DYSGLYCEMIA ASSOCIATED WITH ANTIPSYCHOTIC USE: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract Background Antipsychotics (APs) are the cornerstone of treatment for schizophrenia spectrum disorders (SSDs) and are approved for treatment of affective disorders including bipolar disorder (BD). However, AP use is associated with severe metabolic consequences including weight gain, dyslipidemia, and dysglycemia. Although studies indicate that glucose dysfunction can occur independently of weight gain, AP-induced glycemic changes are most often considered to be a consequence of AP-induced weight gain. Aim & Objectives The aims of this review are to 1) specifically clarify the effect of APs on glucose homeostasis independently of weight gain and 2) determine whether APs similarly impact glycemic control independently of drug class and treatment duration. Method: We searched MEDLINE, EMBASE, PsychINFO, CENTRAL, CINAHL, and Web of Science to identify all randomized controlled trials (RCTs) that compared the effect of APs on glucose metabolism to placebo (PBO), with no restriction on psychiatric diagnosis. Random effects meta-analyses examined glucose dysfunction as both continuous and dichotomous outcomes, with subgroup analyses conducted for study length, AP type, and age (child/adolescent vs. adult). Results Of 20954 references identified in our search, 70 RCTs in patients with SSDs (N=40 studies) and BD (N=30 studies) met our inclusion criteria. In both populations, AP use was associated with a significantly greater increase in fasting glucose compared to placebo (mean difference (MD) SSD = 0.05 mmol/L [0.01, 0.09], p=0.02, I2=0%, n=8542 AP vs. n=3333 PBO; MD BD = 0.10 mmol/L [0.05, 0.15], p<0.0001, I2=46%, n=6018 AP vs. n=4137 PBO). Sub-group analyses revealed that neither study length nor AP type altered this finding. Nevertheless, in patients with BD, significantly impacted AP-induced alterations in blood glucose, with adults showing a greater increase (p=0.03, I2=79%). Plasma insulin was also significantly increased by AP exposure (MD SSD = 13.97 pmol/L [6.42, 21.51], p=0.0003, I2=0%, n=3678 AP vs. n=1169 PBO; MD BD = 12.85 pmol/L [1.14, 24.56], p=0.03, I2=55%, n=2111 AP vs. n=1676 PBO), with a significant subgroup difference according to AP type in both groups. There was an additional effect of study length on plasma insulin in individuals with SSDs (p=0.03, I2=79.8%). Importantly, the strength of the effect of different APs on glucose did not appear to follow the established hierarchy of weight gain liabilities outlined in the literature. Specifically, so-called weight neutral APs such as ziprasidone and lurasidone produced comparable dysglycemia to APs traditionally associated with significant weight gain like olanzapine (SSD: p=0.45, I2 =0%; BD: p=0.46, I2=0%). Furthermore, AP exposure did not appear to have a significant effect on or hyperglycemia. Discussion & Conclusion Our review demonstrates that both short- and long-term exposure to APs is associated with a significant increase in dysglycemia risk as indicated by drug-induced elevations in fasting blood glucose and insulin. Furthermore, all APs cause some degree of regardless of exposure time and established propensities for AP-induced weight gain. Further studies are required to better understand how AP use contributes to dysglycemia, including temporal changes throughout the treatment course, and how these effects could potentially be mitigated using metabolic interventions.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".