6715 Short Adult Height in Childhood Cancer Survivors: Prevalence, Risk Factors, and Genetic Contribution
Notice bibliographique
Résumé
Abstract Disclosure: T. Yoshida: None. J. Baedke: None. F. Wang: None. W. Moon: None. Y. Sapkota: None. J. Miguel Martínez: None. T.E. Merchant: None. C.L. Wilson: None. K.K. Ness: None. M.M. Hudson: None. Y. Yasui: None. A. Delaney: None. Background: Survivors of childhood cancer are at elevated risk for short adult height (SAH) due to cancer and/or its treatment. In the general population, height is a highly polygenic trait; heritability is estimated to be 70-80%. However, the contribution of genetic factors to SAH among childhood cancer survivors is unknown. In addition, the contribution of chemotherapy agents to risk of SAH among survivors has not been established. We assessed: 1) prevalence of SAH; 2) contribution of genetic factors; and 3) impact of cancer therapy including chemotherapy, on SAH in a large cohort of childhood cancer survivors. Methods: Participants included 4461 childhood cancer survivors aged ≥18 years (female 47.5%, mean age 33.2 years old) with measured height information. SAH was defined as height <3rd percentile for age and sex based on the Centers for Disease Control growth charts. Cancer and treatment history were extracted from medical records. We calculated multi-ancestry height polygenic score (PGS) using the latest methodology developed from 5.4 million individuals of diverse ancestries with more than 1 million variants, where lower score associates with shorter height. With a random sample of 75% of survivors, we fit a multivariable logistic regression model for SAH with the PGS, chemotherapy exposures/doses, corticosteroid exposures/doses, and established risk factors for SAH (e.g., age at cancer diagnosis, radiotherapy exposure) as covariates (main model). The remaining 25% of survivors served for validation of the main model and for the calculation of the population attributable fractions (PAF) of the PGS and cancer treatments. Results: The prevalence of SAH was 8.9% among all survivors (9.3% in males; 8.5% in females) and differed widely by primary cancer diagnosis. SAH was associated with lower PGS [odds ratio (OR) 0.47, 95% confidence interval (CI) 0.40-0.55 for a one standard deviation increase], alkylating agent exposure of >12000 mg/m2 (OR 2.19, 95% CI 1.41-3.38, vs. non-exposure), and spinal radiotherapy (OR 3.65, 95% CI 2.23-6.00, vs. non-exposure). Radiotherapy exposure to the hypothalamic-pituitary region and younger age at cancer diagnosis was also associated with SAH in a dose-response manner. The area under the ROC curve of the main model in the validation dataset was 0.80 (95% CI 0.74-0.87), suggesting good predictive ability for SAH by the model. The PAF of SAH calculated from the multiplicative logistic regression model of SAH was 85.7% for cancer treatments and 30.2% and 60.2% for having PGS below the median and the 90th percentile, respectively. Conclusions: Cancer treatments are the primary contributor to SAH risk among survivors with a PAF of 85.7%. Exposure to high-dose alkylating agents contributes to this along with radiotherapy. Inherited genetic factors also affect SAH among survivors but to a much lesser degree than cancer treatments. Presentation: 6/3/2024
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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 | 0,000 | 0,001 |
| 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 | 0,000 | 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) | 0,003 | 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 ».