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Record W2789176396 · doi:10.3389/fnins.2017.00741

The Complex Relationship between Antipsychotic-Induced Weight Gain and Therapeutic Benefits: A Systematic Review and Implications for Treatment

2018· review· en· W2789176396 on OpenAlexaff
Alex Raben, Victoria Marshe, Araba Chintoh, Ilona Gorbovskaya, Daniel J. Müller, Margaret Hahn

Bibliographic record

VenueFrontiers in Neuroscience · 2018
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Mental Health
KeywordsClozapineOlanzapineAntipsychoticPsycINFOMedicineSchizophrenia (object-oriented programming)RisperidoneMEDLINEPsychopathologyPsychiatryWeight gainAmisulpridePsychosisInternal medicineBody weight

Abstract

fetched live from OpenAlex

BACKGROUND. Antipsychotic-induced weight gain (AIWG) and other adverse metabolic effects represent serious side effects faced by many patients with psychosis that can lead to numerous comorbidities and which reduce the lifespan. While the pathophysiology of AIWG remains poorly understood, numerous studies have reported a positive association between AIWG and the therapeutic benefit of antipsychotic medications. OBJECTIVES. To review the literature to 1) determine if AIWG is consistently associated with thera-peutic benefit and 2) investigate which variables may mediate such an association. DATA SOURCES. MEDLINE, Google Scholar, Cochrane Database and PsycINFO databases were searched for articles containing all the following exploded MESH terms : schizophrenia [AND] antipsy-chotic agents/neuroleptics [AND] (weight gain [OR] lipids [OR] insulin [OR] leptin) [AND] treatment outcome. Results were limited to full-text, English journal articles. RESULTS. Our literature search uncovered 31 independent studies which investigated an AIWG-therapeutic benefit association with a total of 6063 enrolled individuals diagnosed with schizophrenia or another serious mental illness receiving antipsychotic medications. Twenty-two studies found a positive association while, 10 studies found no association and one study reported a negative association. Study variables including medication compliance, sex, ethnicity, or prior antipsychotic exposure did not appear to consistently affect the AIWG-therapeutic benefit relationship. In contrast, there was some evidence that controlling for baseline BMI/psychopathology, duration of treatment and specific agent studied (i.e. olanzapine (OLZ) or clozapine (CLZ)) strengthened the relationship between AIWG and therapeutic benefit. LIMITATIONS. There were limitations of the reviewed studies in that many had small sample sizes, and/ or were retrospective. The heterogeneity of the studies also made comparisons difficult and publi-cation bias was not controlled for. CONCLUSIONS. An AIWG-therapeutic benefit association may exist and is most likely to be ob-served in OLZ and CLZ-treated patients. The clinical meaningfulness of this association remains unclear and weight gain and other metabolic comorbidities should be identified and treated to the same targets as the general population. Further research should continue to explore the links between therapeutic benefit and metabolic health with emphasis on both pre-clinical work and well-designed prospective clinical trials examining metabolic parameters associated, but also occurring independently to AIWG.

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)0.017
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0120.016
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.218
GPT teacher head0.417
Teacher spread0.198 · 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 designSystematic review
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

Citations112
Published2018
Admission routes1
Has abstractyes

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