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Enregistrement W4307490127 · doi:10.1101/2022.10.24.22281370

Genetically-proxied anti-diabetic drug target perturbation and risk of cancer: a Mendelian randomization analysis

2022· preprint· en· W4307490127 sur OpenAlexafffund
James Yarmolinsky, Emmanouil Bouras, Andrei‐Emil Constantinescu, Kimberley Burrows, Caroline J. Bull, Emma E. Vincent, Richard M. Martin, Olympia Dimopoulou, Sarah Lewis, Vı́ctor Moreno, Marijana Vujković, Kyong‐Mi Chang, Benjamin F. Voight, Philip S. Tsao, Marc J. Gunter, Jochen Hampe, Annika Lindblom, Andrew J. Pellatt, Paul D.P. Pharoah, Robert E. Schoen, Steven Gallinger, Mark A. Jenkins, Rish K. Pai, Dipender Gill

Notice bibliographique

RevuemedRxiv · 2022
Typepreprint
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetic Associations and Epidemiology
Établissements canadiensLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Organismes subventionnairesOffice of Research Infrastructure Programs, National Institutes of HealthOntario Ministry of Research and InnovationDepartment of Health and Social CareMedical Research CouncilConseil Régional des Pays de la LoireNational Institutes of HealthCentre Hospitalier Universitaire de NantesCancer Research UKWorld Health OrganizationKWF KankerbestrijdingUniversity Hospitals Bristol NHS Foundation TrustWereld Kanker Onderzoek FondsZonMwNational Cancer InstituteUniversity of BristolDiabetes UKU.S. Department of Health and Human ServicesWorld Cancer Research Fund InternationalU.S. Department of Veterans AffairsOffice of Research and DevelopmentNIHR Bristol Biomedical Research CentreFred Hutchinson Cancer Research CenterNational Institute for Health and Care ResearchWorld Cancer Research FundCentre International de Recherche sur le CancerEuropean Commission
Mots-clésMendelian randomizationLinkage disequilibriumGenome-wide association studySingle-nucleotide polymorphismOncologyPeroxisome proliferator-activated receptor gammaGenetic associationTCF7L2BiologyGeneticsInternal medicineMedicineGeneGenotypePeroxisome proliferator-activated receptorGenetic variants

Résumé

récupéré en direct d'OpenAlex

Abstract Aims/hypothesis Epidemiological studies have generated conflicting findings on the relationship between anti-diabetic medication use and cancer risk. Naturally occurring variation in genes encoding anti-diabetic drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk. Methods We developed genetic instruments for three anti-diabetic drug targets (peroxisome proliferator activated receptor gamma, PPARG; sulfonylurea receptor 1, ABCC8; glucagon-like peptide 1 receptor, GLP1R) using summary genetic association data from a genome-wide association study (GWAS) of type 2 diabetes in 69,869 cases and 127,197 controls in the Million Veteran Program. Genetic instruments were constructed using cis -acting genome-wide significant ( P <5×10 −8 ) single-nucleotide polymorphisms (SNPs) permitted to be in weak linkage disequilibrium (r 2 <0.20). Summary genetic association estimates for these SNPs were obtained from GWAS consortia for the following cancers: breast (122,977 cases, 105,974 controls), colorectal (58,221 cases, 67,694 controls), prostate (79,148 cases, 61,106 controls), and overall (i.e. site-combined) cancer (27,483 cases, 372,016 controls). Inverse-variance weighted random-effects models adjusting for linkage disequilibrium were employed to estimate causal associations between genetically-proxied drug target perturbation and cancer risk. Colocalisation analysis was employed to examine robustness of findings to violations of Mendelian randomization (MR) assumptions. A Bonferroni correction was employed as a heuristic to define associations from MR analyses as “strong” and “weak” evidence. Results In Mendelian randomization analysis, genetically-proxied PPARG perturbation was weakly associated with higher risk of prostate cancer (OR for PPARG perturbation equivalent to a 1 unit decrease in inverse-rank normal transformed HbA 1c : 1.75, 95% CI 1.07-2.85, P =0.02). In histological subtype-stratified analyses, genetically-proxied PPARG perturbation was weakly associated with lower risk of ER+ breast cancer (OR 0.57, 95% CI 0.38-0.85; P =6.45 × 10 −3 ). In colocalisation analysis however, there was little evidence of shared causal variants for type 2 diabetes liability and cancer endpoints in the PPARG locus, though these analyses were likely underpowered. There was little evidence to support associations of genetically-proxied PPARG perturbation with colorectal or overall cancer risk or genetically-proxied ABCC8 or GLP1R perturbation with risk across cancer endpoints. Conclusions/interpretation Our drug-target MR analyses did not find consistent evidence to support an association of genetically-proxied PPARG, ABCC8 or GLP1R perturbation with breast, colorectal, prostate or overall cancer risk. Further evaluation of these drug targets using alternative molecular epidemiological approaches may help to further corroborate the findings presented in this analysis. Research in context What is already known about this subject? Anti-diabetic medication use is variably linked to both increased and decreased cancer risk in conventional epidemiological studies It is unclear whether these associations represent causal relationships What is the key question? What is the association of genetically-proxied perturbation of three anti-diabetic drug targets (PPARG, ABCC8, GLP1R) with risk of breast, colorectal, prostate and overall cancer risk? What are the new findings? Genetically-proxied PPARG perturbation was weakly associated with higher risk of prostate cancer and lower risk of ER+ breast cancer There was little evidence that liability to type 2 diabetes and these cancer endpoints shared one or more causal variants in the PPARG locus, a necessary precondition to infer causality between PPARG perturbation and cancer risk How might this impact on clinical practice in the foreseeable future? Our drug-target Mendelian randomization analyses did not find consistent evidence to support a link between genetically-proxied perturbation of PPARG, ABCC8, and GLP1R and risk of breast, colorectal, prostate and overall cancer risk These findings suggest that on-target effects of PPARG agonists, sulfonylureas, and GLP1R agonists are unlikely to confer large effects on breast, colorectal, prostate, or overall cancer risk

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,039
score de la tête « metaresearch » (Gemma)0,060
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,209

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0390,060
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,005
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,002
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,260
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission2
Résumé présentoui

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