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

Associations between antipsychotics-induced weight gain and brain networks of impulsivity

Given the unpredictable rapid onset and ubiquitous consequences of weight gain induced by antipsychotics, there is a pressing need to get insights into the underlying processes at the brain system level that will allow stratification of "at risk" patients. The pathophysiological hypothesis at hand is focused on brain networks governing impulsivity that are modulated by neuro-inflammatory processes. To this aim, we investigated brain anatomy and functional connectivity in patients with early psychosis (median age: 23 years, IQR = 21-27) using anthropometric data and magnetic resonance imaging acquired one month to one year after initiation of AP medication. Our analyses included 19 patients with high and rapid weight gain (i.e., ≥5% from baseline weight after one month) and 23 patients with low weight gain (i.e., <5% from baseline weight after one month). We replicated our analyses in young (26 years, IQR = 22-33, N = 102) and middle-aged (56 years, IQR = 51-62, N = 875) healthy individuals from the general population. In early psychosis patients, higher weight gain was associated with poor impulse control score (β = 1.35; P = 0.03). Here, the observed brain differences comprised nodes of impulsivity networks - reduced frontal lobe grey matter volume (Pcorrected = 0.007) and higher striatal volume (Pcorrected = 0.048) paralleled by disruption of fronto-striatal functional connectivity (R = -0.32; P = 0.04). Weight gain was associated with the inflammatory biomarker plasminogen activator inhibitor-1 (β = 4.9, P = 0.002). There was no significant association between increased BMI or weight gain and brain anatomy characteristics in both cohorts of young and middle-aged healthy individuals. Our findings support the notion of weight gain in treated psychotic patients associated with poor impulse control, impulsivity-related brain networks and chronic inflammation.

The genetics of bipolar disorder with obesity and type 2 diabetes

BACKGROUND

Bipolar disorder (BD) presents with high obesity and type 2 diabetes (T2D) and pathophysiological and phenomenological abnormalities shared with cardiometabolic disorders. Genomic studies may help define if they share genetic liability. This selective review of BD with obesity and T2D will focus on genomic studies, stress their current limitations and guide future steps in developing the field.

METHODS

We searched electronic databases (PubMed, Scopus) until December 2021 to identify genome-wide association studies, polygenic risk score analyses, and functional genomics of BD accounting for body mass index (BMI), obesity, or T2D.

RESULTS

The first genome-wide association studies (GWAS) of BD accounting for obesity found a promising genome-wide association in an intronic gene variant of TCF7L2 that was further replicated. Polygenic risk scores of obesity and T2D have also been associated with BD, yet, no genetic correlations have been demonstrated. Finally, human-induced stem cell studies of the intronic variant in TCF7L2 show a potential biological impact of the products of this genetic variant in BD risk.

LIMITATIONS

The narrative nature of this review.

CONCLUSIONS

Findings from BD GWAS accounting for obesity and their functional testing, have prompted potential biological insights. Yet, BD, obesity, and T2D display high phenotypic, genetic, and population-related heterogeneity, limiting our ability to detect genetic associations. Further studies should refine cardiometabolic phenotypes, test gene-environmental interactions and add population diversity.

Psychological trauma occurring during adolescence is associated with an increased risk of greater waist circumference in Early Psychosis patients treated with psychotropic medication

BACKGROUND

It has been suggested that exposure to Childhood Trauma [CT] may play a role in the risk of obesity in Early Psychosis [EP] patients; however, whether this is independently of age at exposure to CT and the medication profile has yet to be investigated.

METHODS

113 EP-patients aged 18-35 were recruited from the Treatment and Early Intervention in Psychosis Program [TIPP-Lausanne]. Body Mass Index [BMI], Weight Gain [WG] and Waist Circumference [WC] were measured prospectively at baseline and after 1, 2, 3, 6 and 12 months of weight gain inducing psychotropic treatment. Patients were classified as Early-Trauma and Late-Trauma if the exposure had occurred before age 12 or between ages 12 and 16 respectively. Generalized Linear Mixed-Models were adjusted for age, sex, socioeconomic status, baseline BMI, medication and for diagnosis of depression.

RESULTS

Late-Trauma patients, when compared to Non-Trauma patients showed greater WCs during the follow-up [p = 0.013]. No differences were found in any of the other follow-up measures.

CONCLUSIONS

Exposition to CT during adolescence in EP-patients treated with psychotropic medication is associated with greater WC during the early phase of the disease. Further investigation exploring mechanisms underlying the interactions between peripubertal stress, corticoids responsiveness and a subsequent increase of abdominal adiposity is warranted.

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

Metabolic side effects such as weight gain and disturbed lipid metabolism are often observed in the treatment of atypical antipsychotic drugs (AAPDs), which contribute to an excessive prevalence of metabolic syndrome among schizophrenic patients. Great individual differences are observed but the underlying mechanisms are still uncertain. Research on pharmacogenomics indicates that gene polymorphisms involved in the pathways controlling food intake and lipid metabolism may play a significant role. In this review, relevant genes (HTR2C, DRD2, LEP, NPY, MC4R, BDNF, MC4R, CNR1, INSIG2, ADRA2A) and genetic polymorphisms related to metabolic side effects of AAPDs especially dyslipidemia were summarized. Apart from clinical studies, in vitro and in vivo evidence is also analyzed to support related theories. The association of central and peripheral mechanisms is emphasized, enabling the possibility of using peripheral gene expression to predict the central status. Novel methodological development of pharmacogenomics is in urgent need, so as to provide references for individualized medication and further to shed some light on the mechanisms underlying AAPD-induced lipid disturbances.

Cardiometabolic Adverse Effects and Its Predictors in Children and Adolescents With First-Episode Psychosis During Treatment With Quetiapine-Extended Release Versus Aripiprazole: 12-Week Results From the Tolerance and Effect of Antipsychotics in Children and Adolescents With Psychosis (TEA) Trial

OBJECTIVE

To investigate cardiometabolic effects and their predictors in youths with first-episode psychosis (FEP) treated with quetiapine-extended release (ER) versus aripiprazole.

METHOD

Youths with FEP who were 12 to 17 years of age were randomized to quetiapine-ER or aripiprazole in the 12-week, double-blinded, Tolerability and Efficacy of Antipsychotics (TEA) trial. Primary outcome was change in body weight; secondary outcomes were changes in body mass index (BMI) and waist circumference (WC), blood pressure (BP), heart rate, and lipid and glucose metabolism parameters. Possible predictors of cardiometabolic changes were examined.

RESULTS

Altogether, 113 patients (schizophrenia-spectrum disorders = 93%; age [mean ± SD] = 15.7 ± 1.4 years; male participants = 30.1%) were randomized to quetiapine-ER (n = 55) or aripiprazole (n = 58). Quetiapine-ER led to significant increases in body weight (4.88 kg, 95% CI = 3.92-5.83, p < .0001), BMI z-score (0.43, 95% CI = 0.33-0.53, p < .0001), and WC z-score (0.97, CI = 0.7-1.23, p < .0001). Changes were significantly smaller with aripiprazole (all between-group p values <.0001): body weight: 1.97 kg (CI = 0.97-2.97, p = .0001), BMI z-score: 0.10 (CI = -0.01 to 0.20, p = .0646), and WC z-score: 0.18 (CI = -0.09 to 0.45, p = .1968). Lipid and glucose metabolism parameters increased significantly at week 4 and week 12 only with quetiapine-ER (p range = 0.0001-0.037). Quetiapine-ER was associated with an increased occurrence of obesity, elevated blood lipids and hyperinsulinemia (p range = 0.004-0.039). Early weight gain, obesity, or type 2 diabetes in the family significantly predicted weight and BMI gain at week 12.

CONCLUSION

In youths with FEP, quetiapine-ER was associated with significantly greater weight gain and adverse changes in metabolic outcomes than was aripiprazole. Early weight gain must be addressed and family lifestyle factors taken into consideration when treating youths with antipsychotics.

CLINICAL TRIAL REGISTRATION INFORMATION

Tolerance and Effect of Antipsychotics in Children and Adolescents With Psychosis (TEA); https://clinicaltrials.gov; NCT01119014.

Megakaryocyte volume modulates bone marrow niche properties and cell migration dynamics

All hematopoietic cells that develop in the bone marrow must cross the endothelial barrier to enter the blood circulation. Blood platelets, however, are released by bigger protrusions of huge progenitor cells, named megakaryocytes, and enter the blood stream as so-called proplatelets before fragmenting into mature platelets. Recently, a second function of megakaryocytes has been identified, as they modulate the quiescence of hematopoietic stem cells, mostly via different soluble factors. We know from light sheet fluorescence microscopy images that megakaryocytes are distributed throughout the bone marrow facing a dense vascular network. Here, we used such three-dimensional images to provide a realistic simulation template reflecting the in vivo cell-vessel distributions resulting in reliable whole-bone analysis in silico Combining this approach with an automated image analysis pipeline, we found that megakaryocytes influence migration of neutrophils and hematopoietic stem cells, and thus act as biomechanical restrainers modulating cell mobility and extravasation. Indeed, as a consequence of increased megakaryocyte volumes in platelet-depleted mice neutrophil mobility was reduced in these animals.

New findings in pharmacogenetics of schizophrenia

PURPOSE OF REVIEW

This review highlights recent advances in the investigation of genetic factors for antipsychotic response and side effects.

RECENT FINDINGS

Antipsychotics prescribed to treat psychotic symptoms are variable in efficacy and propensity for causing side effects. The major side effects include tardive dyskinesia, antipsychotic-induced weight gain (AIWG), and clozapine-induced agranulocytosis (CIA). Several promising associations of polymorphisms in genes including HSPG2, CNR1, and DPP6 with tardive dyskinesia have been reported. In particular, a functional genetic polymorphism in SLC18A2, which is a target of recently approved tardive dyskinesia medication valbenazine, was associated with tardive dyskinesia. Similarly, several consistent findings primarily from genes modulating energy homeostasis have also been reported (e.g. MC4R, HTR2C). CIA has been consistently associated with polymorphisms in the HLA genes (HLA-DQB1 and HLA-B). The association findings between glutamate system genes and antipsychotic response require additional replications.

SUMMARY

The findings to date are promising and provide us a better understanding of the development of side effects and response to antipsychotics. However, more comprehensive investigations in large, well characterized samples will bring us closer to clinically actionable findings.

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

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.

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

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.