Abstract B028: Co-occurring social determinants of endometrial cancer disparities in All of Us
Notice bibliographique
Résumé
Abstract Background: Endometrial cancer (EC) incidence is rising, especially among Black women and those under 50. Social determinants of health (SDoH), non-medical factors shaped by lived environments, contribute to EC disparities, yet are often studied in isolation, limiting our understanding of how real-world SDoH patterns influence EC risk. Methods: Using bipartite network modeling, a machine learning algorithm that autonomously identifies clusters of co-occurring exposures, we analyzed co-occurring SDoH indicators in a case-control EC cohort from the All of Us database (vs. 8). We hypothesized that Black women would be overrepresented in clusters marked by socioeconomic disadvantage and that these clusters would be associated with increased prevalence odds of EC, particularly early-onset EC (EC diagnosis at age < 50 years). Age-matched cases and controls (1:2 ratio) with complete SDoH data were weighted to address survey bias. Logistic regression assessed associations between cluster membership and EC prevalence. Results: Our study cohort (ncases = 530; ncontrols = 1,060) was composed of 1,394 (90.9% of cases; 86.0% of controls) White participants, 95 (3.8% of cases; 7.1% of controls) Black participants, and 101 (5.3% of cases, 6.9% of controls) participants of other races. Of 129 participants aged < 50 years, 43 (33%) had early onset EC based on diagnosis at age < 50 years. Five distinct SDoH clusters emerged with a biclustered modularity Q of 0.216 (p = 0.047), indicating modest but statistically significant community structure. Detected clusters ranged from Cluster 1—characterized by poor English language proficiency, Cluster 2— defined by high discrimination, delayed care, and limited affordability of care, Cluster 3— defined by inadequate healthcare coverage and poor neighborhood-level infrastructure, Cluster 4—characterized by socioeconomic hardship, and Cluster 5—reflecting low social support and poor neighborhood cohesion. Cluster 4 (socioeconomic hardship) and Cluster 5 (low social support) had the highest proportions of EC cases (40.7% and 35.6%, respectively; 28.2%-31.6% for clusters 1-3). Clusters 2 and 4 had the highest within-cluster proportions of Black patients (8.4% and 9.1%, respectively; 3.8%-5.6% for all other clusters). In race-adjusted models, Cluster 4 membership was significantly associated with increased odds of EC (OR = 1.54, 95% CI: 1.18–2.02), while Black race showed a protective association after adjusting for cluster membership (OR = 0.48, 95% CI: 0.29–0.79). Race-adjusted logistic regression revealed that Cluster 4 membership conferred over a fourfold increased odds of early-onset EC (OR = 4.11, 95% CI: 1.65–10.23). Conclusion: Co-occurring SDoH exposures, particularly socioeconomic disadvantage, are strongly associated with EC risk and early onset. After adjusting for SDoH cluster membership, Black individuals were less likely to be diagnosed with early-onset EC. These findings underscore the need for intersectional approaches to address EC disparities. Citation Format: Oyomoare Osazuwa-Peters, Drew Neish, Jesus Gonzalez. Bosquet, Rebecca Previs, Tomi Akinyemiju. Co-occurring social determinants of endometrial cancer disparities in All of Us [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B028.
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 | 0,000 | 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».