Tristram A. Lett

Pharmacogenetics of antipsychotic-induced weight gain: review and clinical implications

Second-generation antipsychotics (SGAs), such as risperidone, clozapine and olanzapine, are the most common drug treatments for schizophrenia. SGAs presented an advantage over first-generation antipsychotics (FGAs), particularly regarding avoidance of extrapyramidal symptoms. However, most SGAs, and to a lesser degree FGAs, are linked to substantial weight gain. This substantial weight gain is a leading factor in patient non-compliance and poses significant risk of diabetes, lipid abnormalities (that is, metabolic syndrome) and cardiovascular events including sudden death. The purpose of this article is to review the advances made in the field of pharmacogenetics of antipsychotic-induced weight gain (AIWG). We included all published association studies in AIWG from December 2006 to date using the Medline and ISI web of knowledge databases. There has been considerable progress reaffirming previous findings and discovery of novel genetic factors. The HTR2C and leptin genes are among the most promising, and new evidence suggests that the DRD2, TNF, SNAP-25 and MC4R genes are also prominent risk factors. Further promising findings have been reported in novel susceptibility genes, such as CNR1, MDR1, ADRA1A and INSIG2. More research is required before genetically informed, personalized medicine can be applied to antipsychotic treatment; nevertheless, inroads have been made towards assessing genetic liability and plausible clinical application.

Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates

INTRODUCTION Advances in image segmentation of magnetic resonance images (MRI) have demonstrated that multi-atlas approaches improve segmentation over regular atlas-based approaches. These approaches often rely on a large number of manually segmented atlases (e.g. 30-80) that take significant time and expertise to produce. We present an algorithm, MAGeT-Brain (Multiple Automatically Generated Templates), for the automatic segmentation of the hippocampus that minimises the number of atlases needed whilst still achieving similar agreement to multi-atlas approaches. Thus, our method acts as a reliable multi-atlas approach when using special or hard-to-define atlases that are laborious to construct. METHOD MAGeT-Brain works by propagating atlas segmentations to a template library, formed from a subset of target images, via transformations estimated by nonlinear image registration. The resulting segmentations are then propagated to each target image and fused using a label fusion method. We conduct two separate Monte Carlo cross-validation experiments comparing MAGeT-Brain and basic multi-atlas whole hippocampal segmentation using differing atlas and template library sizes, and registration and label fusion methods. The first experiment is a 10-fold validation (per parameter setting) over 60 subjects taken from the Alzheimers Disease Neuroimaging Database (ADNI), and the second is a five-fold validation over 81 subjects having had a first episode of psychosis. In both cases, automated segmentations are compared with manual segmentations following the Pruessner-protocol. Using the best settings found from these experiments, we segment 246 images of the ADNI1:Complete 1Yr 1.5 T dataset and compare these with segmentations from existing automated and semi-automated methods: FSL FIRST, FreeSurfer, MAPER, and SNT. Finally, we conduct a leave-one-out cross-validation of hippocampal subfield segmentation in standard 3T T1-weighted images, using five high-resolution manually segmented atlases (Winterburn et al., 2013). RESULTS In the ADNI cross-validation, using 9 atlases MAGeT-Brain achieves a mean Dices Similarity Coefficient (DSC) score of 0.869 with respect to manual whole hippocampus segmentations, and also exhibits significantly lower variability in DSC scores than multi-atlas segmentation. In the younger, psychosis dataset, MAGeT-Brain achieves a mean DSC score of 0.892 and produces volumes which agree with manual segmentation volumes better than those produced by the FreeSurfer and FSL FIRST methods (mean difference in volume: 80 mm(3), 1600 mm(3), and 800 mm(3), respectively). Similarly, in the ADNI1:Complete 1Yr 1.5 T dataset, MAGeT-Brain produces hippocampal segmentations well correlated (r>0.85) with SNT semi-automated reference volumes within disease categories, and shows a conservative bias and a mean difference in volume of 250 mm(3) across the entire dataset, compared with FreeSurfer and FSL FIRST which both overestimate volume differences by 2600 mm(3) and 2800 mm(3) on average, respectively. Finally, MAGeT-Brain segments the CA1, CA4/DG and subiculum subfields on standard 3T T1-weighted resolution images with DSC overlap scores of 0.56, 0.65, and 0.58, respectively, relative to manual segmentations. CONCLUSION We demonstrate that MAGeT-Brain produces consistent whole hippocampal segmentations using only 9 atlases, or fewer, with various hippocampal definitions, disease populations, and image acquisition types. Additionally, we show that MAGeT-Brain identifies hippocampal subfields in standard 3T T1-weighted images with overlap scores comparable to competing methods.

Treating Working Memory Deficits in Schizophrenia: A Review of the Neurobiology

Cognitive deficits are a core feature of schizophrenia. Among these deficits, working memory impairment is considered a central cognitive impairment in schizophrenia. The prefrontal cortex, a region critical for working memory performance, has been demonstrated as a critical liability region in schizophrenia. As yet, there are no standardized treatment options for working memory deficits in schizophrenia. In this review, we summarize the neuronal basis for working memory impairment in schizophrenia, including dysfunction in prefrontal signaling pathways (e.g., γ-aminobutyric acid transmission) and neural network synchrony (e.g., gamma/theta oscillations). We discuss therapeutic strategies for working memory dysfunction such as pharmacological agents, cognitive remediation therapy, and repetitive transcranial magnetic stimulation. Despite the drawbacks of current approaches, the advances in neurobiological and translational treatment strategies suggest that clinical application of these methods will occur in the near future.

Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders

Background Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism spectrum disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene variation may be related to brain morphology to confer risk for ASD or schizophrenia is unknown. Method/Principal Findings 53 healthy individuals between 18–59 years of age were genotyped at 11 single nucleotide polymorphisms of the NRXN1 gene. All subjects received structural MRI scans, which were processed to determine cortical gray and white matter lobar volumes, and volumes of striatal and thalamic structures. Each subjects sensorimotor function was also assessed. The general linear model was used to calculate the influence of genetic variation on neural and cognitive phenotypes. Finally, in silico analysis was conducted to assess potential functional relevance of any polymorphisms associated with brain measures. A polymorphism located in the 3′ untranslated region of NRXN1 significantly influenced white matter volumes in whole brain and frontal lobes after correcting for total brain volume, age and multiple comparisons. Follow-up in silico analysis revealed that this SNP is a putative microRNA binding site that may be of functional significance in regulating NRXN1 expression. This variant also influenced sensorimotor performance, a neurocognitive function impaired in both ASD and schizophrenia. Conclusions Our findings demonstrate that the NRXN1 gene, a vulnerability gene for SCZ and ASD, influences brain structure and cognitive function susceptible in both disorders. In conjunction with our in silico results, our findings provide evidence for a neural and cognitive susceptibility mechanism by which the NRXN1 gene confers risk for both schizophrenia and ASD.

Association study of polymorphisms in leptin and leptin receptor genes with antipsychotic-induced body weight gain

BACKGROUND Antipsychotic-induced weight gain (AIWG) is a serious side-effect of antipsychotic medication leading to metabolic syndrome and increased cardiovascular morbidity. Unfortunately, there are still no valid predictors to assess an individuals risk to gain weight. Previous studies have indicated an impact of genetic variation in the genes encoding leptin, LEP, and leptin receptor, LEPR, on AIWG, but results have not been conclusive. Thus, we investigated polymorphisms in both genes for an association with AIWG. METHODS A total of 181 schizophrenic and schizoaffective patients treated with various antipsychotics were included. In a small subset of patients, leptin plasma levels were additionally obtained. Five polymorphisms in LEP and LEPR (LEP: rs7799039 (-2548G/A polymorphism), rs10954173, rs3828942; LEPR: rs1327120, rs1137101 (Q223R polymorphism) were genotyped using TaqMan assays. Statistical association with % weight change from baseline weight was performed using ANCOVA with baseline weight as covariate. RESULTS ANCOVA showed a non-significant trend for genotype association of the rs7799039 marker (p=.068). No significant association of the other LEP and LEPR SNPs with AIWG was detected. However, we found a significant association between a haplotype of LEP rs7799039G-rs10954173G-rs3828942G (p=.035) and AIWG. The rs7799039 G-allele (p=.042) and G-allele of rs3828942 (p=.032) were associated with higher weight gain. CONCLUSION Our study supports the hypothesis of an impact of LEP gene variation on AIWG. Limitations of our study include heterogeneous samples, short treatment duration and multiple comparisons. Our findings were compared to previous studies in detail in order to provide the readers with a more conclusive picture. However, further studies are warranted including more gene variants and interaction analyses with other genes of the leptin-melanocortin pathway.

ANK3, CACNA1C and ZNF804A gene variants in bipolar disorders and psychosis subphenotype

Abstract Objectives. The ANK3, CACNA1C and ZNF804A genes have been implicated in both bipolar disorders (BPD) and schizophrenia (SCZ). It has been suggested that BPD with psychosis may be a clinical manifestation of genes overlapping between BPD and SCZ. We therefore tested the association of these genes with BPD in a large family-based sample, and then dissected the phenotype into psychosis present or absent subgroups. Methods. We genotyped four high interest single nucleotide polymorphisms from ANK3 (rs10994336, rs9804190), CACNA1C (rs1006737), and ZNF804A (rs1344706). Family based association testing (FBAT) was performed on 312 families, and within psychotic (N = 158) and non-psychotic BPD (N = 119) subgroups. Results. In the whole sample, we found a nominal association in ZNF804A (rs1344706, P = 0.046), and a trend in CACNA1C (rs1006737, P = 0.077). In the psychotic BPD subgroup, as hypothesized, stronger signals were observed in ZNF804A (P = 0.019) and CACNA1C (P = 0.017). We found no association in the ANK3 markers, but the rs10994336 variant was nominally associated with non-psychotic BPD (P = 0.046). Exploratory analysis revealed the rs1344706 variant was also implicated in suicide-attempt behaviour (P = 0.038). Conclusions. These tentative results are consistent with the hypothesis that the subphenotype of BPD with psychosis may represent a clinical manifestation of shared genetic liability between BPD and SCZ.

Brain White Matter Development Is Associated with a Human-Specific Haplotype Increasing the Synthesis of Long Chain Fatty Acids

The genetic and molecular pathways driving human brain white matter (WM) development are only beginning to be discovered. Long chain polyunsaturated fatty acids (LC-PUFAs) have been implicated in myelination in animal models and humans. The biosynthesis of LC-PUFAs is regulated by the fatty acid desaturase (FADS) genes, of which a human-specific haplotype is strongly associated with ω-3 and ω-6 LC-PUFA concentrations in blood. To investigate the relationship between LC-PUFA synthesis and human brain WM development, we examined whether this FADS haplotype is associated with age-related WM differences across the life span in healthy individuals 9–86 years of age (n = 207). Diffusion tensor imaging was performed to measure fractional anisotropy (FA), a putative measure of myelination, of the cerebral WM tracts. FADS haplotype status was determined with a single nucleotide polymorphism (rs174583) that tags this haplotype. Overall, normal age-related WM differences were observed, including higher FA values in early adulthood compared with childhood, followed by lower FA values across older age ranges. However, individuals homozygous for the minor allele (associated with lower LC-PUFA concentrations) did not display these normal age-related WM differences (significant age × genotype interactions, pcorrected < 0.05). These findings suggest that LC-PUFAs are involved in human brain WM development from childhood into adulthood. This haplotype and LC-PUFAs may play a role in myelin-related disorders of neurodevelopmental origin.

The putative functional rs1045881 marker of neurexin-1 in schizophrenia and clozapine response

Neurexin-1 (NRXN1) modulates recruitment of NMDA receptors. Furthermore, clozapine reduces hyperactivity of NMDA receptors. Thus, regulation of the NRXN1 gene may mediate the efficacy of clozapine at reducing cortical hyperactivity. We examined the putative functional SNP, rs1045881, for association with schizophrenia, and the potential role of this SNP in clozapine response. The rs1045881 variant was not significantly associated with schizophrenia (N=302 case-control pairs), but with clozapine response (N=163; p=0.030). Baseline and BPRS scores after six months revealed a trend for rs1045881 genotype by treatment interaction (p=0.079). In the post hoc analysis, a significant association between BPRS negative symptoms score and genotype was observed (p=0.033). These results suggest that the rs1045881 NRXN1 polymorphism may influence clozapine response.

Prefrontal White Matter Structure Mediates the Influence of GAD1 on Working Memory.

The glutamic acid decarboxylase 1 (GAD1) gene is a major determinant of γ-aminobutyric acid (GABA), the most abundant inhibitory neurotransmitter modulating local neuronal circuitry. GABAergic dysfunction and expression of GAD1 have been implicated in the pathophysiology of schizophrenia, and in working memory impairment. We examined the influence of the functional GAD1 rs3749034 variant on white matter fractional anisotropy (FA), cortical thickness, and working memory performance in schizophrenia patients and healthy controls (N=197). Using transcranial magnetic stimulation with electroencephalography (TMS-EEG), we subsequently examined the effect of rs3749034 on long-interval cortical inhibition (LICI) in the dorsolateral prefrontal cortex (DLPFC) in schizophrenia patients and healthy controls (N=66). We found that the rs3749034 T-allele carrier risk group had lower voxel-wise FA in the prefrontal cortex region (PFWE-corrected<0.05) but not cortical thickness. Mixed-model regression revealed a significant effect on attentional processing and working memory across four performance measures (F1,182=11.5, P=8 × 10−4). FA in the prefrontal cortex was associated with digit-span performance. Voxel-wise mediation analysis revealed that the effect GAD1 on poorer digit-span performance statistically predicted the lower white matter FA (PFWE-corrected<0.05). In exploratory analysis, we found a prominent GAD1 genotype-by-diagnosis interaction on DLPFC LICI (F1,56=14.3, P=4.1 × 10−4). Our findings converge on variation in GAD1 gene predicting a susceptibility mechanism that affects white matter FA, GABAergic inhibitory neurotransmission in the DLPFC, and working memory performance. Furthermore, via voxel mediation of FA and TMS-EEG intervention, we provide evidence for a potentially causal mechanism through which aberrant DLPFC GABA signaling may contribute to working memory dysfunction.

Exploratory study on association of genetic variation in TBC1D1 with antipsychotic‐induced weight gain

Previous studies have shown that antipsychotics with high propensity for antipsychotic‐induced weight gain (AIWG) influence glucose transporter type 4 (GLUT4) mediated glucose intake. Variation in the gene encoding TBC1 domain family member 1 (TBC1D1), a Rab‐GTPase activating protein regulating GLUT4 trafficking, has been associated with obesity. Therefore, we investigated the impact of TBC1D1 polymorphisms on AIWG.