Diagnostic dynamic contrast-enhanced magnetic resonance imaging blood-brain barrier assessment combined with plasma biomarkers for mild cognitive impairment

Abstract

BACKGROUND

The role of cerebral microvascular dysfunction in early cognitive impairment and dementia has become increasingly recognized. Furthermore, pathological changes in both Alzheimer’s disease and vascular dementia are almost always associated with cerebral hemodynamic deficits.

AIM

To investigate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) assessment of the blood-brain barrier (BBB) in combination with relevant plasma biomarkers for mild cognitive impairment (MCI).

METHODS

This study selected 50 patients with non-amnestic MCI (na-MCI group), 52 patients with amnestic MCI (a-MCI group), and 55 healthy elderly controls (control group). The Chinese version of the Montreal cognitive assessment (MoCA), auditory verbal learning test (AVLT), Hamilton anxiety/depression scale (HAMA/HAMD), and activity of daily living (ADL) scales were used to analyze the characteristics of mental and behavioral symptoms of patients with MCI. The DCE-MRI technique was used to assess the contrast enhancement kinetics. The Patlak model was utilized to analyze the BBB permeability (volume transfer constants). Further, fasting blood was was used to quantify plasma homocysteine (Hcy), β-amyloid protein (Aβ) 40, Aβ42, human phosphorylated tau-181 protein (p-tau181), intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and plasminogen activator inhibitor-1 (PAI-1) levels, as well as serum neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) concentrations.

RESULTS

The na-MCI and a-MCI groups demonstrated significantly lower MoCA and AVLT-Huashan version scores, and statistically higher HAMA, HAMD, and ADL scores compared to the control group. Moreover, the a-MCI group showed notably higher HAMA, HAMD, and ADL scores compared to the na-MCI group. Cranial MRI results revealed significant disparities in cerebral blood flow in the left and right frontal lobes, temporal lobes, hippocampi, cuneus, precuneus, parietal lobes, basal ganglia, and occipital lobes between the a-MCI and na-MCI groups. Compared to healthy controls, patients with MCI demonstrated a smaller amplitude of hippocampal contrast enhancement kinetics and a slower decay rate, indicating smaller vascular volume and increased BBB permeability. Further, Hcy, p-tau181, ICAM-1, VCAM-1, PAI-1, and NFL levels were substantially higher in the a-MCI group than in the na-MCI group, whereas the Aβ42 level was significantly lower. We did not observe any significant differences in Aβ40 and GFAP levels.

CONCLUSION

Patients with MCI may have experienced cerebrovascular system changes in the hippocampal region. Disorders associated with changes in cerebral blood supply may begin before pathophysiological changes are visible by imaging, which provides references for the assessment and treatment of patients with cognitive disorders. Further, DCE-MRI provides a noninvasive approach to diagnose subtle BBB leakage associated with cerebrovascular pathology.

Key Words: Dynamic contrast-enhanced magnetic resonance imaging; Blood-brain barrier; Mild cognitive impairment; Auditory verbal learning test; Montreal cognitive assessment

Core Tip: Mild cognitive impairment (MCI) is considered a transitional state between normal aging and dementia and has several contributing factors. Recent literature indicates that blood-brain barrier (BBB) damage may play a key role in MCI, providing new targets for therapeutic intervention. Currently, magnetic resonance imaging (MRI) techniques are predominantly used for BBB integrity assessment. This study uses dynamic contrast-enhanced MRI to calculate BBB permeability and combines biochemical indicators to understand BBB damage in patients with MCI.

INTRODUCTION

Mild cognitive impairment (MCI) is a syndrome of significant cognitive decline of greater magnitude than would be expected based on a person’s age and educational level. It is considered a transitional state between normal aging and dementia[1]. Patients with MCI may have problems in memory, language, attention, reasoning, or other cognitive functions while maintaining their daily activities, which distinguishes it from dementia[2]. Several factors, including medical conditions, contribute to MCI[3,4]. Therefore, early effective cognitive impairment treatment is crucial, as early detection and intervention may help slow down or prevent further cognitive decline. Further, patients with MCI are at high risk for Alzheimer’s disease (AD). If detected early, drug interventions and rehabilitation training can help reduce AD incidence by approximately 30%[5], thereby indicating clinical significance for improving patients’ quality of life. The early stage of AD is MCI, which is defined as objective cognitive impairments that do not influence instrumental activities of daily living (IADL); thus, it is considered a transitional period between normal cognition and dementia[6].

The role of cerebral microvascular dysfunction in early cognitive impairment and dementia is increasingly understood[7]. The pathological changes in both AD and vascular dementia are associated with cerebral hemodynamic disorders[8,9]. Recent studies have found that blood-brain barrier (BBB) damage may play a key role in this process and provide new targets for therapeutic interventions[10-13]. The BBB consisted of capillary endothelial cells, pericytes, astrocyte endfeet, and extracellular matrix. The integrity of BBB endothelial cells and their surrounding structures is crucial for maintaining the BBB function[14]. BBB destruction is the earliest initiating factor of cognitive dysfunction and is independent of the effect of β-amyloid protein (Aβ) 42 phosphorylated tau protein in vivo[15]. Studies have revealed blood-derived protein accumulation, BBB-specific cell degeneration, and vascular endothelial damage in the brain parenchyma of deceased patients with dementia[16,17]. The neuroinflammatory reaction caused by BBB disruption induces white matter damage, demyelination, and axonal integrity loss, as well as nerve fiber structure destruction that interrupts cortical-subcortical loop connections, ultimately resulting in cognitive dysfunction[18]. Magnetic resonance imaging (MRI) techniques with contrast agents (CA) are typically used for BBB integrity assessment to detect the passive diffusion of the small-molecule CA through the damaged BBB[19]. In recent years, dynamic contrast-enhanced MRI (DCE-MRI) has been increasingly used for assessing subtle leakage from the BBB in cerebrovascular and neurodegenerative diseases, such as cerebral small vessel disease and AD[20]. These studies demonstrated increased BBB leakage, confirming the role of BBB disruption in MCI and AD pathophysiology. BBB leakage may be a quantitative functional marker of cerebrovascular or neurodegenerative disease, indicating pathophysiologic changes associated with disease progression or treatment response before structural effects occur, such as white matter high-intensity lesions [21].

Recently, a latent early stage of disease progression known as subjective cognitive decline has gained attention. Recently approved disease-modifying therapies have focused attention on early AD detection using biomarkers[22]. BBB breakdown is associated with Aβ accumulation, and the Aβ deposit-containing senile plaques constitute its main pathological feature. Aβ is categorized by the C-terminus structure of Aβ40 or Aβ42, which can be detected in both cerebrospinal fluid and blood[23]. High homocysteine (Hcy) is toxic to neurons and improves amyloid neurotoxicity[24]. Among the possible peripheral biomarkers, phosphorylated tau-181 protein (p-tau181) is gaining increased interest as an early and more precise indicator of typical degeneration in AD[25]. Further, similar markers include plasminogen activator inhibitor-1 (PAI-1), intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), serum neurofilament light chain (NFL), glial fibrillary acidic protein (GFAP) levels, etc. However, their performance in detecting MCI still requires further investigation. Moreover, studies on the reproducibility of DCE-MRI methods for subtle BBB leakage in nontumor tissues remain lacking. We hypothesized that DCE-MRI detected alterations in cerebral blood supply impairment in patients with cognitive impairment before imaging pathophysiologic changes were evident. Therefore, we utilized DCE-MRI to continuously capture CA content in blood vessels and brain tissue, assess the degree of CA leakage, calculate BBB permeability, and combine biochemical indicators to understand BBB damage in patients with MCI.

MATERIALS AND METHODS

Study population

An analysis was conducted on outpatient and inpatient patients with MCI in the Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University from October 2022 to February 2024. Inclusion criteria were: (1) All patients should meet the diagnostic criteria for MCI proposed by Petersen et al[26], consisting of a chief complaint of memory decline, which could be confirmed by family members or insiders, objective evidence of memory impairment, normal overall cognitive function remained, normal activities of daily living (ADL), and patient not reaching the diagnostic criteria for dementia, with no other physical or mental diseases that could cause brain dysfunction; (2) Age of ≥ 60 years and ≤ 85 years; (3) All patients underwent head MRI; and (4) Both the patient and his/her guardian signed the informed consent form. Exclusion criteria were: (1) Age < 60 or > 85 years; (2) Presence of serious internal medicine system diseases, such as liver and kidney, hematological, or other systemic diseases; (3) Inflammatory neuropsychological and mental diseases; (4) Drug abuse history; (5) Cognitive impairment caused by other diseases, such as epilepsy, Parkinson’s disease, and encephalitis; (6) Diseases that cause permanent decline in memory or other mental functions; and (7) Unqualified imaging data that could not ensure accurate interpretation. This study enrolled 102 patients with MCI. Specifically, 52 cases were determined as amnestic MCI (a-MCI), who had subjective memory decline reported by patients themselves or confirmed by their families as well as objective evidence of memory impairment that was inconsistent with their age. Further, 50 patients were classified as non-amnestic MCI (na-MCI). Meanwhile, 55 healthy individuals from the physical examination center of our hospital within the same time frame were selected as the control group. The inclusion criteria for the control group were: (1) Independent behavioral capacity and a normal neurological function examination; (2) Normal cognitive function scores; and (3) Comparable sex and age with the MCI group. The exclusion criteria were identical to those of the MCI group. All participants signed an informed consent form before the study commenced, and the research was approved by the ethics committee of the hospital.

Patient data collection

General patient information, including sex, age, occupation, and educational level, was collected according to medical records. Medical history-related information, such as present medical status, medical history, smoking and drinking history, family history, medication history, and positive neurological signs were assessed.

Scale assessment

The same chief physician uniformly trained all personnel involved in the scale assessment to ensure the quality of the scale assessment and reduce variability among different assessors.

Cognitive function assessment: Overall cognitive function was evaluated using the Montreal cognitive assessment (MOCA), consisting of visuospatial/executive function, naming, attention, language, abstraction, delayed recall, computational power, and orientation. On a 30-point scale, the score was positively associated with the patient’s cognitive function, with > 26 points indicating normal, 18-26 denoting MCI, 10-17 representing moderate cognitive impairment, and < 10 exhibiting severe cognitive impairment. The cut-off score of MOCA was ≤ 13 for illiteracy, ≤ 19 for primary school, and ≤ 24 for secondary school and higher.

Memory function assessment: The auditory verbal learning test (AVLT) was utilized to evaluate memory function from immediate recall, long-delayed recall, and recognition domains. The presence or absence of episodic memory impairment was identified according to the AVLT-Huashan version (AVLT-H) delayed recall score (N5 dimension). The total score of the N5 dimension was 12, with a higher score indicating a better memory function. The critical value for memory impairment at 50-60 years old was ≤ 4 points, 60-70 years old as ≤ 3 points, and > 70 years old as ≤ 2 points.

Participants’ emotional status assessment: The Hamilton anxiety/depression rating scale (HAMA/HAMD) was used to evaluate participants’ emotional status. Those with severe anxiety and depression were excluded from this study.

General ADL assessment: The patients’ ADL before and after treatment was evaluated with the ADL scale from the perspective of basic ADL and IADL. The total score ranged from 20 to 80 points, with a score of ≤ 26 points indicating normal and > 26 points denoting functional decline of varying degrees. Higher scores indicate worse ADL.

Multimodal MRI examination: Cranial MRI scans were completed using a 3.0T SIGNA Premier MRI System (GE Company, United States). During the scanning process, scans of T1 weighted image (T1WI), T2 weighted image (T2WI), diffusion-weighted imaging, arterial spin labeling (ASL), DCE, and enhanced 3-dimensional brain volume (3D-BRAVO) sequences were sequentially completed. Raw MRI images were transmitted to the GE AW version 4.7 workstation for post-analysis and processing with software. The scanning parameters of each sequence include T1WI and T2WI sequences conducted with fast spin echoes. T1WI scanning parameters were: repetition time (TR)/echo time (TE) of 2534 ms/29 ms, field of view (FOV) of 240 mm × 240 mm, layer thickness of 6 mm, and spacing of 0. T2WI scanning parameters were: TR/TE of 3204 ms/102.9 ms, FOV of 240 mm × 240 mm, slice thickness of 6 mm, and spacing of 0. Scanning parameters of pseudo-continuous ASL (3D-ASL) using turbo spin echo were: TR/TE of 4848 ms/10.5 ms, matrix of 512 × 8, FOV of 240 mm × 240 mm, layer thickness of 4 mm, spacing of 0, and post-labeling delay of 2000 ms. The DCE-perfusion-weighted imaging sequence used an axial gradient echo T1WI sequence scanning, with TR/TE of 3.4 mm/1.5 ms, matrix of 160 × 128, FOV of 220 mm × 220 mm, layer thickness of 5 mm, spacing of 0, and a total of 5 flip angles (3°, 6°, 9°, 12°, and 15°). DCE examinations were performed 60 times after completing five sets of T1WI plain scans. After the fifth acquisition, 0.2 mg/kg gadolinium diamine (CA) (Omniscan, Shanghai General Electric Pharmaceutical Co., Ltd.) was injected through the elbow vein at a rate of 3 mL/second, after which normal saline was injected at the same rate. Finally, the parameters of 3D-BRAVO enhanced sequence scanning were: TR/TE of 7.2 ms/2.9 ms, matrix of 256 × 256, FOV of 256 mm × 256 mm, layer thickness of 0.5 mm, and overlapping spacing of 0 mm. Each participant’s imaging pictures and the first image of dynamic scanning were converted into standard images in MATLAB 12 using VMware workstation 8, Dcm²AsiszImg, and dcm²niix. Subsequently, all remaining images were registered to the standard images using Realign in SPM12. The volume transfer constant (Ktrans) was obtained, and the BBB permeability was analyzed with the Patlak model. Ktrans was the volume transfer constant from plasma to the interstitial space, representing CA uptake. The CA BBB leakage was measured using the CA concentration in the blood vessels and brain tissue. The higher the Ktrans value, the higher the BBB permeability in that area.

APOE gene detection

Genomic DNA was isolated and purified on an automated QIAsymphony SP system (Qiagen) using the QIAsymphony DSP DNA mini kit (Qiagen GmbH Hilden, Germany) following the manufacturer’s guidelines. APOE genotyping was conducted on a CFX96 real-time polymerase chain reaction detection system (Bio-Rad, Hercules, CA, United States) using the Real-Q APOE genotyping kit (Biosewoom, Seoul, Korea) following the manufacturer’s instructions. An APOE carrier is the presence of one or two copies of the APOE allele.

Biochemical index level detection

Fasting venous blood was drawn in the early morning on an empty stomach into anticoagulant tubes. Subsequently, serum and plasma were isolated through centrifugation. Plasma Hcy levels were quantified with an Abbott I2000 chemiluminescence immunoassay analyzer. Plasma Aβ40, Aβ42, human p-tau181, ICAM-1, VCAM-1, PAI-1, NFL, and GFAP levels were measured following the operational instructions of the respective kits. All the kits were procured from Shanghai Jianglai Biotechnology Co., Ltd.

Statistical analysis

Statistical Package for the Social Sciences version 25.0 software was used for statistical analysis. Continuous data were expressed as the mean ± SD, and independent sample t-tests were used for inter-group samples. Count data were presented as rates, and a χ² test was adopted for between-group comparisons. Pearson correlation analysis was conducted to analyze correlations, with P values of < 0.05 representing statistical significance.

RESULTS

General information about the three groups

There was no significant difference in age, sex, body mass index, marital status, occupation, living conditions, drinking history, smoking history, or basic diseases among the three groups (P > 0.05); however, there were statistically significant differences in educational level and physical exercise among the three groups (P < 0.05) (Table 1).

Table 1 Comparison of general information among three groups, mean ± SD.

Characteristic	Control group (n = 55)	na-MCI group (n = 50)	a-MCI group (n = 52)	F value	P value
Age (years)	70.24 ± 3.44	71.18 ± 4.29	71.33 ± 4.82	1.063	0.348
Gender				0.062	0.969
Male	32	30	30
Female	23	20	22
Body mass index (kg/m2)	23.22 ± 2.10	23.40 ± 1.23	22.83 ± 1.24	1.685	0.189
Marital status				1.109	0.575
Married or remarried	37	29	31
Unmarried or divorced	18	21	21
Education level				12.484	0.014
Illiteracy	7	14	20
Primary school	19	21	16
Secondary school and above	29	15	16
Occupation				3.414	0.491
Unemployed	11	15	18
Farmer	25	20	17
Retired	19	15	17
Living conditions				3.228	0.199
Solitary	17	24	21
Non-solitary	38	26	31
Regular exercise				9.040	0.011
With	39	24	23
Without	16	26	29
History of smoking	28	35	33	4.193	0.123
History of drinking	28	33	35	3.166	0.205
Hypertension	31	32	30	0.709	0.702
Diabetes mellitus	24	23	26	0.442	0.802
Hyperlipidemia	27	22	25	0.300	0.861
APOE carrier		12	15	0.308	0.579

a-MCI: Amnestic mild cognitive impairment; na-MCI: Non-amnestic mild cognitive impairment.

Scores of various scales in three groups

Patients with na-MCI and a-MCI had statistically lower scores on MoCA and AVLT-H-N5 scales compared to healthy elderly controls, whereas their HAMA, HAMD, and ADL scores were significantly higher (P < 0.05). We did not identify any statistical significance in MoCA and AVLT-H-N5 scores between the na-MCI and a-MCI groups (P > 0.05). However, HAMA, HAMD, and ADL scores were significantly higher in the a-MCI group than the na-MCI group (P < 0.05) (Table 2).

Table 2 Comparison of various scale scores among three groups, mean ± SD.

	Control group (n = 55)	na-MCI group (n = 50)	a-MCI group (n = 52)	F value	P value
MoCA	28.05 ± 0.85	21.86 ± 2.05a,1	21.23 ± 2.13a,1	247.0	< 0.0001
AVLT-H-N5	6.16 ± 1.44	2.24 ± 0.69a,1	2.31 ± 0.58a,1	275.8	< 0.0001
HAMA	3.31 ± 2.10	5.16 ± 1.02a,1	7.06 ± 1.00a,1,2	85.25	< 0.0001
HAMD	1.95 ± 0.23	6.00 ± 0.64a,1	7.15 ± 0.61a,1,2	1488	< 0.0001
ADL	23.09 ± 1.17	26.24 ± 1.55a,1	28.58 ± 1.21a,1,2	234.9	< 0.0001

^aP < 0.05.

¹P vs control group.

²P vs non-amnestic mild cognitive impairment group.

MoCA: Montreal cognitive assessment; AVLT-H-N5: Auditory verbal learning test-Huashan version delayed recall score (N5 dimension); HAMA: Hamilton anxiety scale; HAMD: Hamilton depression scale; ADL: Activity of daily living; a-MCI: Amnestic mild cognitive impairment; na-MCI: Non-amnestic mild cognitive impairment.

Cerebral blood flow in three groups

According to cranial MRI results, there were significant differences in cerebral blood flow (CBF) in the left and right frontal lobes, temporal lobes, hippocampal heads, hippocampi, cuneus, precuneus, parietal lobes, and basal ganglia of patients in the na-MCI and a-MCI groups compared to the control group (P < 0.05). Moreover, there were notable differences in CBF in the left and right frontal lobes, temporal lobes, hippocampi, cuneus, precuneus, parietal lobes, basal ganglia, and occipital lobes between the a-MCI group and the na-MCI group (P < 0.05) (Table 3).

Table 3 Comparison of cerebral blood flow among three groups, mean ± SD.

CBF/(mL/100 g/minute)	Right					Left
	Control group (n = 55)	na-MCI group (n = 50)	a-MCI group (n = 52)	F value	P value	Control group (n = 55)	na-MCI group (n = 50)	a-MCI group (n = 52)	F value	P value
Frontal lobe	69.40 ± 3.51	70.72 ± 2.15a,1	53.37 ± 2.58a,1,2	606.4	< 0.0001	63.34 ± 3.25	59.60 ± 2.27a,1	48.92 ± 2.60a,1,2	390.5	< 0.0001
Temporal lobe	53.31 ± 1.85	43.40 ± 1.37a,1	47.64 ± 1.42a,1,2	528.1	< 0.0001	55.66 ± 2.02	51.67 ± 1.62a,1	48.89 ± 1.39a,1,2	213.7	< 0.0001
Hippocampal head	58.83 ± 2.63	51.38 ± 1.65a,1	48.71 ± 1.94a,1,2	325.7	< 0.0001	53.77 ± 2.51	49.06 ± 2.20a,1	49.66 ± 2.42a,1	61.72	< 0.0001
Hippocampus	51.32 ± 2.15	50.16 ± 1.40a,1	45.87 ± 2.09a,1,2	117.8	< 0.0001	49.78 ± 1.94	46.78 ± 1.61a,1	45.10 ± 1.98a,1,2	87.63	< 0.0001
Cuneus	55.20 ± 2.68	43.33 ± 1.55a,1	43.24 ± 1.64a,1	607.8	< 0.0001	52.23 ± 2.41	41.69 ± 1.09a,1	46.90 ± 1.54a,1,2	455.1	< 0.0001
Precuneus	52.56 ± 2.36	49.67 ± 1.20a,1	44.74 ± 1.58a,1,2	256.8	< 0.0001	55.17 ± 2.26	45.37 ± 1.34a,1	43.22 ± 1.50a,1,2	701.6	< 0.0001
Parietal lobe	49.91 ± 2.23	48.37 ± 1.66a,1	45.13 ± 1.37a,1,2	97.41	< 0.0001	48.24 ± 1.34	45.61 ± 1.19a,1	42.53 ± 0.98a,1,2	311.9	< 0.0001
Basal ganglia	70.93 ± 2.38	67.66 ± 1.75a,1	60.67 ± 1.49a,1,2	394.2	< 0.0001	70.53 ± 2.25	67.42 ± 2.24a,1	64.63 ± 1.74a,1,2	106.6	< 0.0001
Occipital lobe	47.98 ± 2.28	48.21 ± 1.67	45.46 ± 1.49a,1,2	35.10	< 0.0001	48.20 ± 1.62	47.61 ± 1.69	45.25 ± 1.23a,1,2	55.12	< 0.0001

^aP < 0.05.

¹P vs control group.

²P vs non-amnestic mild cognitive impairment group.

CBF: Cerebral blood flow; a-MCI: Amnestic mild cognitive impairment; na-MCI: Non-amnestic mild cognitive impairment.

BBB permeability in three groups

The na-MCI and a-MCI groups demonstrated remarkably higher Ktrans values in the left frontal lobe, temporal lobe, and hippocampal area of the brain compared to the control group. Ktrans values in the left and right frontal lobes and temporal lobes were significantly lower and those in the left and right hippocampal regions were considerably higher in the a-MCI group than in the na-MCI group (P < 0.05) (Table 4).

Table 4 Comparison of blood-brain barrier permeability among three groups, mean ± SD.

Ktrans	Right					Left
	Control group (n = 55)	na-MCI group (n = 50)	a-MCI group (n = 52)	F value	P value	Control group (n = 55)	na-MCI group (n = 50)	a-MCI group (n = 52)	F value	P value
Frontal lobe	0.01 ± 0.01	0.13 ± 0.01a,1	0.03 ± 0.01a,1,2	2126	< 0.0001	0.09 ± 0.01	0.19 ± 0.02a,1	0.02 ± 0.01a,1,2	1901	< 0.0001
Temporal lobe	0.05 ± 0.02	0.10 ± 0.02a,1	0.08 ± 0.02a,1,2	83.51	< 0.0001	0.02 ± 0.01	0.11 ± 0.01a,1	0.02 ± 0.01a,2	1380	0.867
Hippocampal area	0.02 ± 0.01	0.06 ± 0.01a,1	0.13 ± 0.03a,1,2	451.6	< 0.0001	0.01 ± 0.01	0.03 ± 0.01a,1	0.09 ± 0.03a,1,2	251.0	< 0.0001

^aP < 0.05.

¹P vs control group.

²P vs non-amnestic mild cognitive impairment group.

a-MCI: Amnestic mild cognitive impairment; na-MCI: Non-amnestic mild cognitive impairment.

Biochemical indexes in three groups

We compared the biochemical indexes among the three groups. Hcy, p-tau181, ICAM-1, VCAM-1, PAI-1, NFL, and GFAP levels were notably increased in the na-MCI and a-MCI groups compared to the control group (P < 0.05), whereas the Aβ42 level was significantly decreased. We did not identify a significant difference in the Aβ40 level between the three groups (P > 0.05). Moreover, Hcy, p-tau181, ICAM-1, VCAM-1, PAI-1, and NFL levels were remarkably higher in the a-MCI group compared to the na-MCI group (P < 0.05). Further, the Aβ42 level was lower (P < 0.05), while there were no differences between Aβ40 and GFAP levels (P > 0.05) (Table 5).

Table 5 Comparison of the levels of biochemical indexes of the subjects, mean ± SD.

Biochemical indexes	Control group (n = 55)	na-MCI group (n = 50)	a-MCI group (n = 52)	χ2/t value	P value
Hcy (μmol/L)	10.47 ± 2.91	12.62 ± 2.15a,1	13.40 ± 2.21a,1,2	20.42	< 0.0001
Aβ42 (pg/mL)	129.38 ± 15.49	107.20 ± 7.66a,1	89.87 ± 12.17a,1,2	138.4	< 0.0001
Aβ40 (pg/mL)	151.65 ± 40.52	147.34 ± 21.94	147.63 ± 34.30	0.278	0.758
p-tau181 (pg/mL)	14.22 ± 1.99	31.77 ± 4.69a,1	40.66 ± 4.11a,1,2	695.1	< 0.0001
ICAM-1 (g/mL)	117.29 ± 16.43	215.99 ± 25.44a,1	286.60 ± 33.44a,1,2	577.9	< 0.0001
VCAM-1 (mg/L)	1.79 ± 0.26	2.23 ± 0.31a,1	2.50 ± 0.51a,1,2	49.07	< 0.0001
PAI-1 (ng/L)	314.59 ± 32.81	366.83 ± 36.77a,1	397.75 ± 45.68a,1,2	63.26	< 0.0001
NFL (pg/mL)	18.55 ± 4.90	39.22 ± 6.77a,1	46.70 ± 6.44a,1,2	311.1	< 0.0001
GFAP (ng/mL)	4.00 ± 0.23	8.68 ± 1.09a,1	9.48 ± 3.19a,1	125.0	< 0.0001

^aP < 0.05.

¹P vs control group.

²P vs non-amnestic mild cognitive impairment group.

a-MCI: Amnestic mild cognitive impairment; na-MCI: Non-amnestic mild cognitive impairment; Hcy: Homocysteine; Aβ: β-amyloid protein; p-tau181: Phosphorylated tau-181 protein; ICAM-1: Intercellular adhesion molecule-1; VCAM-1: Vascular cell adhesion molecule-1; PAI-1: Plasminogen activator inhibitor-1; NFL: Neurofilament light chain; GFAP: Glial fibrillary acidic protein.

DISCUSSION

In this study on BBB calculation in MCI, we performed DCE-MRI and assessed Ktrans and BBB permeability. Multiple studies on biochemical indicators have preliminarily confirmed BBB damage in cognitive impairment, but clear evidence remains lacking.

In this study, we first compared the general data of patients with MCI and healthy controls. The results revealed significant inter-group differences in educational level and physical exercise. Research has indicated that the MCI prevalence decreases with increasing educational levels[27]. Cognitive impairment can be accelerated and exacerbated with age or other risk factors. Early education promotes the development of intelligence. Education level may affect the cortical thickness of the left lower frontal lobe brain functional area associated with processing speed, indicating that those with higher educational attainment may have more brain reserves[28]. Therefore, organizing cultural and intellectual activities for the elderly delays the decline of brain function and improves the compensatory ability of brain aging through cognitive stimulation. Further, physical exercise improves nervous system function and prevents cardio-cerebrovascular and metabolic diseases[29], in addition to being an effective way to adjust emotions, which is beneficial to the cognition of the elderly to enhance overall cognitive function. Lifestyle habits are among the main behavioral factors that affect cognitive function. Further, patients with na-MCI demonstrated significant differences in smoking history compared to healthy controls, but patients with a-MCI did not. Smoking results in atherosclerotic plaque formation, thereby affecting cerebral blood supply and impairing cognitive function, although the effects of smoking on cognitive function in older adults have relied on the degree to which nicotine binds to cholinergic nicotinic receptors in the body[30]. Thus, the statistical results of smoking history were different between the na-MCI group and the a-MCI group. Further, we investigated the number of APOE gene carriers among patients with cognitive impairment. The results revealed no significant difference in the number of APOE gene carriers between patients with a-MCI and na-MCI, potentially related to the limited sample size in this particular research. The APOE gene is located on chromosome 19 and encodes a 299 amino acid glycoprotein. Polymorphisms in the APOE gene can result in alterations in the transformation, metabolism, and functionality of lipoproteins within the body and are closely associated with cardiovascular and cerebrovascular disorders[31]. Shi et al[32] revealed that APOE polymorphism is intimately associated with the cognitive function of patients suffering from central nervous system demyelinating diseases.

DCE-MRI findings revealed that the CBF of the left and right frontal lobe, temporal lobe, hippocampal head, hippocampus, cuneus, precuneus, parietal lobe, and basal ganglia was statistically higher in the na-MCI and a-MCI groups than in the control group. The results are comparable with those of a meta-analysis[33] of 244 studies on CBF and CBF velocity in patients with MCI and AD, involving 13644 participants in 60 regions. Participants with AD had reduced resting CBF of the whole brain compared to healthy controls. However, participants with MCI exhibited a decrease in CBF in 10 areas of little impact, particularly in the precuneus. To evaluate BBB permeability, we measured Ktrans, which is the rate of CA entering the extravascular space per volume of tissue, and the CA concentration in plasma[34]. The Ktrans values in the left frontal lobe, temporal lobe, and hippocampal region of the brain were substantially increased in the na-MCI and a-MCI groups compared to in the control group. Ktrans values in the left and right frontal lobes and temporal lobes of the brain were significantly lower in the a-MCI group than in the na-MCI group, whereas the Ktrans values in the left and right hippocampal regions were markedly higher. Overall, patients in the na-MCI and a-MCI groups experienced altered BBB permeability compared to healthy controls. Several studies have revealed that increased BBB permeability is crucial to age-related persistent vascular damage. As age increases, basal layer thickness increases and the number of tight connections in the central nervous system decreases, which may increase BBB permeability by improving paracellular and intercellular transport[35]. However, considering the few brain regions involved in this study, the reliability may require further validation. Meanwhile, as BBB permeability changes are a complex process, Ktrans differences may be associated with multiple factors such as gender, living environment, smoking, alcohol consumption, and stress[36,37]. MCI itself is a disease with subtle BBB damage; thus, measurement results are more susceptible to interference. Therefore, the difference in BBB permeability only represents a trend.

Finally, we conducted tests on the biochemical indicators of participants. The results indicated that Hcy, Aβ42, p-tau181, ICAM-1, VCAM-1, PAI-1, NFL, and GFAP levels in the na-MCI and a-MCI groups were significantly increased compared to the control group (P < 0.05). There were no significant differences in Aβ40 levels between the three groups. Further, Hcy, p-tau181, ICAM-1, VCAM-1, PAI-1, and NFL levels were notably higher in the a-MCI group than in the na-MCI group, whereas no significant variation was detected in the Aβ42, Aβ40, and GFAP levels. Hcy is a sulfur-containing amino acid and a metabolite of methionine demethylation[38]. The results indicate that increased plasma Hcy levels and decreased APN levels are associated with MCI. Aβ has a three-dimensional structure with a β-type fold, with strong self-aggregation and easy formation of insoluble precipitates. It primarily consists of two forms, Aβ40 and Aβ42. Aβ42 presents lower in the brain than Aβ40; however, it is easier to aggregate and form deposits, which is the major pathological factor for senile plaque formation in the brain tissue of AD[39]. Previous studies found decreased plasma Aβ42 levels in patients with AD, which was increased in patients with MCI compared to healthy controls. The decrease in soluble Aβ42 demonstrates a more pronounced correlation with AD symptoms than Aβ plaques, and AD symptom occurrence may potentially be ascribed to soluble Aβ42 reduction[40]. Recent studies have revealed that plasma p-tau181 can be used to identify patients with AD dementia and distinguish between individuals with positive and negative Aβ[41]. Hyperphosphorylated tau protein forms neurofibrillary tangles, inducing neuroinflammation, oxidative stress, and energy metabolism disorders, thereby causing cerebral cell ischemia and hypoxia, synaptic dysfunction, and irreversible neuron damage. Eventually, this culminates in brain atrophy and progression to dementia after the brain’s decompensation[42]. Tau protein hyperphosphorylation in the brain damages neurons and synapses, thereby increasing neurofibrillary tangle formation, consequently reducing BBB function of Aβ42 and p-tau181, whose blood concentrations can be detected with enzyme-linked immunosorbent assay. Cognitive impairment constitutes a state of chronic inflammation. ICAM-1 and VCAM-1 are important intercellular adhesion molecules that can facilitate adhesion at the inflammatory loci, thereby activating vascular endothelium, aggravating tissue inflammatory injury, and expediting inflammation progression[43]. PAI-1 has been associated with diabetes and metabolic syndrome. Angelucci et al[44] revealed that cerebrospinal fluid PAI-1 levels were elevated in patients with AD and that plasma PAI-1 levels progressively increased as dementia progressed. Further, they demonstrated a remarkable correlation between the plasma PAI-1 level and cognitive function. Moreover, PAI-1 modulates Aβ deposition and tau protein phosphorylation through a brain-derived neurotrophic factor-c-Jun N-terminal kinase/c-Jun mechanism that affects cognitive function[45]. Therefore, PAI-1 is considered a predisposing factor for AD. NFL, a principal constituent of neurofilament proteins, serves as a marker of axonal damage. Neurofilament proteins are essential to maintain axonal stability and neural signal transduction. Serum NFL levels are significantly increased in patients with neurodegenerative diseases[46,47]. Conversely, GFAP is a biological marker of acute cerebral hemorrhage that has neuroprotective effects. It is specifically expressed by astrocytes, primarily in the form of soluble proteins and intermediate microfilament proteins[48]. Astrocytes in the brain injury area secrete more neurotrophic factors, which are beneficial for the repair and growth of central nervous system cells.

This study has some limitations. First, this is a single-center study consisting of limited cases. Second, due to the small sample size, the effects of BBB permeability on MCI progression cannot be determined. Third, the BBB permeability of patients with different natures, such as gender, has not been further investigated.

CONCLUSION

In summary, cerebrovascular system changes may have occurred in the hippocampus of MCI. DCE-MRI provides a noninvasive means to diagnose subtle BBB leakage associated with cerebrovascular pathology.