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The Actinobacteria-to-Proteobacteria (A/P) Ratio: A Novel Oral Microbial Marker for Metabolic Syndrome
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DOI：10.1016/j.identj.2026.109499.png)
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Abstract
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Introduction and aims: Oral microbial biomarkers for metabolic syndrome (MetS), in contrast to its well-characterized relationship with the gut microbiome, have not been well defined. This study aims to systematically identify composite oral microbial indices by analyzing a large-scale cohort. Methods: We analyzed oral rinse samples from 3911 U.S. individuals (aged >= 14 years), integrating 16S rRNA sequencing with metabolic phenotyping. We used principal coordinates analysis and permutational multivariate analysis of variance to assess /3-diversity separation, linear discriminant analysis effect size analysis to identify microbial signatures, multivariable-adjusted logistic regression with restricted cubic splines (RCS) to evaluate MetS associations, Cox proportional hazards models to analyze all-cause mortality in the MetS population, and stratified analyses to test for effect modification by age, BMI and medication use. Results: MetS participants exhibited higher BMI and were older than non-MetS controls (P < .001). Oral microbiome /3-diversity diverged significantly in MetS despite stable a-diversity, marked by enrichment of Actinobacteria and depletion of Proteobacteria (P < .001). In adjusted models, the highest tertile of Actinobacteria was associated with 42% higher odds of MetS (aOR = 1.42, 95% CI: 1.06-1.91), whereas the highest tertile of Proteobacteria was associated with 42% lower odds (aOR = 0.58, 95% CI: 0.43-0.79). Capturing this dysbiotic shift, the composite Actinobacteria-to-Proteobacteria (A/P) ratio demonstrated a superior predictive value than either phylum alone (aOR = 1.58, 95% CI: 1.08-2.30). RCS analysis demonstrated a linear association of the A/P ratio with MetS and all-cause mortality among individuals with MetS. Kaplan-Meier analysis confirmed significantly reduced survival in the high-A/P ratio group (P < .001).
Keywords:
Oral microbiome
Biodiversity
Dysbiosis
Biomarkers
16S rRNA
Metabolic syndrome
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