Abstract LB-162: Causal role of body mass index in lung cancer in mendelian randomization analysis accounting for genetic pleiotropism
Notice bibliographique
Résumé
Abstract Background: Body mass index (BMI) is inversely associated with lung cancer in observational studies, despite obesity increases risk for many other cancers. Mendelian Randomization (MR) analysis was previously employed to clarify the causal role of BMI in lung cancer. However, the results of MR were potentially biased by BMI-SNPs with pleiotropic effects on smoking and hence violating the assumption of MR. Objective: To examine the causal role of BMI in lung cancer using MR analysis after excluding BMI-SNPs with potential pleiotropic effects on smoking. Methods: The OncoArray data from the Transdisciplinary Research in Cancer of the Lung (TRICL) team of the International Lung Cancer Consortium (ILCCO) were used. To obtain a valid genetic instrument for MR analysis, we first identified a set of 241 BMI-SNPs (P<10-5) from the GIANT consortium, then filtered out SNPs with potential pleiotropic effects on smoking using causal network inference, and obtained a final set of 46 SNPs to calculate the genetic risk score for BMI (GRS46). Logistic regression model tested its association with lung cancer adjusting for age, sex, and genetic principle components. We compared results of GRS46 with those from GRS241 that included pleiotropic SNPs. Results: A total of 32,240 subjects of European descent (17,976 lung cancer cases and 14,264 controls) were included in the analysis. The average age was 63.0 (SD: 10.5) years, 61.2% were male, 59.5% were overweight (BMI: 25.0-29.9 kg/m2) or obese (BMI≥30 kg/m2), and 79.3% were ever smokers. GRS46 was positively associated with lung cancer (OR: 1.57, 95% CI: 1.20-2.07) in both never smokers (OR: 2.18, 95% CI: 1.10-4.31) and ever smokers (OR: 1.40, 95% CI: 1.02-1.92). GRS46 was significantly associated with increased risk of non-small cell lung cancer (OR: 1.69, 95% CI: 1.21-2.36), adenocarcinoma (OR: 1.76, 95% CI: 1.19-2.60), and squamous cell carcinoma (OR: 1.98, 95% CI: 1.18-3.33), but not with small cell lung cancer (SCLC) (OR: 1.29, 95% CI: 0.63-2.64). In contrast, GRS241 was significantly associated with SCLC (OR: 1.57, 95% CI: 1.21-2.05), which is highly attributable to smoking, but not associated with lung cancer subgroups that are not or less attributable to smoking: never smokers (OR: 1.00, 95% CI: 0.78-1.29) and adenocarcinoma (OR: 1.07, 95% CI: 0.92-1.24). The pleiotropic smoking-associated SNPs in GRS241 might have confounded the results in these subgroups. Conclusion: Increased BMI is a risk factor for lung cancer in both never and ever smokers and for major histological subtypes after accounting for genetic pleiotropism. We demonstrated that previous MR analyses that used a BMI-GRS including SNPs with pleiotropic effects on smoking could yield spurious results. Funding: This work was support by R21 CA202529 (Wang/Ho). Confirmation: These data were not published previously and will not publish prior to the dates of the AACR Annual Meeting 2018. Citation Format: Yiqun Wu, Jee-Young Moon, Christopher I. Amos, Rayjean J. Hung, Gloria Y. Ho, Tao Wang. Causal role of body mass index in lung cancer in mendelian randomization analysis accounting for genetic pleiotropism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-162.
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 | 0,020 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».