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  • 1
    Online Resource
    Online Resource
    Peripheral lymphocyte signaling pathway deficiencies predict treatment response in first-onset drug-naïve schizophrenia
    Elsevier BV ; 2022
    In:  Brain, Behavior, and Immunity Vol. 103 ( 2022-07), p. 37-49
    Details
    In: Brain, Behavior, and Immunity, Elsevier BV, Vol. 103 ( 2022-07), p. 37-49
    Type of Medium: Online Resource
    ISSN: 0889-1591
    URL: Article
    DOI: 10.1016/j.bbi.2022.03.016
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 2
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    Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals
    Springer Science and Business Media LLC ; 2019
    In:  Translational Psychiatry Vol. 9, No. 1 ( 2019-11-07)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-11-07)
    Abstract: Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (training set patient group), 37 subthreshold individuals who developed MDD within two or four years (extrapolation test set patient group), and 86 subthreshold individuals who did not develop MDD within four years (shared reference group). To ensure the development of a robust and reproducible model, we applied feature extraction and model averaging across a set of 100 models obtained from repeated application of group LASSO regression with ten-fold cross-validation on the training set. This resulted in a 12-feature prediction model consisting of six serum proteins (AACT, APOE, APOH, FETUA, HBA and PHLD), three sociodemographic factors (body mass index, childhood trauma and education level) and three depressive symptoms (sadness, fatigue and leaden paralysis). Importantly, the model demonstrated a fair performance in predicting future MDD diagnosis of subthreshold individuals in the extrapolation test set (AUC = 0.75), which involved going beyond the scope of the model. These findings suggest that it may be possible to detect disease indications in subthreshold individuals up to four years prior to diagnosis, which has important clinical implications regarding the identification and treatment of high-risk individuals.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-019-0623-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
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  • 3
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    Multimodel inference for biomarker development: an application to schizophrenia
    Springer Science and Business Media LLC ; 2019
    In:  Translational Psychiatry Vol. 9, No. 1 ( 2019-02-11)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-02-11)
    Abstract: In the present study, to improve the predictive performance of a model and its reproducibility when applied to an independent data set, we investigated the use of multimodel inference to predict the probability of having a complex psychiatric disorder. We formed training and test sets using proteomic data (147 peptides from 77 proteins) from two-independent collections of first-onset drug-naive schizophrenia patients and controls. A set of prediction models was produced by applying lasso regression with repeated tenfold cross-validation to the training set. We used feature extraction and model averaging across the set of models to form two prediction models. The resulting models clearly demonstrated the utility of a multimodel based approach to make good (training set AUC  〉  0.80) and reproducible predictions (test set AUC  〉  0.80) for the probability of having schizophrenia. Moreover, we identified four proteins (five peptides) whose effect on the probability of having schizophrenia was modified by sex, one of which was a novel potential biomarker of schizophrenia, foetal haemoglobin. The evidence of effect modification suggests that future schizophrenia studies should be conducted in males and females separately. Future biomarker studies should consider adopting a multimodel approach and going beyond the main effects of features.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-019-0419-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
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  • 4
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    Converging evidence points towards a role of insulin signaling in regulating compulsive behavior
    Springer Science and Business Media LLC ; 2019
    In:  Translational Psychiatry Vol. 9, No. 1 ( 2019-09-12)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-09-12)
    Abstract: Obsessive–compulsive disorder (OCD) is a neuropsychiatric disorder with childhood onset, and is characterized by intrusive thoughts and fears (obsessions) that lead to repetitive behaviors (compulsions). Previously, we identified insulin signaling being associated with OCD and here, we aim to further investigate this link in vivo. We studied TALLYHO/JngJ (TH) mice, a model of type 2 diabetes mellitus, to (1) assess compulsive and anxious behaviors, (2) determine neuro-metabolite levels by 1 H magnetic resonance spectroscopy (MRS) and brain structural connectivity by diffusion tensor imaging (DTI), and (3) investigate plasma and brain protein levels for molecules previously associated with OCD (insulin, Igf1, Kcnq1, and Bdnf) in these subjects. TH mice showed increased compulsivity-like behavior (reduced spontaneous alternation in the Y-maze) and more anxiety (less time spent in the open arms of the elevated plus maze). In parallel, their brains differed in the white matter microstructure measures fractional anisotropy (FA) and mean diffusivity (MD) in the midline corpus callosum (increased FA and decreased MD), in myelinated fibers of the dorsomedial striatum (decreased FA and MD), and superior cerebellar peduncles (decreased FA and MD). MRS revealed increased glucose levels in the dorsomedial striatum and increased glutathione levels in the anterior cingulate cortex in the TH mice relative to their controls. Igf1 expression was reduced in the cerebellum of TH mice but increased in the plasma. In conclusion, our data indicates a role of (abnormal) insulin signaling in compulsivity-like behavior.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-019-0559-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
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  • 5
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    Diagnostic model development for schizophrenia based on peripheral blood mononuclear cell subtype-specific expression of metabolic markers
    Springer Science and Business Media LLC ; 2022
    In:  Translational Psychiatry Vol. 12, No. 1 ( 2022-10-30)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-10-30)
    Abstract: A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls ( n  = 26) and recent-onset schizophrenia patients ( n  = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells ( F  = 10.75, P  = 0.002, Q  = 0.024 and F  = 21.58, P  = 2.8 × 10 −5 , Q  = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes ( F  = 21.46, P  = 2.9 × 10 −5 , Q  = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66–0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients ( n  = 34) from healthy controls ( n  = 39) with an AUC of 0.75 (95% CI: 0.64–0.86), and also differentiated schizophrenia patients ( n  = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder ( n  = 68), with an AUC of 0.83 (95% CI: 0.75–0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-022-02229-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
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  • 6
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    Leptin Serum Levels are Associated With GLP-1 Receptor Agonist-Mediated Effects on Glucose Metabolism in Clozapine- or Olanzapine-Treated, Prediabetic, Schizophrenia Patients
    Oxford University Press (OUP) ; 2020
    In:  Schizophrenia Bulletin Open Vol. 1, No. 1 ( 2020-01-01)
    Details
    In: Schizophrenia Bulletin Open, Oxford University Press (OUP), Vol. 1, No. 1 ( 2020-01-01)
    Abstract: We previously demonstrated that the glucagon-like peptide-1 receptor agonist (GLP-1RA) liraglutide significantly reduced glucometabolic disturbances and body weight vs placebo in prediabetic, overweight, or obese schizophrenia-spectrum disorder patients treated with clozapine or olanzapine. Here, we aimed to identify potential biomarkers of prediabetes and the GLP-1RA-induced effects on glucose tolerance in schizophrenia patients treated with clozapine or olanzapine. Methods Multiplexed immunoassays were used to measure 8 proteins (adiponectin, C-reactive protein, interleukin-1 receptor antagonist, leptin, macrophage migration inhibitory factor, prolactin, receptor for advanced glycation end products, and vascular endothelial growth factor [VEGF]) in fasting prediabetic and non-prediabetic patients with schizophrenia-spectrum disorder, the prediabetic patients receiving 16-week randomized treatment with liraglutide or placebo. Results Serum adiponectin (P = .004) and VEGF (P = .019) levels were significantly lower in prediabetic (n = 81) than non-prediabetic schizophrenia-spectrum disorder patients (n = 32). Adiponectin levels increased significantly (P = .022) and leptin levels decreased significantly (P = .017) following treatment with liraglutide (n = 39) vs placebo (n = 42). Importantly, patients receiving liraglutide who had higher baseline leptin levels showed significantly larger reductions in the primary endpoint, the 75-g oral glucose tolerance test value, than patients with lower baseline leptin levels (P = .009). Conclusion These results provide new evidence for metabolic alterations associated with prediabetes and GLP-1RA treatment in the context of schizophrenia. They suggest that leptin may be a valuable biomarker predicting GLP-1RA-induced improvement in glucose tolerance in overweight or obese schizophrenia-spectrum disorder patients with prediabetes treated with clozapine or olanzapine. These findings require further validation in larger numbers of individuals.
    Type of Medium: Online Resource
    ISSN: 2632-7899
    URL: Article
    DOI: 10.1093/schizbullopen/sgaa044
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 3040502-6
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  • 7
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    Evidence of microglial activation following exposure to serum from first-onset drug-naïve schizophrenia patients
    Elsevier BV ; 2018
    In:  Brain, Behavior, and Immunity Vol. 67 ( 2018-01), p. 364-373
    Details
    In: Brain, Behavior, and Immunity, Elsevier BV, Vol. 67 ( 2018-01), p. 364-373
    Type of Medium: Online Resource
    ISSN: 0889-1591
    URL: Article
    DOI: 10.1016/j.bbi.2017.10.003
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
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  • 8
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    Schizophrenia-risk and urban birth are associated with proteomic changes in neonatal dried blood spots
    Springer Science and Business Media LLC ; 2017
    In:  Translational Psychiatry Vol. 7, No. 12 ( 2017-12-18)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 7, No. 12 ( 2017-12-18)
    Abstract: In the present study, we tested whether there were proteomic differences in blood between schizophrenia patients after the initial onset of the disorder and controls; and whether those differences were also present at birth among neonates who later developed schizophrenia compared to those without a psychiatric admission. We used multiple reaction monitoring mass spectrometry to quantify 77 proteins (147 peptides) in serum samples from 60 first-onset drug-naive schizophrenia patients and 77 controls, and 96 proteins (152 peptides) in 892 newborn blood-spot (NBS) samples collected between 1975 and 1985. Both serum and NBS studies showed significant alterations in protein levels. Serum results revealed that Haptoglobin and Plasma protease C1 inhibitor were significantly upregulated in first-onset schizophrenia patients (corrected P   〈  0.05). Alpha-2-antiplasmin, Complement C4-A and Antithrombin-III were increased in first-onset schizophrenia patients (uncorrected P- values 0.041, 0.036 and 0.013, respectively) and also increased in newborn babies who later develop schizophrenia ( P- values 0.0058, 0.013 and 0.044, respectively). We also tested whether protein abundance at birth was associated with exposure to an urban environment during pregnancy and found highly significant proteomic differences at birth between urban and rural environments. The prediction model for urbanicity had excellent predictive performance in both discovery (area under the receiver operating characteristic curve (AUC) = 0.90) and validation (AUC = 0.89) sample sets. We hope that future biomarker studies based on stored NBS samples will identify prognostic disease indicators and targets for preventive measures for neurodevelopmental conditions, particularly those with onset during early childhood, such as autism spectrum disorder.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-017-0027-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
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  • 9
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    A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data
    Springer Science and Business Media LLC ; 2021
    In:  Translational Psychiatry Vol. 11, No. 1 ( 2021-01-12)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-01-12)
    Abstract: The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD ( N  = 126) from those with correct MDD diagnosis ( N  = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD ( N  = 98) from MDD ( N  = 112) and subclinical low mood ( N  = 120), respectively. Validation in participants with a previous diagnosis of BD ( N  = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-020-01181-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 10
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    Towards reproducible MRM based biomarker discovery using dried blood spots
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-03-27)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-03-27)
    Abstract: There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation ( r  = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/srep45178
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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