OP36 Trans fatty acid biomarkers and incident type 2 diabetes: pooled analysis of 10 prospective cohort studies in the fatty acids and outcomes research consortium (FORCE)
Notice bibliographique
Résumé
Background Type 2 diabetes (T2D) is a major risk factor associated with cardiometabolic diseases, and a major contributor towards mortality and morbidity, given its rapidly rising prevalence worldwide. In experimental studies, trans-fatty acids (TFAs) exert harmful biologic effects that may affect T2D risk, but findings from observational studies remain inconclusive, especially for biomarkers which provide an objective advantage with less recall bias and estimation errors. By pooling multiple studies, we may also increase generalizability, statistical power, and address potential interactions by subgroups. Therefore, we assessed prospective associations between circulating biomarkers of individual TFAs and incident T2D in a large, diverse sample. Methods We pooled ten prospective cohort or nested-case-control studies from Australia, Germany, Iceland, UK, and the USA to perform an analysis using harmonized individual level data for TFA biomarkers and incident T2D. Fatty acids (FAs) were measured in plasma phospholipid, red blood cell membrane phospholipid, or total plasma collected between 1990–2008 from 22,711 participants aged ≥18 years without prevalent diabetes. Evaluated TFAs included trans-16:1n-9, sum of trans-18:1 isomers (trans-18:1n6 to trans-18:1n12), sum of trans-18:2 isomers (cis/trans-18:2, trans/cis-18:2, trans/trans-18:2), and individual trans-18:2 isomers. The multivariable-adjusted relative risk or odds ratio was estimated for each cohort by lipid compartments using a pre-specified protocol for definitions of exposures, covariates, and outcomes for statistical analysis. Association estimates were pooled using fixed-effects inverse-variance weighted meta-analysis. Results During an average maximum of 14 years of follow-up, 2,244 cases of incident T2D were identified. Median levels of TFAs across cohorts were 0.05–0.18% total FAs for trans-16:1n-9, 0.09–2.05% for total trans-18:1, 0.10–0.73% for total trans-18:2, and 0.01–0.36% for individual trans-18:2 isomers. In overall pooled analysis, TFAs evaluated per inter-quintile range were not significantly associated with risk of T2D. Relative risks for individual TFAs were 1.02 (0.78–1.32) for trans-16:1n-9, 0.92 (0.79–1.08) for total trans-18:1, 1.16 (0.98–1.37) for trans/trans-18:2, 0.98 (0.79–1.21) for cis/trans-18:2, 0.93 (0.76–1.14) for trans/cis-18:2, and 0.90 (0.78–1.04) for total trans-18:2. Findings were consistent when TFAs were assessed categorically by study-specific quintiles, and when associations were pooled within lipid compartment (phospholipids or total plasma). Conclusion We found that biomarker levels of TFAs were not significantly associated with risk of incident T2D in this international pooling project. Findings may reflect no effect of circulating TFA on T2D or be influenced by mixed TFA sources (industrial or ruminant), or to a general decline in TFA exposure during this period. Associations with T2D for higher levels of TFA biomarkers should be investigated.
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 | 0,023 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,018 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».