Association of Atypical Antipsychotics With Lipid Abnormalities in Adult Patients With Schizophrenia: A Scoping...

Atypical antipsychotics (AAs) are commonly used in the treatment of schizophrenia and are often preferred as first-line therapy over typical antipsychotics (TAs) due to their lower risk of extrapyramidal side effects. Both groups are efficacious in treating symptoms of schizophrenia, but increasing research has highlighted AAs as being associated with a risk of developing dyslipidaemia. Existing research has pointed to the need for more data focusing on the effects of individual AAs on dyslipidaemia in this population.

The scoping review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping reviews (PRISMA-ScR) checklist. The thematic analysis was used to synthesize data from 12 studies selected through structured searches across six databases. Inclusion criteria focused on primary studies between 2015 and 2025 involving adult patients with schizophrenia (18-65 years) treated with AAs and reporting on lipid abnormalities. The themes were identified via Braun and Clarke's six-step framework.

Clozapine and olanzapine were most strongly associated with increased LDL, total cholesterol, and triglycerides, and reduced HDL. Aripiprazole and lurasidone showed minimal impact. Identified biomarkers included asprosin, IGFBP-2, MIF, and white blood cell counts. Pharmacogenetic markers such as APOA1 gene polymorphisms and specific SNPs were also linked to lipid profile variability. Anthropometric indicators like waist-to-hip ratio were correlated with dyslipidaemic risk.

The review shows significant associations between specific AAs and lipid abnormalities, particularly with clozapine and olanzapine. Biomarkers and genetic polymorphisms offer promising avenues for monitoring and personalized treatment. Evidence for certain AAs, such as amisulpride, paliperidone, and ziprasidone, remains sparse, highlighting the need for further targeted research. These findings support informed prescribing and the development of predictive tools to mitigate metabolic risks in the treatment of schizophrenia.