Q. Zhao1, A. Baranova2,3, H. Cao2, F. Zhang1,4
1. Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China; 2. School of Systems Biology, George Mason University, Fairfax, 22030, USA; 3. Research Centre for Medical Genetics, Moscow, 115478, Russia; 4. Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
Corresponding Author: Fuquan Zhang, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China, zfqeee@126.com
J Prev Alz Dis 2024;
Published online June 13, 2024, http://dx.doi.org/10.14283/jpad.2024.113
Abstract
BACKGROUND: The preceding evidence indicates a close correlation between imbalances in the gut microbiome and Alzheimer’s disease (AD), yet the direct causal relationship remains unclear. Our objective is to investigate this potential causal connection.
METHODS: We obtained summary results from two significant genome-wide association studies (GWAS) on gut microbiota (the MibioGen consortium and the Dutch Microbiome Project), along with one GWAS summary result for AD. Using a two-sample Mendelian randomization (TSMR) analysis, we examined the potential causal effects of gut microbiota on AD.
RESULTS: Our TSMR analysis revealed that 16 gut bacterial taxa were linked to a reduced risk of AD. These included phylum Tenericutes, classes Bacilli and Clostridia along with its order Clostridiales, family Bacteroidaceae, genus Bacteroides, and species Bifidobacterium bifidum (OR: 0.867~0.971, P ≤ 0.045). Conversely, the presence of 12 taxa correlated with an increased risk of AD. These comprised class Actinobacteria and its family Coriobacteriaceae, as well as class Betaproteobacteria, its order Burkholderiales, and its family Sutterellaceae (OR: 1.042~1.140, P ≤ 0.035).
CONCLUSION: Our research uncovered evidence suggesting certain gut bacterial species might play a causal role in AD risk, providing a fresh angle for AD treatment strategies.
Key words: Mendelian randomization, gut microbiome, Alzheimer’s disease, brain-gut axis.
Introduction
Alzheimer’s disease (AD) refers to the specific onset and course of age-related cognitive and functional decline (1). AD is the most common cause of dementia, accounting for 50% to 75% of cases. It stands as one of the most expensive, deadly, and burdensome diseases of this century, exerting a significant impact on individuals and society alike (2). The number of patients is expected to sharply increase by 2050, surpassing 115 million (3). The defining pathologies of AD are amyloid plaques and neurofibrillary tangles (4). AD is a complex, multifactorial disease caused by genetic and environmental factors, alone or in interaction, ultimately leading to the premature death of nerve cells (5). Besides aging, the most apparent pathogenic factor, epidemiological studies have also suggested that heredity and family history play roles as risk factors for AD. It is currently believed that about 70% of the risk of AD can be attributed to genetic factors (6).
Recent cross-sectional studies have demonstrated that gut microbial composition may serve as an indicator of preclinical AD (7). Moreover, numerous observational studies have highlighted a strong correlation between gut microbiota and AD development, suggesting that restoring healthy gut microbiota could potentially inhibit or ameliorate AD symptoms and progression (8, 9). The human gut microbiota encompasses several phyla, with Firmicutes (including Lactobacilli), Bacteroides, Actinobacteria (containing Bifidobacteria), and Proteobacteria being the predominant ones, accounting for approximately 90% of the gut microbiota. Other phyla present in smaller proportions include Fusobacteria and Verrucobacteria (10). Often referred to as our «second brain,» the gut houses millions of nerve cells, which collectively form an extensive network known as the enteric nervous system. Primarily connected through the vagus nerve, this network forms the gut-brain axis, through which the gut microbiota can influence various complex behaviors associated with hereditary neurological disorders (11, 12). Dysregulation of the gut microbiota has been implicated in the pathogenesis of AD, and the existence of the gut-brain axis prompts further investigation into whether the gut microbiota plays a causative role in AD (13).
Mendelian randomization (MR) is a causal inference method that uses genetic variation as an instrumental variable (IV) for assumed risk factors. It uses summary statistics from genome-wide association studies (GWAS) to infer the association between exposure and outcome (14). This analytical approach is now widely used to infer causality from a genetic perspective (15-18). In this study, we used the two-sample MR (TSMR) analysis to explore the causal effects of gut microbiota on AD (19).
Methods
GWAS summary datasets
The GWAS summary results utilized in this analysis were sourced from publicly available data. For the AD outcome, the dataset comprised 86,531 cases and 676,386 controls (N = 762,917) (20). Summary data on the gut microbiota were gathered from two primary sources: the Dutch Microbiome Project (Dutch cohort) and the international consortium MibioGen (MibioGen cohort). The Dutch cohort (21) primarily stems from the Dutch Microbiome Project, which investigated the composition and function of the gut microbiome in 8,208 individuals. Excluding 15 unknown families and genera of gut microbial taxa, we specifically utilized GWAS data for 207 taxa, omitting the relevant metabolic pathway sections. MibioGen cohort (22) comprises GWAS summary statistics from 18,340 participants, encompassing a total of 212 taxa across 35 families, 20 orders, 16 classes, 9 phyla, and 131 genera. All participants were of European descent, and ethical approval was obtained for all original studies.
TSMR analysis
We conducted a TSMR analysis to investigate the relationship of the gut microbiome on AD. This analysis utilized three complementary methods integrated into the R package «TwoSampleMR» (version 0.5.6) (19): inverse variance weighted (IVW), weighted median, and MR-Egger. These methods have distinct assumptions regarding horizontal pleiotropy. MR analysis requires fulfilling three main assumptions about each instrumental variable (IV): 1) It is closely related to exposure; 2) It is not related to any confounding factors that affect the exposure-outcome association; 3) It does not influence outcomes (except by association with exposure) (23). The IVW model was our primary TSMR approach (24), assuming zero intercepts and providing consistent causality estimates through fixed-effects meta-analysis. The other two models, weighted median and MR-Egger were utilized as complementary methods for assessment of sensitivity. The MR-Egger model assumes that pleiotropic effects are independent and applies weighted linear regression of outcome coefficients to exposure coefficients, using intercepts of MR-Egger regression to assess mean-level pleiotropy (24). When the MR-Egger intercepts are significantly different from zero, IVs are not all effective. Heterogeneity was assessed using both I2 statistics and Cochran’s Q test (25). When I2 < 0.25 and P > 0.05 criteria are satisfied, there is no heterogeneity in the included IVs. If I2 > 0.25 and P < 0.05, a random effects model is prescribed for subsequent TSMR analysis. Finally, we performed a leave-one-out (LOO) sensitivity analysis and excluded IVs one by one to test whether our MR results were robust. A causal effect of the gut microbiome on AD was considered present if the IVW yielded a significance level of P < 0.05.
In the TSMR analysis, single-nucleotide polymorphisms (SNPs) meeting genome-wide significance (P < 5 × 10-8) were chosen as IVs and further filtered using a clumping r2 cutoff of 0.001 within a 10 Mb window, utilizing data from the 1000 Genomes Project Phase 3 (EUR). If the number of IVs fell below 10, a somewhat more lenient threshold of 1×10-5 was applied to select IVs. During TSMR analysis, we removed SNPs not present in the outcome dataset and those with palindromic alleles and intermediate frequencies. We harmonized each pair of exposure and outcome datasets by aligning the effect alleles between them.
Results
TSMR analysis
Our TSMR analysis revealed that the gut microbiota has positive or negative causal effects on AD, and the microbiota identified in the two different gut microbiota datasets differed.
TSMR results from Dutch cohort suggest that class Bacilli, family Bacteroidaceae, and species Clostridium asparagiforme, and Bifidobacterium bifidum reduce AD risk (OR: 0.941~0.971, P ≤ 0.04), but class Betaproteobacteria, orders Burkholderiales, and Coriobacteriales, families Sutterellaceae, and Coriobacteriaceae, and species Veillonella unclassified increase AD risk (OR: 1.042~1.120, P ≤ 0.035) (Table 1, and Figure 1). MibioGen cohort suggests that phylum Tenericutes, classes Mollicutes, and Clostridia, order Clostridiales, family Bacteroidaceae, and genera Ruminiclostridium9, Bacteroides, LachnospiraceaeUCG004, Anaerotruncus, Intestinimonas, Clostridiuminnocuumgroup, Eggerthella, and Butyrivibrio (OR: 0.867~0.954, P ≤ 0.045) were associated with a reduced risk of AD, but classes Negativicutes, and Actinobacteria, orders Pasteurellales, and Selenomonadales, and families Pasteurellaceae, and Lactobacillaceae (OR: 1.072~1.140, P ≤ 0.018) were associated with an increased risk of AD (Table 2, and Figure 1). It is worth noting that the TSMR analysis results of family Bacteroidaceae in both Dutch and MibioGen cohorts indicate a significant anti-AD effect.
Table 1. Causal effects of the gut microbiome (Dutch cohort) on AD
AD: Alzheimer’s disease; CI: confidence interval; OR: odds ratio; P: P value.
Table 2. Causal effects of the gut microbiome (MibioGen cohort) on AD
AD: Alzheimer’s disease; CI: confidence interval; OR: odds ratio; P: P value.
Figure 1. Causal effects of the gut microbiome on AD
AD: Alzheimer’s disease; CI: confidence interval; Dutch: gut microbiota data from the Dutch Microbiome Project; MibioGen: gut microbiota data from the international consortium MibioGen; OR: odds ratio.
MR sensitivity analysis showed that the directions of causal effect estimates across the set of applied techniques were largely the same (Supplementary Table 1-2). No directional pleiotropy was detected in the results of the MR-Egger model (P > 0.05). The Cochran’Q test and the I2 statistics showed no heterogeneity between most of the effect estimates, with a few showing heterogeneity, including that for phylum Tenericutes, classes Mollicutes and Actinobacteria, order Coriobacteriales, and families Coriobacteriaceae and Lactobacillaceae (Supplementary Table 1-2). The robustness of some results was confirmed by the LOO sensitivity analysis, including those for phylum Tenericutes, classes Betaproteobacteria, Mollicutes, Negativicutes, and Clostridia, orders Selenomonadales and Burkholderiales, family Lactobacillaceae, genus Butyrivibrio, and species Clostridium asparagiforme. For other datasets, the LOO analysis suggests that single or multiple SNPs with potential to influence the causal effect; therefore, these results need to be interpreted with caution (Supplementary Figure 1-3).
Discussion
Some amyloid fibrils produced by gut microbiota can breach the intestinal epithelium and the blood-brain barrier, exerting various effects on amyloid-beta deposition in the brain and potentially triggering the onset of AD. Additionally, the bioactive metabolites they generate can traverse the blood-brain barrier, influencing cognition either directly or indirectly through immune, neuroendocrine, or vagal mechanisms (26). Furthermore, the gut microbiota may impact AD pathogenesis by disrupting tau regulation. Dysregulation of gut microbiota could lead to compromised microglial activity, thereby potentially exacerbating AD progression. Furthermore, its dysregulation might contribute to AD development by influencing oxidative stress levels in the central nervous system (13).
Our study indicates that certain gut bacterial taxa have either positive or negative causal effects on AD. Among these, the majority of gut bacterial taxa that decrease the risk of AD belong to the classes Bacilli and Clostridia, while others such as classes Actinobacteria increase the risk of AD.
A new exopolysaccharide (EPS) was isolated and identified from Bacillus amyloliquefaciens RK3. Purified EPS has an anti-AD effect in a mouse model (27). Aging and nerve cell degeneration are important risk factors for the development of AD (28). Bacillus subtilis demonstrates proficiencies in quorum-sensing peptide synthesis and gut-associated biofilm formation. A study reported that Bacillus subtilis significantly delayed these two harmful processes in the Caenorhabditis elegans AD model (29). Moreover, EPS from Bacillus maritimus MSM1 shows promise as an anti-AD material in an in vitro model (30). Interestingly, treatment with Clostridium butyricum attenuates microglial-mediated neuroinflammation in a mouse model by regulating the metabolite butyrate-mediated gut microbiota-gut-brain axis to prevent AD (31). The researchers found that subjects with a higher abundance of Actinobacteria were 1.16 times more likely to develop AD compared with the healthy control group (32). Furthermore, the evaluation of bacterial infections in brain tissue from AD patients and non-AD control groups revealed that Aggregatibacter actinomycetemcomitans (Actinobacteria) may cause AD by upregulating tau phosphorylation to trigger inflammation and neurotoxicity (33).
Previously, in three MR studies, Blautia, which is genetically driven by the host, was found to have an increased protective effect on AD risk (OR: 0.88) (34); The genus Allisonella (OR: 1.235), genus Lachnospiraceae FCS020 group (OR: 1.374) significantly increased the risk of AD, while the family Defluviitaleaceae (OR: 0.771), and order Bacillales (OR: 0.786) significantly decreased the risk of AD (35); Betaproteobacteria, Deltaproteobacteria, Desulfovibrionales, Desulfovibrionaceae and Eubacterium hallii group were suggestively associated with the risk of AD (36). Additionally, two other MR studies partially overlap with our findings. Their results also show the causal effect of Actinobacteria at the class level (OR: 1.027) and Lactobacillaceae at family level (OR: 1.027) on higher risk of AD, while Ruminiclostridium9 at genus level (OR: 0.969) on lower risk of AD (37); Higher abundances of Actinobacteria at the class level (OR: 1.210) were found to be positively associated with an elevated risk of AD (38). Discordant results between these studies may be attributed to the small study sample size of previous studies as well as sample heterogeneity.
TSMR uses genetic variation as an instrumental variable for identifying and quantifying causal associations. Since gamete formation follows the Mendelian law of inheritance stating that parental alleles are randomly assigned to each offspring, genetic variation is not affected by traditional confounding factors such as age, exposure to drugs or other environmental factors, socioeconomic status, learned behavior, etc. As each set of genetic variants is inherited from parents and present at birth, then remains stable throughout life, its associations with various outcomes are truly causal. That is why MR is capable of overcoming both problems, confounding and reverse causation, which interfere with the interpretation of the observations made in traditional epidemiological studies (39). Furthermore, we utilized two extensive datasets on intestinal microbial composition with minimal overlap and a large dataset on AD to enhance the statistical power of genetic discovery. However, our study does have certain limitations. MR analyses may suffer from biases stemming from multiple effects, thus we evaluated hypotheses using various models. While we exclusively examined genetic factors for both diseases, it’s important to note that gut microbiota can be influenced by environmental factors like dietary habits or health status, and gene-environment correlation may introduce biased associations between genotypes and phenotypes. Caution is warranted in interpreting the results due to the differences in the taxonomic hierarchy of bacterial groups from the two gut microbiota datasets, and possible bias introduced by the different populations where the two datasets were derived. Additionally, our analysis was confined to individuals of European ancestry, hence, our findings may not be generalizable to other populations.
Conclusion
Our study suggests that 12 gut bacterial taxa increase the risk of AD, whereas 16 exhibit protective effects against AD. These positive and negative causal relationships bolster the prospect of leveraging gut microbe composition for the treatment and prevention of AD in the future.
Ethics approval and consent to participate: Ethical approval was obtained in all original studies.
Consent for publication: Not applicable.
Availability of data and materials: All data generated or analyzed during this study are included in this published article and its supplementary information files.
Competing interests: The authors declare that they have no competing interests.
Funding: None.
Authors’ contributions: FZ: Conceptualization; Investigation; Data Curation; Supervision; Project Administration. QZ: Methodology; Validation; Formal Analysis; Writing – Original Draft; Writing – Review & Editing; Visualization. AB: Validation; Formal Analysis; Writing – Review & Editing. HC: Validation; Formal Analysis; Writing – Review & Editing. All authors contributed to the revision of the manuscript. All authors approved the final version.
Acknowledgments: The authors thank all investigators and participants from the groups for sharing these data.
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