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Record W1983408983 · doi:10.1055/s-2002-36391

Obesity and Related Metabolic Abnormalities during Antipsychotic Drug Administration: Mechanisms, Management and Research Perspectives

2002· review· en· W1983408983 on OpenAlexaff
Trino Baptista, N.M.K. Ng Ying Kin, Serge Beaulieu, Enma Araujo de Baptista

Bibliographic record

VenuePharmacopsychiatry · 2002
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersPfizer
KeywordsOlanzapineSertindoleClozapineAntipsychoticMedicineInsulin resistanceHaloperidolRisperidoneDyslipidemiaInternal medicineAppetiteOrlistatAtypical antipsychoticEndocrinologyFluphenazineDiabetes mellitusPharmacologySchizophrenia (object-oriented programming)ObesityPsychiatryWeight lossDopamine

Abstract

fetched live from OpenAlex

Excessive body weight gain (BWG) is a common side effect of some typical and atypical antipsychotic drugs (APs). Convergent evidence suggests a hierarchy in the magnitude of BWG that may be induced by diverse agents, being very high for clozapine and olanzapine; high for quetiapine, zotepin, chlorpromazine, and thioridazine; moderate for risperidone and sertindole; and low for ziprazidone, amisulpiride, haloperidol, fluphenazine, pimozide, and molindone. BWG may be related to increased appetite that is due to drug interaction with the brain monoaminergic and cholinergic systems and to the metabolic/endocrine effects of hyperprolactinemia. Subjects with schizophrenia and bipolar disorders manifested a significantly high prevalence of diabetes, even before the introduction of atypical APs. However, clozapine and olanzapine appear to display a high propensity to induce glucose dysregulation and dyslipidemia. Sudden BWG, insulin resistance, increased appetite, and related endocrine changes also may be involved in the development of glucose intolerance and dyslipidemia in predisposed individuals. Patients should be informed of these side effects in order to prevent excessive BWG, and their blood glucose and lipids should be monitored before treatment and then at regular intervals. Nutritional advice must be given and regular physical exercise recommended. An appropriate selection of APs ought to be based on drug efficacy for specific patients and assessment of relevant risk factors such as propensity to gain weight; family or personal history of diabetes or hyperlipidemia; and elevated fasting serum glucose, lipid, or insulin levels. At present, there is no standardized pharmacological treatment for AP-induced BWG. Some studies have assessed the effects of agents such as amantadine, orlistat, metformin, nizatidine, and topiramate on AP-induced BWG. Further studies will provide tools to identify patients at high risk for obesity and metabolic abnormalities during AP administration. Excessive body weight gain (BWG), glucose intolerance, and dyslipidemia during treatment with antipsychotic drugs (APs) were reported in the late 1950s [14,101]. However, after 1990, interest in these problems increased noticeably, mainly because of the high propensity of some new atypical APs to induce these side effects (Fig.1). The APs are currently used in diverse mental disorders. Hence, excessive BWG and metabolic dysfunction are not exclusive of subjects with schizophrenia. In the case of bipolar disorders, AP-induced BWG may be additive to that induced by mood stabilizers [14,48,101]. The clinical features [2,14,24,133,139,140] and mechanisms [14,34,68,87,93,101,130] of BWG and metabolic dysfunction have been previously reviewed. In this article, we focus on a unified theory to explain these side effects, based on the interaction of APs with brain neurotransmitters involved in appetite regulation. This review comprises the following sections: 1) the clinical features of AP-induced BWG; 2) the effects of APs on carbohydrate and lipid metabolism in humans and experimental animals; 3) mechanisms involved in BWG, glucose, and lipid dysregulation; 4) strategies for prevention and treatment of these side effects; and 5) research perspectives in the field. The following sources were consulted: MEDLINE, Cochrane database system, and PsychINFO. Numerous articles referred to in leading articles also were consulted. The literature on this subject has increased so rapidly that it was impossible to include all the data recently published. For the first two sections, references that illustrate current controversies in the field were selected.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.057
GPT teacher head0.399
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations230
Published2002
Admission routes1
Has abstractyes

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