Notice bibliographique
Résumé
Dear Editor, The recent manuscript analyzing whether there was a correlation between cyclosporine levels and dyslipidemia after renal transplantation in 1391 kidney transplant recipients (1) was read with great interest. Dyslipidemia is a common problem after renal transplantation and a potentially modifiable factor (2). This is particularly important because cardiovascular morbidity significantly contributes to the below-average life expectancy found in renal transplant patients (3). In their cross-sectional study of 1391 subjects, Hosseini et al. found a very high prevalence of hypercholesterolemia (58.8%) and hypertriglyceridemia (86.6%) (1). Using univariate correlation analysis, they found only a weak correlation between cyclosporine levels two hours post-intake (C2) and dyslipidemia (Pearson correlation coefficient 0.18 and 0.16 for hypercholesterolemia and hypertriglyceridemia, respectively). C2-monitoring is the preferred method for assessing cyclosporine exposure, although there are some challenges with timely C2 blood sampling, and some patients may suffer toxicity or rejection when switching from trough level (C0) to C2 monitoring (4). Using logistic multivariate regression, only serum creatinine was associated with hyperlipidemia. Hosseini’s study did not assess the correlation of steroid levels or the steroid dose and dyslipidemia, even though steroid therapy is a well-known risk factor for hyperlipidemia and steroid avoidance has been a powerful tool to reduce the prevalence of hyperlipidemia in liver transplantation (5). Hosseini’s study has several other limitations. No validation of the fasting state occurred. The results of the logistic regression were not well documented, and it is unclear what factors were included in the multivariate analysis. The limitations of the cross-sectional retrospective study are not well discussed, and such a study can only assess an association, no causality. Nonetheless, the stronger correlation of the hypertriglyceridemia with the GFR (measured as serum creatinine) is interesting; it is disappointing that targeting optimal calcineurin inhibitor exposure has little effect on hyperlipidemia. The authors call for prospective trials targeting better lipid control, with the hope that long-term outcomes will improve. The authors are correct in their assessment that chronic kidney disease (CKD)-related complications after renal transplantation are poorly managed (3, 6, 7). Often, there is undertreatment of dyslipidemia. Longevity after renal transplantation could be improved significantly if similar multidisciplinary clinics were introduced as for CKD in the primary kidneys (8). It is of the utmost importance to determine whether targeting conventional cardiovascular risk factors can effectively modify cardiovascular morbidity (9). In a large study of national data, Sciarretta et al. found no association of renal damage with cardiovascular disease and the individual cardiovascular risk profile (9). It is also important to determine which intervention is most effective and what novel therapies can be employed to lower triglycerides. There is no known effective treatment of hypertriglyceridemia. Dietary approaches such as supplementation with omega-3 fatty acids should be studied prospectively (10). Nonetheless, the undersigned is delighted that the attention is shifting towards modifiable long-term complications after renal transplantation. Cardiovascular risk factors are among the most significant factors affecting long-term outcomes in renal transplant recipients and are responsible for deaths with functioning graft. CKD is a major risk factor for cardiovascular morbidity following transplantation, and has a high prevalence in both renal and non-renal transplant patients. The relationship between impaired GFR and dyslipidemia needs to be studied further and effective therapeutic interventions have to be found.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».