Human gut microbiome aging clock based on taxonomic profiling and deep learning
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Abstract
Graphical Abstract Abstract The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous analyses of gut microflora revealed associations between specific microbes and host health and disease status, genotype and diet. Here, we developed a method of predicting biological age of the host based on the microbiological profiles of gut microbiota using a curated dataset of 1,165 healthy individuals (3,663 microbiome samples). Our predictive model, a human microbiome clock, has an architecture of a deep neural network and achieves the accuracy of 3.94 years mean absolute error in cross-validation. The performance of the deep microbiome clock was also evaluated on several additional populations. We further introduce a platform for biological interpretation of individual microbial features used in age models, which relies on permutation feature importance and accumulated local effects. This approach has allowed us to define two lists of 95 intestinal biomarkers of human aging. We further show that this list can be reduced to 39 taxa that convey the most information on their host’s aging. Overall, we show that (a) microbiological profiles can be used to predict human age; and (b) microbial features selected by models are age-related.
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In a study using metagenomic profiles from over 4,000 healthy people aged 18-90, an algorithm predicted individuals' ages within approximately six years of their actual age.
“In a study that used metagenomic profiles from over 4,000 healthy people aged 18–90, the authors were able to construct an algorithm that predicted individuals’ ages within approximately six years of their actual age (98).”
From Signalling cognition: The gut microbiota and hypothalamic-pituitary-adrenal axis (2023) · cites this paper as [98]
The Galkin et al. microbiome clock had similar accuracy and variance to clocks trained on other omics modalities, and in a separate cohort, it predicted individuals with type 1 diabetes mellitus to be significantly older.
“Their clock had similar accuracy and variance to clocks trained on other omics modalities, and in a separate cohort, the microbiome clock predicted individuals with type 1 diabetes mellitus to be significantly older.”
From Measuring biological age using omics data (2022) · cites this paper as [137]
Galkin et al. predicted chronological age from gut microbiome taxonomic composition using deep neural network models.
“Galkin et al.137 predicted chronological age from gut microbiome taxonomic composition using deep neural network models.”
From Measuring biological age using omics data (2022) · cites this paper as [137]
The abundance of certain well-studied beneficial microorganisms, such as Akkermansia muciniphila, had a larger impact on the Galkin et al. model age predictions than the majority of other microorganisms detected.
“They also observed that the abundance of certain well-studied beneficial microorganisms, such as Akkermansia muciniphila, had a larger impact on the model age predictions than the majority of other microorganisms detected.”
From Measuring biological age using omics data (2022) · cites this paper as [137]
Galkin et al. compared multiple methods, settling on a simple neural network architecture to predict age and detect accelerated microbiome ageing in patients with diabetes mellitus.
“the authors compared multiple methods, settling on a simple neural network architecture to predict age and detect accelerated microbiome ageing in patients with diabetes mellitus.”
From Measuring biological age using omics data (2022) · cites this paper as [137]
Galkin et al. made the first attempt to develop an ageing clock based on microbiome sequencing data.
“The first attempt to develop an ageing clock based on microbiome sequencing data”
From Measuring biological age using omics data (2022) · cites this paper as [137]
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- Authors
- Galkin, F., Mamoshina, P., Aliper, A., Lane, E., Moskalev, V., Gladyshev, V. N., & Zhavoronkov, A.
- Affiliations
- University of Liverpool (GB)
- Hong Kong Science and Technology Parks Corporation (HK)
- Brigham and Women's Hospital (US)
- Buck Institute for Research on Aging (US)
- Corresponding author
- Zhavoronkov, Alex
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 23
- Issue
- 6
- Pages
- 101199
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC7298543
- OpenAlex
- W3028405346
- Cited by
- 292
- References
- 67
- Citations by year
- 2026: 40 · 2025: 40 · 2024: 55 · 2023: 43 · 2022: 34 · 2021: 35 · 2020: 33 · 2019: 12
- FWCI
- 11.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- American Society for Microbiology (20200075127)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Keywords
- gut microbiota, aging clocks, age prediction, deep neural network, aging biomarkers, metagenomics, microbial succession
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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