Proteomics signature of physical activity and risk of multimorbidity of cancer and cardiometabolic diseases
Notice bibliographique
Résumé
Abstract Background Cancer, cardiovascular diseases (CVD), and type 2 diabetes (T2D) may co-occur, a condition referred to as multimorbidity. Physical activity is inversely associated with each of these diseases; however, the biologic pathways underlying these relationships remain incompletely understood. Methods In 33,806 UK Biobank participants, we derived a proteomic signature (high-throughput panel of 2,911 proteins assessed by Olink array) of moderate-to-vigorous physical activity using linear and LASSO regressions in a two-step procedure to prospectively assess associations with physical activity-related cancers (1,108 cases), CVD (3,445 cases), T2D (1,363 cases), as well as progression to multimorbidity (420 cases). Multivariable Cox regression estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for each identified protein, as well as their linear combination (proteomics signature score), separately for each outcome and with adjustment for physical activity. Pathway enrichment analysis and protein-protein interaction networks were used to gain insights into the systemic interplay of the identified proteins. Results After correction for multiple testing, 223 proteins were selected in the physical activity signature. Proteins involved in food intake, metabolism, and cell growth regulation (e.g., LEP, MSTN, TGFBR2) were inversely associated with physical activity. Proteins involved in immune cell adhesion and migration, as well as cartilage and muscle integrity (e.g., integrins, COMP, MYOM3) were positively associated with physical activity. Several proteins upregulated by physical activity were inversely associated with disease risk (e.g., integrins, PI3, CLEC4A for cancer risk, or LPL, IGFBP1, LEP for T2D risk). Similarly, various proteins were downregulated by physical activity and positively associated with disease risk (e.g., CD38, TGFA for CVD risk). For multimorbidity, proteins inversely related to physical activity generally aligned with expected risk patterns, while positively associated proteins exhibited mixed effects, with inverse and positive associations. The proteomics signature score was inversely associated with the risk of cancer (HR per interquartile range: 0.87; 95% CI: 0.78, 0.96) and T2D (HR: 0.66; 95% CI: 0.60, 0.72), after adjustment for physical activity, but not with CVD (HR: 0.93; 95% CI: 0.85, 1.03) and progression towards multimorbidity. Conclusions These findings suggest that the inverse relationships between physical activity and risk of major chronic diseases may be explained by the maintenance of tissue integrity and the proper regulation of immune and metabolic processes. Further studies are needed to determine the causal nature of these associations.
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 | 0,001 | 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 | 0,001 | 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) | 0,002 | 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 ».