Epigenetic age biomarkers and risk assessment in adult spinal deformity: a novel association of biological age with frailty and disability
Notice bibliographique
Résumé
OBJECTIVE: Surgery for spinal deformity has the potential to improve pain, disability, function, self-image, and mental health. These surgical procedures carry significant risk and require careful selection, optimization, and risk assessment. Epigenetic clocks are age estimation tools derived by measuring the methylation patterns of specific DNA regions. The study of biological age in the adult deformity population has the potential to shed insight onto the molecular basis of frailty and to improve current risk assessment tools. METHODS: Adult patients who underwent deformity surgery were prospectively enrolled. Preoperative whole blood samples were used to assess epigenetic age and telomere length. DNA methylation patterns were quantified and processed to extract 4 principal component (PC)-based epigenetic age clocks (PC Horvath, PC Hannum, PC PhenoAge, and PC GrimAge) and the instantaneous pace of aging (DunedinPACE). Telomere length was assessed using both quantitative polymerase chain reaction (telomere to single gene [T/S] ratio) and a methylation-based telomere estimator (PC DNAmTL). Patient demographic and surgical data included age, BMI, American Society of Anesthesiologists Physical Status Classification System class, and scores on the Charlson Comorbidity Index, adult spinal deformity frailty index (ASD-FI), Edmonton Frail Scale (EFS), Oswestry Disability Index, and Scoliosis Research Society-22r questionnaire (SRS-22r). Medical or surgical complications within 90 days of surgery were collected. Spearman correlations and beta coefficients (β) from linear regression, adjusted for BMI and sex, were calculated. RESULTS: Eighty-three patients were enrolled with a mean age of 65 years, and 45 were women (54%). All patients underwent posterior fusion with a mean of 11 levels fused and 33 (40%) 3-column osteotomies were performed. Among the epigenetic clocks adjusted for BMI and sex, DunedinPACE showed a significant association with ASD-FI (β = 0.041, p = 0.002), EFS (β = 0.696, p = 0.026), and SRS-22r (β = 0.174, p = 0.013) scores. PC PhenoAge showed associations with ASD-FI (β = 0.029, p = 0.028) and SRS-22r (β = 0.159, p = 0.018) scores. PC GrimAge showed associations with ASD-FI (β = 0.029, p = 0.037) and SRS-22r (β = 0.161, p = 0.025) scores. Patients with postoperative complications were noted to have shorter telomere length (T/S 0.790 vs 0.858, p = 0.049), even when the analysis controlled for BMI and sex (OR = 1.71, 95% CI 1.07-2.87, p = 0.031). CONCLUSIONS: Epigenetic clocks showed significant associations with markers of frailty and disability, while patients with postoperative complications had shorter telomere length. These data suggest a potential role for aging biomarkers as components of surgical risk assessment. Integrating biological age into current risk calculators may improve their accuracy and provide valuable information for patients, surgeons, and payers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».