Les perturbations métaboliques liées à la prise d’antipsychotiques de seconde génération : revue de littérature et prise en charge
Bibliographic record
Abstract
Resume Objectifs : Discuter des perturbations metaboliques, dependantes et independantes du gain ponderal, liees aux antipsychotiques atypiques de deuxieme generation et de leur prise en charge. Sources des donnees : Une revue de la litterature scientifique a ete effectuee par la consultation de Pubmed. Des etudes cliniques et des meta-analyses relatives aux effets metaboliques des divers antipsychotiques atypiques et publiees de 1997 a 2009 ont ete retenues. Analyses des donnees : En plus de causer des gains de poids importants (olanzapine = clozapine > quetiapine = risperidone > ziprasidone = aripiprazole), les antipsychotiques atypiques sont associes au developpement de resistance a l’insuline, et donc ultimement du diabete de type 2, ainsi qu’a la presence de dyslipidemie. Ces perturbations metaboliques sont principalement dues au developpement d’un exces de gras autour des visceres abdominaux. Cependant, certains individus n’ayant pas eu de gain de poids ont tout de meme developpe une resistance a l’insuline ou une dyslipidemie, ce qui incite a penser que les antipsychotiques ont une action directe sur le metabolisme du glucose et des lipides. Diverses hormones peptidiques, dont la leptine, seraient impliquees dans le developpement de tels problemes. On recommande d’exercer un suivi etroit des differents parametres biologiques (poids, tension arterielle, glycemie a jeun, bilan lipidique, etc.) des patients sous antipsychotiques atypiques de meme qu’une prise en charge rapide du developpement de complications metaboliques. Conclusion : Malgre le fait que les antipsychotiques atypiques constituent un arsenal therapeutique interessant, leur profil metabolique n’est pas negligeable et doit etre considere lors de l’introduction d’un tel traitement. Abstract Purpose: To discuss the metabolic side effects of second generation atypical antipsychotics, whether dependent or independent of weight gain. Their management will also be discussed. Data sources: A review of the scientific literature was done using Pubmed. Selected were clinical studies and meta-analyses that were published in 1997–2009 pertaining to the metabolic side effects of various atypical antipsychotics. Data analysis: In addition to causing significant weight gain (olanzapine = clozapine > quetiapine = risperidone > ziprasidone = aripiprazole), atypical antipsychotics are associated with insulin resistance and thus ultimately with type 2 diabetes and with dyslipidemia. These metabolic disturbances are mainly due to the presence of excess fat surrounding the abdominal viscera. However, some people, having experienced no weight gain, have developed insulin resistance or dyslipidemia, which suggests that antipsychotics have a direct action on lipid and glucose metabolism. Various peptide hormones such as leptin may be implicated in the development of such problems. We recommend strict monitoring of biological parameters (weight, blood pressure, fasting blood glucose, lipid panel, etc.) of patients treated with atypical antipsychotics. Rapid management of metabolic complications is indicated. Conclusion: Despite the fact that atypical antipsychotics constitute an interesting therapeutic option, their metabolic profile cannot be ignored and must be considered upon treatment initiation. Key words: antipsychotics; weight gain; diabetes; dyslipidemia; leptin
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".