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Cited by 167Gut microbiota and healthOral microbiology and periodontitis researchGenetics, Aging, and Longevity in Model Organisms

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Human Skin, Oral, and Gut Microbiomes Predict Chronological Age

Open access
Huang, Shi, Haiminen, Niina, Carrieri, Anna-Paola, Hu, Rebecca, Jiang, Lingjing, Parida, Laxmi, Russell, Baylee, Allaband, Celeste, Zarrinpar, Amir, Vázquez-Baeza, Yoshiki, Belda-Ferre, Pedro, Zhou, Hongwei, Kim, Ho-Cheol, Swafford, Austin D., Knight, Rob, Xu, Zhenjiang Zech·2020·mSystems
DOI 10.1128/msystems.00630-19PMC PMC7018528
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Abstract

Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person’s age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site’s microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases.

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Crossref · 10.1128/msystems.00630-19

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Figures

FIG 1 The distinct capability for age prediction from gut (A), oral (B), and skin (C) microbiomes. Spline fit to the data is also shown (blue curve). Although the skewed age distribution in the skin or oral microbiota data set may decrease the accuracy of age prediction for the older adults, it will not affect the conclusions about the relative abilities of different human microbiomes to predict age. Prediction performances at increasing numbers of microbial species were obtained by retraining the random forest classifier on the top-ranking features (ASVs), shown in terms of mean absolute error (MAE) from gut (D), oral (E), and skin (F) microbiota identified with previous random forest models trained in different cohorts. Data are from Qiita studies 11757, 10317, 550, 1841, 1774, 2010, 2024, 2202, 11052, and 10052.
FIG 1 The distinct capability for age prediction from gut (A), oral (B), and skin (C) microbiomes. Spline fit to the data is also shown (blue curve). Although the skewed age distribution in the skin or oral microbiota data set may decrease the accuracy of age prediction for the older adults, it will not affect the conclusions about the relative abilities of different human microbiomes to predict age. Prediction performances at increasing numbers of microbial species were obtained by retraining the random forest classifier on the top-ranking features (ASVs), shown in terms of mean absolute error (MAE) from gut (D), oral (E), and skin (F) microbiota identified with previous random forest models trained in different cohorts. Data are from Qiita studies 11757, 10317, 550, 1841, 1774, 2010, 2024, 2202, 11052, and 10052.CC BY · PubMed Central
FIG 2 The skin microbiota age prediction model can be applied across forehead and hand microbiota. (A) The microbiota age of hand (orange) or forehead (blue) as calculated by a random forest model trained on the hand (upper scatterplots) or forehead (lower scatterplots) subsets; lines indicate spline fit. (B) The prediction accuracy of age regression models dependent on skin body sites and their cross-applications compared to random permutations. The vertical lines indicate the prediction accuracy (mean absolute error) of age models trained in forehead (orange) or hand (blue) sites and their testing on the other site, while the gray histograms show the MAE distribution in 1,000 permutations of age values in either training or testing data. (C) Cross-prediction matrix reporting prediction performances as MAE values obtained using a random forest model on ASV relative abundances. Matrix values refer to the MAE values obtained by training the regressor on the data set of the corresponding row and applying it to the data set of the corresponding column. The prediction accuracies between sexes are higher than those between body sites.
FIG 2 The skin microbiota age prediction model can be applied across forehead and hand microbiota. (A) The microbiota age of hand (orange) or forehead (blue) as calculated by a random forest model trained on the hand (upper scatterplots) or forehead (lower scatterplots) subsets; lines indicate spline fit. (B) The prediction accuracy of age regression models dependent on skin body sites and their cross-applications compared to random permutations. The vertical lines indicate the prediction accuracy (mean absolute error) of age models trained in forehead (orange) or hand (blue) sites and their testing on the other site, while the gray histograms show the MAE distribution in 1,000 permutations of age values in either training or testing data. (C) Cross-prediction matrix reporting prediction performances as MAE values obtained using a random forest model on ASV relative abundances. Matrix values refer to the MAE values obtained by training the regressor on the data set of the corresponding row and applying it to the data set of the corresponding column. The prediction accuracies between sexes are higher than those between body sites.CC BY · PubMed Central

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  1. 10.1128/mSystems.00031-18 · DOI · Paper page

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Metadata

Authors
Huang, S., Haiminen, N., Carrieri, A.-P., Hu, R., Jiang, L., Parida, L., Russell, B., Allaband, C., Zarrinpar, A., Vázquez-Baeza, Y., Belda-Ferre, P., Zhou, H., Kim, H.-C., Swafford, A. D., Knight, R., & Xu, Z. Z.
Affiliations
  • University of California San Diego (US)
  • IBM Research - Thomas J. Watson Research Center (US)
  • IBM (United Kingdom) (GB)
  • Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, California, USA
  • Zhujiang Hospital (CN)
  • IBM Research - Almaden (US)
  • Nanchang University (CN)
Corresponding author
  • Knight, Rob
  • Xu, Zhenjiang Zech
ORCID
Venue
mSystems
Publisher
American Society for Microbiology
Published
2020-02-11
Volume
5
Issue
1
ISSN
2379-5077 (ISSN-L)
DOI
10.1128/msystems.00630-19
PMC
PMC7018528
OpenAlex
W3006148598
Cited by
167
References
27
Citations by year
2026: 27 · 2025: 22 · 2024: 35 · 2023: 24 · 2022: 27 · 2021: 24 · 2020: 7
FWCI
6.65
Type
journal article
Language
en
Open access
Licence
Locations
  • mSystems (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Institutes of Health (KL2TR000099, UL1TR001442, T32OD017863, DK120515)
  • National Heart, Lung, and Blood Institute (R01HL148801)
  • National Institute of Mental Health (R21MH117780)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R03DK114536, DK120515, K08DK102902, P30DK120515, P30DK063491)
  • National Center for Advancing Translational Sciences
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
chronological age estimation, skin microbiome, oral microbiota, gut microbiota, aging, age-related diseases

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