Genetic correlations between gut microbiome genera, Alzheimer’s disease diagnosis, and APOE genotypes: a polygenic risk score study

doi:10.21203/rs.3.rs-2292371/v1

Background: A growing body of evidence suggests that dysbiosis of the human gut microbiota is associated with neurodegenerative diseases like Alzheimer’s disease (AD) via neuroinflammatory processes across the microbiota-gut-brain axis (MGBA). The gut microbiota affects brain health through the secretion of toxins and short-chain fatty acids, which modulates gut permeability and numerous immune functions. Observational studies indicate that AD patients have reduced microbiome diversity, which could contribute to the pathogenesis of the disease. Uncovering the genetic basis of microbial abundance and its effect on AD could suggest lifestyle changes that may reduce an individual’s risk for the disease.

Methods: Using the largest genome-wide association study (GWAS) of gut microbiota genera from the MiBioGen consortium, we conducted the polygenic risk score (PRS) analysis with the “best-fit” model implemented in PRSice-2 and determine the genetic correlation between 119 genera and AD in a discovery sample (case/control: 1,278/1,293). We then replicated our findings in an independent sample (case/control: 799/778) and further confirmed the correlation with meta-analysis. Finally, we conducted a linear regression analysis to assess the correlation between the PRSs for the significant genera and the APOE genotypes.

Results: In the discovery sample, 20 gut microbiota genera were initially identified as genetically associated with AD case/control status. Three genera (Eubacterium fissicatena as a protective factor, Collinsella, and Veillonella as a risk factor) were replicated in the replication sample. The meta-analysis confirmed that ten genera have a significant correlation with AD, four of which were significantly associated with the APOErs429358 risk allele in a direction consistent with their protective/risk designation in AD association. Notably, the proinflammatory genus Collinsella, identified as a risk factor for AD, was positively correlated with the APOErs429358 risk allele in both samples.

Conclusion: Host genetic factors influencing the abundance of ten genera are significantly associated with AD, suggesting that these genera may serve as biomarkers and targets for AD treatment and intervention. Our results highlight that proinflammatory gut microbiota might promote AD development through interaction with APOE. Larger datasets and functional studies are required to understand their causal relationships.

Biological sciences/Genetics

Biological sciences/Neuroscience

Alzheimer’s disease

microbiome

polygenic risk scores (PRSs)

genome-wide association studies (GWASs)

neuroinflammation

APOE

dysbiosis

Alzheimer’s disease (AD), the most common form of dementia, is a neurodegenerative disorder characterized by a multitude of pathological and clinical hallmarks such as a progressive decline in cognitive function and the buildup of toxic β-amyloid and tau proteins (1,2). Due to the growing elderly population worldwide, the number of individuals with dementia is projected to reach 150 million globally by the year 2050 (3). Despite this growing burden on world health, the mechanisms underlying the disease pathology are not fully understood, impeding the development of optimally effective treatments (4). Neuroinflammation has emerged as a key feature of AD with mechanistic and treatment implications due to the central role of microglia and inflammation in brain health (5,6). There remains an urgent need to understand the genetic risk factors and pathological basis of neuroinflammation in AD so that individuals with a higher risk can be identified for earlier intervention.

Recently, an association between dysbiosis of the gut microbiome and neuroinflammation has been hypothesized to drive AD. The gut microbiota comprises a complex community of microorganism species that reside in our gastrointestinal ecosystem; alterations in the gut microbiota have been reported to influence not only various gut disorders but also brain disorders such as AD (7,8). The human gut microbiota has been suggested to modulate brain function and behavior via the microbiota-gut-brain axis (MGBA), a bidirectional communication system connecting neural, immune, endocrine, and metabolic pathways (9). Observational studies across multiple countries show reductions in gut microbiota diversity in AD patients compared to cognitively normal controls (10–12). Current research indicates that bacteria populating the gut microbiota are capable of releasing lipopolysaccharide (LPS) and amyloids, which may induce microglial activation in the brain and contribute to the production of proinflammatory cytokines associated with the pathogenesis of AD (13). The secretion of these biomolecules also harms the integrity of the MGBA and blood-brain barrier (BBB), which worsens with increasing dysbiosis (8,14). The composition of the human gut microbiota and risk for AD have been suggested as heritable traits (2,15). Apolipoprotein E ε4 (APOE ε4), the most well-established risk gene for AD, has recently been shown to correlate with microbiome composition in humans and mouse models of AD (16–18). However, few studies have explored the correlation between APOE alleles and microbiome taxa at the human genomic level. In this study, we aim to determine the genetic correlation between the abundance of gut microbial genera and AD diagnosis. We further investigate if gut microbial genera are correlated with APOE ε4 alleles.

One promising approach to exploring this relationship is the use of polygenic risk score (PRS) analyses. PRS is an overall estimate of an individual’s genetic liability for a specific trait. PRSice-2 is designed to calculate the PRS by aggregating and quantifying the effect of many single-nucleotide polymorphisms (SNPs) in the genome weighted by their effect sizes from genome-wide association studies (GWASs) (19). This approach has previously been used to explore the genetic relationship between gut microbial abundance and complex traits like bone mineral density, rheumatoid arthritis, and depression (20–22). In the present study, we used this approach to determine the genetic relationship between 119 microbial genera and AD diagnosis. With the largest GWAS of the human gut microbiota (23), we first conducted PRS analyses in an AD discovery sample to identify the genera genetically correlated with AD. We then verified our results in a replication study and meta-analysis. The correlation between the top genera and the APOE genotypes was further analyzed by linear regression analysis.

3.1 Study design overview

The overall design of our study is shown in Fig. 1. Briefly, we used PRSice-2 (19) to calculate PRSs for individuals from our discovery sample. PRSs were calculated based on the summary statistics for 119 microbial genera from the MiBioGen consortium. The significant association between genera and AD diagnosis was determined when the “best-fit” PRS model had a Bonferroni-corrected p < 0.00042 (0.05/119 = 0.00042). We then replicated the results in an independent sample. We conducted logistic regression analyses between the PRSs of associated genera and AD diagnosis to generate relative odds ratios (OR) for meta-analysis. The multivariate logistic regression model was used to determine if the correlation between the PRSs of the associated genera and AD diagnosis was affected by sex, age, and APOE genotypes. Furthermore, we conducted a linear regression analysis to evaluate the genetic association between the PRSs of ten significant genera and the APOE genotypes of individuals in our discovery and replication samples. This study was approved by our institutional review board (IRB) at the University of Nevada Las Vegas (UNLV).

3.2 Data sources

3.2.1 Microbiome GWAS summary statistics

For our “base” GWAS data, we obtained summary statistics from the MiBioGen consortium initiative (www.mibiogen.org) (23), which is the largest, multi-ethnic genome-wide meta-analysis of the gut microbiome to date (Table 1). Participants (n = 18,340) were recruited in the original participating studies, where 16S rRNA gene sequencing profiles from each individual were utilized to characterize their gut microbiota using SILVA as a reference database (24) with truncation of the taxonomic resolution down to the genus level. In total, 31 loci from host genetic variations were associated with gut microbiota taxa abundance at the genome-wide significant threshold (p < 5.0 x 10^− 8), most of which (n = 24) were at the genus level (23). We chose the effect sizes of the host SNP-microbiota associations (beta) for our analyses. Beta represents how the host genetic loci affect the relative abundance of each microbiome taxa (mbQTLs) (23). In the present study, we limited our analyses to the summary statistics from the 119 microbial genera, as 16S rRNA sequencing correlates more accurately with the functional role of gut microbiota at lower taxonomic levels (25).

Table 1

Information for studies used in our analyses.
Variable	Consortium or Study	PMID	Year	Sample Size	Ethnic Group
Gut microbiota	MiBioGen	33462485	2021	18340	Multi-ethnic
Alzheimer’s Disease	ADc12	19001172	2008	2571	Multi-ethnic
	dbGaP phs000168.v2.p2 NIA/LOAD consent 1 and 2	19001172	2008	1278 cases/1293 controls	Multi-ethnic
	GenADA	17998437	2008	1577	Caucasian
	dbGap phs000219.v1.p1	17998437	2008	799 cases/778 controls	Caucasian
The Multi-ethnic cohorts of the MiBioGen study include 16 European cohorts (n = 13,266), 1 Middle Eastern cohort (n = 481), 1 East Asian cohort (n = 811), 1 American Hispanic/Latin cohort (n = 1,097), 1 African-American cohort (n = 114), and 4 cohorts of multi-ancestry individuals (n = 2,571). The Multi-ethnic cohort of the NIA/LOAD study contains African-American and Caucasian individuals.

3.2.2 AD genotyping data (target data): discovery and replication samples

From dbGaP (https://www.ncbi.nlm.nih.gov/gap/), we requested genotyping data from the National Institute of Aging/Late-onset Alzheimer’s Disease Study (NIA/LOAD) cohort consents 1 and 2 (ADc12) as our discovery sample (26), and the Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease (GenADA) Study as our replication sample (27). In our study, AD cases were considered as any individual with dementia diagnosed with definite, probable, or possible AD at any point in their clinical course, according to the Criteria proposed in 1984 by the National Institute of Neurological and Communicative Disorders and Stroke, and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) (28). Included controls were neurologically evaluated individuals who were age-matched cognitively normal. Unspecified dementia, unconfirmed controls, and controls with other neurological diseases from the original study were removed from this study, resulting in 1,278/1,293 cases/controls in the discovery sample ADc12, and 799/778 cases/controls in the replication sample GenADA. Demographic characteristics of the ADc12 and Gen/ADA samples are listed in Table 2, along with two major APOE SNP genotype information. More detailed descriptions of the data can be found in previous studies (26,27,29).

Table 2

Demographic characteristics of the target data (ADc12 and GenADA) with *APOE* SNP genotyping.
			Discovery Sample (ADc12)
			Cases	Controls	Total	Cases	Controls	Total
Age, mean ± SD			1278	1293	2571	799	778	1577
Age, mean ± SD			76.57 ± 6.71*	70.27 ± 10.29		72.24 ± 8.41**	73.40 ± 7.92
Sex (Male/Female)			443/ 835	471/822	914/1657	339/460	276/502	615/962
APOE SNP Genotype	rs429358	T/T, n(%)	409 (32.0)	847 (65.5)	1256 (48.9)	296 (37.0)	589 (75.7)	885 (56.1)
		T/C, n(%)	682 (53.4)	414 (32.0)	1096 (42.6)	397 (49.7)	177 (22.8)	574 (36.4)
		C/C, n(%)	187 (14.6)	32 (2.5)	219 (8.5)	106 (13.3)	12 (1.5)	118 (7.5)
		C/C, n(%)	1206 (94.4)	1128 (87.2)	2334 (90.8)	739 (92.5)	661 (85.0)	1400 (88.8)
	rs7412	C/T, n(%)	71 (5.6)	159 (12.3)	230 (8.9)	60 (7.5)	113 (14.5)	173 (11.0)
		T/T, n(%)	1 (0.0)	6 (5.1)	7 (0.3)	0 (0.0)	4 (0.5)	4 (0.3)
Both discovery (ADc12) and replication (GenADA) samples were downloaded from dbGaP. Age, mean ± SD. For each case, their “Age” was the age at onset (AAO). For each control, their “Age” was the age at examination (AAE). p = 5.97 x 10^− 71 when AAO compared to AAE in the discovery sample. *p = 4.94 x 10^− 3 when AAO compared to AAE in the replication sample.

To maximize genetic variants, we conducted imputation for both discovery and replication samples at the Michigan Imputation Server (minimac4) (https://imputationserver.sph.umich.edu) (30). The 1,000 Genome Phase 3v5 was used as a reference. After the imputation, standard quality control was performed with the Plink command (--maf 0.01 --hwe 1e-6 --geno 0.01 --mind 0.01) (31,32). The final datasets were composed of 2,571 individuals with 9,997,692 SNPs in the discovery sample, and 1,577 individuals with 8,914,585 SNPs in the replication sample.

3.3 Polygenic Risk Score (PRS) analyses via PRSice-2 software

PRSice-2 software was designed to evaluate the genetic correlation between different traits when provided GWAS summary statistics data from a base trait (base data) and genotyping data from a target trait (target data) (19). PRS is a numerical approximation of an individual’s genetic liability weighed by the allele number and effect size from a set of genetic variants estimated from GWAS summary statistics (33). This approach is called polygenic risk score (PRS) analyses, by which investigators can assess an individual’s genetic risk to a trait of interest and the genetic correlation between different traits (33). As mentioned above, our base GWAS data were from the 119 microbiome genera in the MiBioGen consortium study (23). Our target samples were the discovery sample ADc12 (26) and the replication sample GenADA (27). In our study, we first calculated PRSs for the 119 microbiome genera in the discovery sample to determine the genetic correlation between the PRSs and AD diagnosis. PRSs for each genus were calculated from the PRSice-2 program for the “best-fit” PRS model. For this purpose, a range of p-value thresholds was set from 5 x 10^− 8 to 1 with an incremental interval of 0.00005 (--interval 0.00005 --lower 5e-08) with LD clumping (--clump-kb 250kb --clump-p 1.0 --clump-r2 0.1) (19). To validate the significantly associated genera from the discovery sample, we conducted PRS analyses for the significant genera in an independent sample.

To evaluate the overall association from our discovery and replication samples, we used z-score normalized the "best-fit" PRSs of the top 20 genera and performed a simple logistic regression analysis between the z-score normalized PRS’s from the “best-fit” threshold and AD diagnosis. We then conducted a meta-analysis on both samples using the R package metafor v3.8-1 (34). A forest plot was generated to visualize the overall AD protective and risk effects across the significant genera using the “forestplot” R package (35). We also created boxplots to compare the PRSs for those significant genera between AD patients and controls in the discovery sample. Box plots were generated by using the R program ggplot2 v.0.4.0 (36). Multivariate logistic regression analysis was conducted by adding sex, age, and APOE genotypes (rs429358, rs7412) as covariates.

3.4 Linear regression analyses between APOE genotypes and PRSs for the ten significant genera

Two APOE SNPs, rs429358 minor allele C and rs7412 major allele C, are well-known risk factors for AD (37,38). We performed linear regression analyses to determine the genetic correlation between the PRSs of the ten significant genera and the two APOE SNPs. The association was further evaluated by linear regression analysis adjusted for sex and age. Boxplots were created using the R packages ggplot2 (v3.3.6), ggpubr (v0.4.0), and stats (v0.1.0) (35,36,39).

3.5 Statistical Analyses

The p-value threshold for significant association in the discovery sample and meta-analysis was set as p < 4.20 x 10^− 4 (0.05/119 with Bonferroni correction) (40). For the replication sample, one-side significant level p < 0.005 (0.1/20 with Bonferroni correction) was used. For all other statistical analyses, such as linear regression analysis, the ANOVA test, and Wilcoxon signed-rank test, p < 0.05 was considered significant. The Wilcoxon signed-rank method was used to test two interrelated samples to see if their sample proportion ranks differed (41). The ANOVA method was utilized to test the association between the PRSs for genera and APOE genotypes (42).

4.1 PRSs for ten microbiome genera were significantly associated with AD diagnosis.

We first calculated the PRSs for the 119 microbiome genera for each individual from the discovery sample (ADc12) using the PRSice-2 program (19). We found that 20 out of the 119 genera were significantly associated with AD diagnosis using the “best-fit” model (p < 4.20 x 10^− 4) (Table 3). Among these 20 significant genera, six were identified as likely risk genera and 14 potentially protective genera for AD diagnosis. Risk genera included Alistipes and Bacteroides from the Bacteroidetes phylum, Lachnospira and Veillonella from the Firmicutes phylum, and Collinsella and Sutterella from the Actinobacteria and Pseudomonadota phyla, respectively. The most significant risk genus was Bacteroides (R² = 0.011, p = 3.32 x 10^− 6) at the “best-fit” p-value threshold of 0.179 which included 71,984 SNPs. For protective genera, eleven out of fourteen were from the Firmicutes phylum (Anaerostipes, Candidatus Soleaferrea, Catenibacterium, Eisenbergiella, Eubacterium coprostanoligenes group, Eubacterium fissicatena group, Eubacterium nodatum group, Intestinibacter, Lachnospiraceae UCG-008, Oscillibacter, and Roseburia), two were from Actinobacteria (Adlercreutzia and Gordonibacter), and one was from Bacteroidetes (Prevotella 9). The most significant protective genus was Intestinibacter (R² = 0.015, p = 1.01 x 10^− 7) at the “best-fit” p-value threshold of 0.190 with 70,292 SNPs.

Table 3

Association between significant microbiome genera and AD diagnosis from “best-fit” PRSice-2 model.
Genera	Samples	Threshold	R²	P	Coeff.	SE	SNP#	SampleSize
Adlercreutzia*	ADc12	0.3705	0.0068	3.57E-04	-1870.66	524.00	101798	2571
	GenADA	0.0020	0.0036	3.91E-02	-224.26	108.71	1684	1577
Alistipes	ADc12	0.1676	0.0080	1.04E-04	2581.83	665.28	67574	2571
	GenADA	0.3246	0.0023	1.03E-01	-2953.44	1810.61	99785	1577
Anaerostipes	ADc12	0.0032	0.0078	1.18E-04	-469.15	121.86	2834	2571
	GenADA	0.2989	0.0054	1.18E-02	4134.46	1641.01	96076	1577
Bacteroides*	ADc12	0.1792	0.0114	3.32E-06	3930.96	845.35	71984	2571
	GenADA	0.0009	0.0037	3.68E-02	221.55	106.10	922	1577
Candidatus Soleaferrea*	ADc12	0.0002	0.0090	3.48E-05	-71.29	17.22	177	2571
	GenADA	0.0199	0.0020	1.28E-01	-404.97	265.96	12431	1577
Catenibacterium	ADc12	0.3839	0.0082	8.54E-05	-1276.68	324.95	71434	2571
	GenADA	0.0831	0.0080	2.11E-03	1068.34	347.56	25728	1577
Collinsella	ADc12	0.0002	0.0073	1.78E-04	125.63	33.51	230	2571
	GenADA	0.0003	0.0143	4.36E-05	229.15	56.06	335	1577
Eisenbergiella*	ADc12	0.1049	0.0082	9.64E-05	-858.39	220.13	42727	2571
	GenADA	0.0439	0.0066	5.28E-03	-1131.73	405.70	22002	1577
Eubacterium coprostanoligenes	ADc12	0.1020	0.0086	7.19E-05	-1085.22	273.37	48130	2571
	GenADA	0.0101	0.0038	3.36E-02	707.65	333.01	7483	1577
Eubacterium fissicatena	ADc12	0.0404	0.0070	3.00E-04	-418.31	115.71	19567	2571
	GenADA	0.0008	0.0090	1.17E-03	-142.85	44.01	663	1577
Eubacterium nodatum*	ADc12	0.4353	0.0072	2.52E-04	-1240.65	338.95	95579	2571
	GenADA	0.1259	0.0025	8.82E-02	-805.51	472.45	42817	1577
Gordonibacter*	ADc12	0.0330	0.0089	5.57E-05	-254.45	63.13	16821	2571
	GenADA	0.0116	0.0039	3.30E-02	-351.65	164.98	7135	1577
Intestinibacter*	ADc12	0.1903	0.0154	1.01E-07	-3014.37	566.17	70292	2571
	GenADA	0.0024	0.0024	9.22E-02	-246.05	146.10	2152	1577
Lachnospira*	ADc12	0.0034	0.0067	3.30E-04	530.79	147.83	2997	2571
	GenADA	0.0004	0.0021	1.11E-01	113.71	71.44	439	1577
LachnospiraceaeUCG008	ADc12	0.0784	0.0081	1.08E-04	-776.50	200.53	35344	2571
	GenADA	0.0041	0.0017	1.57E-01	197.29	139.40	3224	1577
Oscillibacter	ADc12	0.0124	0.0068	3.08E-04	-630.36	174.67	8305	2571
	GenADA	0.0051	0.0027	7.55E-02	302.62	170.24	3884	1577
Prevotella9*	ADc12	0.0084	0.0075	1.57E-04	-556.34	147.21	6609	2571
	GenADA	0.0008	0.0031	5.47E-02	-147.32	76.68	819	1577
Roseburia*	ADc12	0.2061	0.0100	1.35E-05	-3564.78	819.18	78446	2571
	GenADA	0.0037	0.0027	7.64E-02	-366.88	207.02	3135	1577
Sutterella	ADc12	0.4928	0.0071	2.53E-04	3594.16	982.20	124985	2571
	GenADA	0.0002	0.0009	3.08E-01	-34.75	34.10	156	1577
Veillonella	ADc12	0.0070	0.0081	8.83E-05	539.94	137.72	5406	2571
	GenADA	0.0021	0.0085	1.56E-03	369.43	116.78	1843	1577
The association from the “best-fit” threshold was generated from PRSice-2 with a range of p-value thresholds from 5 x 10^− 8 to 1 and incremental interval of 5 x 10^− 5; R²: Variance explained by the PRS model; P: p-value of model fit for the association; Coeff: Coefficient of the model; SE: standard error; # of SNPs: Number of SNPs included in the model at the specified threshold. Genera in bold are three genera identified to have a genetically significant association with AD in both discovery and replicate samples. *: indicated ten genera that have the same direction in both discovery and replicate samples. Seven genera, originally identified to be significantly associated with AD in the discovery sample, did not survive the replicate analysis due to the opposite direction in the replicate sample.

To replicate our findings for the top 20 genera in the discovery sample, we conducted a replication analysis in an independent sample. Two risk-associated genera (Collinsella and Veillonella) and one protective genus (Eubacterium fissicatena), were significantly associated with AD diagnosis in the replication sample (p < 0.005). Ten other genera did not reach significance but had the same effect direction as in the discovery sample. To evaluate the overall association, we conducted a meta-analysis with both the discovery and replication samples. As a result, a total of ten genera, including the three genera identified from the replication sample, were significantly associated with AD diagnosis (See Table S1). The meta-analysis results from the top ten genera were visualized in the Forest plot (Fig. 2).

Six genera—Adlercreutzia, Eubacterium nodatum group, Eisenbergiella, Eubacterium fissicatena group, Gordonibacter, and Prevotella9—were identified as protective, while four genera—Collinsella, Bacteroides, Lachnospira, and Veillonella—were identified as a risk factor for AD. From the meta-analysis, Eisenbergiella was identified as the strongest protective factor for AD with p = 1.39 x 10^− 6 and OR = 0.857 (95% CI 0.805–0.912), and Collinsella was identified as the strongest risk factor for AD p = 4.47 x 10^− 8 and OR = 1.188 (95% CI 1.117–1.264). The meta-analysis also found three genera to have a suggestive association (0.00042 < p < 0.05) with AD diagnosis, of which all were potential protective factors (Intestinibacter, Candidatus Soleaferrea, and Roseburia) (See Table S1). In addition, seven genera—Alistipes, Anaerostipes, Catenibacterium, Eubacterium coprostanoligenes group, Lachnospiraceae UCG-008, Oscillibacter, and Sutterella—originally identified to be associated with AD, did not show any association due to the opposite effects in the replication sample. Next, a multivariate logistic regression analysis, including sex, age, and two APOE genotypes (rs429358 and rs7412) as covariates, was used to determine any confounding effects on the association between the ten significant genera and AD diagnosis. As shown in Supplementary Table S2, the ten significant genera remained significantly associated with AD diagnosis in the discovery sample (p < 0.05), which suggested that the genetic association between PRSs for the top ten genera and AD diagnosis was independent of age, sex, and APOE genotypes. As expected, age and APOE were strongly associated with AD in the multivariate logistic regression analysis. Specifically, age and rs429358 minor allele C were risk factors because of their positive correlation with AD diagnosis, while rs7412 minor allele T was a protective factor due to its negative correlation with AD diagnosis. However, sex did not show any association with AD in our study.

To better visualize the difference of PRSs between AD cases and controls, we constructed a boxplot along with the Wilcoxon signed-rank test (41) for the ten significant genera in the discovery sample. As compared to cognitively normal controls, Fig. 3A showed that AD patients had lower PRSs for the six likely protective genera (Adlercreutzia, Eubacterium nodatum group, Eisenbergiella, Eubacterium fissicatena group, Gordonibacter, and Prevotella9). On the other hand, Fig. 3B showed AD patients had higher PRSs for the four risk genera (Bacteroides, Collinsella, Lachnospira, and Veillonella). These results were consistent with our PRS model and logistic regression analysis between PRSs and AD diagnosis.

4.2 Correlation between PRSs for the top ten significant genera and APOE genotypes

APOE is a well-known genetic risk for AD (37,38). Depending on the alleles of two SNPs rs429358 and rs7412, the human APOE gene has three alleles (ε2, ε3, and ε4) (38). The ε4 allele is the most influential risk factor for AD beyond age; a single ε4 allele increases one’s risk by 3 to 4-fold compared with the ε2 or ε3 allele (37). Several studies have been conducted for the potential links between the APOE genotypes (rs429358 and rs7412) and the gut microbiota (16–18), but not at the whole genomic level. For this reason, we sought to determine whether there was a genetic link between the PRSs for the ten significant genera and the APOE genotypes. Linear regression analyses were performed between the z-score normalized "best-fit" PRSs for the top ten significant genera and APOE minor alleles at rs429358 and rs7412. The meta-analysis showed that four out of ten significant genera were correlated with APOE rs429358 risk allele C (p < 0.05) (Table 4). Notably, Collinsella was the only genus that was positively correlated with AD diagnosis and APOE risk allele C at rs429358 in both discovery and replication samples (p < 0.05) (Table 3 and Table 4). PRSs for three genera—Adlercreutzia, Eubacterium nodatum, and Prevotella9—identified negatively correlated with AD diagnosis showed negative correlation with APOE risk allele C at rs429358.

Table 4

Association between PRSs for ten significant gut microbiota genera and *APOE* rs429358.
Trait	Samples	Coeff.	SE	z-value	P	OR(95%CI)
Adlercreutzia	ADc12	-0.051	0.031	-1.643	0.1006	0.95(0.89–1.01)
	GenADA	-0.093	0.040	-2.309	0.0211	0.91(0.84–0.99)
	meta-analysis	-0.067	0.025	-2.712	0.0067	0.94(0.89–0.98)
Bacteroides	ADc12	0.019	0.031	0.608	0.5434	1.02(0.96–1.08)
	GenADA	0.014	0.040	0.350	0.7263	1.01(0.94–1.10)
	meta-analysis	0.017	0.025	0.695	0.4871	1.02(0.97–1.07)
Collinsella	ADc12	0.137	0.031	4.413	1.06E-5	1.15(1.08–1.22)
	GenADA	0.118	0.040	2.930	0.0034	1.12(1.04–1.22)
	meta-analysis	0.130	0.025	5.283	1.27E-7	1.14(1.09–1.19)
Eisenbergiella	ADc12	-0.105	0.031	-3.382	0.0007	0.90(0.85–0.96)
	GenADA	0.007	0.040	0.180	0.8575	1.01(0.93–1.09)
	meta-analysis	-0.052	0.056	-0.924	0.3553	0.95(0.85–1.06)
Eubacterium Fissicatena	ADc12	-0.111	0.031	-3.557	0.0004	0.90(0.84–0.95)
	GenADA	-0.010	0.040	-0.252	0.8011	0.99(0.91–1.07)
	meta-analysis	-0.064	0.050	-1.270	0.2042	0.94(0.85–1.04)
Eubacterium Nodatum	ADc12	-0.097	0.031	-3.130	0.0018	0.91(0.85–0.96)
	GenADA	-0.043	0.040	-1.080	0.2804	0.96(0.88–1.04)
	meta-analysis	-0.077	0.026	-2.912	0.0036	0.93(0.88–0.98)
Gordonibacter	ADc12	-0.108	0.031	-3.460	0.0005	0.90(0.84–0.95)
	GenADA	-0.014	0.040	-0.352	0.7248	0.99(0.91–1.07)
	meta-analysis	-0.064	0.047	-1.383	0.1668	0.94(0.86–1.03)
Lachnospira	ADc12	0.047	0.031	1.524	0.1277	1.05(0.99–1.11)
	GenADA	0.047	0.040	1.169	0.2425	1.05(0.97–1.13)
	meta-analysis	0.047	0.025	1.921	0.0548	1.05(1.00-1.10)
Prevotella9	ADc12	-0.046	0.031	-1.486	0.1373	0.95(0.90–1.01)
	GenADA	-0.074	0.040	-1.836	0.0666	0.93(0.86–1.01)
	meta-analysis	-0.057	0.025	-2.299	0.0215	0.94(0.90–0.99)
Veillonella	ADc12	0.092	0.031	2.946	0.0032	1.10(1.03–1.17)
	GenADA	-0.027	0.040	-0.664	0.5071	0.97(0.90–1.05)
	meta-analysis	0.035	0.059	0.596	0.5513	1.04(0.92–1.16)
Genera in bold are four genera identified to have genetically significant correlation with APOE rs429358 minor allele C in the meta-analysis (p < 0.05). Collinsella was the only genus that showed significant correlation in both discovery and replication samples.

To illustrate the correlations between PRSs for Collinsella and APOE risk allele C at rs429358, we constructed a boxplot along with ANOVA analysis (36,43). As shown in Fig. 4, a positive correlation between PRSs for Collinsella and APOE risk allele C at rs429358 was found in the discovery sample (p = 2.1 x 10^− 5). This positive correlation indicated that a genetic factor determining Collinsella abundance was more likely to occur in individuals with APOE minor allele C (CC and TC) as compared to individuals with two T alleles (TT) at rs429358.

Overall, our results showed that Collinsella was a risk factor for AD diagnosis and showed a positive correlation with APOE risk allele C at rs429358. On the other hand, three genera identified as protective factors (Eubacterium nodatum group, Adlercreutzia, and Prevotella9) for AD diagnosis showed a negative correlation with APOE risk allele C at rs429358 (Table 4). These associations indicate that certain microbial genera and APOE may contribute to disease modulation in some similar biological pathways, synergizing in disease risk or protective effects. The associations between PRSs for the four genera and the APOE rs429358 risk allele were independent of sex and age, as the results remained significant after adjustment for these cofactors (Supplementary Table S3). For the APOE genotype at rs7412, we did not see any significant correlation with the PRSs for the top 10 genera.

4.3 Association between microbiome abundance and APOE genotypes

To further investigate APOE genotypes association with the abundance of all the gut microbiota genera, we retrieved summary statistics for the two APOE SNPs rs429358 and rs7412 directly from the 119 genera GWAS summary statistics in the MiBioGen consortium study. As shown in Table 5, rs429358 was marginally correlated with the abundance of ten genera, and rs7412 was marginally associated with the abundance of eight genera (p < 0.05). Together, these findings indicate that the APOE genotypes may have some impact on the microbiome abundance at the genus level and that the association may synergistically contribute to the risk for human diseases such as AD. Our results open the door for future studies to explore the role of the interaction between APOE and the gut microbiota and find a new target for treatment in human diseases.

Table 5

List of gut microbiome genera that were nominally associated with *APOE* SNPs rs429358 and rs7412.
rsID (CHR:BP:Effect Allele)	Microbiome Genera	Beta	SE	SZ	P	N	# cohorts
rs429358 (19:45411941:C)	Bacteroides	-0.044	0.015	-2.881	0.0040	18173	23
	Butyricimonas	-0.044	0.020	-2.241	0.0250	10657	23
	Dorea	0.030	0.015	2.127	0.0334	17494	23
	Eubacterium coprostanoligenes	0.033	0.015	2.120	0.0340	17261	23
	Faecalibacterium	-0.035	0.015	-2.188	0.0287	17960	23
	Olsenella	0.064	0.033	1.993	0.0462	3721	13
	Parasutterella	0.040	0.019	2.216	0.0267	11291	23
	Senegalimassilia	0.050	0.024	2.207	0.0273	6888	21
	Veillonella	0.044	0.021	2.032	0.0421	9194	23
rs7412 (19:45412079:T)	Butyricicoccus	0.048	0.021	2.468	0.0136	15637	20
	Collinsella	0.052	0.023	2.214	0.0268	12811	19
	Coprococcus3	0.050	0.022	2.344	0.0191	14323	20
	Eubacterium hallii	0.053	0.021	2.453	0.0142	14846	20
	Lachnospiraceae UCG001	-0.071	0.027	-2.534	0.0113	9357	20
	Olsenella	-0.103	0.043	-2.346	0.0190	3721	13
	Ruminococcaceae UCG004	0.080	0.028	2.901	0.0037	9049	20
	Senegalimassilia	0.082	0.032	2.584	0.0098	6508	18
SNP data was extracted from GWAS summary statistics of human gut microbiome abundance conducted among 24 multi-ethnic cohorts in the MiBioGen consortium (www.mibiogen.org) (24). rsID (CHR:BP:Effect Allele): the rs-number of single nucleotide polymorphisms, chromosome number (CHR), base pair (BP), with the effect allele (genome assembly GRCh37/hg19). Microbiome: only contains the genus level with significant abundance associated with APOE two SNPs. Beta: Beta coefficient. SE: Standard Error. SZ: Weighted sum of z-scores. P: p-value (p < 0.05 was considered as nominal association between the microbiome genera and APOE). N: sample count. # Cohorts: Number of cohorts involved.

The microbiota is a complex ecosystem that comprises more than 100 trillion symbiotic microbial cells in the human body, of which 95% inhabit the human gut (44). The bacteria from phylum Firmicutes and Bacteroidetes form a significant proportion (90%) of the adult gut microbiota, while Actinobacteria composes the rest (45). Recently, significant evidence has shown that the gut microbiota influences normal systemic physiological homeostasis and that dysbiosis of gut microbiota may contribute to the pathogenesis of brain diseases, including AD. The gut microbiota interacts with the central nervous system (CNS) across the MGBA via microbial components, metabolic products, and neural stimulation. In this study, we leveraged the extensive GWAS data to study the genetic correlation between gut microbiota genera and AD diagnosis. PRSs for 20 genera were initially found significantly associated with AD in the discovery sample, three of which were replicated in the independent replication study. A further meta-analysis from our discovery and replication samples identified a strong genetic association between ten gut microbiota genera and AD diagnosis. Six genera were negatively associated with AD diagnosis and four genera were positively correlated with AD diagnosis. “Negative association” means that the abundance of these genera is lower in AD patients as compared to normal controls. Thus, PRSs for such genera are regarded as a protective factor for the disease. Similarly, “positive association” means that the abundance of those genera is higher in AD cases as compared to normal controls, indicating their PRSs would be seen as a risk factor for the disease. Genera identified as a protective factor were primarily from the Firmicutes phylum (Eubacterium nodatum group, Eisenbergiella, and Eubacterium fissicatena group) as well as from Actinobacteria (Adlercreutzia, Gordonibacter) and Bacteroidetes (Prevotella9). Positively correlated, or risk-associated genera were from phyla including Firmicutes (Lachnospira and Veillonella), Actinobacteria (Collinsella), and Bacteroidetes (Bacteroides).

In the discovery sample, the correlation of the ten significant genera remained statistically significant after being adjusted for sex, age, and two APOE SNPs (rs429358 and rs7412), suggesting that the genetic correlation between the ten genera and AD diagnosis was independent of age, sex, or APOE genotypes. In addition, we found that four of the ten significant genera showed a strong correlation with the APOE rs429358 risk allele C via linear regression analysis. Interestingly, the genera showing a positive correlation with APOE rs429358 risk allele C tend to have a positive (risk) association with AD, while the genera showing a negative correlation with APOE rs429358 risk allele C have a negative (protective) association with AD.

In our analyses, Collinsella from the phylum Actinobacteria was identified as a risk factor for AD in both the discovery and replication samples. Collinsella was also positively correlated with APOE rs429358 risk allele C in both samples. The abundance of Collinsella in the gut has been previously associated with rheumatoid arthritis, atherosclerosis, and Type-2 diabetes (46–48). Importantly, an increased abundance of this genus has also been observed in AD transgenic mice and AD patients (49,50). Our findings provide evidence at the human genomic level of a connection between Collinsella and AD that supports previous observational studies. At the molecular level, this connection is possibly driven by the pro-inflammatory effects of the Collinsella genus. In a human intestinal epithelial cell line, the presence of Collinsella increased the expression of inflammatory cytokines (IL-17A) and chemokines (CXCL1, CXCL5). Collinsella also increased gut permeability by reducing the expression of tight-junction proteins (51). Furthermore, the strong association between Collinsella and APOE rs429358 risk allele C in our study may provide new insight into the pathogenesis of AD. For example, a study found that Collinsella correlates with higher serum levels of total cholesterol and low-density lipoprotein (LDL) cholesterol in healthy adults (52), which may be correlated with the interaction between Collinsella and APOE. Functional studies that further explore the relationship between Collinsella, lipid metabolism, and inflammatory signals would help to elucidate how their interaction influences AD and other diseases.

Three genera of the Firmicutes phylum—Eubacterium nodatum group, Eisenbergiella, and Eubacterium fissicatena group—had a negative association with AD diagnosis. Eisenbergiella, Eubacterium fissicatena group, and Eubacterium nodatum group are known to contain species that metabolize the short-chain fatty acid (SCFA) butyrate from dietary carbohydrates (53–56). Butyrate is a major SCFA metabolite in the colon that might be a critical mediator of the colonic inflammatory response. Alongside its anti-inflammatory properties, butyrate is also essential in maintaining tight junctions that prevent dysbiotic gut permeability (57,58). Despite their production of butyrate, several studies have identified Eisenbergiella and Eubacterium nodatum group as microbial features associated with neurodegenerative diseases. A notable study of patients with AD and vascular dementia found that the gut abundance of these genera could be used to discriminate severe dementia patients against those with mild or moderate dementia (59). High serum levels of the IgG antibody against oral Eubacterium nodatum were associated with lower AD risk in another study (60). This suggests that oral and gut populations of the same microbial taxa may have different etiologies with the same disease, however, our base data covers only the gut abundance of microbiota. Nevertheless, we are the first to report a protective association between genetically-predicted Eisenbergiella, Eubacterium nodatum group, and Eubacterium fissicatena group abundance with AD, but more studies are needed to understand how these three genera may interact with the pathology of AD.

In addition, we identified two Firmicutes genera as risk factors for AD (Lachnospira and Veillonella), with Veillonella being validated in the replication sample. Recently, it was reported that AD patients have an abundance of Veillonella in their oral microbiome (61). In the gut, it has been shown that an overabundance of species like V.parvula promotes intestinal inflammation by activating macrophages via the lipopolysaccharide-Toll-like receptor 4 (LPS-TLR4) pathway (62). The dual association of oral and gut abundance of Veillonella with disease points to this genus as a target for therapeutics and a potential bridge between conditions like gut inflammation and periodontitis with AD. On the other hand, gut Lachnospira and Veillonella species have also been identified as beneficial or commensal to gut health, such as Lachnospira being protective against Crohn’s disease, or Veillonella interacting with Streptococcus species to modulate immune responses in the small intestine (63,64). In an observational study from a Chinese group, patients with AD had decreased Lachnospira at the genus level compared with healthy controls (65). However, this may reflect national differences in diet or the genetics of microbial abundance, as our study uses mostly Caucasian subjects from the United States in our discovery and replication samples.

The Bacteroidetes genera, Prevotella9 and Bacteroides, were identified as protective and risk factors, respectively, in our meta-analysis. There is a complex relationship between Prevotella and Bacteroides abundance and intestinal diseases (66). In humans, Prevotella is more common in populations with plant-based and high-carbohydrate diets (67). Conversely, Bacteroides is more abundant in those consuming “western” diets high in protein and fat (68). One major study showed that Prevotella was higher in individuals with greater adherence to Mediterranean diets, which is thought to be protective against neurodegenerative diseases (69–71). The protective effects of Prevotella abundance may come from the positive dietary effects on the genus. Our association of higher genetically-predicted Bacteroides abundance with AD risk supports the findings of previous observational studies (11,72,73). Bacteroides species are capable of secreting LPS as an endotoxic biomolecule, which has been implicated in pathological endothelial dysfunction of the gut and can induce neuroinflammation in microglia cells (74–76). However, it should be noted that a meta-analysis including Chinese studies found no risk association between Bacteroides and AD (12), which may again reflect national differences in diet and microbial abundance.

Two protective genera, Gordonibacter and Adlercreutzia, are from the Actinobacteria phylum. These genera tend to produce metabolites beneficial to mitochondrial function, namely Urolithin-A (UA) and Equol (77,78). UA is an anti-inflammatory compound that enhances mitophagy, the removal of dysfunctional mitochondria in a cell (79). Impaired mitophagy is part of the pathogenesis of AD, as well as general aging processes, making UA and Gordonibacter species promising targets for therapeutics against the disease (80). Equol is an estrogen-like compound that reduces microglial inflammation when stimulated by LPS and downregulates genes in neurons related to apoptosis (81). The positive effects of these bacterial metabolites could drive the protective association of Gordonibacter and Adlercreutzia abundance with AD that we found in this study.

The strengths of our study include the use of the largest available GWAS of gut microbiota taxa to date, and the identification of multiple genera genetically associated with AD in the discovery and replication samples after a strict Bonferroni correction. The use of logistic regression analysis alongside our initial PRS analyses allowed us to adjust for potential confounders, such as sex, age, and APOE alleles, and further validate that the association was independent of those confounders. Additionally, we are the first to study the genetic correlation between the gut microbiota and the APOE gene at the human genomic level.

There are several limitations to our study. First, the sample size for our base microbiome GWAS may not be large enough to truly cover the effect size of the host genetic variants, even though the MiBioGen study has a larger sample size compared to other microbiomes GWASs. Because of this, we may not have enough power to detect some of the associations in our meta-analysis that were considered significant in the discovery sample. Future studies with larger sample sizes would be more capable of drawing solid conclusions about the genetic connection between gut microbiota and AD. As the microbiome is also highly influenced by lifestyle and environmental factors, the lack of information on these confounders in our base and target data prevents the subtyping of patients. Second, our genotyping data was specifically drawn from individuals in the United States, which limits the generalization of our conclusions when applied to other nations or ethnic groups. More diverse genotyping datasets would enable us to capture the variability in risk for AD across different ethnicities. In addition, covariates included in our logistic regression models are limited by what was available from our genotyping data, which means we were unable to account for clinical biomarkers of AD outside of age, sex, and APOE genotypes. Third, the 16S rRNA sequencing used to generate genetic associations in the “base” GWAS only provides taxa resolution from the phylum to the genus level. Fully understanding the role of bacterial taxa that may drive the pathology of AD will require methods that can capture the abundance of individual species and their mechanistic impact on the MGBA.

Overall, our novel findings provide new insights into the interplay of the gut microbiota on AD. Genetic associations with the abundance of certain bacterial genera inhabiting the gut correlate with AD diagnosis in risk and protective directions. Risk-associated genera, such as Collinsella, have been previously tied to neuroinflammatory processes across the MGBA, while protection-associated genera like Gordonibacter are known to secrete metabolites that promote gut and brain health. PRSs for four genera were further identified as significant associations with the APOE genotype at rs429358. Our results advance the understanding of how gut dysbiosis may play a role in the pathology of AD. Future investigations with larger cohorts of AD patients from different ethnic backgrounds and more powerful microbiome GWASs are needed to better understand these genetic associations. Functional studies are also required to establish causality between particular gut microbiota and AD pathology.

AD: Alzheimer’s Disease

APOE: Apolipoprotein-E

BBB: Blood-Brain-Barrier

MGBA: Microbiota-Gut-Brain-Axis

LPS: Lipopolysaccharide

TLR4: Toll-Like-Receptor 4

SNP: Single Nucleotide Polymorphism

GWAS: Genome-Wide Association Study

OR: Odds Ratio

IRB: Institutional Review Board

UNLV: University of Nevada Las Vegas

NIA/LOAD: National Institute of Aging/Late-onset Alzheimer’s Disease Study

GenADA: Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease

NINCDS-ADRDA: National Institute of Neurological and Communicative Disorders and Stroke, and the Alzheimer's Disease and Related Disorders Association

LDL: Low-Density Lipoprotein

SCFA: Short Chain Fatty Acid

UA: Urolithin-A

mbQTL: Microbial Quantitative Trait Loci

Ethical Approval and Consent to Participate

We are using the existing data for this study. The informed consent was obtained from all subjects and/or their legal guardian(s) in the original studies. Contributing studies received ethical approval from their respective institutional review boards (IRB). This study was performed per the Declaration of Helsinki and approved by the IRB at the University of Nevada-Las Vegas (IRB #00002305, 10/12/2021).

Consent for Publication

Not applicable.

Availability of data and materials

The Full GWAS summary statistics of mbQTLs analyzed during this study are available at https://mibiogen.gcc.rug.nl/. Genotyping data for the NIA/LOAD and GenADA cohorts are available at https://www.ncbi.nlm.nih.gov/gap/ with their respective accession numbers from Table 1. All data generated in this study are included in the manuscript and Supplementary Tables 1-3.

Competing Interests

The authors declare that they have no competing interests.

Funding

This research was funded to J.C. in part by NIH grant P20GM121325, NIH grant P20GM121325-02S1, and NIH grant P20GM121325_03S2.

Author contribution

J.C. was responsible for the conception, sample request, data analyses, and writing the manuscript. D.C., Y.L., and M.J.C. were responsible for data analyses and writing the manuscript. M.L.Z., J.M.C, and J.D. were responsible for data analyses. X.C., E.O., J.L.C, and J.E. were responsible for writing, critiquing, and revising the manuscript. All authors have read and agreed to the content of the manuscript before sending it to the journal for publication.

Acknowledgements

We thank the patients for their participation in the MiBioGen consortium, NIA/LOAD ADc12 Cohort, and GenADA studies, and the original investigators who conducted these studies and made the data available.

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No competing interests reported.

SupplementaryTables13.xlsx

Genetic correlations between gut microbiome genera, Alzheimer’s disease diagnosis, and APOE genotypes: a polygenic risk score study

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Materials And Methods

3.1 Study design overview

3.2 Data sources

3.2.1 Microbiome GWAS summary statistics

3.2.2 AD genotyping data (target data): discovery and replication samples

3.3 Polygenic Risk Score (PRS) analyses via PRSice-2 software

3.4 Linear regression analyses between APOE genotypes and PRSs for the ten significant genera

3.5 Statistical Analyses

Results

4.1 PRSs for ten microbiome genera were significantly associated with AD diagnosis.

4.2 Correlation between PRSs for the top ten significant genera and APOE genotypes

4.3 Association between microbiome abundance and APOE genotypes

Discussion

Limitations

Conclusions

Abbreviations

Declarations