Development of multivariable models to predict change in Body Mass Index within a clinical trial population of psychotic individuals

Abstract

Many antipsychotics promote weight gain, which can lead to non-compliance and relapse of psychosis. By developing models that accurately identify individuals at greater risk of weight gain, clinicians can make informed treatment decisions and target intervention measures. We examined clinical, genetic and expression data for 284 individuals with psychosis derived from a previously published randomised controlled trial (IMPACT). These data were used to develop regression and classification models predicting change in Body Mass Index (BMI) over one year. Clinical predictors included demographics, anthropometrics, cardiac and blood measures, diet and exercise, physical and mental health, medication and BMI outcome measures. We included genetic polygenic risk scores (PRS) for schizophrenia, bipolar disorder, BMI, waist-hip-ratio, insulin resistance and height, as well as gene co-expression modules generated by Weighted Gene Co-expression Network Analysis (WGCNA). The best performing predictive models for BMI and BMI gain after one year used clinical data only, which suggests expression and genetic data do not improve prediction in this cohort.

Evidence that genes involved in hedgehog signaling are associated with both bipolar disorder and high BMI

“…Our hypothesis that genetic variants might contribute to explain the increased comorbidity between BD and obesity is in contrast with findings from a recent study showing that the inclusion of genetic data in a model comprising clinical characteristics did not improve prediction of BMI or BMI gain after 1 year in a sample of 284 patients with psychosis 29 . However, in this study only 32 patients had a diagnosis of BD, suggesting the need to conduct further studies specifically including BD patients.…”

Is It Possible to Predict the Future in First-Episode Psychosis?

“…Combining the genetic findings with clinical risk factors for weight gain has resulted in modest improvements when compared to only using clinical factors. Whereas, in one study genetic data (SNPs from GWA studies of BMI and candidate gene studies) increased the prediction accuracy compared to using clinical data alone ( 150 ), in another study adding data from PRSs did not improve the prediction of weight gain compared to the clinical information ( 148 ).…”

“…Other reported risk factors for weight gain include female sex, a non-white ethnic background, negative symptoms, poor social functioning, and co-medications, while smoking and cannabis use have been associated with less weight gain ( 141 – 146 ). A dysregulated glucose metabolism may also mark an increased risk of weight gain ( 147 , 148 ). Early weight gain predicted further weight increase in a longer follow-up ( 149 ).…”

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

“…13 (19%) of 67 studies included genetic variables to develop prediction models. [41][42][43][44][45][47][48][49][50][74][75][76][77] Most studies including genetic information (nine [69%] of 13) were conducted using secondary research data and compared the performance of multiple AI algorithms. Genetic information was extracted from genetic variant genotyping data and expression profiles.…”

“…41,76 In eight studies, both genetic and clinical factors were combined as model input variables. [42][43][44][45]47,[74][75][76] In most cases, genetic factors were found to be associated with outcomes and contributed to the model performance. In two studies, the value of adding genetic variables was evaluated.…”