Prediction of early weight gain during psychotropic treatment using a combinatorial model with clinical and genetic markers

Vandenberghe, Frederik; Saigí-Morgui, Núria; Delacrétaz, Aurélie; Quteineh, Lina; Crettol, Séverine; Ansermot, Nicolas; Gholam‐Rezaee, Mehdi; Gunten, Armin von; Conus, Philippe; Eap, Chin B.

doi:10.1097/fpc.0000000000000249

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…We also find an association between BMI and number of weight gain drugs prescribed. A recent study predicted early weight gain in individuals starting initial treatment with weight-promoting psychotropic drugs, and found that age and baseline BMI were significantly associated with strong weight gain 64 . In contrast to our results, they found that addition of genetic data (18 genes previously associated with weight gain) improved model performance 64 .…”

Section: Discussionmentioning

confidence: 99%

“…A recent study predicted early weight gain in individuals starting initial treatment with weight-promoting psychotropic drugs, and found that age and baseline BMI were significantly associated with strong weight gain 64 . In contrast to our results, they found that addition of genetic data (18 genes previously associated with weight gain) improved model performance 64 . We tested SNPs previously associated with BMI, in addition to polygenic risk scores and found that adding candidate genes did not improve model performance relative to PRS alone.…”

Section: Discussionmentioning

confidence: 99%

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

Harrison

Gaughran

Murray

et al. 2017

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Harrison

Gaughran

Murray

et al. 2017

Sci Rep

View full text Add to dashboard Cite

show abstract

“…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 ).…”

Section: Outcome Prediction: Current Evidencementioning

confidence: 98%

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

et al. 2018

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The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features—like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis—are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.

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“…Selective CB 1 receptor antagonist SR141716A (rimonabant) ameliorates diet-induced obesity of mice through enhancement of fatty acid oxidation and energy expenditure in white adipocytes (Jbilo et al, 2005), or modulating macrophage inflammatory mediators via gut microbiota alterations (Mehrpouya-Bahrami et al, 2017). Clinical trials proved that rimonabant decreased body weight and waist circumference in overweight or obese patients (Van Gaal et al, 2005;Pi-Sunyer et al, 2006). The 3813G allele at the exon 4 of CNR1 is associated with obesity-related phenotypes like waist circumference and subscapular skinfold thickness in adult men (Russo et al, 2007).…”

Section: The Cannabinoid 1 Receptormentioning

confidence: 99%

“…A multigene risk-model has showed promising results of predicting antipsychotic-induced weight gain (Tiwari et al, 2014). Moreover, the combinatorial model of genetic and clinical data could help to identify patients at high risk for early weight gain (Vandenberghe et al, 2016). On the other hand, the more we know about the mechanism of the metabolic side effects of atypical antipsychotics, the better we can tackle with this troublesome adverse drug reaction.…”

Section: Future Perspectivementioning

confidence: 99%

Progress in Genetic Polymorphisms Related to Lipid Disturbances Induced by Atypical Antipsychotic Drugs

Cao

et al. 2020

Front. Pharmacol.

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Prediction of early weight gain during psychotropic treatment using a combinatorial model with clinical and genetic markers

Abstract: These results may help to implement genetic testing before starting psychotropic drug treatment to identify patients at risk of important WG.

Cited by 10 publications

References 38 publications

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

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

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

Progress in Genetic Polymorphisms Related to Lipid Disturbances Induced by Atypical Antipsychotic Drugs

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