	r = 0.333
	p = 1.08×10^–8	p = 3.15×10^–8	p = 2.26×10^–4	p = 0.002	p = 0.001	p = 2.93×10^–5
CA1-head	r = 0.455	r = 0.525	r = 0.426	r = 0.437	r = 0.425	r = 0.443
	p = 3.62×10^–7	p = 1.95×10^–9	p = 2.00×10^–6	p = 1.00×10^–6	p = 2.00×10^–6	p = 8.18×10^–8
CA1-body	r = 0.327	r = 0.389	r = 0.288	r = 0.351	r = 0.338	r = 0.329
	p = 3.81×10^–4	p = 1.90×10^–5	p = 0.002	p = 1.30×10^–4	p = 2.34×10^–4	p = 3.45×10^–5
CA3-head	r = 0.482	r = 0.551	r = 0.456	r = 0.437	r = 0.396	r = 0.438
	p = 5.54×10^–8	p = 2.17×10^–10	p = 3.51×10^–7	p = 1.00×10^–6	p = 1.30×10^–5	p = 1.00×10^–8
CA3-body	r = 0.483	r = 0.511	r = 0.399	r = 0.416	r = 0.398	r = 0.428
	p = 5.26×10^–8	p = 6.27×10^–9	p = 1.10×10^–5	p = 4.00×10^–6	p = 1.20×10^–5	p = 2.00×10^–6
CA4-head	r = 0.480	r = 0.549	r = 0.444	r = 0.431	r = 0.395	r = 0.437
	p = 6.74×10^–8	p = 2.59×10^–10	p = 7.48×10^–7	p = 2.00×10^–6	p = 1.40×10^–5	p = 1.00×10^–6
CA4-body	r = 0.585	r = 0.590	r = 0.451	r = 0.423	r = 0.428	r = 0.464
	p = 8.44×10^–12	p = 4.73×10^–12	p = 4.87×10^–7	p = 3.00×10^–6	p = 2.00×10^–6	p = 2.03×10^–7
molecular_layer_HP-head	r = 0.530	r = 0.589	r = 0.473	r = 0.467	r = 0.462	r = 0.482
	p = 1.29×10^–9	p = 5.47×10^–12	p = 1.05×10^–7	p = 1.61×10^–7	p = 2.36×10^–7	p = 5.74×10^–8
molecular_layer_HP-body	r = 0.572	r = 0.613	r = 0.461	r = 0.465	r = 0.466	r = 0.472
	p = 2.84×10^–11	p = 4.26×10^–13	p = 2.46×10^–7	p = 1.85×10^–7	p = 1.71×10^–7	p = 1.13×10^–7
GC-ML-DG-head	r = 0.496	r = 0.571	r = 0.464	r = 0.451	r = 0.416	r = 0.461
	p = 1.93×10^–8	p = 3.37×10^–11	p = 1.94×10^–7	p = 4.65×10^–7	p = 4.00×10^–6	p = 2.51×10^–7
GC-ML-DG-body	r = 0.586	r = 0.593	r = 0.470	r = 0.433	r = 0.441	r = 0.484
	p = 7.67×10^–12	p = 3.55×10^–12	p = 1.30×10^–7	p = 1.00×10^–6	p = 8.97×10^–7	p = 4.82×10^–8

The correlation analyses were performed using partial Pearson’s correlation with age, gender, education, APOE ɛ4 status and TICV as covariates. All p values survived FDR correction for multiple comparison at P_FDR < 0.05 using Benjamini-Hochberg criterion (α=0.05). TICV, total intracranial volume; FDR, false-discovery rate; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; AVLT, Auditory Verbal Learning Test; IR, immediate recall; SR, short-term delayed recall; LR, long-term delayed recall; CA, cornu ammonis; GC-ML-DG, granule cell molecular layer of dentate gyrus; HP, hippocampal.

Table 4 presents correlation matrices of the plasma biomarker concentration with the cognition and memory test scores in all groups. Plasma p-tau181 and p-tau217 levels were significantly correlated with all neuropsychological scores after adjusting for age, sex, education, TICV, APOE ɛ4 carrier status, BMI, and CrCl (FDR correction). However, significant correlations were not observed between Aβ₄₂, Aβ₄₀, and Aβ₄₂/Aβ₄₀ ratio and any neuropsychological score.

Table 4

Partial correlation between the plasma biomarker concentrations and the neuropsychological test scores in all participants

plasma biomarkers	MMSE	MoCA	AVLT-IR	AVLT-SR	AVLT-LR	AVLT-recognition
Aβ₄₂	r = 0.014	r = 0.022	r = 0.016	r = 0.031	r = 0.018	r = 0.065
	p = 0.963	p = 0.821	p = 0.868	p = 0.747	p = 0.961	p = 0.341
Aβ₄₀	r = 0.069	r = 0.141	r = 0.131	r = 0.124	r = 0.108	r = 0.035
	p = 0.468	p = 0.136	p = 0.166	p = 0.192	p = 0.253	p = 0.763
Aβ₄₂/Aβ₄₀	r = 0.057	r = 0.096	r = 0.124	r = 0.116	r = 0.097	r = 0.074
	p = 0.550	p = 0.312	p = 0.192	p = 0.221	p = 0.307	p = 0.434
p-tau217	r = –0.381	r = –0.440	r = –0.296	r = –0.351	r = –0.335	r = –0.280
	p = 3.10×10^–5 *	p = 1.01×10^–7 *	p = 1.00×10^–3 *	p = 1.40×10^–5 *	p = 2.87×10^–4 *	p = 3.00×10^–3 *
p-tau181	r = –0.281	r = –0.276	r = –0.281	r = –0.332	r = –0.330	r = –0.199
	p = 3.00×10^–4 *	p = 3.00×10^–4 *	p = 2.00×10^–3 *	p = 3.23×10^–4 *	p = 3.59×10^–4 *	p = 2.40×10^–2 *

The correlation analyses were performed using partial Pearson’s correlation with age, gender, APOE ɛ4 status, BMI, CrCl and TICV as covariates. P values in asterisk survived FDR correction for multiple comparison at P_FDR < 0.05 using Benjamini-Hochberg criterion (α = 0.05). TICV, total intracranial volume; CrCl, creatinine clearance; FDR, false-discovery rate; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; AVLT, Auditory Verbal Learning Test; IR, immediate recall; SR, 5-min delay recall; LR, 20-min delay recall; Aβ, amyloid-β.

Mediation analysis

A mediation analysis was conducted to clarify the nature of the associations between hippocampal subfield volumes, plasma biomarkers, and cognitive function. Considering the diversity of hippocampal subfields and cognitive scores, we only selected the indicators with the strongest correlations with each other. That is, the subiculum-body was the mediator variable, and MoCA and AVLT-recognition were the dependent variables; the independent variables included plasma p-tau181 and p-tau217. Figure 4 presents the mediation analysis results. The subiculum body partially mediated the association between MoCA scores and the p-tau181 (indirect effect = –0.109, 95% CI:–0.188 to –0.023), and p-tau217 (indirect effect = –0.116, 95% CI:–0.180 to –0.035) concentrations (Fig. 4A, B). Similarly, the subiculum body also partially mediated the association between AVLT-recognition scores and p-tau181 (indirect effect = –0.112, 95% CI:–0.182 to –0.015), and p-tau217 (indirect effect = –0.123, 95% CI: –0.195 to –0.018) concentrations (Fig. 4C, D).

Fig. 4

Conceptual diagram of mediation analysis. In the mediation model, all paths are reported as standardized coefficients, namely, the total effect of X on Y (c) = the indirect effect of X on Y through M (a×b) + the direct effect of X on Y (c^′). A) Subiculum-body volume mediated the correlation between p-tau 181 concentration and MoCA scores. B) Subiculum-body volume mediated the correlation between p-tau 217 concentration and MoCA scores. C) Subiculum-body volume mediated the correlation between p-tau 181 concentration and AVLT-recognition scores. D) Subiculum-body volume mediated the correlation between p-tau 217 concentration and AVLT-recognition scores. ^*p < 0.05, ^**p < 0.01; ^***p < 0.001. MoCA, Montreal Cognitive Assessment; AVLT, Auditory Verbal Learning Test.

DISCUSSION

The current study investigated the diagnostic value of specific hippocampal subfields for MCI and AD participants and the associations of these subfield volumes with plasma biomarkers and cognitive performance. As expected, the progressive hippocampal subfield atrophy was not homogeneous across the different stages of cognitive decline (MCI and dementia). We found that the CA1-body and subiculum-body volumes could accurately differentiate those with MCI from those with CN, while the subiculum-body, CA4-body molecular_layer_HP-body, and GC-ML-DG-body could accurately distinguish those with AD from those with MCI or CN. Pairwise correlations existed among the plasma biomarker concentrations, hippocampal subfield volumes, and cognitive performance. Furthermore, the hippocampal subfield volume partially mediated the relationships between plasma p-tau 217, p-tau181, and cognitive performance.

This study demonstrated that hippocampal volume atrophy in participants with MCI and AD is not uniformly distributed but affects some specific subfields more than others. Specifically, Specifically, compared with the CN group, the volumes of the subiculum body, CA1-head, CA1-body, CA4-body, molecular_layer_HP-head, molecular_layer_HP-body, and GC-ML-DG-body in the MCI group were significantly reduced, but not the volume of all subfields, which was significantly atrophied in the AD group compared with the CN and MCI groups. Furthermore, the subiculum-body volume best discriminated the correct group membership of the AD, MCI, and CN participants, followed by the GC-ML-DG-body, CA4-body, and molecular_layer_HP-body. This was true both when we compared the CN and MCI groups and when we considered the AD group with respect to CN or MCI. Notably, the CA1 volume demonstrated best between the CN and MCI groups. These results were broadly consistent with those of previous studies [10, 40], which noted that volume reductions in the subfields, such as CA1, subiculum, and DG were linked with AD. However, the results regarding which subfields atrophy the earliest in the course of the disease remain controversial. Giovanni et al. reported that the hippocampal atrophy pattern was nonhomogeneous in patients with AD, with prevalent involvement of the presubicular-subicular complex from the MCI stage that remained evident in the AD dementia stage. However, the CA1 subfield was least involved in AD-related atrophy, and its volume did not differ among the CN, MCI, and AD groups [14]. These findings were also consistent with the neuropathological observations reported by Mizutani and Kasahara [41, 42], who agreed that in patients with AD, the subiculum and presubiculum volumes show atrophic changes but differ in the volume reduction pattern in Ammon’s horn subfields. It has been hypothesized that the volume loss of these structures could be caused by isomorphic fibrillary gliosis resulting from early and severe degeneration of the perforant pathway while penetrating the hippocampus through the subicular field from the entorhinal cortex to the DG [14]. Notably, the CA1 subfield was found to be significantly atrophic in another study that used the same automated method as described in the present study and included patients with similar dementia severity [43]. Similarly, previous studies using volumes of hippocampal subfields highlighted focal atrophy of the CA1 in the pre-dementia or even preclinical stages of AD [44], as well as in patients with MCI [45]. These studies revealed that the CA1 showed the highest and earliest sensitivity in neuropathological and AD imaging studies [46]. There may be many reasons for these controversial results, but two are notable. First, the inconsistencies might be due to the severity of dementia in the group of patients with AD and the criteria adopted to define MCI. Both dementia severity in patients with AD and the pattern of cognitive impairment in patients with MCI (only amnestic or amnestic plus other domains) significantly affected the expected pattern of regional hippocampal atrophy [47]. We selected a relatively large group of patients with AD and MCI to investigate regional atrophy progression across different stages of the disease. All patients with AD included in this study were in the mild phase of the disease, and patients with severe dementia were not included due to a lack of cooperation. Second, the inconsistencies might be due to differing hippocampal subfield segmentation methods, which differ widely between protocols, ranging from manual segmentation [48] to half-automated [49] to fully automated segmentation procedures [50], as well as the use of different software packages. Automated methods use geometric rules as criteria for defining subfields, whereas manual segmentation and three-dimensional surface reconstruction methods use anatomical points derived from central nervous system atlases. There were differences the CA1 and subiculum definitions in previous studies. For instance, as Mueller et al. admitted, the boundary used in that study was chosen ‘because it could be easily and reliably identified although by doing so, parts of the presubiculum and subiculum proper were counted towards CA1 [51]. The choice of these reference points determined whether a larger or smaller portion of the hippocampal formation would be included in the CA1 or in the contiguous subfields, and the probability of finding a significant volumetric difference between CN and AD participants. Furthermore, most current studies have used FreeSurfer 6.0, resulting in 12 subfields, whereas we used the latest version, FreeSurfer 7.2, which yields elaborate demarcations of subfields and greater segmentation accuracy [38] and further divides each subfield into the head and body. In our study, in addition to the subiculum-body and Ammon’s horn fields, we observed atrophy of the molecular_layer_HP-head, molecular_layer_HP-body, and GC-ML-DG-body volumes in the MCI group compared with the CN group, which has not been reported in previous studies. These regions have not been subdivided using the existing hippocampal segmentation methods used in previous studies. Therefore, our findings extend and partially explain the contradictory results.

The present study also identified significant positive correlations between the volume of all hippocampal subfields and neuropsychological test scores. This correlation suggests that hippocampal atrophy corresponds to cognitive impairment. Notably, the results highlighted the differential effects of various subfield volumes on cognitive impairment. We observed that the volume of the subiculum body demonstrated the highest sensitivity and largest effect sizes, followed by the molecular_layer_HP-body, molecular_layer_HP-head, GC-ML-DG-body, CA4-body, and other subfields. These findings are consistent with those of most previous studies [27, 52], showing that the hippocampus plays an important role in memory and cognitive function, as discussed below. Disruptions in the classic hippocampal trisynaptic circuit (EC-DG-CA3-CA1), including the perforant pathway, could explain why subiculum atrophy is most significantly associated with comprehensive cognitive and memory impairments [53]. More specifically, according to Hyman et al., episodic memory deficits in patients result from anatomical and functional segregation of the hippocampus from associative sensory inputs due to degeneration of the perforant pathway [54, 55]. In addition, neuronal loss in the subiculum further compromises the anatomical and functional communication of the hippocampus with the extrahippocampal-connected regions [42, 56]. Similarly, the DG, composed of a granule cell and a molecular layer, is thought to be the neurogenesis center in the hippocampus [57] and plays a key role in mnemonic processes [58] and spatial learning [59]. CA1 has been the focus of previous pathophysiological studies, showing significant loss in neurons and synapses in AD [60, 61]. This neuronal loss has also been associated with global cognitive performance [62]. Moreover, the CA1 and CA3 are subfields crucially involved in episodic memory processes, such as pattern separation and completion [63]. Overall, regional hippocampal atrophy appears to be a reliable indicator of cognitive and memory function.

Importantly, we found significant correlations between the hippocampal subfield volumes and plasma biomarkers across all groups. To our knowledge, no other studies have explored this issue. Previous studies have shown significant associations of CSF p-tau on hippocampal subfield volumes [22]. Similar to the findings of these studies, here, distinct associations were noted between plasma p-tau217, and p-tau181 levels and specific hippocampal subfield volumes. Notably, current studies have reported similar performances between blood-based and CSF biomarkers in detecting brain amyloid and tau deposition in AD [64, 65]. Therefore, our study provides supporting evidence for the utility of plasma biomarkers in detecting neuropathological changes in hippocampal subfields. Our results suggest that p-tau217 is significantly associated with the subiculum body, presubiculum body, CA4-body, molecular_layer_HP (head and body), and GC-ML-DG (head and body), highlighting the differential effects of tau pathology on various hippocampal subfields. In particular, these subfields present early atrophic changes that gradually worsen with the progression of AD clinical symptoms. Strikingly, the magnitudes of the associations of the subfields with the plasma biomarkers corresponded with the magnitudes of their associations with cognitive performance. Furthermore, our mediation analyses revealed that subiculum-body volume significantly mediates the correlations between MoCA and AVLT-recognition scores and plasma p-tau181 and p-tau217 concentrations. Based on these results, we speculate that tau pathological alterations may contribute to hippocampal subfield atrophy, further affecting cognitive deficits in AD patients. Our results are consistent with those of most previous studies [24], demonstrating that AD pathology may lead to cognitive decline through hippocampal atrophy. However, the associations between plasma p-tau217 and the above subfields reflect, to some degree, the topography reported in the autopsy literature. For example, previous postmortem studies have indicated that the subiculum accumulates tau during the early stages of AD [66]. Neuropathological studies have suggested that neurofibrillary tangles are initially deposited in the CA1, followed by the subiculum, CA2, CA3, CA4, and dentate gyrus [16]. The DG and CA4 are affected by Aβ from Thal-stage III [67] and higher and p-tau deposition from NFT-stage IV/V [3]. However, our results showed that plasma p-tau181 levels only significantly correlated with the subiculum and presubiculum body volumes, indicating that plasma p-tau181 is not a sufficiently sensitive biomarker to reflect tau pathology compared with p-tau217. Previous findings suggesting that p-tau217 outperforms p-tau181 in differentiating and accurately diagnosing AD support these results [68]. However, after FDR correction, we did not observe any significant correlation between hippocampal subfield volume and plasma amyloid biomarkers (Aβ₄₂/Aβ₄₀ ratio, Aβ₄₂, or Aβ₄₀ levels). There are three possible explanations for these observations. First, Aβ leads to neuronal damage or neurodegeneration through the complex pathological interplay of tau, neuroinflammation, and other factors and ultimately to hippocampal subfields atrophy [69, 70]. Second, plasma Aβ₄₀ and Aβ₄₂ levels are affected by cardiovascular comorbidities, medication, and genetic variation, resulting in inaccurate reflections of their relationship to the hippocampal subfields. Third, compared with the new ultrasensitive technologies such as single molecule array and mass spectrometry assays, the current ELISA methods may lack sufficient precision [71], resulting in the low accuracy of plasma Aβ₄₂, Aβ₄₀, and the Aβ₄₂/Aβ₄₀ ratio for diagnosing the Aβ state defined by CSF or PET. A more comprehensive study using structural MRI and molecular markers may further clarify the neurobiological mechanisms of subregion-specific hippocampal atrophy. In addition, some studies have shown differences in Aβ deposition according to age, sex and APOE ɛ4 carriership. A Previous study showed that age at onset of late-onset AD has been associated with divergent disease progression according to sex and APOE ɛ4 carrier status, while education seems to be more protective for women and APOE ɛ4 carriers when it comes to cognitive decline [72]. Wang et al. [73] investigated the effects of interactions of sex with APOE ɛ4 carrier status on region-specific Aβ deposition by amyloid-PET in the brains of 204 patients with MCI in different age groups. They concluded that there was an interaction of sex with APOE ɛ4 carrier status on Aβ deposition of MCI patients in different ages, and that younger women, especially those who are APOE ɛ4 carriers, have more Aβ deposition in the brain. Thus, further stratification according to sex, age, and APOE ɛ4 carrier status may reveal even more significant findings with sufficiently large samples.

Our study’s strengths include the use of the latest version of FreeSurfer 7.2, which has a higher segmentation accuracy than the previous version 6.0. Furthermore, we used detailed data from each participant, including blood samples, MRI scans, and neuropsychological evaluation data. However, this study has some limitations. First, owing to the relatively small sample size, the current study only investigated the relationship between plasma biomarkers and hippocampal subfield volumes across all groups without separate analyses for each group. This is regrettable because the accumulation rate of these pathological proteins may differ across various clinically defined stages of the disease. Second, although the Aβ₄₂/Aβ₄₀ ratio in AD group showed a trend of being lower than that in CN and MCI groups, the difference was not statistically significant. The reason why there was no significant difference in the Aβ₄₂/Aβ₄₀ ratio among the three groups may be due to the relatively small sample size. Third, the cross-sectional design of this study made it impossible to conclude the impact of disease progression on hippocampal subfield atrophy and the temporal association between hippocampal subfield volumes, plasma biomarkers, and cognitive impairment. Hence, future studies with large sample size and longitudinal data are necessary. Finally, the AD diagnosis in this study was based on the 2011 NIA-AA criteria, rather than on the A/T(N) framework testing through CSF or PET analyses.

Conclusions

We found that the hippocampal subfields have distinct atrophy patterns in the MCI stage that predominantly involve the subiculum-body, CA1, molecular_layer_HP, CA4-body, and GC-ML-DG-body, which are still evident in the dementia stages. Furthermore, various hippocampal subfields exhibit differential vulnerabilities to AD-associated pathological changes and may partially mediate the effects of plasma p-tau on cognition impairment. Additionally, the subiculum-body may be the most clinically meaningful subfield in AD, with the strongest associations with plasma biomarkers and cognitive performance and high diagnostic value for MCI and AD participants. Finally, the p-tau217 might be a promising blood biomarker associated with hippocampal subfield atrophy and cognitive decline. These findings enhance our understanding of the pathophysiological mechanisms of AD, which could contribute to the early identification of patients at a higher risk of dementia conversion and provide effective biomarkers for assessing disease progression.

AUTHOR CONTRIBUTIONS

Jing Cao (Data curation; Formal analysis; Investigation; Methodology; Software; Visualization; Writing – original draft; Writing – review & editing); Yating Tang (Data curation; Formal analysis; Writing – original draft); Shujian Chen (Methodology); Siqi Yu (Data curation); Ke Wan (Software); Wenwen Yin (Data curation); Wenhui Zhen (Data curation); Wenming Zhao (Methodology); Xia Zhou (Formal analysis; Methodology); Xiaoqun Zhu (Funding acquisition; Project administration; Resources; Supervision); Zhongwu Sun (Funding acquisition; Project administration; Resources; Supervision).

Footnotes

ACKNOWLEDGMENTS

We thank the participants for their cooperation during this study.

FUNDING

This work was supported by funding from the Key Research and Development Projects of Anhui Province (202104j07020031); the Natural Science Foundation of Anhui Province (2108085MH274); the Scientific research projects of universities in Anhui Province, major projects (2022AH040159); the Anhui Medical University Scientific Research Fund (2020xkj170).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

All data are available from the corresponding author upon reasonable request.

The supplementary material is available in the electronic version of this article:.

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