ArticleClinical epigenetics2026
Association of accelerated biological aging with obstructive sleep apnea symptoms and identification of a candidate biomarker gene signature.
Article in Clinical epigenetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundObstructive sleep apnea (OSA) is a common sleep-related breathing disorder that is linked to cardiovascular, metabolic, and neurocognitive complications. However, its diagnosis relies on polysomnography, which is complex and resource-intensive, leading to frequent underdiagnosis. Emerging evidence suggests that accelerated biological aging may contribute to OSA pathophysiology, but systematic assessments using biological age metrics are limited.
methodsData from the National Health and Nutrition Examination Survey (NHANES) were analyzed to evaluate associations between biological age acceleration and symptom-based OSA risk. Weighted multivariable logistic regression was used to assess the relationships of KDM-Age and PhenoAge accelerations with symptom-based OSA risk. Bioinformatics analyses of the GSE135917 dataset identified aging-related differentially expressed genes (DEGs). Machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were used to screen hub genes, which were validated in both external cohorts and a chronic intermittent hypoxia (CIH) mouse model.
resultsHigher KDM-Age and PhenoAge accelerations were independently associated with increased symptom-based OSA risk (both P < 0.001). Thirty aging-related DEGs were identified, which were mainly enriched in senescence, inflammatory, and immune pathways. Three hub genes-RBBP4, UCHL1, and ERRFI1-were selected by machine learning and exhibited favorable discriminative potential across validation datasets and the CIH model. In addition, an integrated three-gene predictive model demonstrated promising discriminative ability in the training set and acceptable predictive performance in independent validation datasets. A nomogram integrating these genes showed good calibration and demonstrated value as an exploratory analytical tool at this stage.
conclusionsAccelerated biological aging is significantly associated with symptom-based OSA risk. The identified three-gene candidate biomarker signature links aging-related alterations to OSA and warrants further validation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.