The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies

© The Author(s) 2017 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases ‘type 2 diabetes, coronary artery disease, hypertension’ and/or for the risk factors ‘blood pressure, obesity, plasma lipid levels, insulin and glucose related traits’. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and ‘depression’ or ‘depressive disorder’ or ‘depressive symptoms’ or ‘bipolar disorder’ or ‘lithium treatment response in bipolar disorder’, or ‘serotonin reuptake inhibitors treatment response in major depression’. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases

Kernel learning for ligand-based virtual screening: discovery of a new PPARgamma agonist

Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results

Hydrogen production for fuel cell application in an autothermal micro-channel reactor

Results concerning the coupling of the steam reforming and total oxidn. of methanol in a two-passage reactor are presented. A com. available copper-based catalyst is used for the steam reforming. For the total oxidn., a highly active cobalt oxide catalyst was developed. Both catalysts are used in form of thin layers immobilized on the wall of the microchannels. Reactor design and operating conditions are based on kinetic models developed under isothermal conditions in microstructured reactors. For the oxidn. reaction, complete conversion of methanol (>99%) at temps. >250 Deg is obsd. For the steam reforming, the hydrogen and CO2 selectivity is >96% for methanol conversion up to 90%. Besides the steady state, the dynamic behavior of the coupled system is studied. It is shown that the transient behavior is mainly detd. by the thermal inertia of the system. [on SciFinder (R)

Drug delivery of 6-bromoindirubin-3’-glycerol-oxime ether employing poly(d,l-lactide-co-glycolide)-based nanoencapsulation techniques with sustainable solvents

Background Insufficient solubility and stability of bioactive small molecules as well as poor biocompatibility may cause low bioavailability and are common obstacles in drug development. One example of such problematic molecules is 6-bromoindirubin-3'-glycerol-oxime ether (6BIGOE), a hydrophobic indirubin derivative. 6BIGOE potently modulates the release of inflammatory cytokines and lipid mediators from isolated human monocytes through inhibition of glycogen synthase kinase-3 in a favorable fashion. However, 6BIGOE suffers from poor solubility and short half-lives in biological aqueous environment and exerts cytotoxic effects in various mammalian cells. In order to overcome the poor water solubility, instability and cytotoxicity of 6BIGOE, we applied encapsulation into poly(d,l-lactide-co-glycolide) (PLGA)-based nanoparticles by employing formulation methods using the sustainable solvents Cyrene™ or 400 g/mol poly(ethylene glycol) as suitable technology for efficient drug delivery of 6BIGOE. Results For all preparation techniques the physicochemical characterization of 6BIGOE-loaded nanoparticles revealed comparable crystallinity, sizes of about 230 nm with low polydispersity, negative zeta potentials around − 15 to − 25 mV, and biphasic release profiles over up to 24 h. Nanoparticles with improved cellular uptake and the ability to mask cytotoxic effects of 6BIGOE were obtained as shown in human monocytes over 48 h as well as in a shell-less hen’s egg model. Intriguingly, encapsulation into these nanoparticles fully retains the anti-inflammatory properties of 6BIGOE, that is, favorable modulation of the release of inflammation-relevant cytokines and lipid mediators from human monocytes. Conclusions Our formulation method of PLGA-based nanoparticles by applying sustainable, non-toxic solvents is a feasible nanotechnology that circumvents the poor bioavailability and biocompatibility of the cargo 6BIGOE. This technology yields favorable drug delivery systems for efficient interference with inflammatory processes, with improved pharmacotherapeutic potential

Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD

Retinal Vascular Occlusion after COVID-19 Vaccination : More Coincidence than Causal Relationship? Data from a Retrospective Multicentre Study

Background: To investigate whether vaccination against SARS-CoV-2 is associated with the onset of retinal vascular occlusive disease (RVOD). Methods: In this multicentre study, data from patients with central and branch retinal vein occlusion (CRVO and BRVO), central and branch retinal artery occlusion (CRAO and BRAO), and anterior ischaemic optic neuropathy (AION) were retrospectively collected during a 2-month index period (1 June–31 July 2021) according to a defined protocol. The relation to any previous vaccination was documented for the consecutive case series. Numbers of RVOD and COVID-19 vaccination were investigated in a case-by-case analysis. A case– control study using age- and sex-matched controls from the general population (study participants from the Gutenberg Health Study) and an adjusted conditional logistic regression analysis was conducted. Results: Four hundred and twenty-one subjects presenting during the index period (61 days) were enrolled: one hundred and twenty-one patients with CRVO, seventy-five with BRVO, fifty-six with CRAO, sixty-five with BRAO, and one hundred and four with AION. Three hundred and thirty-two (78.9%) patients had been vaccinated before the onset of RVOD. The vaccines given were BNT162b2/BioNTech/Pfizer (n = 221), followed by ChadOx1/AstraZeneca (n = 57), mRNA1273/Moderna (n = 21), and Ad26.COV2.S/Johnson & Johnson (n = 11; unknown n = 22). Our case–control analysis integrating population-based data from the GHS yielded no evidence of an increased risk after COVID-19 vaccination (OR = 0.93; 95% CI: 0.60–1.45, p = 0.75) in connection with a vaccination within a 4-week window. Conclusions: To date, there has been no evidence of any association between SARS-CoV-2 vaccination and a higher RVOD risk

HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders

Bipolar afective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratifcation are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identifed genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × ­10−3; FDR< 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common infammatory/autoimmune processes, our fndings strongly suggest that HLA-mediated low infammatory background may contribute to the efcient response to Li in BD patients, while an infammatory status overriding Li anti-infammatory properties would favor a weak response

Association of Attention-Deficit/Hyperactivity Disorder and Depression Polygenic Scores with Lithium Response: A Consortium for Lithium Genetics Study

Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients (N = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosum and in a model including all three PRSs and other covariates, and the PRS of ADHD (β = −0.14; 95% confidence interval [CI]: −0.24 to −0.03; p value = 0.010) and MDD (β = −0.16; 95% CI: −0.27 to −0.04; p value = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34–1.93; p value = 2e−7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD