Intestinal Microflora Changes in Patients with Mild Alzheimer’s Disease in a Chinese Cohort

Abstract

Background:

Understanding the relationship between Alzheimer’s disease (AD) and intestinal flora is still a major scientific topic that continues to advance.

Objective:

To determine characterized changes in the intestinal microbe community of patients with mild AD.

Methods:

Comparison of the 16S ribosomal RNA (rRNA) high-throughput sequencing data was obtained from the Illumina MiSeq platform of fecal microorganisms of the patients and healthy controls (HC) which were selected from cohabiting caregivers of AD patients to exclude environmental and dietary factors.

Results:

We found that the abundance of several bacteria taxa in AD patients was different from that in HC at the genus level, such as Anaerostipes, Mitsuokella, Prevotella, Bosea, Fusobacterium, Anaerotruncus, Clostridium, and Coprobacillus. Interestingly, the abundance of Akkermansia, an emerging probiotic, increased significantly in the AD group compared with that in the HC group. Meanwhile, the quantity of traditional probiotic Bifidobacteria of the AD group also rose.

Conclusion:

These alterations in fecal microbiome of the AD group indicate that patients with mild AD have unique gut microbial characteristics. These specific AD-associated intestinal microbes could serve as novel potential targets for early intervention of AD.

Keywords

intestinal microflora mild Alzheimer’s disease therapeutic targets

INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by the progressive decline of cognitive functions such as memory, language, and visual space. It is the most common type of dementia and also the fourth leading cause of death in the global aging population [1]. As of 2019, there are about 9.83 million Chinese people suffering from AD. As the aging society intensifies, the number of patients continues to increase. By the middle of this century, the population living with AD in China is expected to grow to 40 million [2, 3]. Although AD has been investigated for more than a century, its pathogenesis is still not fully understood [4]. It is currently believed that the cleavage of amyloid-β protein precursor (AβPP) and production of AβPP fragment amyloid-β (Aβ), as well as aggregation of highly phosphorylated tau protein, leads to reduced synaptic strength, synaptic loss, and neurodegeneration. Metabolic factors, vascular lesions, and inflammatory changes are also involved in the process of AD [5]. In addition, the role of intestinal flora and microbial-gut-brain axis in the occurrence and development of AD is an increasingly concerned research topic [6].

The human gastrointestinal tract is a complex ecological environment that contains about 10¹⁴ microorganisms of heterogeneous population, which belongs to 1,000 different species [7]. These microorganisms make significant influence on the host [8]. Compositions of the gut microbiomes are associated with various diseases, such as colon cancer, autoimmune diseases, inflammatory bowel disease, and Clostridium difficile infection [9]. In most cases, gut microorganisms act as prebiotics, for example, maintaining the integrity of the intestinal barrier, inhibiting pathogens from adhering to the surface of the intestine and producing bioactive substances such as γ-aminobutyric acid, norepinephrine, tryptophan, and short chain fatty acids (SCFAs) [10, 11]. Likewise, gut microbiota plays an important role on central nervous system and communicates with the brain bidirectionally [12]. Brain signals alter the gut environment through the vagus nerve, and changes in the gut microflora conversely affect mood and pain centers through the vagus nerve and central nervous system signaling pathways. Germ-free animals were employed to demonstrate the significant effect of gut microbiota on the morphological and functional development of separate parts of the brain [13]. Meanwhile, more and more evidence emerge about how AD can change intestinal microbiota. Currently, studies have shown that the ileum and colon microbiota of rats dramatically changed since the establishment of AD model with bilateral intraventricular injection of Aβ₄₂ [14]. Additionally, traumatic frontal lobe brain injury could lead to extensive changes in intestinal structures and function, affecting intestinal permeability [15]. Nonetheless, AD is a disease of continuous spectrum, changes in the flora at different stages of the disease are not yet fully understood.

In addition, intervention on mild AD patients based on microbial gut-brain axis has come into people’s sight [16]. For this purpose, we performed bacterial 16S ribosomal gene sequencing analysis on stool samples from patients with mild AD and their healthy caregivers living with them, trying to identify a target for AD intervention.

MATERIALS AND METHODS

Characteristics of the study population

All participants were recruited consecutively from the Memory Clinic, Department of Neurology at the First Affiliated Hospital of Shandong First Medical University from March to September 2020. All participants had been living in Shandong province (Eastern China) for a long time.

Each participant underwent a complete medical history evaluation, neurological and neuropsychological assessment including the Mini-Mental State Examination (MMSE), clinical dementia rating (CDR), and the Montreal Cognitive Assessment (MoCA). Mild AD patients (AD group, 13 cases) and healthy caregivers living with the patients (HC group, 13 cases) were screened as the research objects. Inclusion criteria for patients with mild AD were as follows: 1) meeting the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [17]; following the guidelines of the National Institute of Neurological and Communicative Disorders and the Stroke and Alzheimer Disease and Related Disorders Association [18]; 2) CDR = 1; 3) patients or their legally authorized caregivers were informed of the purpose of this study and provided with written informed consent. Exclusion criteria: 1) a history of using cholinesterase inhibitors, N-methyl-D-aspartic acid receptor antagonists and other nootropic drugs; 2) other diseases causing cognitive impairment, such as vascular dementia, Lewy body dementia, frontotemporal dementia, Parkinson’s disease (PD), traumatic dementia, or serious primary mental and psychological problems; 3) a history of serious heart, lung, liver, kidney problems, and other serious systemic diseases; 4) inability to cooperate with the scale assessment due to communication obstacles caused by aphasia or other reasons; 5) a history of using antibiotics or probiotics within two months before collecting the stool samples. This study was approved by the Ethics Committee of the First Hospital Affiliated with Shandong First Medical University (Ethics No. 2021S109).

There were no significant differences between the AD and HC groups in terms of age, gender, years of education, body mass index (BMI), diabetes, or hypertension (Table 1). Compared with the HC group, AD patients had lower MMSE and MOCA scores (23.2±2.95 versus 26.0±3.22, p = 0.312 and 19.3±1.65 versus 23.5±2.99, p < 0.001, Table 1). The course of AD in the patients in this study was 2.7±0.5 years.

Table 1

Clinical and demographic data of patients with AD and healthy controls

Variables	AD (n = 13)	HC (n = 13)	p
Age, y (SD)	72.0 (4.50)	71.3 (6.30)	0.7237
Male, n (%)	6.0 (46.15)	5.0 (38.46)	0.6914
MMSE scores, mean (SD)	23.2 (2.95)^*	26.0 (3.22)	0.0312
MoCA scores, mean (SD)	19.3 (1.65)^***	23.5 (2.99)	< 0.001
BMI, mean (SD)	22.7 (2.86)	22.0 (2.30)	0.56
Diabetes (%)	7.0 (53.85)	6.0 (46.15)	0.6949
Hypertension (%)	8.0 (61.54)	7.0 (53.85)	0.6914
Education, y (SD)	10.4 (5.16)	8.8 (4.95)	0.4233

BMI, body mass index; MMSE, Mini–Mental State Examination; MoCA, Montreal cognitive assessment; SD, standard deviation. Age, BMI, education, MMSE scores, and MoCA scores are expressed as means (SD). Sex, hypertension, and diabetes are expressed as a proportion; p, analysis of Welch’s t test, or χ² test.

Fecal sample collection

All participants involved in the study resided at home, where fecal sample collection occurred. Fecal samples were returned by overnight delivery sample collection kits, packaged within insulated containers and chilled with frozen gel packs. All samples included in this study arrived chilled and were processed and frozen the day following home collection. Upon receipt, chilled samples were weighed, scored on the Bristol stool scale, subsampled (∼100 mg) into prepared sterile bead beating tubes, and stored at −80°C until processing.

Polymerase chain reaction (PCR) and Miseq sequencing

DNA extraction was performed using the DNeasy PowerSoil Kit (QIAGEN, Inc., Netherlands) following the manufacturer’s instructions. Variable regions 3–4 of the 16S rRNA gene were amplified with the forward primer 5′-ACTCCTACGGGAGGCAGCA-3′, and the reverse primer 5′-GGACTACHVGGGTWTCTAAT-3′. Barcodes (7-bp sample-specific) were incorporated into the primers for multiplex sequencing. The PCR program was set as follows: 98°C, 10 min; 25 cycles of 98°C, 15 s; 55°C, 30 s; 72°C, 30 s; and 72°C, 5 min. After agarose gel electrophoresis, the PCR amplicons were purified twice using the Agencourt AMPure Kit (Beckman Coulter, Milan, Italy) and quantified using the PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA). An equimolar amplicon pool was obtained and paired-end 2×300 bp sequencing was performed using the Illumina MiSeq platform with MiSeq Reagent Kit V3 (Shanghai Personal Biotechnology Co., Ltd, Shanghai, China). The sequencing data were analyzed using QIIME package 13 (version 1.8.0). Raw sequencing reads with exact matches to the barcodes were identified as valid sequences and assigned to corresponding samples. To guarantee a higher level of accuracy, sequences were excluded from analysis if they (a) were < 150 nucleotides in length, (b) had average Phred scores of < 20, or (c) contained ambiguous bases or mononucleotide repeats of > 8 bp. Paired-end reads were aligned using FLASH [19], and delineation of operational taxonomic units (OTUs) was conducted with UCLUST at a 97% cutoff [20]. A representative sequence of each OTU was selected and subjected to BLAST to assign taxonomic classification using the Greengenes 16S rRNA gene database. Alpha and beta diversity were performed utilizing QIIME. Raw sequence reads in FASTQ format were available in Sequence Read Archive under study accession PRJNA792014.

Statistical analysis

Alpha biodiversity analysis between OTU frequencies was performed with the use of alpha_rarefaction.py pipeline implemented in the QIIME package. Statistics were calculated with the use of compare_alpha_diversity.py (QIIME) script for each biodiversity index. Statistical analysis was performed with two tests: t-parametric and t-non-parametric.

Beta diversity (β-diversity) metrics were computed using normalized OTU-level data in R and included Bray-Curtis dissimilarity, weighted UniFrac and unweighted UniFrac. To detect differences in richness and alpha diversity (α-diversity) between groups, we used independent two-sample t-tests for normally distributed measures or Mann-Whitney U tests for non-normally distributed measures in SPSS. To detect statistical differences in beta diversity metrics between groups, we used ANOSIM in the vegan package in R.

Differential abundance of taxa between AD and control groups was determined at the OTU level using the DESeq2 package of R. Results were expressed as log2 fold change in AD participants relative to control participants. Relative abundance comparisons at the genus, family, and phylum levels were performed on normalized data in Mothur using 10,000 iterations of metastats (a statistical method employing non-parametric t-tests), Fisher’s exact tests and false discovery rate (FDR) correction to detect differentially abundant features. The linear discriminate analysis (LDA) effect size (LEfSe) implementation in Mothur was used to detect differences in AD and control groups. LEfSe is an algorithm for high-dimensional biomarker discovery [20]. We performed LEfSe analysis on the website https://huttenhower.sph.harvard.edu/galaxy to identify differentially abundant taxa with both statistical significance and biological relevance.

We utilized PICRUSt to detect predicted functional differences in microbial communities between AD and control participants. Briefly, OTUs were re-assigned PICRUSt-compatible taxonomic classifications using the Greengenes version 13.5 reference database and then normalized by 16S rRNA copy number. The resulting normalized OTU table was then used for prediction of KEGG orthologs based on bacterial composition.

To assess the potential prediction effect for AD, multivariable logistic regression models based on the relative abundance of the fecal microbiota were built in a stepwise manner. The Hosmer-Leme show test was performed to assess the goodness of fit in the prediction model. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were calculated to evaluate the predictor performance of the final model.

RESULTS

By sequencing the V3-V4 region of 16S rRNA genes, the composition and diversity of the intestinal flora of the AD and the HC groups were investigated and the differences between the AD and the HC groups were evaluated through the intestinal flora analysis. In the fecal samples, the final OTU data set for the AD and HC groups consisted of 3228 OTUs, classified to 215 genera, 111 families, 56 orders, 31 classes, and 17 phyla. According to Fig. 1A-E, changes in intestinal flora were clearly observed in both groups. To verify the differences between the AD and HC groups, Chao1, ACE, Shannon, and Simpson indices of each community were calculated, which have been widely used to calculate Alpha diversity. Although there was no significant difference, Chao1 and ACE indices which were affected by species richness showed that the diversity of microorganisms in the HC group was higher than that in the AD group (Fig. 1F). Shannon and Simpson indices between the two groups did not show differences.

Fig. 1

Composition of the gut microbiome and α-diversity indices of fecal microbiota in the AD and HC groups. Relative abundance of (A) phylum-level, (B) class-level, (C) order-level, (D) family-level and (E) genus-level microbial taxa in patients with AD and HC groups. (F) α-diversity indices of fecal microbiota in patients with the AD and HC groups. Box plots depict differences in the fecal α-diversity indices according to Chao 1 index, ACE index, Shannon index, Simpson index based on OUT counts. Each box plot represents median, interquartile range, minimum and maximum values.

Despite significant inter-individual variations, a nonmetric multidimensional scaling (NMDS) based on the unweighted UniFrac, and weighted UniFrac algorithms also divided the two groups into different clusters. Differences in NMDS distance values between different groups were profiled by boxplot (Fig. 2A, B). Combined with the results of the statistical test, it could be seen that composition and abundance of the fecal flora in the AD group were not identical in the HC group. Weighted (qualitative, ANOSIM R = 0.0882, p = 0.053) UniFrac and unweighted (quantitative, ANOSIM R = 0.2234, p = 0.008) UniFrac of the AD and HC groups were also displayed significantly different states (Fig. 2C, D).

Fig. 2

Beta diversity measures between the AD and HC groups. Beta diversity expressed in (A) unweighted UniFrac NMDS and multi-group comparison boxplot of unweighted UniFrac distance, (B) weighted UniFrac NMDS and multi-group comparison boxplot of weighted UniFrac distance (each box plot represents median, interquartile range, minimum, and maximum values), (C) unweighted UniFrac PCoA, weighted UniFrac PCoA, and (D) PCA based on the distance matrix of UniFrac dissimilarity of the fecal microbiota communities in AD and HC groups. PCA: principal coordinates analysis, PCoA: principal coordinates analysis, NMDS: nonmetric multidimensional scaling.

The LEfSe and metastats were used to determine the main differences in the gut microbiota and to find indicator taxa between the AD and HC groups. Based on the LEfSe, we compared the taxa of stool samples from the AD and HC groups and visualized the results of the Kruskal-Wallis rank sum test through the LEfSe cladogram (Fig. 3A, B). Using logarithmic linear discriminant analysis score > 2.0 and p < 0.01, we found that the abundance of 3 phyla and 9 genera in the AD group was significantly higher than in the HC group. Abundance of 1 phylum and 8 genera was considerably lower than in the HC group. Through metastats analysis, a pairwise comparison test was performed on the difference in the number of sequences (absolute abundance) of each taxon between the AD and HC groups (Fig. 3C, D). At the genus level, the AD group had lower abundance of 14 genera (Anaerostipes, Mitsuokella, Prevotella, Bosea, Lachnobacterium, Aeromicrobium, Coprococcus, Tsukamurella, Ru minococcus, Megamonas, Comamonas, Fimbriimonas, Faecalibacterium, and Labrys), whereas it had 14 genera of higher abundance (Fusobacterium, Anaerotruncus, Clostridium, Coprobacillus, Cryocola, Enterobacter, Robiginitalea, Clostridium, Moryella, Akkermansia, Sutterella, Bifidobacterium, Pyramidobacter, Bacteroides).

Fig. 3

Taxonomic differences of fecal microbiota in the AD patients and the HC individuals. (A) Linear discriminant analysis (LDA) effect size (LEfSe) and (B) LEfSe cladogram analysis revealed significant bacterial differences in fecal microbiota between the AD patients and normal controls (LDA score > 2, p < 0.01). The top 20 taxa with the most significant differences at (C) the phylum level and (D) the genus level measured by metastats (p < 0.05).

Through Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt), based on Greengenes 16S rRNA gene full-length sequence database, the bacterial population sequencing results were divided by reference to OTU and comparison to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for prediction of metabolic function of the flora. After comparison of 45 KEGG pathways in level 2, four KEGG pathways with significant differences between the AD and HC groups were determined. We found that signaling molecules and interaction, transport and catabolism increased significantly in the AD group (Fig. 4A). Eight pathways in level 3, including the lysosome, bacterial toxins and phosphatidylinositol signaling system, were significantly increased in the microbial communities of the AD group, while the transcription factors were significantly reduced (Fig. 4B).

Fig. 4

KEGG pathways predicted with PICRUSt. KEGG pathways with significant differences between the AD and HC groups (A) at level 2 and, (B) at level 3. PICRUSt: phylogenetic investigation of communities by reconstruction of unobserved states.

We evaluated the value of six abundant genera (Bifidobacterium, Akkermansia, Coprococcus, Anaerostipes, Sutterella, Coprobacillus) as potential biomarkers between the AD and HC groups. The differential features of these genera are shown in Fig. 5. We used differential bacteria as a predictor to generate the area under the receiver operating characteristic curves to obtain the area under the curve (AUC) ranging from 0.72 to 0.85. We found that Sutterella was the best discriminant predictor (AUC: 0.797). In a nutshell, these key differential genera could be used as potential biomarkers for discriminating healthy controls from AD patients.

Fig. 5

ROC curves calculated using the predominant fecal microbial genera in discriminating AD from HC.

DISCUSSION

We examined that AD contributes to primary morbidity and mortality of the central nervous system degenerative disease. There is still a deficiency of maneuverable, cost-effective, sensitive, and specific peripheral biomarkers and safe, effective therapies for AD [21, 22]. Current medications, such as the three cholinesterase inhibitors which are already on the market, along with disease-modifying drugs like Memantine and Aducanumab, only provide symptomatic relief and have limited delay to the onset or progression of the disease. One of the main causes is that the etiology of AD is still not fully understood. Multiple known pathogenesis are furthermore associated with each other, aggravating the severity of AD, so a combination therapy targeting multiple factors looks more promising [23, 24]. In addition, the blood-brain barrier limits drug delivery and efficacy, which is another important barrier against drug development [25]. As a bidirectional communication system, the microbiome-gut-brain axis not only allows the central nervous system to control and maintain intestinal homeostasis, but also connects the surrounding intestinal function with emotional and cognitive functions, and allows the gut to directly or indirectly affect brain function through neural, endocrine, immune and metabolic signals [26]. Currently, there is evidence that gut microbiota plays a pivotal role in the regulation of human health. The relationship between gut microbiota and central nervous system diseases, particularly neurodegenerative diseases, including AD and PD has received increasing attention [27, 28]. Therefore, based on the differences of gut microbiota in different stages of progression of AD, exploring the biological correlation of intestinal flora related to AD and determining its relationship with cognitive function may provide a new method for the diagnosis of AD. Intervention of microbiome-gut-brain axis could be beyond the limitation of blood-brain barrier to some extent, and is expected to become a novel scheme of combined treatment of AD.

So far, most studies on AD intestinal flora have focused on AD patients and randomly selected healthy controls. Separate living environments between the two groups, including diets and other environmental factors, may introduce variations and interfere with research conclusions. Moreover, nootropic drugs for AD treatment such as cholinesterase inhibitors and N-methyl-D-aspartic acid receptor antagonists can also influence the composition of the intestinal flora. In current research, intestinal microbes of mild AD patients who have not taken any nootropic drug and their cohabiting healthy caregivers were analyzed with bioinformatics methods. The abovementioned interference is eliminated to the greatest extent to render the most reliable data in the microbiota communities’ analysis.

Admittedly, dysfunction of the fecal microbial communities probably is involved in the occurrence and development of AD. According to the sequencing results, the AD group had lower community richness than the HC group, while higher microbial diversity is usually considered as a sign of good health. Moreover, differences in compositions of gut microbial communities between the AD and HC groups were also evident. In general, the difference between α-diversity and β-diversity reveals the imbalance of the gut microbial communities in mild AD patients [29].

Additionally, PICRUSt analysis revealed extensive functional changes in the gut microbiome. Age-related changes in microbiome suggest that AD patients host fewer microbial functions than healthy individuals on various metabolism and biosynthesis processes. ROC analysis was carried out to determine the correlation between intestinal microflora and the disease. Any change of gut microbial communities may be an element influencing the central nervous system and persistence of the disease [30]. Our results could provide novel clues for early AD diagnosis and provide possible new targets for the intervention of this disease. The possibility of utilizing microbial patterns for development of diagnostic tools, based on bacterial “biomarkers”, becomes a promising aspect of microbiome research [31].

We have observed significant changes in the composition of the intestinal flora of the AD group. As an indicator of aging [32], the ratio of Firmicutes/Bacteroidetes decreases and the abundance of Bacteroidetes increases significantly in the intestine. In participants with AD, we observed an increase in the phylum of Bacteroidetes, which was reflected by Bacteroides at the OTU and genus level. Bacteroidetes contains a large number of Gram-negative symbiotic bacteria, with lipopolysaccharide (LPS) as their main outer membrane component [33]. LPS is considered as one of the major causes of inflammation [34], which is capable of triggering systemic inflammation and release of pro-inflammatory cytokines after translocation from the gut to systemic circulation, leading to a weakening of the tightness of the intestinal wall, especially after binding to chylomicron particles [35]. These functions contribute to a variety of intestinal and systemic diseases [36], including autoimmune diseases, inflammatory bowel disease, and AD [37]. In vitro studies have also shown that the accumulation of LPS causes the production and release of pro-inflammatory cytokines (including IL-1, IL-6, and TNF-α), promotes the formation of Aβ peptide fibers, activates microglia and triggers neuroinflammation, leading to oxidative stress and promoting the occurrence and development of AD [38].

Interestingly, most genera with significantly reduced abundance in the AD group belong to Firmicutes, which is in agreement with the results of previous studies [39]. Firmicutes can produce different types of SCFAs [40]. SCFAs are under a wide variety of roles in human health, which can provide nutrients to the host and colonic epithelium, and regulate the permeability of the mucosal barrier. SCFAs, especially butyric acid, which weakens chronic inflammation, are thought to promote barrier formation [41, 42]. Butyric acid induces mucin synthesis and strengthens connections between epithelial cells, preventing inflammation and bowel leakage syndrome. Intestinal microbial metabolite butyrate can prevent intestinal inflammation, ulcerative colitis, and colorectal cancer and attenuate microglia-mediated neuroinflammation via regulating the microbe-gut-brain axis [43], which may mediate the treatment of AD. Meanwhile, these reduced abundance genera may also cause host metabolic and immune disorders, such as obesity, diabetes, etc. [44, 45]. Actually, insulin resistance is also believed to increase the risk of AD [46].

Bifidobacteria can protect human health by downregulating the inflammatory factors in the intestinal mucosa [47, 48]. Previous studies have demonstrated that oral Bifidobacteria can inhibit inflammation and immune response gene expression, reduce malondialdehyde, triglycerides, and very-low-density lipoprotein and improve cognitive function. Furthermore, Bifidobacteria in the intestine can metabolize glutamate to produce gamma aminobutyric acid. And regulating the gamma aminobutyric acid level in the cerebral cortex can improve cognitive, emotion, and behavior. We found that the abundance of Bifidobacterium in AD patients was significantly greater than that in the HC group, which is consistent with the study of Shengdi Chen [49]. It is speculated that the reason why the abundance of Bifidobacteria in AD patients in this study was higher than in the HC individuals may be that we enrolled patients with mild AD, whose Bifidobacteria populations were still at the increasing stage. This change may be a potential sign for mild AD diagnosis.

As an emerging probiotic, Akkermansia muciniphila has attracted significant attention. However, beneficial effects of A. muciniphila may depend on the mucin content of the corresponding environment. A. muciniphila was found to regulate the body’s metabolic balance and immune tolerance [50] while Sait et al. found that it is capable of pathogenic activity toward an invertebrate host [51]. Studies have shown that the abundance of A. muciniphila is negatively correlated with clinical indicators of AD and anti-inflammatory cytokines, and positively correlated with medial temporal lobe atrophy [39, 52]. It has also been reported that A. muciniphila can cause infection in mice with normal intestinal microbes [53]. Zhang et al. [54] found that phylum Verrucomicrobia was dramatically increased by 6-fold in 8–12-month-old AD mice relative to age-matched controls. Depending on our current observations, Akkermansia is not always a beneficial bacterium. In the development of AD in the elderly, Akkermansia induces mucin degradation, intestinal mucosa permeability increase, and potential cognitive dysfunction through the brain-gut axis.

The composition of the gut microbiota does not always show univocal features, but may vary depending on dietary habits, disease progression, geographic location and medical treatment [55]. Ling et al. [56] found that Faecalibacterium, Roseburia, Clostridium sensu stricto, etc., decreased significantly in AD patients in southern China, yet traditional beneficial genera, such as Bifidobacterium and Akkermansia, had drastically increased. Similar results have been widely confirmed in patients in southern and western China [39, 57]. These results are consistent with our findings in Shandong province in eastern China. The Italian amyloid-positive patients exhibited a lower abundance of Eubacterium rectale and a higher richness of Escherichia/Shigella in the stools compared to amyloid-negative patients and controls. Furthermore, amyloid-positive patients showed a lower abundance of Bacteroides fragilis than controls [58]. An American study demonstrated that compared to control participants, AD participants exhibited decreased Actinobacteria which were mostly driven by changes in Bifidobacterium.

Compared to studies of gut microbiota in AD patients, more evidence exists on the relationship of gut microbes and their metabolites dysregulation in patients with PD. It is reported that anti-inflammatory-related bacteria, such as Blautia, Coprococcus, and Roseburia, are significantly reduced in the feces of PD patients [59]. Unger et al. have shown that the fecal samples of PD patients have higher quantities of bacteria of the genus Bifidobacterium and Enterobacteriaceae family [60]. Further evidence suggests that Verrucomicrobiaceae (Akkermansia muciniphila), Ruminococcaceae, Lactobacillus, unclassified Firmicutes and Bifidobacterium have increased abundance and decreased levels of Lachnospiraceae in PD patients [61, 62]. Both AD and PD are the most common degenerative diseases of the central nervous system. Although AD is mainly manifested as cognitive impairment and PD is recognized as motor impairment, they share the same pathological changes of α-synuclein. Actually, PD patients also suffer from cognitive dysfunction to some extent at later stages. AD and PD patients demonstrate a high degree of similarity in the changes of intestinal flora, which suggests that intervention therapy of AD could learn from the successful experience of PD treatment, such as employment of probiotics [63 –66] (Bifidobacterium bifidum, Lactobacillus acidophilus, etc.) to intervene in the intestinal flora or fecal microbiota transplantation performance [67].

Probiotics supplementation delayed the decline of neurocognitive function, reduced the risk of AD, and significantly improved the cognitive ability of AD patients [68]. These effects may be due to the restoration of the gut microbiome but are also due to contrast with other AD-related pathological events, such as oxidative stress and insulin resistance [69]. Psychobiotics, which beneficial effects on the brain are bacterially mediated, are currently being investigated as direct or adjunctive therapies for psychiatric and neurodevelopmental disorders and possibly for neurodegenerative disease and may emerge as novel therapeutic options in the clinical management of brain disorders.

Footnotes

ACKNOWLEDGMENTS

This work was supported by the Nature Science Foundation of Shandong Province (ZR2020MC004, ZR2015HL120, ZR2021QH243), the Nature Science Joint Foundation of Shandong Province (ZR2021LSW022), the Science and Technology Project of University of Jinan (XKY2030), the Project of Research Leader Studio of University Funding 20 items in Jinan (2019GXRC058), Clinical Science and Technology Innovation Program of Jinan (202019191), Technological SMEs’ innovation ability improvement project (2021TSGC1279), and Major Subject Projects of Key Disciplines of University of Jinan (1420707).

Authors’ disclosures available online ().

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