Evidence map›Paper›PMID 41997961›Full record

ArticleNature communications2026

Oral microbiome signatures predict biological age and host health.

Jia-Jun Zhao, Ming Hu, Siyan Li, Qianqian Wang, Qiufen Mo, Huilin Yu

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Intersections of gastrointestinal dysbiosis of the gut microbiome and aging.World journal of gastrointestinal pathophysiology · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jia-Jun Zhao *State Key Laboratory for Development and Utilization of Forest Food Resources, College of Food and Health, Zhejiang A&F University, Hangzhou, Zhejiang, China.
Ming Hu *Department of Nutrition, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
Siyan Li *State Key Laboratory for Development and Utilization of Forest Food Resources, College of Food and Health, Zhejiang A&F University, Hangzhou, Zhejiang, China.
Qianqian WangState Key Laboratory for Development and Utilization of Forest Food Resources, College of Food and Health, Zhejiang A&F University, Hangzhou, Zhejiang, China.
Qiufen MoState Key Laboratory for Development and Utilization of Forest Food Resources, College of Food and Health, Zhejiang A&F University, Hangzhou, Zhejiang, China.
Huilin YuState Key Laboratory for Development and Utilization of Forest Food Resources, College of Food and Health, Zhejiang A&F University, Hangzhou, Zhejiang, China. yuhl0323@126.com.ORCID http://orcid.org/0000-0001-7635-4276

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying robust, non-invasive biomarkers of biological age is key to preventive medicine. While gut aging clocks exist, the oral microbiome remains underexplored as a quantitative biomarker. Using oral microbiome data from two NHANES cohorts (N = 4,675), we identified 64 age-dependent bacterial genera and developed a machine learning model predicting chronological age, with generalizability in an independent external cohort (N = 1,293). We derived an Oral Microbiome Aging Acceleration (OMAA) Score as the residual of predicted age against chronological age. The OMAA Score independently predicted all-cause mortality (HR = 1.05, P = 0.024) and frailty (OR = 1.05, P = 0.008), correlated with impaired kidney function (lower eGFR: β = -0.066, P = 5.22×10

Indexed as

AgingMicrobiotaMouthAgedBacteriaBiomarkersFemaleHumansMachine LearningMaleNutrition SurveysRisk FactorsBiomarkers

Identifiers

PMID41997961
PMCPMC13347023

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.