Inflammation and LDL cholesterol contribute independently to the progression of early human atherosclerotic plaque
Notice bibliographique
Résumé
Abstract Background The role of inflammatory processes in the risk of ischemic events (despite intensive lipid-lowering treatment) has received considerable attention in recent years, mainly amongst secondary prevention patients. Both dyslipidemia and inflammation contribute to the pathophysiology of atherosclerosis, and both predict future ischemic events. However, the interplay between lipids and inflammation in the early phases of human atherosclerosis [i.e., subclinical atherosclerosis (SA) and primary prevention] is largely unknown. Objectives To investigate the relative contribution of inflammation and LDLc as determinants of risk of SA progression in a cohort of middle-aged, asymptomatic individuals. Methods Participants from the PESA study (median age 45 years, 36% female) underwent 3 visits (at 3-year intervals) involving serial blood testing, physical examinations, 3D vascular ultrasound assessments of peripheral arteries (bilateral carotids and femorals), and coronary artery calcium scoring (CACS). Multiple linear regression models with global plaque volume (GPV, mm3) at 6-year follow-up (FU) as the primary outcome were performed to investigate its key determinants, first based on standard cardiovascular risk factors (i.e., smoking, diabetes, systolic blood pressure, and LDLc) and then followed by the potential additional role of mean levels of inflammatory markers [white blood cell count (WBC), fibrinogen, oxidized LDL (oxLDL), and high-sensitivity C reactive protein (hs-CRP)] and lipoprotein a [Lp(a)]. Results The 3,471 participants had mostly low risk lipid profiles according to current guidelines, and 2,013 (58%) had some SA (GPV >0mm3) at 6-year FU. Overall, mean WBC and mean fibrinogen had highly significant associations with the extent of GPV at 6-year FU after adjusting for age, sex and CVRFs. Mean oxLDL showed a weaker, yet significant association with greater GPV at 6-year FU, but hs-CRP and Lp(a) did not. WBC had the strongest statistical associations with extent of SA, equivalent in strength to the known link of LDLc with SA, followed by fibrinogen (Figure 1). For WBC the marked trend in risk only occurred at levels above its median. Baseline LDLc and the mean inflammatory marker levels predicted GPV at 6 years alongside each other but not in a synergistic manner (i.e., no statistical interactions in determining GPV at 6 years were found). Similar patterns were observed for separate analyses of femoral plaque, carotid plaque and CACS (Figure 2). Conclusion In a cohort of middle-aged, asymptomatic individuals without dyslipidemia according to current standards, both LDLc and inflammation (especially WBC) are associated with progression of SA, but independently of each other. Our results suggest inflammatory risk is relevant across the life continuum of atherosclerosis and should not be regarded as only a residual risk in secondary prevention.Figure 1 Figure 2
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| 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 | 0,001 | 0,001 |
| 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 ».