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Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

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Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.

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Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

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Aim. To identify subgroups of patients with primary osteoarthritis of the hip joint (pHOA) with similar imaging and laboratory findings, disease evolution, and response to conventional therapies. Methods. We performed further statistical analyses on patient data from two published, double-blind, randomized, and placebo-controlled studies (DB-RCTs), which examined the effects of intra-articular corticosteroids (ia-CSs), hyaluronic acid (ia-HA)-KИ-109-3-0008/14.01.2014, and intravenous bisphosphonates (iv-BPs) -KИ- 109-3-0009/14.01.2014 compared to the country's standard pHOA therapy. The data span an 8-year follow-up of 700 patients with pHOA, including: 1. Clinical parameters (WOMAC-A, B, C, and T; PtGA). 2. Laboratory markers (serum calcium and phosphate levels; 25-OH-D and PTH, markers for bone sCTX-I and cartilage uCTX-II turnover). 3. Radiological indicators: X-ray stage (Kellgren-Lawrence (K/L) and model (Bombelli/OOARSI), width (mJSW), speed (JSN mm/year), and zone of maximum narrowing of the joint space (max-JSN)-determining the type of femoral head migration (FHM). 4. DXA indicators: bone geometry (HAL; NSA; and MNW); changes in regional and total bone mineral density (TH-BMD, LS-BMD, and TB-BMD). 5. Therapeutic responses (OARSI/MCII; mJSW; JSNmm/yearly) to different drug regimens (iv-BP -zoledronic acid (ZA/-5 mg/yearly for 3 years)); ia-CS 40 mg methylprednisolone acetate, twice every 6 months; and ia-HA with intermediate molecular weight (20 mg/2 mL × 3 weekly applications, two courses every 6 months) were compared to standard of care therapy (Standard of Care/SC/), namely D3-supplementation according to serum levels (20-120 ng/mL; target level of 60 ng/mL), simple analgesics (paracetamol, up to 2.0 g/24 h), and physical exercises. The abovementioned data were integrated into a non-supervised hierarchical agglomerative clustering analysis (NHACA) using Ward's linkage method and the squared Euclidean distance to identify different endophenotypes (EFs). Univariate and multivariate multinomial logistic regression analyses were performed to determine the impact of sex and FHM on clinical and radiographic regression of pHOA. Results. A baseline cluster analysis using incoming (M0) patient data identified three EFs: hypertrophic H-HOA, atrophic A-HOA, and intermediate I-HOA. These EFs had characteristics that were similar to those of patients grouped by radiographic stage and pattern ('H'-RPs, 'I'-RPs, and 'A'-RPs), p < 0.05). The repeated cluster analysis of M36 data identified four EF pHOAs: 1. Hypertrophic (slow progressors, the influence of the type of femoral head migration (FHM) outweighing the influence of sex on progression), progressing to planned total hip replacement (THR) within 5 (K/LIII) to 10 (K/LII) years. 2. Intermediate (sex is more important than the FHM type for progression) with two subgroups: 2#: male-associated (slow progressors), THR within 4 (K/LIII) to 8 years. (K/LII). 2* Female-associated (rapid progressors), THR within 3 (K/LIII) to 5 (K/LII) years. 3. Atrophic (rapid progressors; the influence of FHM type outweighs that of sex), THR within 2 (K/LIII) to 4 (K/LII) years. Each EF, in addition to the patient's individual progression rate, was also associated with a different response to the aforementioned therapies. Conclusions. Clinical endophenotyping provides guidance for a personalized approach in patients with pHOA, simultaneously assisting the creation of homogeneous patient groups necessary for conducting modern genetic and therapeutic scientific studies.

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BACKGROUND: Alzheimer's disease is a neurodegenerative disorder characterized by severe cognitive, behavioral, and psychological symptoms, such as dementia, cognitive decline, apathy, and depression. There are no accurate methods to diagnose the disease or proper therapeutic interventions to treat AD. Therefore, there is a need for novel diagnostic methods and markers to identify AD efficiently before its onset. Recently, there has been a rise in the use of imaging techniques like Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) as diagnostic approaches in detecting the structural and functional changes in the brain, which help in the early and accurate diagnosis of AD. In addition, these changes in the brain have been reported to be affected by variations in genes involved in different pathways involved in the pathophysiology of AD. METHODOLOGY: A literature review was carried out to identify studies that reported the association of genetic variants with structural and functional changes in the brain in AD patients. Databases like PubMed, Google Scholar, and Web of Science were accessed to retrieve relevant studies. Keywords like 'fMRI', 'Alzheimer's', 'SNP', and 'imaging' were used, and the studies were screened using different inclusion and exclusion criteria. RESULTS: 15 studies that found an association of genetic variations with structural and functional changes in the brain were retrieved from the literature. Based on this, 33 genes were identified to play a role in the development of disease. These genes were mainly involved in neurogenesis, cell proliferation, neural differentiation, inflammation and apoptosis. Few genes like FAS, TOM40, APOE, TRIB3 and SIRT1 were found to have a high association with AD. In addition, other genes that could be potential candidates were also identified. CONCLUSION: Imaging genetics is a powerful tool in diagnosing and predicting AD and has the potential to identify genetic biomarkers and endophenotypes associated with the development of the disorder.

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BACKGROUND: Women demonstrate a memory advantage when cognitively healthy yet lose this advantage to men in Alzheimer's disease. However, the genetic underpinnings of this sex difference in memory performance remain unclear. METHODS: We conducted the largest sex-aware genetic study on late-life memory to date (Nmales  = 11,942; Nfemales  = 15,641). Leveraging harmonized memory composite scores from four cohorts of cognitive aging and AD, we performed sex-stratified and sex-interaction genome-wide association studies in 24,216 non-Hispanic White and 3367 non-Hispanic Black participants. RESULTS: We identified three sex-specific loci (rs67099044-CBLN2, rs719070-SCHIP1/IQCJ-SCHIP), including an X-chromosome locus (rs5935633-EGL6/TCEANC/OFD1), that associated with memory. Additionally, we identified heparan sulfate signaling as a sex-specific pathway and found sex-specific genetic correlations between memory and cardiovascular, immune, and education traits. DISCUSSION: This study showed memory is highly and comparably heritable across sexes, as well as highlighted novel sex-specific genes, pathways, and genetic correlations that related to late-life memory. HIGHLIGHTS: Demonstrated the heritable component of late-life memory is similar across sexes. Identified two genetic loci with a sex-interaction with baseline memory. Identified an X-chromosome locus associated with memory decline in females. Highlighted sex-specific candidate genes and pathways associated with memory. Revealed sex-specific shared genetic architecture between memory and complex traits.

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Indoleamine 2,3-dioxygenase 2 (IDO2) is an enzyme of the tryptophan-kynurenine pathway that is constitutively expressed in the brain. To provide insight into the physiological role of IDO2 in the brain, behavioral and neurochemical analyses in IDO2 knockout (KO) mice were performed. IDO2 KO mice showed stereotyped behavior, restricted interest and social deficits, traits that are associated with behavioral endophenotypes of autism spectrum disorder (ASD). IDO2 was colocalized immunohistochemically with tyrosine-hydroxylase-positive cells in dopaminergic neurons. In the striatum and amygdala of IDO2 KO mice, decreased dopamine turnover was associated with increased α-synuclein level. Correspondingly, levels of downstream dopamine D1 receptor signaling molecules such as brain-derived neurotrophic factor and c-Fos positive proteins were decreased. Furthermore, decreased abundance of ramified-type microglia resulted in increased dendritic spine density in the striatum of IDO2 KO mice. Both chemogenetic activation of dopaminergic neurons and treatment with methylphenidate, a dopamine reuptake inhibitor, ameliorated the ASD-like behavior of IDO2 KO mice. Sequencing analysis of exon regions in IDO2 from 309 ASD samples identified a rare canonical splice site variant in one ASD case. These results suggest that the IDO2 gene is, at least in part, a factor closely related to the development of psychiatric disorders.

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While there is substantial agreement on the diagnostic criteria for autism spectrum disorder, it is also acknowledged that it has a broad range of clinical presentations. This can complicate the diagnostic process and aggravate the choice of the most suitable rehabilitative strategy for each child. Attentional difficulties are among the most frequently reported comorbidities in autism spectrum disorder. We investigated the role of SNAP-25 polymorphisms. Synaptosome-associated protein 25 (SNAP25) is a presynaptic membrane-binding protein; it plays a crucial role in neurotransmission and has already been studied in numerous psychiatric disorders. It was also seen to be associated with hyperactivity in children with autism spectrum disorder. We collected clinical, behavioral and neuropsychological data on 41 children with a diagnosis of autism spectrum disorder, and then genotyped them for five single-nucleotide polymorphisms of SNAP-25. Participants were divided into two groups according to the Autism Diagnostic Observation Schedule (ADOS-2) Severity Score. In the group with the highest severity score, we found significant associations of clinical data with polymorphism rs363050 (A/G): children with the GG genotype had lower total IQ, more severe autistic functioning and more attentional difficulties. Our research could be the starting point for outlining a possible endophenotype among patients with autism spectrum disorder who are clinically characterized by severe autistic functioning and significant attentional difficulties.

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INTRODUCTION: Studies that have compared the cognitive alterations of the children of parents with bipolar disorder (CPBD) versus the children of control parents (CCP), present heterogeneous results due to the studies' methodological differences, the age of the population studied, and the lack of standardisation of the measures used for the different neurocognitive domains. The objective was to compare the neurocognitive profile of CPBD versus CCP to observe if there are differences that could be proposed as possible endophenotypes of BD. RESULTS: A total of 107 individuals (51 CPBD, and 56 CCP) with ages between 6 and 16 (mean, 12.2±2.80) years of age were evaluated. Seventy-four point five percent of the CPBD group had some disorder compared to 67.9% of the CCP group. Tests such as letter-F phonemic verbal fluency, letter-S phonemic verbal fluency, overall F-A-S phonemic verbal fluency, story recall and retrieval, and Wisconsin perseverative errors showed a difference with a small effect size, but with a high degree of uncertainty. CONCLUSIONS: The CPBD did not have differences in their neurocognitive profile in comparison with CCP. Both groups have a high prevalence of psychopathology, which is a factor that could explain the lack of differences in neurocognitive performance.

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BACKGROUND: Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults. OBJECTIVE: To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome. METHODS: Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up). RESULTS: Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities. CONCLUSIONS: Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

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INTRODUCTION: Schizophrenia is a severe mental illness causing significant impairment in personal, family, social, educational, occupational, and other important areas of life. While there is no widely accepted endophenotype, peripheral blood cells may serve as an accessible model of intracellular changes in schizophrenia. METHODS: We reviewed the literature on the query "peripheral blood mononuclear cells AND schizophrenia" in Medline (Pubmed), selecting studies that searched for specific biomarkers of schizophrenia. We considered both diagnostic biomarkers and biomarkers of therapeutic response, specific schizophrenia disorders or differential diagnostic biomarkers. RESULTS: We retrieved 41 articles matching the search criteria, among which were studies that considered changes in the production of pro-inflammatory and anti-inflammatory markers, proteins, receptors, enzyme activity, and gene expression as potential biomarkers. CONCLUSION: Approaches analysing a biological axis or a group of related biomarkers may hold the greatest promise for identifying schizophrenia. In addition, pharmacological status, smoking status, inflammatory markers and glucose metabolites, the presence of comorbidities should be considered. Certain biomarkers, while not specific for the diagnosis of schizophrenia, may indicate the prognosis and effectiveness of treatment in the established diagnosis.

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Subfertility is a global health issue, and as many as 30% of cases are attributed to unexplained reasons. A hypercaloric, high-fat diet stimulates the expansion of pro-inflammatory gut microbiota with a consequent rise in circulating lipopolysaccharides. Adverse gut microbiota remodeling can exacerbate insulin resistance, while sex and thyroid hormones may influence the variability in gut microbiota. This cross-sectional study included 150 participants and was designed to determine a biochemical, nutritional-related pattern that may distinguish subfertile from fertile individuals and couples. A panel of 28 biomarkers was assessed. Four biochemical phenotypes of unexplained subfertility were found, including two metabolic and two immune, when assessed using binary logistic regression models. Two phenotypes were distinguished in women: cardio-metabolic with atherogenic dyslipidemia (LowHDL-cholesterol: OR = 10.9; p < 0.05) and autoimmune thyroid disorder (Highanti-thyroid-peroxidase: OR = 5.5; p < 0.05) and two in men: hepato-metabolic with elevated liver injury enzymes (HighHOMA-IR: OR = 6.1; p < 0.05) and immune type-2 response (HighIgE: OR = 6.4; p < 0.05). The chances of a couple's subfertility rose with the number of laboratory components of metabolic syndrome in the couple (OR = 1.7; p < 0.05) and if at least one partner had an elevated total IgE level (>100 kU/L) (OR = 6.5; p < 0.05). This study found that unexplained subfertility may be accompanied by mutually overlapping immune and metabolic dysregulations in individuals and couples. We propose one-time laboratory diagnostics taking into account the lipid profile, insulin resistance, anti-thyroid-peroxidase, and total IgE in both males and females with unexplained subfertility. This may allow for a one-time assessment of targeted medical and nutritional interventions and help optimize patients' health. The gut-organ axes related to subfertility are discussed in the context of the obtained results.

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Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-ß (Aß) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.

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Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

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Background: Myeloid cells, including monocytes, macrophages, microglia, dendritic cells and neutrophils are a part of innate immunity, playing a major role in orchestrating innate and adaptive immune responses. Microglia are the resident myeloid cells of the central nervous system, and many Alzheimer's disease (AD) risk loci are found in or near genes that are highly or sometimes uniquely expressed in myeloid cells. Similarly, inflammatory bowel disease (IBD) loci are also enriched for genes expressed by myeloid cells. However, the extent to which there is overlap between the effects of AD and IBD susceptibility loci in myeloid cells remains poorly described, and the substantial IBD genetic maps may help to accelerate AD research. Methods: Here, we leveraged summary statistics from large-scale genome-wide association studies (GWAS) to investigate the causal effect of IBD (including ulcerative colitis and Crohn's disease) variants on AD and AD endophenotypes. Microglia and monocyte expression Quantitative Trait Locus (eQTLs) were used to examine the functional consequences of IBD and AD risk variants enrichment in two different myeloid cell subtypes. Results: Our results showed that, while PTK2B is implicated in both diseases and both sets of risk loci are enriched for myeloid genes, AD and IBD susceptibility loci largely implicate distinct sets of genes and pathways. AD loci are significantly more enriched for microglial eQTLs than IBD. We also found that genetically determined IBD is associated with a lower risk of AD, which may driven by a negative effect on the accumulation of neurofibrillary tangles (beta=-1.04, p=0.013). In addition, IBD displayed a significant positive genetic correlation with psychiatric disorders and multiple sclerosis, while AD showed a significant positive genetic correlation with amyotrophic lateral sclerosis. Conclusion: To our knowledge, this is the first study to systematically contrast the genetic association between IBD and AD, our findings highlight a possible genetically protective effect of IBD on AD even if the majority of effects on myeloid cell gene expression by the two sets of disease variants are distinct. Thus, IBD myeloid studies may not help to accelerate AD functional studies, but our observation reinforces the role of myeloid cells in the accumulation of tau proteinopathy and provides a new avenue for discovering a protective factor.

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The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.

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PURPOSE: PACG is one of the leading causes of blindness where lens thickness is a major risk factor for narrow-angle individuals. To our knowledge, no literature has been reported on candidate gene for lens thickness as a quantitative trait (QT). Here, we performed a genome-wide association analysis on lens thickness in the narrow-angle individuals. MATERIALS AND METHODS: We conducted a genome-wide association study (GWAS) in the narrow angle individuals to investigate comprehensive genetic insights on lens thickness. RESULTS: In QT-GWAS, we identified 145 genome-wide suggestive significant loci in the discovery cohort. Subsequently, we observed 13 SNPs that showed statistical significance around the region of PTRRM. Regional association analysis for top significant genotyped variants identified PTPRM as the most likely candidate for increased LT. Integrative bioinformatic analyses confirmed that the associated genomic region has potential regulatory roles for modulating transcription as enhancers. In the replication cohort, the sentinel genotype SNP was further associated significantly (P-value =0.000448) with high LT individuals. In both cohorts, the T allele of rs1941137 in the PTPRM gene indicates as a risk allele for the increased LT. CONCLUSION: In this study, we discovered evidence of a genomic association between chromosomal areas around the PTPRM and increased lens thickness, resulting in a narrow angle. The regulatory components corresponding to PTPRM variations might have a role in the thicker lens. We report that the genomic region near PTPRM, a gene of potential interest, is associated with increased lens thickness.

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Background: Bipolar disorder (BD) is a chronic multifactorial disorder that presents with cognitive impairment as one of its main features, in patients as well as in their first-degree relatives. However, the profile of cognitive dysfunction in BD patients and their relatives is not yet well defined. Various neurocognitive deficits have been proposed as endophenotypes for BD. In the present study, we explored the susceptibility to neurocognitive deficits in BD patients and their siblings compared to healthy controls. Method: A sample consisting of patients diagnosed with BD (N=37), their unaffected siblings (N=30) and a healthy control group (N=39) was assessed using the Brief Assessment of Cognition for Affective Disorders (BAC-A) battery of tests in various cognitive domains: memory, processing speed, working memory, reasoning and problem solving, and affective processing. Results: Compared to healthy controls, BD patients and their unaffected siblings showed deficits in attention and motor speed, or processing speed as measured by the Symbol coding task (p = 0.008), as well as a similar degree of impairment (p = 1.000). Limitations: The lack of statistically significant findings in the other cognitive domains could be related to differences in task difficulty. Most patients were taking psychotropic medication with varying effects on cognition and being treated as outpatients, implying a currently higher level of functioning, which may limit extrapolation of the sample to the general population of BD patients. Conclusions: These results support the view of considering processing speed as an endophenotype for bipolar disorder.