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Enregistrement W2009321876 · doi:10.1097/00007890-200111270-00002

Posttransplantation Diabetes Mellitus in FK-506-Treated Renal Transplant Recipients: Analysis of Incidence and Risk Factors. Transplantation 2001; 72: 1655.

2001· article· en· W2009321876 sur OpenAlexaboutno aff
Bart Maes, Dirk Kuypers, T. Messiaen, P. Evenepoel, Chantal Mathieu, Willy Coosemans, Jacques Pirenne, Yves Vanrenterghem

Notice bibliographique

RevueTransplantation · 2001
Typearticle
Langueen
DomaineMedicine
ThématiquePancreatic function and diabetes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineTacrolimusDiabetes mellitusAzathioprineTransplantationDyslipidemiaInternal medicineIncidence (geometry)Risk factorAdverse effectGastroenterologySurgeryDiseaseEndocrinology

Résumé

récupéré en direct d'OpenAlex

COMMENTARY Diabetes mellitus developing de novo following solid organ transplantation was reported in pioneering studies almost 40 years ago (1,2). The high doses of corticosteroids employed in those early reports often led to hyperglycemia that required insulin treatment. The introduction of azathioprine and the immunophilin-binding drugs permitted the use of lower doses of corticosteroids (1,2). Complete withdrawal of corticosteroid therapy subsequently became routine in some centers (1). However, posttransplantation diabetes has persisted because of intrinsic diabetogenic effects of cyclosporine and tacrolimus (FK-506) (1,2). Studies in the 1990s suggested a higher risk of diabetes in renal and liver transplant recipients treated with tacrolimus than in those receiving cyclosporine (1,3). Despite improved outcomes, ensuring long-term graft survival remains a challenge. Moreover, cardiovascular complications have emerged as the major cause of premature death in organ transplant recipients (4). Adverse effects of corticosteroids and immunophilin-binding drugs have been implicated in reduced graft and patient survival. Dose-dependent glucose intolerance and dyslipidemia are often accompanied by hypertension in transplant recipients. When present in combination, these risk factors confer a greatly increased risk of cardiovascular disease (5). Corticosteroids exacerbate this situation (1,2). The incidence of disordered glucose metabolism in transplant recipients reported in the literature has probably been underestimated. Diagnostic criteria differ between studies, often with only the more marked degrees of hyperglycemia being reported (1). In nontransplantation populations, even minor glucose intolerance is associated with an increased long-term risk of cardiovascular disease (6). Such dysglycemia often remains undetected. The category of impaired glucose tolerance, for example, can only be diagnosed by a 75-g oral glucose tolerance test. In this issue of Transplantation, Maes and colleagues (7) report a study using the recently revised diagnostic criteria (8,9). The new American Diabetes Association criteria, which are based on fasting blood glucose concentrations (8), were applied to 139 consecutive renal transplant recipients, none of whom had had recognized diabetes preoperatively. Maintenance immunosuppressive therapy comprised tacrolimus and low-dose methylprednisolone, the third agent being either mycophenolate mofetil or azathioprine (neither of which is regarded as having appreciable diabetogenic effects). With appropriate confirmatory tests (which are necessary if classic osmotic symptoms are absent) 32% met the revised criteria for diabetes during the first year posttransplantation. A further 15% had impaired fasting glucose (IFG). The latter category lies between normality and diabetes mellitus. Subjects with lesser degrees of glucose intolerance have a higher risk of progression to type 2 diabetes (and an increased risk of cardiovascular disease) (8,9). Whether the prevalence of type 2 diabetes will increase with a longer duration of follow-up is uncertain, particularly because some subjects had reverted to normal at one year (7). What are the factors that predict posttransplantation IFG and diabetes? Advanced age, a history of diabetes in a close relative, a personal history of glucose intolerance, and non-Caucasian ethnicity have been identified as risk factors in other retrospective studies (1,2). It is noteworthy that these characteristics also predispose to type 2 diabetes in the general population, obesity being a major factor in the majority of cases. Maes et al. confirmed these observations, also identifying cumulative corticosteroid dose and high trough blood concentrations of tacrolimus as risk factors for postoperative hyperglycemia. Hyperglycemia tended to be detected during episodes of acute rejection when high-dose methylprednisolone was given (and plasma glucose was perhaps monitored more closely). Other factors may be relevant to the emergence of posttransplantation hyperglycemia. Thus, in univariate analysis, a tendency to higher pretransplantation serum triglyceride concentration was associated with posttransplantation IFG or diabetes. Hypertriglyceridemia is common in dialysis patients, and in nontransplantation populations is regarded (along with low HDL cholesterol levels) as a prominent feature of the insulin resistance (or metabolic) syndrome of cardiovascular risk factors (9). This pattern of dyslipidemia has also been implicated in impaired long-term graft survival in organ transplant recipients. Interestingly, a lower cumulative dose of tacrolimus during the initial 3 months (resulting in similar or higher trough blood levels) was also associated with posttransplantation hyperglycemia. The study of Maes et al. highlights the continuing high incidence of posttransplantation disturbances of glucose metabolism. Use of oral glucose tolerance tests might well have identified additional subjects with impaired glucose tolerance. In nontransplantation populations, the 2-hr plasma glucose concentration following a 75-g oral glucose challenge appears to be a more reliable predictor of cardiovascular mortality than is fasting hyperglycemia (10). Clearly, vigilance is required; posttransplantation dysglycemia and other cardiovascular risk factors should be sought and treated, taking care to minimize the potential for drug interactions. What of alternatives to conventional triple immunosuppressive regimens? Reports of toxic effects of tacrolimus on insulin synthesis and secretion have not deterred investigators from exploring its use in pancreatic and islet transplantation (11,12). Fortunately, drugs with reduced potential to induce diabetes (e.g. sirolimus, mycophenolate mofetil) have allowed lower doses of tacrolimus to be used. Moreover, the detrimental effects of corticosteroids may be avoidable (4). For example, a corticosteroid-free regimen of low-dose tacrolimus, sirolimus, and the monoclonal antibody daclizumab, has recently been successfully employed in islet transplants by the Edmonton group (13). Tailored immunosuppressive therapy that seeks to maximize benefits while minimizing adverse effects by avoiding calcineurin inhibitors is another option that is currently being explored. However, this approach requires careful judgment, because rejection rates may increase (14,15). The need for additional well-designed clinical trials is clear (16).

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,016
Tête enseignante GPT0,258
Écart entre enseignants0,242 · 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

Citations5
Publié2001
Routes d'admission1
Résumé présentoui

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