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[Preprint]. 2024 Sep 5:2024.09.04.24313004.

doi: 10.1101/2024.09.04.24313004.

Gut Microbiome Compositional and Functional Features Associate with Alzheimer's Disease Pathology

Jea Woo Kang¹, Lora A Khatib^{2

3}, Margo B Heston¹, Amanda H Dilmore², Jennifer S Labus^{4

5

6}, Yuetiva Deming¹, Leyla Schimmel⁷, Colette Blach⁸, Daniel McDonald², Antonio Gonzalez², MacKenzie Bryant², Karenina Sanders², Ara Schwartz², Tyler K Ulland^{1

9}, Sterling C Johnson¹, Sanjay Asthana¹, Cynthia M Carlsson¹, Nathaniel A Chin¹, Kaj Blennow¹⁰, Henrik Zetterberg^{10

11

12

13

14

1}, Federico E Rey¹⁵; Alzheimer Gut Microbiome Project Consortium; Rima Kaddurah-Daouk^{7

16

17}, Rob Knight^{2

18

19

20

21}, Barbara B Bendlin^{1

22}

Affiliations

¹ Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA Address: 600 Highland Ave, J5/1 Mezzanine, Madison, WI, USA 53792.
² Department of Pediatrics, University of California San Diego, La Jolla, California, USA Address: 9461 Gilman Dr, La Jolla, CA, USA 92093.
³ Neurosciences Graduate Program, University of California San Diego, La Jolla, California, USA Address: 9500 Gilman Dr, La Jolla, CA, USA 92093.
⁴ Integrative Biostatistics and Bioinformatics Core (IBBC) at the Goodman-Luskin Microbiome Center Address: 42-210 CHS, Los Angeles, CA, USA 90095.
⁵ G. Oppenheimer Center for Neurobiology of Stress and Resilience Address: 10833 Le Conte Ave, Los Angeles, CA, USA 90095.
⁶ UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA Address: 100 Medical Plaza, Los Angeles, CA, USA 90095.
⁷ Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA Address: 905 W Main St, Durham, NC, USA 27701.
⁸ Duke Molecular Physiology Institute, Duke University, Durham, NC, USA Address: 300 N Duke St, Durham, NC, USA 27701.
⁹ Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA Address: 1685 Highland Ave, Madison, WI, USA 53705.
¹⁰ Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Address: Blå stråket 15, vån 3 SU/Sahlgrenska 413 45 Göteborg, Sweden.
¹¹ Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden Address: Blå stråket 5, 413 45 Göteborg, Sweden.
¹² Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK Address: Queen Square, London WC1N 3BG, United Kingdom.
¹³ UK Dementia Research Institute at UCL, London, UK Address: 6th Floor, Maple House, Tottenham Ct Rd, London W1T 7NF, United Kingdom.
¹⁴ Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China Address: Units 1501-1502, 1512-1518, 15/F, Building 17W, Hong Kong Science Park, Shatin, N.T., Hong Kong.
¹⁵ Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA Address: 1550 Linden Dr, Madison, WI, USA 53706.
¹⁶ Duke Institute of Brain Sciences, Duke University, Durham, NC, USA Address: 308 Research Dr, Durham, NC, USA 27710.
¹⁷ Department of Medicine, Duke University, Durham, NC, USA Address: 40 Duke Medicine Circle, 124 Davison Building, Durham, NC, USA 27710.
¹⁸ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA Address: Franklin Antonio Hall, Jacobs School of Engineering, 9500 Gilman Dr, La Jolla, CA, USA 92093.
¹⁹ Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA Address: 3235 Voigt Dr, La Jolla, CA, USA 92093.
²⁰ Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA Address: 3234 Matthews Ln, La Jolla, CA, USA 92093.
²¹ Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Address: 3223 Voigt Dr, La Jolla, CA, USA 92093.
²² Wisconsin Alzheimer's Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA Address: 610 Walnut Street, 9th Floor, Madison, WI, USA 53726.

PMID: 39281749
PMCID: PMC11398448
DOI: 10.1101/2024.09.04.24313004

Gut Microbiome Compositional and Functional Features Associate with Alzheimer's Disease Pathology

Jea Woo Kang et al. medRxiv. 2024.

[Preprint]. 2024 Sep 5:2024.09.04.24313004.

doi: 10.1101/2024.09.04.24313004.

Authors

Affiliations

¹ Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA Address: 600 Highland Ave, J5/1 Mezzanine, Madison, WI, USA 53792.
² Department of Pediatrics, University of California San Diego, La Jolla, California, USA Address: 9461 Gilman Dr, La Jolla, CA, USA 92093.
³ Neurosciences Graduate Program, University of California San Diego, La Jolla, California, USA Address: 9500 Gilman Dr, La Jolla, CA, USA 92093.
⁴ Integrative Biostatistics and Bioinformatics Core (IBBC) at the Goodman-Luskin Microbiome Center Address: 42-210 CHS, Los Angeles, CA, USA 90095.
⁵ G. Oppenheimer Center for Neurobiology of Stress and Resilience Address: 10833 Le Conte Ave, Los Angeles, CA, USA 90095.
⁶ UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA Address: 100 Medical Plaza, Los Angeles, CA, USA 90095.
⁷ Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA Address: 905 W Main St, Durham, NC, USA 27701.
⁸ Duke Molecular Physiology Institute, Duke University, Durham, NC, USA Address: 300 N Duke St, Durham, NC, USA 27701.
⁹ Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA Address: 1685 Highland Ave, Madison, WI, USA 53705.
¹⁰ Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Address: Blå stråket 15, vån 3 SU/Sahlgrenska 413 45 Göteborg, Sweden.
¹¹ Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden Address: Blå stråket 5, 413 45 Göteborg, Sweden.
¹² Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK Address: Queen Square, London WC1N 3BG, United Kingdom.
¹³ UK Dementia Research Institute at UCL, London, UK Address: 6th Floor, Maple House, Tottenham Ct Rd, London W1T 7NF, United Kingdom.
¹⁴ Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China Address: Units 1501-1502, 1512-1518, 15/F, Building 17W, Hong Kong Science Park, Shatin, N.T., Hong Kong.
¹⁵ Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA Address: 1550 Linden Dr, Madison, WI, USA 53706.
¹⁶ Duke Institute of Brain Sciences, Duke University, Durham, NC, USA Address: 308 Research Dr, Durham, NC, USA 27710.
¹⁷ Department of Medicine, Duke University, Durham, NC, USA Address: 40 Duke Medicine Circle, 124 Davison Building, Durham, NC, USA 27710.
¹⁸ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA Address: Franklin Antonio Hall, Jacobs School of Engineering, 9500 Gilman Dr, La Jolla, CA, USA 92093.
¹⁹ Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA Address: 3235 Voigt Dr, La Jolla, CA, USA 92093.
²⁰ Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA Address: 3234 Matthews Ln, La Jolla, CA, USA 92093.
²¹ Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Address: 3223 Voigt Dr, La Jolla, CA, USA 92093.
²² Wisconsin Alzheimer's Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA Address: 610 Walnut Street, 9th Floor, Madison, WI, USA 53726.

PMID: 39281749
PMCID: PMC11398448
DOI: 10.1101/2024.09.04.24313004

Update in

Gut microbiome compositional and functional features associate with Alzheimer's disease pathology.
Kang JW, Khatib LA, Heston MB, Dilmore AH, Labus JS, Deming Y, Schimmel L, Blach C, McDonald D, Gonzalez A, Bryant M, Ulland TK, Johnson SC, Asthana S, Carlsson CM, Chin NA, Blennow K, Zetterberg H, Rey FE; Alzheimer Gut Microbiome Project Consortium; Kaddurah-Daouk R, Knight R, Bendlin BB. Kang JW, et al. Alzheimers Dement. 2025 Jul;21(7):e70417. doi: 10.1002/alz.70417. Alzheimers Dement. 2025. PMID: 40604345 Free PMC article.

Abstract

Background: The gut microbiome is a potentially modifiable factor in Alzheimer's disease (AD); however, understanding of its composition and function regarding AD pathology is limited.

Methods: Shallow-shotgun metagenomic data was used to analyze fecal microbiome from participants enrolled in the Wisconsin Microbiome in Alzheimer's Risk Study, leveraging clinical data and cerebrospinal fluid (CSF) biomarkers. Differential abundance and ordinary least squares regression analyses were performed to find differentially abundant gut microbiome features and their associations with CSF biomarkers of AD and related pathologies.

Results: Gut microbiome composition and function differed between people with AD and cognitively unimpaired individuals. The compositional difference was replicated in an independent cohort. Differentially abundant gut microbiome features were associated with CSF biomarkers of AD and related pathologies.

Discussion: These findings enhance our understanding of alterations in gut microbial composition and function in AD, and suggest that gut microbes and their pathways are linked to AD pathology.

Keywords: Alzheimer’s disease; Biomarkers; Cerebrospinal fluid; Composition; Differential abundance; Function; Gut microbiome; Pathology.

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Conflict of interest statement

Dr. Kaddurah-Daouk in an inventor on a series of patents on use of metabolomics for the diagnosis and treatment of CNS diseases and holds equity in Metabolon Inc., Chymia LLC and PsyProtix. Dr. Rob Knight is a scientific advisory board member, and consultant for BiomeSense, Inc., has equity and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant for DayTwo, and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a cofounder of Micronoma, and has equity and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. Dr. Zetterberg has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Daniel McDonald is a consultant for, and has equity in, BiomeSence, Inc. The terms of this arrangement has been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest polices.

Figures

**Figure 1. Alpha and beta diversity metrics across AD groups.**
(A-C) Shannon, Evenness, and Faith’s PD metrics for individuals categorized by clinical diagnosis (CU vs. Dementia-AD). (D-F) Metrics for amyloid status (Negative vs. Positive). (G-I) Metrics across *APOE* ε4 status (Negative vs. Positive). Each box plot is overlaid with individual data points, enhancing visualization of the data distribution within each group. Kruskal-Wallis test was used to determine statistical significance. (J-L) Differences in beta diversity metrics (Bray Curtis, Weighted UniFrac, and Unweighted UniFrac, respectively) for individuals categorized by clinical diagnosis (CU vs. Dementia-AD). (M-O) Metrics for amyloid status (Negative vs. Positive). (P-R) Metrics across *APOE* ε4 status (Negative vs. Positive). Principal coordinates (PC)1 and PC2 axes represent the most variance in data. Each plot is color-coded by the respective group, highlighting the spatial distribution and clustering based on the dissimilarity indices. PERMANOVA was used to determine statistical significance.

**Figure 2. Differential abundance (DA) across AD groups.**
Forest plots illustrating the DA of microbial features associated with AD groups. (A, D, G, and J) Contrasts in the abundance of various bacterial taxa at (A) phylum, (D) family, (G) genus, and (J) species levels between AD dementia and CU. (B, E, H, and K) The differences in abundance at these taxonomic levels between A+ and A− individuals. (C, F, I, and L) The microbial features differentially abundant between *APOE* ε4+ and *APOE* ε4− groups. The x-axes quantify the log ratio of presence between groups, with values above one indicating a higher abundance in the first-mentioned group. Circles denote “Top” features, indicating a positive association with AD groups (dementia diagnosis, amyloid positivity, and *APOE* ε4 positivity), whereas triangles denote “Bottom” features, indicating a negative association. The lines are color-coded by unique phylum as labeled in the legend. DA analysis was conducted using BIRDMAn.

**Figure 3. Comparative analysis of top and bottom features across AD groups.**
Box plots comparing the distribution of log-transformed ratios of differentially abundant microbial species in relation to diagnosis, amyloid status, and *APOE* ε4 status. (A-C) The log-transformed ratios of microbes for diagnosis groups (AD vs CU). (D-F) The log-transformed ratios of amyloid-related microbes (A+ vs A−). (G-I) The log-transformed ratios of *APOE* ε4-related microbes (*APOE* ε4+ vs *APOE* ε4−). Each column compares the CU and AD groups (A, D, and G), A+ and A− groups (B, E, and H), and *APOE* ε4+ and *APOE* ε4− groups (C, F, and I). Each panel includes a Kruskal-Wallis test statistic and associated P value, indicating the statistical significance of the differences observed.

**Figure 4. Venn-diagram of co-occurrence of microbial features across AD groups.**
The diagrams on the left column (A, C, E, and G) depict the Top (positively-associated) differentially abundant features, while those on the right column (B, D, F, and H) show the Bottom (negatively-associated) differentially abundant features. (A and B) Top and Bottom microbial phyla, respectively. These diagrams identify unique and shared phyla associated with each of the three AD groups. (C and D) Top and Bottom microbial families, respectively. These diagrams highlight the family-level microbial differences that correlate with AD diagnosis, amyloid presence, and *APOE* ε4 genotype presence. (E and F) Top and Bottom microbial genera, respectively. These diagrams provide insight into the genus-level microbial composition influenced by the specified AD groups. (G and H) Top and Bottom microbial species, respectively. These diagrams detail the number of species that are unique and shared across the three AD groups. Each diagram contains colored regions representing intersections between the groups: red for dementia, green for amyloid, and blue for *APOE* ε4. The numbers within each segment of the diagrams indicate the count of microbial features unique to or shared between the conditions. Specific microbial features are listed in Table S2.

**Figure 5. Comparison of log-transformed dementia biomarker ratios in CU and AD dementia across two cohorts.**
(A) The results from the MARS cohort. Box plots show the distribution of log-transformed ratios of top dementia biomarkers to bottom dementia biomarkers for CU individuals (light blue) and AD dementia (dark blue) (Kruskal-Wallis = 31.81, P value < .001). (B) The results from a larger validation cohort (n = 448). Box plots present the distribution of log-transformed ratios of top dementia biomarkers to bottom dementia biomarkers found in the MARS cohort for CU individuals (light blue) and people with AD dementia (dark blue) (Kruskal-Wallis = 5.59, P value = .02). Each point represents an individual sample, with the boxes indicating the interquartile range (IQR) and the whiskers extending to 1.5 times the IQR. The horizontal line within each box denotes the median value.

**Figure 6. Differentially abundant microbial pathways between AD and CU.**
(A) The distribution of the log ratios of Top/Bottom pathway features between AD (orange) and CU (blue) was shown in a box plot. Mann–Whitney U test was performed to determine statistical significance. Asterisks indicate a significant difference between AD (4.90) and CU (2.74) groups in the median of the log ratios of Top/Bottom pathway features (P value < .001). (B) A total of 36 differentially abundant features of microbial species and their corresponding pathways between AD and CU were displayed in a forest plot. Circles denote “Top” features, indicating a positive association with AD, whereas triangles denote “Bottom” features, indicating a negative association. The lines are color-coded by unique species and their corresponding pathways. DA analysis was conducted using BIRDMAn. (C) RPCA on the clinical diagnosis group and a biplot of microbiome pathway features and their corresponding species. Each point represents an individual sample color-coded by the respective group, with CU colored in blue and AD colored in orange. Vectors represent the direction (arrows) and magnitude (length) of the contribution of feature variables to the principal components (PCs). Vectors in red indicate Top features and vectors in green indicate Bottom features. PC1 and PC2 axes represent the most variance in data. Statistical analysis on RPCA was performed with PERMANOVA between AD and CU groups.

**Figure 7. Heatmap illustrating the associations between gut microbiome compositional and functional features and CSF biomarkers in AD and related pathologies.**
(A) This heatmap represents the coefficients of regression analysis between the top and bottom 20 gut microbial species linked to dementia and CSF biomarkers in two groups: Top (more abundant in AD, denoted by the pink bar) and Bottom (less abundant in AD, denoted by the green bar). The color scale indicates the strength and direction of the associations, with red representing positive associations and blue representing negative associations. The intensity of the color corresponds to the magnitude of the coefficient. Listed on the left are the gut microbiome species that were identified as more or less abundant in dementia-AD through BIRDMAn. (B) The heatmap depicts the coefficients of regression analysis between the gut microbial pathways and CSF biomarkers. Coefficients are scaled by colors indicating the strength and direction of the associations, with green representing positive associations and pink representing negative associations. The intensity of the color corresponds to the magnitude (strength) of the coefficient. Microbial species and their associated pathway features are listed on the left of the plot and two groups (Top: more abundant in AD, denoted by the light pink bar; and Bottom: less abundant in AD or more abundant in CU, denoted by the light green bar) from DA analysis using BIRDMAn are displayed on the right of the plot. The biomarkers listed along the bottom include amyloid pathology (Aβ₄₂/Aβ₄₀), tau pathophysiology (pTau₁₈₁ and tTau), neurodegeneration (NfL), synaptic dysfunction and injury (neurogranin and α-synuclein), inflammation (IL-6), and glial activation (S100B, GFAP, YKL-40, and sTREM2). Asterisks indicate the level of statistical significance of the associations: ***P < .001, **P < .01, and *P < .05 (uncorrected).

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This is a preprint.

Gut Microbiome Compositional and Functional Features Associate with Alzheimer's Disease Pathology

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Gut Microbiome Compositional and Functional Features Associate with Alzheimer's Disease Pathology

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Conflict of interest statement

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