Whether non-genetic prognostic factors significantly influence the variable prognosis of antipsychotic-induced weight gain (AIWG) has not yet been systematically explored.

Searches for both randomized and non-randomized studies were undertaken using four electronic databases, two trial registers, and via supplemental searching methods. Unadjusted and adjusted estimates were extracted. Meta-analyses were undertaken using a random-effects generic inverse model. Risk of bias and quality assessments were undertaken using Quality in Prognosis Studies (QUIPS) and Grading of Recommendations Assessment, Development and Evaluation (GRADE), respectively.

Seventy-two prognostic factors were assessed across 27 studies involving 4426 participants. Only age, baseline body mass index (BMI), and sex were suitable for meta-analysis. Age (b=−0.044, 95%CI −0.157–0.069), sex (b=0.236, 95%CI −0.086–0.558), and baseline BMI (b=−0.013 95%CI −0.225–0.200) were associated with nonsignificant effects on AIWG prognosis. The highest quality GRADE rating was moderate in support of age, trend of early BMI increase, antipsychotic treatment response, unemployment, and antipsychotic plasma concentration. Trend of early BMI increase was identified as the most clinically significant prognostic factor influencing long-term AIWG prognosis.

The strong prognostic information provided by BMI trend change within 12 weeks of antipsychotic initiation should be included within AIWG management guidance to highlight those at highest risk of worse long-term prognosis. Antipsychotic switching and resource-intensive lifestyle interventions should be targeted toward this cohort. Our results challenge previous research that several clinical variables significantly influence AIWG prognosis. We provide the first mapping and statistical synthesis of studies examining non-genetic prognostic factors of AIWG and highlight practice, policy, and research implications.

Metabolic side effects of psychotropic medications are a major drawback to patients’ effective treatment. Among the mechanisms underlying their development, DNA methylation may be involved.

The aim of this study was to estimate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and baseline DNA methylation or 1-month DNA methylation changes, using an epigenome-wide approach.

Seventy-nine psychiatric patients recruited as part of PsyMetab study, who started a treatment with either an antipsychotic, a mood stabilizer or mirtazapine were selected. Epigenome-wide DNA methylation was measured using the Illumina Methylation EPIC BeadChip at baseline and after one month of treatment.

A global methylation increase was observed after 1 month of treatment, which was more pronounced in patients whose weight remained stable (i.e., <2.5% weight increase). Epigenome-wide significant methylation changes were observed at 52 loci in the whole cohort and at one site, namely cg12209987, located in an intergenic region within an enhancer, specifically in patients who underwent important early weight gain (i.e., ≥5% weight increase) during the same period of treatment (p<5*10-8). Multivariable analysis confirmed an association between an increase in methylation at this locus and weight gain in the whole cohort (p=0.004). Epigenome-wide association analyses failed to identify any significant link between other metabolic changes (e.g. glucose or lipid levels) and methylation data.

These findings give new insight into the mechanisms of psychotropic drug-induced weight gain. With improved understanding of the metabolic side effects, the use of precision medicine with epigenetics may become possible

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