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Risk stratification for bipolar disorder using polygenic risk scores among young high-risk adults
(2020)
Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD.
Methods: A final sample of 185 young, high-risk German adults (aged 18–35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes.
Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls.
Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Despite the progress to understand inflammatory reactions, mechanisms causing their resolution remain poorly understood. Prostanoids, especially prostaglandin E2 (PGE2), are well-characterized mediators of inflammation. PGE2 is produced in an inducible manner in macrophages (Mϕ) by microsomal PGE2-synthase-1 (mPGES-1), with the notion that it also conveys pro-resolving properties. We aimed to characterize the role of mPGES-1 during resolution of acute, zymosan-induced peritonitis. Experimentally, we applied the mPGES-1 inhibitor compound III (CIII) once the inflammatory response was established and confirmed its potent PGE2-blocking efficacy. mPGES-1 inhibition resulted in an incomplete removal of neutrophils and a concomitant increase in monocytes and Mϕ during the resolution process. The mRNA-seq analysis identified enhanced C-X3-C motif receptor 1 (CX3CR1) expression in resident and infiltrating Mϕ upon mPGES-1 inhibition. Besides elevated Cx3cr1 expression, its ligand CX3CL1 was enriched in the peritoneal lavage of the mice, produced by epithelial cells upon mPGES-1 inhibition. CX3CL1 not only increased adhesion and survival of Mϕ but its neutralization also completely reversed elevated inflammatory cell numbers, thereby normalizing the cellular, peritoneal composition during resolution. Our data suggest that mPGES-1-derived PGE2 contributes to the resolution of inflammation by preventing CX3CL1-mediated retention of activated myeloid cells at sites of injury.
Resveratrol shows beneficial effects in inflammation-based diseases like cancer, cardiovascular and chronic inflammatory diseases. Therefore, the molecular mechanisms of the anti-inflammatory resveratrol effects deserve more attention. In human epithelial DLD-1 and monocytic Mono Mac 6 cells resveratrol decreased the expression of iNOS, IL-8 and TNF-α by reducing mRNA stability without inhibition of the promoter activity. Shown by pharmacological and siRNA-mediated inhibition, the observed effects are SIRT1-independent. Target-fishing and drug responsive target stability experiments showed selective binding of resveratrol to the RNA-binding protein KSRP, a central post-transcriptional regulator of pro-inflammatory gene expression. Knockdown of KSRP expression prevented resveratrol-induced mRNA destabilization in human and murine cells. Resveratrol did not change KSRP expression, but immunoprecipitation experiments indicated that resveratrol reduces the p38 MAPK-related inhibitory KSRP threonine phosphorylation, without blocking p38 MAPK activation or activity. Mutation of the p38 MAPK target site in KSRP blocked the resveratrol effect on pro-inflammatory gene expression. In addition, resveratrol incubation enhanced KSRP-exosome interaction, which is important for mRNA degradation. Finally, resveratrol incubation enhanced its intra-cellular binding to the IL-8, iNOS and TNF-α mRNA. Therefore, modulation of KSRP mRNA binding activity and, thereby, enhancement of mRNA degradation seems to be the common denominator of many anti-inflammatory effects of resveratrol.
The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points.