Forever Chemical PFOS Linked to Gum Disease by Genetic and Multiomics Evidence

Periodontitis, the chronic inflammatory disease that destroys the tissues anchoring our teeth, has long been viewed as a condition of middle and older age. A new integrated study published in iScience argues that the picture is incomplete: gum disease can begin in adolescence, and a ubiquitous environmental chemical may help drive it. The research team, led by Hongcheng Wan and colleagues, combined global epidemiology, human genetics, transcriptomics, machine learning, structural simulation, and laboratory experiments to examine whether perfluorooctane sulfonate, or PFOS, contributes to periodontal disease. PFOS is a member of the per- and polyfluoroalkyl substances, the so-called forever chemicals, and has been used for decades in firefighting foams, stain-resistant coatings, and industrial processes. Its exceptional chemical stability means it persists in water, food, dust, and human tissue, producing chronic low-level exposure in populations worldwide.

The epidemiological starting point came from the Global Burden of Disease 2021 study, which the team mined for data on periodontal diseases among people aged five to twenty from 1999 to 2021. The burden remained substantial throughout the period, with only modest temporal change overall, but the analysis revealed striking heterogeneity across sociodemographic strata. Low- and low-middle sociodemographic index regions consistently showed higher age-standardized prevalence, incidence, and disability-adjusted life-year rates than high-SDI regions, and male adolescents generally carried a slightly heavier burden than females. The authors stress that because PFOS is not a modeled GBD risk factor for periodontal disease, these figures provide descriptive public health context rather than direct evidence of chemical causation. What they do establish is a persistent socioeconomic inequity in adolescent gum disease that motivates the search for modifiable environmental contributors.

That search turned to Mendelian randomization, a genetic technique that uses naturally occurring DNA variants as instrumental variables to test whether an exposure plausibly causes a disease. Using 65 genetic instruments associated with circulating PFOS levels, drawn from more than 8,000 participants in the Canadian Longitudinal Study on Aging, and chronic periodontitis summary statistics from over 195,000 FinnGen participants of European ancestry, the team applied the inverse-variance weighted method as its primary estimator. The result was a statistically significant positive association: genetically predicted PFOS exposure increased periodontitis risk with an odds ratio of 1.148 (95 percent confidence interval 1.053 to 1.251, p = 0.0017). Complementary MR-Egger, weighted-median, and mode-based estimates pointed in the same direction, though several did not reach conventional significance, a pattern the authors attribute to limited statistical power rather than conflicting evidence.

Sensitivity analyses were designed to rule out the artifacts that plague observational studies. Cochran’s Q statistics detected no substantial heterogeneity among instruments, the MR-Egger intercept and MR-PRESSO global tests found no directional horizontal pleiotropy, the funnel plot was largely symmetric, and leave-one-out analyses showed that removing any single variant did not materially change the estimate. The authors are careful about the limits of the design: the analysis relied on a single PFOS GWAS without independent replication or colocalization, the datasets were predominantly European-ancestry and non-adolescent, and linkage disequilibrium confounding cannot be fully excluded. They therefore present the genetic evidence as supportive rather than definitive, while noting it is far stronger than a simple cross-sectional correlation between chemical levels and gum disease.

To uncover the molecular machinery that might mediate such an effect, the researchers merged two public periodontitis transcriptomic datasets, GSE16134 and GSE173078, correcting batch effects with ComBat and surrogate variable analysis. Differential expression analysis identified 1,005 genes altered between diseased and healthy gingival tissue, and weighted gene co-expression network analysis pinpointed disease-associated modules. Intersecting these lists yielded 329 candidate periodontitis-related genes, which the team then overlapped with a curated set of 8,827 PFOS-related human proteins compiled from the Comparative Toxicogenomics Database and prediction tools. The intersection produced 151 putative PFOS-periodontitis targets. Enrichment analyses of these genes converged on blood coagulation, leukocyte migration, phagocytosis, extracellular matrix organization, serine-type peptidase activity, and canonical inflammatory cascades including JAK-STAT, MAPK, TNF, IL-17, and NF-κB signaling, precisely the pathways that govern the destructive host immune response in periodontal tissue.

From the 151 candidates, a machine-learning framework that evaluated 127 model configurations across lasso, support-vector machine, random forest, XGBoost, and other algorithms, with stratified five-fold cross-validation and two independent validation cohorts, distilled a ten-gene biomarker panel: APLNR, AQP9, BTG2, CXCL1, HBD, MME, MMP13, SERPINB9, STK17B, and XBP1. Each gene showed good individual diagnostic ability, with area under the ROC curve exceeding 0.78, and a multigene nomogram built from the panel achieved an AUC of 0.939 with good calibration and favorable decision-curve net benefit. SHAP explainability analysis quantified each gene’s contribution and revealed nonlinear relationships between expression and predicted risk. Many of the genes carry mechanistic plausibility: CXCL1 attracts neutrophils, MMP13 degrades collagen, XBP1 governs endoplasmic reticulum stress and immune function, and STK17B participates in lymphocyte signaling.

Immune deconvolution added a cellular dimension. Applying CIBERSORT to the bulk transcriptomic samples, the team inferred the relative abundance of 22 immune cell subsets and found marked shifts in periodontitis, particularly among macrophages, neutrophils, dendritic cells, T cell subsets, and mast cells. Several core genes correlated positively with pro-inflammatory populations such as M1 macrophages, neutrophils, and activated CD4-positive T cells, and negatively with resting or naive subsets. Single-cell RNA sequencing of peripheral blood from eleven periodontitis patients and ten controls then showed that eight of the detectable core genes were enriched in myeloid and T cell clusters. Ligand-receptor analysis revealed a dense communication network in which macrophages and T cells acted as major hubs, with macrophage migration inhibitory factor, or MIF, emerging as a prominent signaling axis connecting myeloid cells and T cells. Because the single-cell data came from blood rather than gingival lesions, the authors interpret this as evidence of systemic immune dysregulation, and the PFOS-MIF link remains hypothesis-generating.

Structural biology provided a physical rationale. Molecular docking with AutoDock Vina showed that PFOS binds favorably to all ten target proteins, with the strongest predicted affinity for STK17B at minus 10.4 kilocalories per mole. The binding mode was consistent across targets: the chemical’s sulfonate head group forms polar contacts with nearby residues while its perfluoroalkyl tail inserts into a hydrophobic pocket. A 100-nanosecond molecular dynamics simulation in GROMACS confirmed that the PFOS-STK17B complex is conformationally stable, with root-mean-square deviation settling between 0.35 and 0.55 nanometers, a steady radius of gyration, two to four persistent intermolecular hydrogen bonds, and a dominant low-energy basin in the free-energy landscape. The authors caution that docking and simulation establish structural plausibility, not functional proof.

Experimental validation brought the computational chain back to the bench. Human gingival fibroblasts exposed to 64 micromolar PFOS for 24 hours, a concentration selected after a CCK-8 viability assay showed marked toxicity only at higher doses, showed significantly elevated mRNA expression of all ten core genes by RT-qPCR. The authors frame the full picture honestly: the adolescent GBD data offer context, the Mendelian randomization offers quasi-causal genetic evidence in a broader population, and the omics, structural, and in vitro results offer mechanisms and biomarkers, but the datasets were not adolescent-specific, clinical covariates were unavailable, and dose-response relationships remain untested. Still, the convergence is notable. With forever chemicals under intensifying regulatory scrutiny and adolescent gum disease concentrated where dental resources are scarcest, the study argues that PFOS deserves evaluation as a potentially modifiable environmental risk factor for periodontal health, and its ten-gene signature may serve as an early-warning biomarker for environmentally associated periodontal destruction.

Subject of Research: PFOS exposure and its genetic, immune, and molecular links to periodontitis risk

Article Title: PFOS exposure and periodontitis from adolescent burden to genetic and multiomics evidence

Article References: Wan, H., Wang, Z., Peng, H., Wang, X., Xiong, L., & Zou, X. (2026). PFOS exposure and periodontitis from adolescent burden to genetic and multiomics evidence. iScience, 29(11), Article 117905. https://doi.org/10.1016/j.isci.2026.117905

Image Credits: AI Generated

DOI: Not provided

Keywords: PFOS, PFAS, periodontitis, Mendelian randomization, forever chemicals, gingival fibroblasts, WGCNA, machine learning biomarkers, molecular docking, immune infiltration, Global Burden of Disease, adolescent health