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1. Predicting unipolar and bipolar depression using inflammatory markers, neuroimaging and neuropsychological data: a machine learning study

Author

L. Raffaelli, F. Colombo, F. Calesella, L. Fortaner-Uya, I. Bollettini, C. Lorenzi, E. Maggioni, E. Tassi, S. Poletti, R. Zanardi, F. Attanasio, F. Benedetti, and B. Vai

Subjects
Psychiatry, RC435-571
Abstract

Introduction About 60% of bipolar disorder (BD) cases are initially misdiagnosed as major depressive disorder (MDD), preventing BD patients from receiving appropriate treatment. An urgency exists to identify reliable biomarkers for improving differential diagnosis (DD). Machine learning methods may help translate current knowledge on biomarkers of mood disorders into clinical practice by providing individual-level classification. No study so far has combined biological data with clinical data to provide a multifactorial predictive model for DD. Objectives Define a predictive algorithm for BD and MDD by integrating structural neuroimaging and inflammatory data with neuropsychological measures (NM). Two different algorithms were compared: multiple kernel learning (MKL) and elastic net regularized logistic regression (EN). Methods In a sample of 141 subjects (70 MDD; 71 BD), two different models were implemented for each algorithm: 1) structural neuroimaging measures only (i.e. voxel-based morphometry (VBM), white matter fractional anisotropy (FA), and mean diffusivity (MD)); 2) VBM, FA, and MD combined with NM. In a subsample of 71 subjects (36 BD; 38 MDD), two similar models were implemented: 1) VBM, FA, and, MD combined with only NM; 2) VBM, FA, and MD combined with NM and peripheral inflammatory markers. Finally, the best model was selected for comparison with healthy controls (HC). Results Overall, the EN model based on all the modalities achieved the highest accuracy (AUC = 90.2%), outperforming MKL (AUC=85%). EN correctly classified BD and MDD with a diagnostic accuracy of 78.3%, sensitivity of 75%, and specificity of 81.6%. The most significant predictors of BD (variable inclusion probability (VIP) > 80%) were the parahippocampal cingulate, interleukin 9, chemokine CCL5, posterior thalamic radiation, and internal capsule, whereas MDD was best predicted by chemokine CCL23, the anterior cerebellum, and the sagittal stratum. In contrast, NM did not help to differentiate between MDD and BD. However, they help to distinguish patients from HC. Psychomotor coordination and speed of information processing discriminated between MDD and HC (VIP>90%), whereas fluency, working memory, and executive functions differentiated between BD and HC (VIP>80%). Conclusions In summary, BD was predicted by a strong proinflammatory profile, whereas MDD was identified by structural neuroimaging data. A multimodal approach offers additional instruments to improve personalized diagnosis in clinical practice and enhance the ability to make DD. Disclosure of Interest None Declared

Published
2023
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2. Structural neuroimaging differentiates between depressed bipolar disorder and major depressive disorder patients: a machine learning study

Author

F. Calesella, F. Colombo, B. Bravi, L. Fortaner-Uyà, C. Monopoli, E. Tassi, E. Maggioni, I. Bollettini, S. Poletti, F. Benedetti, and B. Vai

Subjects
Psychiatry, RC435-571
Abstract

Introduction Depression is the predominant mood alteration in bipolar disorder (BD), leading to overlapping symptomatology with major depressive disorder (MDD). Consequently, in clinical assessment, almost 60% of BD patients are misdiagnosed as affected by MDD. This calls for the creation of a framework for the differentiation of BD and MDD patients based on reliable biomarkers. Since machine learning (ML) enables to make predictions at the single-subject level, it appears to be particularly suitable for this task. Objectives We implemented a ML pipeline for the differentiation between depressed BD and MDD patients based on structural neuroimaging features. Methods Diffusion tensor imaging (DTI) and T1-weighted magnetic resonance imaging (MRI) data were acquired for 282 depressed BD (n=180) and MDD (n=102) patients. Axial (AD), radial (RD), mean (MD) diffusivity, and fractional anisotropy (FA) maps were extracted from DTI images, and voxel-based morphometry (VBM) measures were obtained from T1-weighted images. Each feature was entered separately into a 5-fold nested cross-validated ML pipeline differentiating between BD and MDD patients, comprising: confound regression for nuisance variables removal (i.e., age and sex), feature standardization, principal component analysis, and an elastic-net penalized regression. The models underwent 5000 random permutations as a test for significance, and the McNemar’s test was used to assess whether there was any significant difference between the models (significance threshold was set to p

Published
2023
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3. Choroid plexus volume as a proxy of neuroinflammation in depression

Author

B. Bravi, E. Melloni, L. Servidio, E. Agnoletto, M. Paolini, S. Poletti, C. Lorenzi, C. Colombo, and F. Benedetti

Subjects
Psychiatry, RC435-571
Abstract

Introduction Choroid plexus (CP) is a physiological barrier, producing cerebrospinal fluid (CSF), neurotrophic, and inflammatory factors. It’s also involved in the neuro-immune axis, facilitating the interplay between central and peripheral inflammation, allowing trafficking of immune cells. Coherently, CP enlargement has been found in psychiatric diseases characterized by inflammatory signature. Although CP volume correlates with central microglia activation in major depressive disorder (MDD), it’s never been directly associated with peripheral markers in mood disorders. Objectives Examine CP volume in mood disorders and healthy controls (HC) in relation to clinical features and peripheral inflammatory markers. Methods CP volume was extracted with FreeSurfer in 72 HC and 152 age- and sex-matched depressed patients: 79 BD and 73 MDD. Plasma analytes in patients were collected through immunoassay technology (Bioplex). We tested for the effect of age by group on CP volume. Then we focused on the interaction between illness duration and diagnosis in predicting CP volume. After testing the effect of specific analytes by diagnosis, we calculated moderated moderation models (SPSS, PROCESS) setting each analyte as independent variable, CP volume as predicted variable and illness duration and diagnosis as moderators. We get the effects’ significance with the likelihood ratio statistic, always controlling for age, sex, and intracranial volume. Results Patients were comparable in illness duration and severity. CP volume is differentially distributed through groups (right: p=0.04; left: p

Published
2023
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4. The different effect of adverse childhood experiences on Theory of Mind brain networks in schizophrenia and healthy controls

Author

A. Pelucchi, F. Calesella, M. Bechi, R. Cavallaro, S. Poletti, B. Vai, and F. Benedetti

Subjects
Psychiatry, RC435-571
Abstract

Introduction Deficit in Theory of Mind (ToM) is a core feature of schizophrenia (SZ), while adverse childhood experiences (ACEs) can contribute to worsen ToM abilities through their effect on brain functioning, structure and connectivity. Objectives Here, we investigated the effects of ACEs on brain functional connectivity (FC) during an affective and cognitive ToM task (AToM, CToM) in healthy control (HC) and SZ, and whether FC can predict the performance at the ToM task and patients’ symptoms severity. Methods The sample included 26 HC and 33 SZ. In an fMRI session, participants performed a ToM task targeting affective and cognitive domains. Whole-brain FC patterns of local correlation (LC) and multivariate pattern analysis (MVPA) were extracted. The significant MVPA clusters were used as seeds in further seed-based connectivity analyses. Second-level analyses were modelled to investigate the interaction between ACEs, the diagnosis, and the task, corrected for age, sex, and equivalent doses of chlorpromazine (p

Published
2023
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5. Inflammatory signature of post-COVID-19 depression

Author

M. Palladini, M. G. Mazza, V. Aggio, S. Spadini, F. Calesella, S. Poletti, P. Rovere-Querini, and F. Benedetti

Subjects
Psychiatry, RC435-571
Abstract

Introduction Persisting and disabling depressive symptomatology represent a prominent feature of the post-acute COVID-19 syndrome. Sars-CoV-2-induced immune system dysregulation mainly result in a cytokine storm. Once in the brain, inflammatory mediators negatively affect neurotransmission, microglia activation, and oxidative stress, possibly disrupting critical brain neurocircuits which underpin depressive symptoms. So far, only inflammatory markers based on leukocyte counts have been linked to depressive outcome in COVID survivors. However, an accurate immune profile of post-COVID depression has yet to be elucidated. Objectives Identify inflammatory mediators that predict post-COVID depression among a panel of cytokines, chemokines, and growth factors, with a machine learning routine. Methods 88 COVID age- and sex-matched survivors’ (age 52.01 ± 9.32) were screened for depressive symptomatology one month after the virus clearance through the Beck Depression Inventory (BDI-13), with 12.5% of the individuals scoring in the clinical range (BDI-13 ≥ 9). Immune assay was performed through Luminex system on blood sampling obtained in the same context. We entered 42 analytes into an elastic net penalized regression model predicting presence of clinical depression, applied within a 5-fold nested cross-validation machine learning routine running in MATLAB. Significance of predictors was evaluated according to variable inclusion probability (VIP), as returned by 5000 bootstraps. Socio-demographics, previous psychiatric history, hospitalization, time after discharge were used as covariates. Results The model reached a balance accuracy of 73% and AUC of 77%, correctly identifying 73% of people suffering from clinically relevant depressive symptoms (Figure1). Depressive symptomatology was predicted by high levels of CCL17, ICAM-1, MIF, whereas CXCL13, CXCL12, CXCL10, CXCL5, CXCL2, CCL23, CCL15, CCL8, GM-CSF showed a protective effect (Figure2). Image: Image 2: Conclusions This is the first study highlighting a putative inflammatory signature of post-COVID depression. Consistently to the immune profile of Major Depressive disorder, upregulation of innate immunity mediators seems to foster depressive symptoms in the aftermath of COVID. Interestingly, recruiters of B and T cells promoting a physiological adaptive response to viral infection also mitigate its psychiatric sequelae. Understanding the biological basis of post-COVID depression could pave the way for personalized treatments capable of reducing its add-on burden. Disclosure of InterestNone Declared

Published
2023
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6. Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach

Author

L. Fortaner-Uyà, C. Monopoli, F. Calesella, F. Colombo, B. Bravi, E. Maggioni, E. Tassi, S. Poletti, I. Bollettini, F. Benedetti, and B. Vai

Subjects
Psychiatry, RC435-571
Abstract

Introduction Every year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD. Objectives The present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA). Methods 91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC). Results Models’ performances are summarized in Table 1.Table 1. Models’ performances. Input features Algorithm Specificity Sensitivity Balanced accuracy AUC VBM SVM 55.00% 50.00% 52.50% 0.55 FA SVM 72.00% 54.00% 63.00% 0.62 VBM and FA MKL 68.00% 54.00% 61.00% 0.58 Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry. Conclusions Overall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients. Disclosure of Interest None Declared

Published
2023
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7. Unsupervised neurobiology-driven stratification of clinical heterogeneity in depression

Author

F. Colombo, F. Calesella, B. Bravi, L. Fortaner-Uyà, C. Monopoli, E. Maggioni, E. Tassi, R. Zanardi, F. Attanasio, I. Bollettini, S. Poletti, F. Benedetti, and B. Vai

Subjects
Psychiatry, RC435-571
Abstract

Introduction One of the main obstacles in providing effective treatments for major depressive disorder (MDD) is clinical heterogeneity, whose neurobiological correlates are not clearly defined. A biologically meaningful stratification of depressed patients is needed to promote tailored diagnostic procedures. Objectives Using structural data, we performed an unsupervised clustering to define clinically meaningful clusters of depressed patients. Methods T1-weighted and diffusion tensor images were obtained from 102 MDD patients. In 64 patients, clinical symptoms, number of stressful life events, severity and exposure to adverse childhood experiences were evaluated using the Beck Depression Inventory (BDI), Schedule of Recent Experiences (SRE), Risky Family Questionnaire (RFQ), and Childhood Trauma Questionnaire (CTQ). Clustering analyses were performed with extracted tract-based fractional anisotropy (TBSS, FSL), cortical thickness, surface area, and regional measures of grey matter volumes (CAT12). Gaussian mixture model was implemented for clustering, considering Support Vector Machine (SVM) as classifier. A 10x2 repeated cross-validation with grid search was performed for hyperparameters tuning and clusters’ stability. The optimal number of clusters was determined by normalized stability, Akaike and Bayesian information criterion. Analyses were adjusted for total intracranial volume, age, and sex. The clinical relevance of the identified clusters was assessed through MANOVA, considering domains of clinical scales as dependent variables and clusters’ labels as fixed factors. Discriminant analysis was subsequently performed to assess the discriminative power of these variables. Results Cross-validated clustering approach identified 2 highly stable clusters (normalized stability=0.316, AIC=-80292.48, BIC=351329.16). MANOVA showed a significant between-clusters difference in clinical scales scores (p=0.038). Discriminant analysis distinguished the two clusters with an accuracy of 78.1%, with BDI behavioural and CTQ minimisation/denial domains showing the highest discriminant values (0.325 and 0.313). Conclusions Our results defined two biologically informed clusters of MDD patients associated with childhood trauma and specific clinical profiles, which may assist in targeting effective interventions and treatments. Disclosure of Interest None Declared

Published
2023
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8. Resting State Functional Connectivity is Associated With Treatment Response in Major Depression: A Real World Study

Author

Y. Harrington, M. Paolini, V. Bettonagli, F. Colombo, S. Poletti, R. Zanardi, and F. Benedetti

Subjects
Psychiatry, RC435-571
Abstract

Introduction Major depressive disorder (MDD) is largely considered the most prevalent psychiatric disorder worldwide. Despite its domineering presence, effective treatment for many individuals remains elusive. Investigation into relevant biological markers, specifically neuroimaging correlates, of MDD and treatment response have gained traction in recent years; however, findings are still inconsistent. Objectives In this study, we aimed to investigate the resting state functional connectivity patterns associated with treatment response in MDD inpatients in a real world setting. Methods Forty-three inpatients suffering from a major depressive episode were recruited from the psychiatric ward at IRCCS San Raffaele Hospital in Milan, Italy. Symptom severity was assessed via the 21-item Hamilton Depression Rating Scale (HDRS). The percentage of decrease in HDRS scores from admission to discharge was then calculated with the formula [(HDRS admission – HDRS discharge) * 100] / HDRS admission. All patients underwent a 3T MRI scan within one week of admission to acquire resting-state fMRI images, which included 200 sequential T2*-weighted volumes. Images were preprocessed using the CONN toolbox, running within Statistical Parametric Mapping (SPM 12). Preprocessing was performed according to a standard pipeline. A voxel-wise metric, intrinsic connectivity contrast (ICC), was implemented to explore the global resting state functional connectivity (rs-FC) patterns associated with treatment response. ICC-derived maps were then entered in the second-level analyses to examine the effect of the percentage of HDRS decrease, including age, sex, admission HDRS score, duration of hospitalization, and antidepressant dose equivalents as nuisance covariates. Results We found that the percentage of HDRS decrease after treatment predicted rs-FC. ICC analysis identified 2 clusters where changes in HDRS scores were significantly associated with rs-FC, with increased connectivity in the supramarginal gyrus (pFDR = 0.002) and decreased connectivity in the amygdala and parahippocampal gyrus (pFDR = 0.047). Conclusions Our results suggest that altered connectivity of the supramarginal gyrus, amygdala and parahippocampal gyrus is related to antidepressant treatment response. Given that these brain areas are implicated in emotional processing and mood, it is conceivable that a better integrity of brain connectivity may facilitate treatment response in major depression. Disclosure of Interest None Declared

Published
2023
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9. Brain correlates of perceived cognitive impairment after covid-19 infection: a multimodal MRI study.

Author

V. Bettonagli, M. Paolini, M. Palladini, M. G. Mazza, P. Rovere Guerini, S. Poletti, and F. Benedetti

Subjects
Psychiatry, RC435-571
Abstract

Introduction Many different long-term neuropsychiatric sequelae of the novel Coronavirus have been described after the pandemic outbreak. One of the most common symptoms in the months following infection is related to “brain fog”. This condition includes several signs of cognitive impairment like mental slowness, deficits in attention, executive functions, processing, memory, learning, and/or psychomotor coordination, which can be perceived on a subjective level and further confirmed by objective data. Since this kind of mental status has been documented in previous viral infections, and the SARS-COV-2 has been characterized by a worldwide diffusion, investigation into this condition in post-covid individuals is warranted. Currently, several hypotheses on its pathophysiology have been put forward, mostly hypothesizing a direct effect of the virus on the central nervous system or indirect consequences of the inflammatory response. Objectives The aim of our research is to analyze brain correlates of subjective cognitive complaints in Covid-19 survivors using multimodal brain imaging. Methods We performed a voxel-based morphometry (VBM) and a resting state functional connectivity analysis on 60 post-COVID-19 individuals recruited from the San Raffaele Hospital in Milan, that underwent a 3 tesla MRI scan. We assessed the perceived cognitive impairment both after the infection and at the time of the MRI scan through the PROMIS Cognitive Abilities scale. The difference of the two scores (delta PROMIS) was calculated as a measure of cognitive improvement over time. Results We found the perceived amelioration of cognitive abilities (delta PROMIS) to be positively associated to grey matter volumes in the bilateral caudate, putamen and pallidum (pFWE: ˂0.001). Moreover, in the resting state fMRI analysis, subjective cognitive status at MRI was found to be associated with functional connectivity between the right putamen and pallidum, and two clusters belonging to the attentional (pFWE: ˂0.001) and salience (pFWE: 0.02) networks. Conclusions This is one of the first studies investigating brain correlates of subjective cognitive impairment after COVID-19 infection; our main finding is the convergence of structural and functional results on brain areas located within the basal ganglia, implying their possible role in the pathophysiology of the condition. Moreover, this research could be interpreted as the first step toward understanding a very complex condition, with potential implications for the development of treatment and neurorehabilitative strategies. Disclosure of InterestNone Declared

Published
2023
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10. Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study

Author

C. Monopoli, L. Fortaner-Uyà, F. Calesella, F. Colombo, B. Bravi, E. Maggioni, E. Tassi, I. Bollettini, S. Poletti, F. Benedetti, and B. Vai

Subjects
Psychiatry, RC435-571
Abstract

Introduction Bipolar patients (BP) frequently have cognitive deficits, that impact on prognosis and quality of life. Finding biomarkers for this condition is essential to improve patients’ healthcare. Given the association between cognitive dysfunctions and structural brain abnormalities, we used a machine learning approach to identify patients with cognitive deficits. Objectives The aim of this study was to assess if structural neuroimaging data could identify patients with cognitive impairments in several domains using a machine learning framework. Methods Diffusion tensor imaging and T1-weighted images of 150 BP were acquired and both grey matter voxel-based morphometry (VBM) and tract-based white matter fractional anisotropy (FA) measures were extracted. Support vector machine (SVM) models were trained through a 10-fold nested cross-validation with subsampling. VBM and FA maps were entered separately and in combination as input features to discriminate BP with and without deficits in six cognitive domains, assessed through the Brief Assessment of Cognition in Schizophrenia. Results The best classification performance for each cognitive domain is illustrated in Table 1. FA was the most relevant neuroimaging modality for the prediction of verbal memory, verbal fluency, and executive functions deficits, whereas VBM was more predictive for working memory and motor speed domains.Table 1. Performance of best classification models. Input feature Balance Accuracy (%) Specificity (%) Sensitivity (%) Verbal Memory FA 60.17 51.31 43 Verbal Fluency FA 57.67 62 53.33 Executive functions FA 60 63.33 56.67 Working Memory VBM 56.50 56 57 Motor speed VBM 53.50 47.67 59.33 Attention and processing speed VBM + FA 58.33 49.17 67.5 Conclusions Overall, the tested SVM models showed a good predictive performance. Although only partially, our results suggest that different structural neuroimaging data can predict cognitive deficits in BP with accuracy higher than chance level. Unexpectedly, only for the attention and processing speed domain the best model was obtained combining the structural features. Future research may promote data fusion methods to develop better predictive models. Disclosure of Interest None Declared

Published
2023
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11. Effect of Neutrophil to Lymphocyte ratio on antidepressant treatment response: moderating effect of sex and mediating effect of Hippocampal volumes.

Author

M. Paolini, Y. Harrington, J. Ernst, R. Zanardi, S. Poletti, and F. Benedetti

Subjects
Psychiatry, RC435-571
Abstract

Introduction In recent years much focus has been put on the role of immune/inflammatory alterations in affecting Major Depression (MDD) development and antidepressant efficacy. Neutrophil-to-lymphocyte ratio (NLR) is an inexpensive inflammatory marker shown to be elevated in depressed patients, with large population studies reporting this effect only in women. However, its relation to treatment response is much less clear. Reduced hippocampal volumes (HV) are among the few consistent brain structural predictors of poor treatment response, and they have been shown to be influenced by inflammatory status. Objectives To investigate the effect of NLR on treatment response in MDD patients, testing a possible moderating role of sex. To investigate the effect of NLR on HV and test a possible mediating role of the latter in the relation between NLR and treatment response. Methods Our study was performed on a sample of 120 MDD inpatients suffering from a non psychotic depressive episode (F=78; M=42). Depression severity was assessed via the Hamilton Depression Rating Scale (HDRS), both at admission and discharge; as a measure of treatment response, delta HDRS was calculated subtracting the two scores. NLR was calculated for each subject. Patients underwent 3T MRI acquisition and bilateral HV were estimated. Results We found a significant moderating effect of sex on the relationship between NLR and Delta HDRS (p < 0.001): a negative relation was found in women (p < 0.001) and a positive one in men (p = 0.042). NLR was found to negatively affect left HV in the whole sample (p = 0.027) and in women (p = 0.038). A positive effect on Delta HDRS was found for both left (p = 0.038) and right (p = 0.027) HV. Finally, we found a significant indirect effect of NLR values on Delta HDRS through left HV in women (95% BCa CI [- 0.948, -0.017]); the direct effect of NLR on Delta HDRS also remained significant (p = 0.002). Conclusions Sex was found to moderate the relation between NLR and treatment response. The detrimental effect in women is in line with previous reports linking inflammation to hampered antidepressant effect; the positive one in men is more surprising: however, the only studies to date on the effect of NLR on antidepressant efficacy report a positive effect in patients with psychotic depression. In women we found NLR to affect treatment response partially through its effect on left HV, providing a possible, albeit incomplete, mechanistic explanation of the effect of inflammatory status on antidepressant efficacy. Disclosure of Interest None Declared

Published
2023
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12. The exploration of interoception construct in COVID-19 survivors

Author

G. D’Orsi, M. Palladini, A. Scalabrini, M. G. Mazza, S. Poletti, P. Rovere-Querini, and F. Benedetti

Subjects
Psychiatry, RC435-571
Abstract

Introduction The new coronavirus disease (COVID-19) has important physical and mental health implications at short and long term. Some inflammatory parameters are implicated in the maintenance of psychiatric symptoms, especially those of anxiety and depression. Additionally, growing literature attributes a role to interoception in several mental health conditions. Objectives We investigated the involvement of the interoception in COVID-19 survivors and its possible associations with psychopathological and inflammatory variables. Methods Our study included 57 people surviving COVID-19 at one month follow-up after recovery. Individual interoceptive accuracy (IA) measure was obtained through heart-beat perception task. A measure of accuracy in external time perception (TA) was also obtained asking people to mentally produce a duration of 10s. Each participant completed State-Trait Anxiety Inventory - STAI-Y; Zung Self-Rating Depression Scale - ZSDS; Beck Depression Inventory - BDI-II; Impact of Events Scale - IES-R and Multidimensional Assessment of Interoceptive Awareness - MAIA. Peripheral inflammation markers were obtained in a subsample of 40 people by a blood sampling conducted at the time of admission and discharge from hospital. Correlation, regression and GLM analyses were performed with SPSS. Mediation analysis were performed with Hayes’ Process tool. Results TA is not associated with IA, symptomatological measures and bodily awareness. Trusting is the only aspect of body awareness associated with IA (p=.021). Noticing (p=.010), Not-distracting (p=.009), Not-worrying (p=.012) and Trusting (p=.001) predict anxiety psychopathology. Poor IA predict anxiety symptomatology (p=.004) and part of this effect is mediated by Trusting [Fig.1]. In the end, platelets count at the time of hospitalization negatively correlates with anxiety symptoms (p=.003). Image: Conclusions COVID-19 hospitalization could be considered a psychophysical traumatic experience which involved mental and physical health and the connection and integration between them. It’s necessary to deepen the different facets of body awareness and IA in post-covid stages and to study how interoceptive dimensions change over time. Further research is needed to investigate the specific role of platelets in prominent anxiety psychopathology detected in COVID-19 survivors, wondering about their possible involvement in the dysfunctional interoception process too. Disclosure of Interest None Declared

Published
2023
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13. White matter integrity and pro-inflammatory cytokines as predictors of antidepressant response in MDD

Author

S. Breit, E. Mazza, S. Poletti, and F. Benedetti

Subjects
white matter integrity, biomarkers, MDD, pro-inflammatory cytokines, Psychiatry, RC435-571
Abstract

Introduction Major depressive disorder (MDD) often involves immune dysregulation with high peripheral levels of pro-inflammatory cytokines that might have an impact on the clinical course and treatment response. Moreover, MDD patients show brain volume changes and white matter (WM) alterations that are already existing in the early stage of illness. Objectives The aim of the present review is to elucidate the association between inflammation and WM integrity and its impact on the pathophysiology and progression of MDD as well as the role of possible novel biomarkers of treatment response to improve MDD prevention and treatment strategies. Methods We conducted an electronic literature search of PubMed on studies that examined the role of inflammation in depression and that focused on WM integrity and pro-inflammatory cytokines as predictors of antidepressant response. Results There is evidence for central effects of peripheral inflammation which could activate microglia which, in turn, might trigger a cascade of inflammatory processes leading to neurotransmitter imbalances. Numerous studies indicated that both altered levels of peripheral inflammatory markers, particularly TNF-α, IL-6, and CRP as well as WM integrity might predict antidepressant treatment outcome. Conclusions Despite mounting evidence on the impact of the immune system on WM microstructure, no study has yet addressed the interaction between the two factors in influencing antidepressant response. There is a lack of reproducible biomarkers predicting treatment response on an individual basis. The availability of such biomarkers would enable more efficient and personalized treatments with a faster treatment response and better prevention of treatment resistance. Disclosure No significant relationships.

Published
2022
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14. Antinuclear antibodies in postinfectious smell loss - a pilot study

Author

U. Walliczek-Dworschak, A.M. Zimmermann, S. Poletti, and T. Hummel

Subjects
antinuclear antibodies, olfaction, autoimmune disease, smell, auto antibodies, Otorhinolaryngology, RF1-547
Abstract

Background: Numerous diseases are mediated by autoimmune mechanisms some of which have been shown to be associated with reduced olfactory function, e.g. systemic lupus erythematosus, Sjögren syndrome, psoriasis vulgaris. Postinfectious smell loss is a frequent cause of olfactory dysfunction. However, its nature is not fully understood, but connections to immune mediated processes might be possible. The primary aim of the present study was therefore to investigate the possible relation of postinfectious smell loss with autoimmune processes. Methods: Fifty-two patients (35 female, 17 male; mean age 57 years ± 12 years) with postinfectious smell loss and 56 controls (21 female, 35 male; mean age 58 years ± 13 years), with olfactory dysfunction of other causes (sinunasal, posttraumatic or idiopathic) were included in the present study. The patients’ olfactory function was measured using the extended test battery of the "Sniffin’ Sticks“. Furthermore venous blood samples were taken and analyzed for antinuclear autoantibodies. Results: Patients showed anti-nuclear antibodies (ANA) significantly more frequently compared to controls, although ANAs were typically only present at cut off levels of 1:100. In addition, there was a significant association between the presence of ANAs and odor thresholds. Conclusions: Results from this study indicate that postinfectious olfactory dysfunction may be associated with autoimmune processes, as they show significantly more ANAs – although not at a clincially significant level - compared to patients with olfactory deficits of other origin.

Published
2019
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15. Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry

Author

Jitendra PS. Sawhney, Veerappa A. Kothiwale, Vikas Bisne, Rajashekhar Durgaprasad, Praveen Jadhav, Manoj Chopda, Velam Vanajakshamma, Ramdhan Meena, Govindan Vijayaraghavan, Kamaldeep Chawla, Jagan Allu, Karen S. Pieper, A. John Camm, Ajay K. Kakkar, Jean-Pierre Bassand, David A. Fitzmaurice, Samuel Z. Goldhaber, Shinya Goto, Sylvia Haas, Werner Hacke, Lorenzo G. Mantovani, Frank Misselwitz, Alexander G.G. Turpie, Martin van Eickels, Freek W.A. Verheugt, Gloria Kayani, Keith A.A. Fox, Bernard J. Gersh, Hector Lucas Luciardi, Harry Gibbs, Marianne Brodmann, Frank Cools, Antonio Carlos Pereira Barretto, Stuart J. Connolly, Alex Spyropoulos, John Eikelboom, Ramon Corbalan, Dayi Hu, Petr Jansky, Jørn Dalsgaard Nielsen, Hany Ragy, Pekka Raatikainen, Jean-Yves Le Heuzey, Harald Darius, Matyas Keltai, Sanjay Kakkar, Jitendra Pal Singh Sawhney, Giancarlo Agnelli, Giuseppe Ambrosio, Yukihiro Koretsune, Carlos Jerjes Sánchez Díaz, Hugo Ten Cate, Dan Atar, Janina Stepinska, Elizaveta Panchenko, Toon Wei Lim, Barry Jacobson, Seil Oh, Xavier Viñolas, Marten Rosenqvist, Jan Steffel, Pantep Angchaisuksiri, Ali Oto, Alex Parkhomenko, Wael Al Mahmeed, David Fitzmaurice, D.Y. Hu, K.N. Chen, Y.S. Zhao, H.Q. Zhang, J.Z. Chen, S.P. Cao, D.W. Wang, Y.J. Yang, W.H. Li, Y.H. Yin, G.Z. Tao, P. Yang, Y.M. Chen, S.H. He, Ying Wang, Yong Wang, G.S. Fu, X. Li, T.G. Wu, X.S. Cheng, X.W. Yan, R.P. Zhao, M.S. Chen, L.G. Xiong, P. Chen, Y. Jiao, Y. Guo, L. Xue, F.Z. Wang, H. Li, Z.M. Yang, C.L. Bai, J. Chen, J.Y. Chen, X. Chen, S. Feng, Q.H. Fu, X.J. Gao, W.N. Guo, R.H. He, X.A. He, X.S. Hu, X.F. Huang, B. Li, J. Li, L. Li, Y.H. Li, T.T. Liu, W.L. Liu, Y.Y. Liu, Z.C. Lu, X.L. Luo, T.Y. Ma, J.Q. Peng, X. Sheng, X.J. Shi, Y.H. Sun, G. Tian, K. Wang, L. Wang, R.N. Wu, Q. Xie, R.Y. Xu, J.S. Yang, L.L. Yang, Q. Yang, Y. Ye, H.Y. Yu, J.H. Yu, T. Yu, H. Zhai, Q. Zhan, G.S. Zhang, Q. Zhang, R. Zhang, Y. Zhang, W.Y. Zheng, B. Zhou, Z.H. Zhou, X.Y. Zhu, S. Kakkar, J.P.S. Sawhney, P. Jadhav, R. Durgaprasad, A.G. Ravi Shankar, R.K. Rajput, K. Bhargava, R. Sarma, A. Srinivas, D. Roy, U.M. Nagamalesh, M. Chopda, R. Kishore, G. Kulkarni, P. Chandwani, R.A. Pothiwala, M. Padinhare Purayil, S. Shah, K. Chawla, V.A. Kothiwale, B. Raghuraman, G. Vijayaraghavan, V.M. Vijan, G. Bantwal, V. Bisne, A. Khan, J.B. Gupta, S. Kumar, D. Jain, S. Abraham, D. Adak, A. Barai, H. Begum, P. Bhattacharjee, M. Dargude, D. Davies, B. Deshpande, P. Dhakrao, V. Dhyani, S. Duhan, M. Earath, A. Ganatra, S. Giradkar, V. Jain, R. Karthikeyan, L. Kasala, S. Kaur, S. Krishnappa, A. Lawande, B. Lokesh, N. Madarkar, R. Meena, P. More, D. Naik, K. Prashanth, M. Rao, N.M. Rao, N. Sadhu, D. Shah, M. Sharma, P. Shiva, S. Singhal, S. Suresh, V. Vanajakshamma, S.G. Panse, Y. Koretsune, S. Kanamori, K. Yamamoto, K. Kumagai, Y. Katsuda, K. Sadamatsu, F. Toyota, Y. Mizuno, I. Misumi, H. Noguchi, S. Ando, T. Suetsugu, M. Minamoto, Hiroshi Oda, K. Shiraishi, S. Adachi, K. Chiba, H. Norita, M. Tsuruta, T. Koyanagi, H. Ando, T. Higashi, K. Okada, S. Azakami, S. Komaki, K. Kumeda, T. Murayama, J. Matsumura, Y. Oba, R. Sonoda, K. Goto, K. Minoda, Y. Haraguchi, H. Suefuji, H. Miyagi, H. Kato, Tadashi Nakamura, Tsugihiro Nakamura, H. Nandate, R. Zaitsu, Yoshihisa Fujiura, A. Yoshimura, H. Numata, J. Ogawa, H. Tatematsu, Y. Kamogawa, K. Murakami, Y. Wakasa, M. Yamasawa, H. Maekawa, S. Abe, H. Kihara, S. Tsunoda, Katsumi Saito, Kazuyuki Saito, T. Fudo, K. Obunai, H. Tachibana, I. Oba, T. Kuwahata, S. Higa, M. Gushiken, T. Eto, H. Yoshida, D. Ikeda, Yoshitake Fujiura, M. Ishizawa, M. Nakatsuka, K. Murata, C. Ogurusu, M. Shimoyama, M. Akutsu, I. Takamura, F. Hoshino, N. Yokota, T. Iwao, K. Tsuchida, M. Takeuchi, Y. Hatori, Y. Kitami, Yoichi Nakamura, R. Oyama, M. Ageta, Hiroyuki Oda, Y. Go, K. Mishima, T. Unoki, S. Morii, Yuhei Shiga, H. Sumi, T. Nagatomo, K. Sanno, K. Fujisawa, Y. Atsuchi, T. Nagoshi, T. Seto, T. Tabuchi, M. Kameko, K. Nii, K. Oshiro, H. Takezawa, S. Nagano, N. Miyamoto, M. Iwaki, Yuichiro Nakamura, M. Fujii, M. Okawa, Masahiko Abe, Masatake Abe, Mitsunori Abe, T. Saito, T. Mito, K. Nagao, J. Minami, T. Mita, I. Sakuma, T. Taguchi, S. Marusaki, H. Doi, M. Tanaka, T. Fujito, M. Matsuta, T. Kusumoto, S. Kakinoki, K. Ashida, N. Yoshizawa, J. Agata, O. Arasaki, M. Manita, M. Ikemura, S. Fukuoka, H. Murakami, S. Matsukawa, Y. Hata, T. Taniguchi, T. Ko, H. Kubo, M. Imamaki, M. Akiyama, M. Inagaki, H. Odakura, T. Ueda, Y. Katsube, A. Nakata, H. Watanabe, M. Techigawara, M. Igarashi, K. Taga, T. Kimura, S. Tomimoto, M. Shibuya, M. Nakano, K. Ito, T. Seo, S. Hiramitsu, H. Hosokawa, M. Hoshiai, M. Hibino, K. Miyagawa, Hajime Horie, N. Sugishita, Yukio Shiga, A. Soma, K. Neya, Tetsuro Yoshida, Tomoki Yoshida, M. Mizuguchi, M. Ishiguro, T. Minagawa, M. Wada, H. Mukawa, F. Okuda, S. Nagasaka, Y. Abe, Sen Adachi, Susumu Adachi, T. Adachi, K. Akahane, T. Amano, K. Aoki, T. Aoyama, H. Arai, S. Arima, T. Arino, H. Asano, T. Asano, J. Azuma, T. Baba, T. Betsuyaku, H. Chibana, H. Date, J. Doiuchi, Y. Emura, M. Endo, Y. Fujii, R. Fujiki, A. Fujisawa, Y. Fujisawa, T. Fukuda, T. Fukui, N. Furukawa, T. Furukawa, W. Furumoto, T. Goto, M. Hamaoka, N. Hanazono, K. Hasegawa, T. Hatsuno, Y. Hayashi, K. Higuchi, K. Hirasawa, H. Hirayama, M. Hirose, S. Hirota, M. Honda, Hideki Horie, T. Ido, O. Iiji, H. Ikeda, K. Ikeda, K. Ikeoka, M. Imaizumi, H. Inaba, T. Inoue, F. Iseki, A. Ishihara, N. Ishioka, N. Ito, T. Iwase, H. Kakuda, J. Kamata, H. Kanai, H. Kanda, M. Kaneko, H. Kano, T. Kasai, T. Kato, Y. Kato, Y. Kawada, K. Kawai, K. Kawakami, S. Kawakami, T. Kawamoto, S. Kawano, J. Kim, T. Kira, H. Kitazawa, H. Kitazumi, T. Kito, T. Kobayashi, T. Koeda, J. Kojima, H. Komatsu, I. Komatsu, Y. Koshibu, T. Kotani, T. Kozuka, Y. Kumai, T. Kumazaki, I. Maeda, K. Maeda, Y. Maruyama, S. Matsui, K. Matsushita, Y. Matsuura, K. Mineoi, H. Mitsuhashi, N. Miura, S. Miyaguchi, S. Miyajima, H. Miyamoto, A. Miyashita, S. Miyata, I. Mizuguchi, A. Mizuno, T. Mori, O. Moriai, K. Morishita, O. Murai, Sho Nagai, Shunichi Nagai, E. Nagata, H. Nagata, A. Nakagomi, S. Nakahara, M. Nakamura, R. Nakamura, N. Nakanishi, T. Nakayama, R. Nakazato, T. Nanke, J. Nariyama, Y. Niijima, H. Niinuma, Y. Nishida, Y. Nishihata, K. Nishino, H. Nishioka, K. Nishizawa, I. Niwa, K. Nomura, S. Nomura, M. Nozoe, T. Ogawa, N. Ohara, M. Okada, K. Okamoto, H. Okita, M. Okuyama, H. Ono, T. Ono, Y. Onuki Pearce, S. Oriso, A. Ota, E. Otaki, Y. Saito, H. Sakai, N. Sakamoto, Y. Sakamoto, Y. Samejima, Y. Sasagawa, H. Sasaguri, A. Sasaki, T. Sasaki, Kazuki Sato, Kiyoharu Sato, M. Sawano, S. Seki, Y. Sekine, Y. Seta, K. Sezaki, N. Shibata, Y. Shiina, H. Shimono, Y. Shimoyama, T. Shindo, H. Shinohara, R. Shinohe, T. Shinozuka, T. Shirai, T. Shiraiwa, Y. Shozawa, T. Suga, C. Sugimoto, Kazuo Suzuki, Keita Suzuki, Shu Suzuki, Shunji Suzuki, Susumu Suzuki, Y. Suzuki, M. Tada, A. Taguchi, T. Takagi, Y. Takagi, K. Takahashi, S. Takahashi, H. Takai, C. Takanaka, S. Take, H. Takeda, K. Takei, K. Takenaka, T. Tana, G. Tanabe, K. Taya, H. Teragawa, S. Tohyo, S. Toru, Y. Tsuchiya, T. Tsuji, K. Tsuzaki, H. Uchiyama, O. Ueda, Y. Ueyama, N. Wakaki, T. Wakiyama, T. Washizuka, M. Watanabe, T. Yamada, T. Yamagishi, H. Yamaguchi, Kenichi Yamamoto, Kentaro Yamamoto, Kunihiko Yamamoto, T. Yamamoto, M. Yamaura, M. Yamazoe, K. Yasui, Y. Yokoyama, K. Yoshida, T.W. Lim, C.K. Ching, C.G. Foo, J.H. Chow, D.D. Chen, F.R. Jaufeerally, Y.M. Lee, G. Lim, W.T. Lim, S. Thng, S.Y. Yap, C. Yeo, S. Oh, H.N. Pak, J.-B. Kim, J.H. Kim, S.-W. Jang, D.H. Kim, D.R. Ryu, S.W. Park, D.-K. Kim, D.J. Choi, Y.S. Oh, M.-C. Cho, S.-H. Kim, H.-K. Jeon, D.-G. Shin, J.S. Park, H.K. Park, S.-J. Han, J.H. Sung, J.-G. Cho, G.-B. Nam, Y.K. On, H.E. Lim, J.J. Kwak, T.-J. Cha, T.J. Hong, S.H. Park, J.H. Yoon, N.-H. Kim, K.-S. Kim, B.C. Jung, G.-S. Hwang, C.-J. Kim, D.B. Kim, J.J. Ahn, H.J. An, H. Bae, A.L. Baek, W.J. Chi, E.A. Choi, E.H. Choi, H.K. Choi, H.S. Choi, S. Han, E.S. Heo, K.O. Her, S.W. Hwang, E.M. Jang, H.-S. Jang, S. Jang, H.-G. Jeon, S.R. Jeon, Y.R. Jeon, H.K. Jeong, I.-A. Jung, Hyeon Jeong Kim, Hyun Ju Kim, Ji Seon Kim, Jung Sook Kim, J.A. Kim, K.T. Kim, M.S. Kim, Sang Hee Kim, Sang Hyun Kim, Y.-I. Kim, C.S. Lee, E.H. Lee, G.H. Lee, H.Y. Lee, H.-Y. Lee, K.H. Lee, K.R. Lee, M.S. Lee, M.-Y. Lee, R.W. Lee, S.E. Lee, S.H. Lee, S. Lee, W.Y. Lee, I.K. Noh, A.R. Park, B.R. Park, H.N. Park, J.H. Park, M. Park, Y. Park, S.-Y. Seo, J. Shim, J.H. Sim, Y.M. Sohn, W.S. Son, Y.S. Son, H.J. Song, H.K. Wi, J.J. Woo, S. Ye, K.H. Yim, K.M. Yoo, E.J. Yoon, S.Y. Yun, P. Angchaisuksiri, S. Chawanadelert, P. Mongkolwongroj, K. Kanokphatcharakun, S. Cheewatanakornkul, T. Laksomya, S. Pattanaprichakul, T. Chantrarat, S. Rungaramsin, S. Silaruks, W. Wongcharoen, K. Siriwattana, K. Likittanasombat, P. Katekangplu, W. Boonyapisit, D. Cholsaringkarl, B. Chatlaong, P. Chattranukulchai, Y. Santanakorn, P. Hutayanon, P. Khunrong, T. Bunyapipat, S. Jai-Aue, P. Kaewsuwanna, P. Bamungpong, S. Gunaparn, S. Hongsuppinyo, R. Inphontan, R. Khattaroek, K. Khunkong, U. Kitmapawanont, C. Kongsin, B. Naratreekoon, S. Ninwaranon, J. Phangyota, A. Phrommintikul, P. Phunpinyosak, K. Pongmorakot, S. Poomiphol, N. Pornnimitthum, S. Pumprueg, S. Ratchasikaew, K. Sanit, K. Sawanyawisuth, B. Silaruks, R. Sirichai, A. Sriwichian, W. Suebjaksing, P. Sukklad, T. Suttana, A. Tangsirira, O. Thangpet, W. Tiyanon, Y. Vorasettakarnkij, T. Wisaratapong, W. Wongtheptien, A. Wutthimanop, S. Yawila, A. Oto, A. Altun, I. Ozdogru, K. Ozdemir, O. Yilmaz, A. Aydinlar, M.B. Yilmaz, E. Yeter, Z. Ongen, M. Cayli, H. Pekdemir, M. Ozdemir, M. Sucu, T. Sayin, M. Demir, H. Yorgun, M. Ersanli, E. Okuyan, D. Aras, H. Abdelrahman, O. Aktas, D. Alpay, F. Aras, M.F. Bireciklioglu, S. Budeyri, M. Buyukpapuc, S. Caliskan, M. Esen, M.A. Felekoglu, D. Genc, B. Ikitimur, E.B. Karaayvaz, S. Kılıç Karataş, S. Okutucu, E. Ozcelik, A. Quisi, H. Sag, L. Sahiner, B.Y. Sayin, T. Seker, D. Uzun Alkan, E. Yildirim, R. Yildirim, F. Yilmaz, V. Yuksekdag, H.L. Luciardi, N. Vensentini, A.C. Ingaramo, G.A. Sambadaro, V. Fernandez Caputi, S.G. Berman, P. Dragotto, A.J. Kleiban, N. Centurion, G. Giacomi, R.A. Ahuad Guerrero, D. Conde, G. Zapata, L.A. Di Paola, J.L. Ramos, R.D. Dran, J. Egido, A.A. Fernandez, M.J. Fosco, S. Sassone, V.A. Sinisi, L.R. Cartasegna, M.A. Berli, O.A. Gomez Vilamajo, F. Ferroni, E.D. Alaguibe, A. Alvarez D'Amelio, C. Arabetti, L. Arias, J.A. Belardi, L. Bergesio, F. Berli, M. Berli, S. Borchowiec, C. Buzzetti, R. Cabrini, V. Campisi, A.L. Cappi, R. Carrizo, F. Colombo Berra, J.P. Costabel, O.J.A. Costamagna, A.A. Damonte, I.N. De Urquiza, F. Diez, M.F. Edén, M. Fanuele, F. Fernandez Voena, M. Foa Torres, C. Funosas, M.P. Giacomi, C.H. Gimenez, E.P. Gurfinkel, M. de L.M. Had, V. Hansen, A.D. Hrabar, M. Ingratta, A. Lopez, G. Maehara, L. Maffei, A. Martinelli, C. Martinelli, J. Matkovich, B. Mautner, A. Meirino, R. Munguia, A. Navarro, V. Novas, G. Perez Prados, J. Pontoriero, R.N. Potito, C. Ricotti, M.A. Rodriguez, F. Rolandi, M.E. Said Palladino, M. Salinger, L.S. Sanziani, P.O. Schygiel, A. Sossich, J.F. Tinto, L. Tonelli, A.L. Tufare, M. Vallejo, M.E. Yunis, M. Zillo, F.J. Zurbrigk, A.C.P. Barretto, D.C. Sobral Filho, J. Jaber, D. Armaganijan, J. Faria Neto, A. Steffens, W. Kunz Sebba Barroso de Souza, J.D. de Souza Neto, J.M. Ribeiro, M. Silveira Teixeira, P.R. Ferreira Rossi, L. Pires, D. Moreira, J.C. Moura Jorge, A. Menezes Lorga Filho, L.C. Bodanese, M. Westerlund Montera, C.H. Del Carlo, T. Da Rocha Rodrigues, F.A. Alves da Costa, A. Lopes, R. Lopes, G.R. Araújo, E.R. Fernandes Manenti, J.F. Kerr Saraiva, J.C. Ferreira Braga, A. Negri, L. Souto, C. Moncada, D. Bertolim Precoma, F. Roquette, G. Reis, R.A. Ramos Filho, E. Lanna Figueiredo, R. Vieira Botelho, C. Munhoz da Fontoura Tavares, C.R. Costantini Frack, J. Abdalla Saad, H.C. Finimundi, C. Pisani, D. Chemello, M. Pereira Martins, C.C. Broilo França, F. Alban, G.B. Aranha Rosito, J.B. de Moura Xavier Moraes Junior, R.T. Tumelero, L. Nigro Maia, R. Simões de Almeida, N.C. do Carmo Borges, L.G. Gomes Ferreira, P. Agliardi, J. Alves de Oliveira Gomes, V. Araujo, M. Arruda Nakazone, T. Barbosa, S. Barroso, E. Belisario Falchetto, H. Bellotti Lopes, M.A. Benez Teixeira Lemos, G. Biazus, L. Borges Queiroz, F.E. Camazzola, M. Caporale, S. Cardoso Boscato, F. Chieza, M.O. Chokr, R. Clemente Mingireanov, N. Codonho Góes, C. Correa, M. Costa, C. Costantini Ortiz, L.S. da Silva, F. da Silva Paulitsch, J.A. da Silveira, E. Daros, G.R. de Araújo, M.I. Del Monaco, C. Dias, M.A. Dias, A.P. Drummond Wainstein, P. Ely Pizzato, D.C. Esteves, P. Fabri, T. Félix Lorenzato Fonseca, E. Fernandes, C. Fonseca, C.R. Frack Costantini, R. Franchin Ferraz, F. Freire, P. Gottardo, D. Guanaes, S. Guizzardi, E. Hettwer Magedanz, F. Igansi, F. Jannuzzi, G. Junior, D. Komar, E.G. Lino, D. Lopes, O. Lourenço da Silva Júnior, E. Lustosa, A.P. Macagnan, M.C. Marinho, M. Mazzoni, G. Melo, L. Mortari, O.M.C.C. Mouco, C. Nanzer Vital, C. Ormundo, S. Oss Emmer, E. Palmegiani, R. Pavani, L. Pereira, V.L. Pereira, R. Perreira, S. Poletti, S.C. Quaia Fortunato, C. Queirantes, N. Ramos Pereira, R.L. Rech, S. Ribeiro, A. Rodrigues, H. Roesch, T. Ruaro Reichert, D. Santos, I. Santos, M. Santos, M.V. Seroqui, S. Silva, L. Soares, L. Spolaor, C. Stoll, N. Toazza Duda, L. Trama, B. Unterkircher, M.V. Valois, T. Vargas, T. Viana, C. Vicente, L. Vidal Armaganijan, R. Vieira Homem, L.G. Vieira Torres, L. Vila Boas, F. Villaça Guimarães Filho, R. Corbalan, G. Eggers, C. Bugueño Gutiérrez, G. Arriagada, S. Potthoff Cardenas, B.A.J. Stockins Fernandez, C. Conejeros, C. Houzvic, P. Marin Cuevas, H. Montecinos, A. Forero, F. Lanas, M. Larico Gómez, G. Charme Vilches, C. Rey, C. Astudillo, J. Aguilar, Y. Campisto, C. Lara, E. Molina, J. Munoz Oyarzon, V. Olguin, M. Vergara, C. Villan, C.J. Sánchez Díaz, J. Illescas Diaz, R. Leal Cantú, M.G. Ramos Zavala, R. Cabrera Jardines, N. Espinola Zavaleta, S. Villarreal Umaña, E. López Rosas, G. Llamas Esperón, G. Pozas, E. Cardona Muñoz, N. Matadamas Hernández, A. Leyva Rendón, N. García Hernández, M. de los Ríos Ibarra, L. Virgen Carrillo, D. López Villezca, C. Hernández Herrera, J.J. López Prieto, R. Gaona Rodríguez, E. Villeda Espinosa, D. Flores Martínez, J. Velasco Barcena, R. Yong, I. Rodríguez Briones, J.L. Leiva Pons, H. Álvarez López, R. Olvera Ruiz, C. Díaz de la Vega, C. Cantú Brito, E. Chuquiure Valenzuela, R. Reyes-Sanchez, A. Bazzoni Ruiz, O. Nandayapa Flores, M. Benavides Gonzalez, R. Arriaga Nava, J.D. Morales Cerda, O. Fierro Fierro, P. Fajardo Campos, T.A.A. Alfaro, S. Altamirano Bellorin, R. Avena, M. Chavarria, I. Espinosa, F. Flores Silva, R.H. Garcia Nava, K. Godoy, E.J. Gonzalez Felix, C.L. Gonzalez Garcia, L.G. Gonzalez Salas, P. Guajardo, S. Hernandez Gonzalez, T. Izquierdo, M.C. Mancilla Ortiz, D. Martinez Vasquez, N. Mendoza, J. Morales, N. Nikitina, S. Ochoa Aybar, A. Ortiz, P. Padilla Macias, F. Perez, J.A. Perez Sanchez, S. Piña Toledano, M. Ramos Gonzalez, C. Rivera Ramos, V. Roa Castro, G. Romero Cardona, M. Ruiz Cornejo, A. Salinas, G. Santana, P. Sida Perez, A.C. Tovar Castaneda, R. Trujillo Cortes, M. Brodmann, K. Lenz, H. Drexel, J. Foechterle, C. Hagn, A. Podczeck-Schweighofer, K. Huber, M. Winkler, B. Schneeweiß, A. Gegenhuber, W. Lang, S. Eichinger-Hasenauer, P. Kaserer, J. Sykora, H. Rasch, M. Pichler, E. Schaflinger, B. Strohmer, R. Breier, K.-M. Ebner, L. Eischer, F. Freihoff, A. Lischka-Lindner, T. Mark, A. Mirtl, A. Said, C. Stöcklöcker, B. Vogel, A. Vonbank, C. Wöhrer, D. Zanolin, F. Cools, G. Paparella, P. Vandergoten, J.-L. Parqué, L. Capiau, G. Vervoort, B. Wollaert, P. Desfontaines, G. Mairesse, T. Boussy, P. Godart, A. De Wolf, J. Voet, A. Heyse, G. Hollanders, W. Anné, J. Vercammen, P. Purnode, I. Blankoff, D. Faes, Y. Balthazar, M. Beutels, P. Maréchal, S. Verstraete, O. Xhaet, H. Striekwold, J. Thoeng, K. Hermans, B. Alzand, A.-K. Ascoop, F. Banaeian, A.-M. Barbuto, A.C. Billiaux, M. Blockmans, C. Bouvy, C. Brike, H. Capiau, T. Casier, A. Conde Y Bolado, D. De Cleen, M. De Coninck, M. de Vos, N. de Weerdt, M. Delforge, M. Delvigne, D. Denie, K. Derycker, E. Deweerd, F. Dormal, S. Drieghe, M. Everaert, T. Eykerman, E. Feys, M. Ghekiere, F. Gits, S. Hellemans, L. Helvasto, C. Jacobs, S. Lips, I. Mestdagh, J. Nimmegeers, V. Piamonte, P. Pollet, A. Postolache, M. Raepers, E. Raymenants, J. Richa, H. Rombouts, J. Salembier, C. Scheurwegs, O. Semeraro, N. Simons, C. Smessaert, W. Smolders, I. Stockman, S. Tahon, V. Thyssen, G. Tincani, F. Van Durme, D. Van Lier, H. Vandekerckhove, Y. Vandekerckhove, D. 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Robiro Robiro, M. Roca, C. Roca Saumell, C. Rodrigo, E. Rodriguez, M. Rodriguez Garcia, S. Saez Jimenez, P. Sanchez Calderon, L. Sanchez Mendez, S. Sanchez Parra, C. Santolaya, M.R. Senan Sanz, A. Seoane Blanco, E. Serralvo, N. Sierra, C. Simon Valero, J. Sorribes Lopez, M. Teixido Fontanillas, M. Terns Riera, G. Tobajas, C. Torres, J. Torres Marques, M. Ubeda Pastor, M. Rosenqvist, A. Wirdby, J. Linden, K. Henriksson, M. Elmersson, A. Egilsson, U. Börjesson, G. Svärd, B. Liu, A. Lindh, L.-B. Olsson, M. Gustavsson, Lars Andersson, Lisbeth Andersson, L. Benson, C. Bothin, A. Hajimirsadeghi, K. Kadir, M. Ericsson, A. Ohlsson, H. Lindvall, P. Svensson, K. Thorne, H. Handel, P. Platonov, B. Eriksson, I. Timberg, K. Romberg, M. Crisby, J.-E. Karlsson, S.A. Jensen, A. Andersson, L. Malmqvist, B. Martinsson, F. Bernsten, J. Engdahl, J. Thulin, A. Hot-Bjelac, P. Stalby, H. Aaröe, E. Ahbeck, H. Ahlmark, F. Al-Khalili, G. Bonkowski, S. Dzeletovic, A.-B. Ekstrand, G.-B. Eriksson, K. Floren, C. Grässjö, S. Hahn, P. Jaensson, B. Jansson, J.-H. Jansson, R.-M. Kangert, A. Koch, D. Kusiak, A. Lettenström, A. Lindberg, C.-J. Lindholm, A. Mannermyr, K. Mansson, M. Millborg, C. Nilsson, A.-M. Ohlin, A. Olofsson, A. Osberg, A. Pedersen, K. Risbecker, K. Rosenberg, J. Samuelsson, M. Shayesteh, K. Skoglund, M. Stjernberg, C. Thorsen, J. Steffel, J.H. Beer, J. Debrunner, D. Amstutz, J. Bruegger, G. Elise, A. Grau, A. Guinand, I. Henriette, E. Saga, S. Winnik, A. Parkhomenko, I. Rudyk, V. Tseluyko, O. Karpenko, S. Zhurba, I. Kraiz, I. Kupnovytska, N. Serediuk, Y. Mostovoy, O. Ushakov, O. Koval, I. Kovalskyi, Y. Svyshchenko, O. Sychov, M. Stanislavchuk, O. Kraydashenko, A. Yagensky, S. Tykhonova, I. Fushtey, R. Belegai, G. Berko, L. Burdeuna, O. Chabanna, I. Daniuk, A. Ivanov, E. Kamenska, P. Kaplan, O. Khyzhnyak, S. Kizim, O. Matova, O. Medentseva, V. Mochonyi, M. Mospan, V. Nemtsova, T. Ovdiienko, O. Palamarchuk, M. Pavelko, R. Petrovskyy, D. Plevak, O. Proshak, S. Pyvovar, L. Rasputina, O. Romanenko, O. Romanova, A. Sapatyi, O. Shumakov, R. Stets, L. Todoriuk, V. Varenov, D. Fitzmaurice, N. Chauhan, D. Goodwin, P. Saunders, R. Evans, J. Leese, P.S. Jhittay, A. Ross, M.S. Kainth, G. Pickavance, J. McDonnell, A. Williams, T. Gooding, H. Wagner, S. Suryani, A. Singal, S. Sircar, R. Bilas, P. Hutchinson, A. Wakeman, M. Stokes, N. Paul, M. Aziz, C. Ramesh, P. Wilson, S. Franklin, S. Fairhead, J. Thompson, V. St Joseph, G. Taylor, D. Tragen, D. Seamark, C. Paul, M. Richardson, A. Jefferies, H. Sharp, H. Jones, C. Giles, M. Page, O. Oginni, J. Aldegather, S. Wetherwell, W. Lumb, P. Evans, F. Scouller, N. Macey, Y. Stipp, R. West, S. Thurston, P. Wadeson, J. Matthews, P. Pandya, A. Gallagher, T. Railton, B. Sinha, D. Russell, J.A. Davies, P. Ainsworth, C.P. Jones, P. Weeks, J. Eden, D. Kernick, W. Murdoch, L. Lumley, R.P. Patel, S.W. Wong, M. Saigol, K. Ladha, K. Douglas, D.F. Cumberlidge, C. Bradshaw, G. Van Zon, K.P. Jones, M.J. Thomas, E. Watson, B. Sarai, N. Ahmad, W. Willcock, J. Cairns, S. Sathananthan, N. de Kare-Silver, A. Gilliland, E. Strieder, A. Howitt, B. Vishwanathan, N. Bird, D. Gray, M. Clark, J. Bisatt, J. Litchfield, E. Fisher, T. Fooks, A.R. Kelsall, E. Alborough, J. Wakeling, M. Parfitt, K. Milne, S. Rogers, R. Priyadharshan, J.L. Oliver, E. Davies, S. Abushal, M. Jacobs, C. Hutton, N.I. Walls, R. Thompson, C. Chigbo, S.M.A. Zaidi, M. Howard, K.C. Butter, S. Barrow, H. Little, I.U. Haq, L. Gibbons, S. Glencross, A.J. McLeod, K. Poland, C. Mulholland, A. Warke, P. Conn, G. Burns, R.N. Smith, S. Lowe, R. Kamath, H.S. Dau, J. Webster, I. Hodgins, S. Vercoe, P.C. Roome, H. Pinnock, J.R.A. Patel, A. Ali, N. Hart, R. Davies, E. Stuart, C.A. Neden, M. Danielsen, R. Heath, P. Sharma, S. Galloway, C. Hawkins, R. Oliver, M. Aylward, S. Mannion, M. Braddick, D. Edwards, A.C. Rothwell, A. Sabir, F. Choudhary, S. Khalaque, A. Wilson, S. Peters, W. Coulson, N. Roberts, A. Heer, S. Coates, B. Ward, D. Jackson, S. Walton, D. Shepherd, M. Sterry, T. Wong, M. Boon, R. Bunney, R. Haria-Shah, R.T. Baron, S. Davies, T. Schatzberger, N. Hargreaves, T. Stephenson, H. Choi, R. Batson, L. Lucraft, T. Myhill, S. Estifano, D. Geatch, J. Wilkinson, R. Veale, K. Forshaw, T. Davies, K. Zaman, P. Vinson, C. Liley, M. Bandrapalli, P. McGinty, R. Wastling, P. McEleny, A. Beattie, P. Cooke, M. Wong, J. Gunasegaram, M. Pugsley, S. Ahmad, C. A'Court, J. Ayers, J. Bennett, S. Cartwright, S. Dobson, C. Dooldeniya, A. Flynn, R. Fox, J. Goram, A. Halpin, A. Hay, P. Jacobs, L. Jeffers, L. Lomax, I. Munro, R. Muvva, M. Nadaph, K. Powell, S. Randfield, D. Redpath, R. Reed, M. Rickenbach, G. Rogers, P.B. Saunders, C. Seamark, J. Shewring, P. Simmons, H. Simper, H. Stoddart, A. Sword, N. Thomas, A. Thomson, H. Gibbs, A. Blenkhorn, B. Singh, W. Van Gaal, W. Abhayaratna, R. Lehman, P. Roberts-Thomson, J. Kilian, D. Coulshed, A. Catanchin, D. Colquhoun, H. Kiat, D. Eccleston, J. French, L. Zimmett, B. Ayres, T. Phan, P. Blombery, D. Crimmins, D. O'Donnell, A. Choi, P. Astridge, M. Arstall, N. Jepson, M. Binnekamp, A. Lee, J. Rogers, G. Starmer, P. Carroll, J. Faunt, A. Aggarwala, L. Barry, C. Batta, R. Beveridge, A. Black, M. Bonner, J. Boys, E. Buckley, M. Campo, L. Carlton, A. Connelly, B. Conway, D. Cresp, H. Dimitri, S. Dixon, M. Dolman, M. Duroux, M. Eskandari, R. Eslick, A. Ferreira-Jardim, T. Fetahovic, D. Fitzpatrick, R. Geraghty, J. Gibbs, T. Grabek, M.H. Modi, K. Hayes, M.P. Hegde, L. Hesketh, B. Hoffmann, B. Jacobson, K. Johnson, C. Juergens, I. Kassam, V. Lawlor, M. Lehman, S. Lehman, D. Leung, S. Mackay, M. MacKenzie, C. McCarthy, C. McIntosh, L. McKeon, H. Morrison, C. Mussap, J.-D. Myers, V. Nagalingam, G. Oldfield, V. O'May, J. Palmer, L. Parsons, K. Patching, T. Patching, V. Paul, M. Plotz, S. Preston, H. Rashad, M. Ratcliffe, S. Raynes, J. Rose, L. Sanders, M. Seremetkoska, H. Setio, S. Shone, P. Shrestha, C. Singh, C. Singleton, N. Stoyanov, S. Sutcliffe, K. Swaraj, J. Tarrant, S. Thompson, I.M. Tsay, M. Vorster, A. Waldman, L. Wallis, E. Wilford, K. Wong, S.J. Connolly, A. Spyropoulos, J. Eikelboom, R. Luton, M. Gupta, A.S. Pandey, S. Cheung, R. Leader, P. Beaudry, F. Ayala-Paredes, J. Berlingieri, J. Heath, G. Poirier, M. Du Preez, R. Nadeau, G. Dresser, R. Dhillon, T. Hruczkowski, B. Schweitzer, B. Coutu, P. Angaran, P. MacDonald, S. Vizel, S. Fikry, R. Parkash, A. Lavoie, J. Cha, B. Ramjattan, J. Bonet, K. Ahmad, L. Aro, T. Aves, K. Beaudry, C. Bergeron, J. Bigcanoe, N. Bignell, L. Breakwell, E. Burke, L. Carroll, B. Clarke, T. Cleveland, S. Daheb, P. Dehghani, I. Denis, Z. Djaidani, P. Dorian, S. Douglass, J. Dunnigan, A. Ewert, D. Farquhar, A. Fearon, L. Ferleyko, D. Fournier, B. Fox, M.-C. Grenier, W. Gulliver, K. Haveman, C. Hines, K. Hines, A.M. Jackson, C. Jean, G. Jethoo, R. Kahlon, S. Kelly, R. Kim, V. Korley, J. Kornder, L. Kwan, J. Largy, C. Lewis, S. Lewis, I. Mangat, R. Moor, J. Navratil, I. Neas, J. Otis, R. Otis, M. Pandey, F. Petrie, A. Pinter, M. Raines, P. Roberts, M. Robinson, G. Sas, S. Schulman, L. Snell, S. Spearson, J. Stevenson, T. Trahey, S. Wong, D. Wright, H. Ragy, A. Abd El-Aziz, S.K. Abou Seif, M.G. El Din, S. El Etriby, A. Elbahry, A. El-Etreby, M. Elkhadem, A. Katta, T. Khairy, A. Mowafy, M. Nawar, A. Ohanissian, A. Reda, M. Reda, H. Salem, N. Sami, S. Samir, M. Setiha, M. Sobhy, A. Soliman, N. Taha, M. Tawfik, E. Zaatout, D. Kettles, J. Bayat, H. Siebert, A. Horak, Y. Kelfkens, R. Garda, T. Pillay, M. Guerra, L. van Zyl, H. Theron, A. Murray, R. Louw, D. Greyling, P. Mntla, V. Ueckermann, R. Loghdey, S. Ismail, F. Ahmed, J. Engelbrecht, A. Ramdass, S. Maharajh, W. Oosthuysen, G. Angel, C. Bester, M. Booysen, C. Boshoff, C. Cannon, S. Cassimjee, C. Chami, G. Conway, A. Davids, L. de Meyer, G. Du Plessis, T. Ellis, L. Henley, M. Karsten, E. Loyd, J. Marks, L. Mavhusa, M. Mostert, A. Page, L. Rikhotso, M. Salie, J. Sasto, F. Shaik, A. Skein, L. Smith, G. Tarr, T. Tau, F. van Zyl, W. Al Mahmeed, G. Yousef, A. Agrawal, M. Nathani, M. Ibrahim, E.M. Esheiba, R. Singh, A. Naguib, M. Abu-Mahfouz, M. Al Omairi, A. Al Naeemi, R. Maruthanayagam, N. Bazargani, A. Wassef, R. Gupta, M. Khan, B. Subbaraman, A. Abdul, A. Al Mulla, S. El Bardisy, P. Haridas, S. Jadhav, K. Magdaluyo, M. Makdad, I. Maqsood, R. Mohamed, N. Sharma, R. Sharma, M. Thanzeel, S.Z. Goldhaber, R. Canosa, P. Rama, E. Blumberg, J. Garcia, P. Mullen, V. Wilson, A. Quick, K. Ferrick, W.M. Kutayli, M. Cox, M. Franco, S. Falkowski, R. Mendelson, M. Williams, S. Miller, S. Beach, A. Alfieri, T. Gutowski, I. Haque, R. Reddy, W. Ahmed, P. Delafontaine, D. Diercks, D. Theodoro, K. Remmel, M. Alberts, R. Ison, H. Noveck, P. Duffy, S. Pitta, D. Nishijima, C. Treasure, N. Asafu-Adjaye, K. Ball, M. Bartlett, M. Bentley, S. Bowers, A. Brown, A. Browne, J. Cameron-Watts, M. Canova, D. Cassidy, K. Cervellione, S. Congal, J. DePauw, A. Dickerson, M. Eley, L. Evans, S. Felpel, K. Ferdinand, D. Fielder, P. Gentry, A. Haideri, F. Hakimi, T. Harbour, E. Hartranft, B. Hawkins, M. Headlee, L. Henson, C. Herrick, T. Hicks, S. Jasinski, A. Jones, L. Jones, P. Jones, S. Karl, M. Keeling, J. Kerr, P. Knowles, J. Langdon, M. Lay, J.A. Lee, T. Lincoln, E. Malone, A. Merliss, D. Merritt, J. Minardo, B. Mooso, C. Orosco, V. Palumbo, M. Parker, T. Parrott, S. Paserchia, G. Pearl, J. Peterson, N. Pickelsimer, T. Purcell, J. Raynor, S. Raziano, C. Richard, T. Richardson, C. Robertson, A. Sage, T. Sanghera, P. Shaw, J. Shoemaker, K. Smith, B. Stephanie, A. Thatcher, H. Theobald, N. Thompson, L. Treasure, T. Tripti, C. Verdi, and V. Worthy

Subjects
Surgery, RD1-811, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background: The Global Anticoagulant Registry in the FIELD–Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. Methods and results: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012–2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P

Published
2018
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16. Olfactory stimulation may modulate the sensation of nasal patency

Author

Y.-T. Chao, A. Nakov, A. Haehner, S. Poletti, and T. Hummel

Subjects
Otorhinolaryngology, General Medicine
Abstract

Background: The sensation of nasal patency can be induced by inhaling menthol, which predominantly produces trigeminal stimulation. It remains unclear whether olfactory stimulation can also induce or modulate the sensation of nasal patency. Methodology: A total of 118 participants (normosmia: n=67, olfactory dysfunction: n=51) were exposed to four odors in a randomized order: 1) phenylethanol (PEA), 2) menthol, 3) a mixture of PEA and menthol, 4) nearly odorless propylene glycol. The odors were presented by nasal clips. After the nasal clip had been removed, the participants rated relative nasal patency (RNP) from - 50 to +50, and their peak nasal inspiratory flow (PNIF) was measured. Repeated measures analysis of variance was used to examine the difference of RNP and PNIF among the four conditions and the influence of olfactory function. Results: The RNPs, other than PNIFs, differed between the four conditions. Menthol induced the highest RNP, followed by the mixed solution, PEA and the odorless condition. Normosmic participants, but not those with olfactory dysfunction, responded to PEA significantly higher than odorless condition with regard to RNP. The correlation analysis showed that the better the subjective or measured olfactory performance, the greater the PEA-induced sensation of nasal patency. Conclusions: A specific olfactory stimulant that selectively induces olfactory perception can also evoke and modulate the sensation of nasal patency. Hence, patients might benefit from exposing themselves to odors in order to relieve the annoying nasal obstruction.

Published
2022

18. Higher Interleukin 13 differentiates patients with a positive history of suicide attempts in major depressive disorder

Author

Federico Calesella, Veronica Aggio, Mario Gennaro Mazza, Francesco Benedetti, Silvia Cazzetta, Cristina Colombo, S. Poletti, Benedetta Vai, Raffaella Zanardi, and Cristina Lorenzi

Subjects
medicine.medical_specialty, business.industry, Interleukin 13, medicine, Major depressive disorder, medicine.disease, Psychiatry, business, RZ400-408, Mental healing
Abstract

Background: Every 40 seconds one person dies from suicide worldwide, half of which are related to depression. Neuroinflammation has been reported in neuropsychiatric conditions, including major depressive disorder (MDD). Preliminary findings suggested a link between inflammation and suicidal behavior. Nevertheless, to the best of our knowledge, no previous study explored how a wide range of cytokines may differentiate lifetime suicidal behaviors (i.e. positive history of suicide attempts) in patients affected by MDD. Methods: In 28 healthy controls (HC), 28 MDD patients with a positive history of attempting suicide (SA), and 28 MDD patients with a negative history (nSA), we determined concentrations of immune analytes using an ultra-sensitive Pro Human Cytokine Group 27-Plex Panel and Chemokine immunoassay panels (Bioplex). To deal with the 27 highly correlated cytokines we applied two different statistical approaches that reduce the dimensionality of data: Principal Component Analysis, exploring if suicidality may be related to a comprehensive derived inflammatory state, and elastic-net penalized regression that allows considering as predictor every single cytokine. Results: Five inflammatory components were identified: none predicted suicidal behavior. Elastic net results prompted IL-13 as a new candidate marker of suicide in patients with MDD. Limitation: The retrospective design of the study and the small sample size preclude the possible causal interpretation and generalizability of reported results. Conclusion: Exploring a wide range of cytokines may provide a new inflammatory signature of suicide useful for understanding pathophysiological underpinnings, developing new therapeutic targets, and allowing the identification of at-risk factors.

Published
2021

37. P.253 Immunoassay quantification and neuroimaging data for differentiating bipolar and unipolar depression: support vector machine on kernels and elastic net approaches

Author

Irene Bollettini, Francesco Benedetti, Mario Gennaro Mazza, Raffaella Zanardi, Benedetta Vai, S. Poletti, Lorenzo Parenti, Silvia Cazzetta, Cristina Lorenzi, Cristina Colombo, Veronica Aggio, and Barbara Barbini

Subjects
Pharmacology, Elastic net regularization, medicine.diagnostic_test, business.industry, Pattern recognition, Support vector machine, Psychiatry and Mental health, Neurology, Neuroimaging, Immunoassay, medicine, Pharmacology (medical), Neurology (clinical), Artificial intelligence, business, Biological Psychiatry, Depression (differential diagnoses)
Published
2020

38. P.201 Higher baseline interleukin 13 marks poor antidepressant response to sleep deprivation and light therapy in bipolar disorder

Author

Francesco Benedetti, Benedetta Vai, S. Poletti, M. Lorenzi Cristina, Silvia Cazzetta, M. Mazza Gennaro, and Cristina Colombo

Subjects
Pharmacology, Light therapy, Oncology, medicine.medical_specialty, business.industry, medicine.medical_treatment, medicine.disease, Psychiatry and Mental health, Sleep deprivation, Neurology, Internal medicine, Interleukin 13, medicine, Antidepressant, Pharmacology (medical), Neurology (clinical), Bipolar disorder, medicine.symptom, Baseline (configuration management), business, Biological Psychiatry
Published
2020

39. The effect of childhood trauma on serum BDNF in bipolar depression is modulated by the serotonin promoter genotype

Author

Oliver Ambrée, Clara Locatelli, Cristina Colombo, S. Poletti, Volker Arolt, Cristina Lorenzi, Francesco Benedetti, Benedetti, Francesco, Ambrã©e, Oliver, Locatelli, Clara, Lorenzi, Cristina, Poletti, Sara, Colombo, CRISTINA ANNA, and Arolt, Volker

Subjects
Adult, Male, medicine.medical_specialty, Bipolar Disorder, Genotype, Bipolar disorder, Context (language use), Serotonin promoter, Childhood trauma, Epigenesis, Genetic, 03 medical and health sciences, 0302 clinical medicine, Neurotrophic factors, Polymorphism (computer science), Internal medicine, mental disorders, medicine, Humans, Child Abuse, Child, Promoter Regions, Genetic, Major depressive episode, Serotonin Plasma Membrane Transport Proteins, Neuroscience (all), Brain-Derived Neurotrophic Factor, General Neuroscience, CTQ tree, Middle Aged, medicine.disease, 030227 psychiatry, BDNF, Endocrinology, behavior and behavior mechanisms, Female, Serotonin, medicine.symptom, Psychology, psychological phenomena and processes, 030217 neurology & neurosurgery, Clinical psychology
Abstract

In healthy humans, both childhood trauma and the short form of the serotonin promoter transporter genotype (5-HTTLPR) are associated with lower levels of brain-derived neurotrophic factor (BDNF). In subjects with bipolar disorder (BD), lower levels of BDNF and a higher degree of childhood trauma were observed compared with healthy controls. However, is still unknown if the functional 5-HTTLPR polymorphisms exerts an effect on both abnormalities. In 40 inpatients affected by a major depressive episode in the course of BD, we genotyped 5-HTTLPR, measured serum BDNF with ELISA, and assessed early adversities by the childhood trauma questionnaire (CTQ). Data were analyzed in the context of the general linear model correcting for age, sex, ongoing lithium treatment, severity of current depression, and CTQ minimization/denial scores to investigate the effect of 5-HTTLPR polymorphism and childhood trauma on BDNF levels. Early trauma were negatively associated with BDNF serum levels (higher CTQ scores, lower BDNF; p = 0.0019). 5-HTTLPR l/l homozygotes showed significantly higher BDNF levels than 5-HTTLPR*s carriers (30.57 ± 6.13 vs 26.82 ± 6.41; p = 0.0309). A separate-slopes analysis showed that 5-HTTLPR significantly influenced the relationship between early trauma and adult BDNF (interaction of 5-HTTLPR with CTQ scores: p = 0.0023), due to a significant relationship between trauma and BDNF in 5-HTTLPR*s carriers, but not among l/l homozygotes. Putatively detrimental effects of childhood trauma exposure on adult BDNF serum levels are influenced by 5-HTTLPR genotype in patients affected by BD. Possible mechanisms include epigenetic modulation of BDNF gene expression, due to different reactivity to stressors in 5-HTTLPR genotype groups.

Published
2017

41. P.4.09 Corticolimbic connectivity mediates the early stress - suicidality relation in 5-HTTLPR S-carrier bipolar depression

Author

Alice Vitali, Francesco Benedetti, Mattia Mascia, Benedetta Vai, S. Poletti, Cristina Lorenzi, Cristina Colombo, Mascia, M., Vai, B., Poletti, S., Lorenzi, C., Vitali, A., Colombo, C., and Benedetti, F.

Subjects
Pharmacology, Psychiatry and Mental health, Neurology, 5-HTTLPR, Pharmacology (medical), Neurology (clinical), Psychology, Biological Psychiatry, Depression (differential diagnoses), Clinical psychology
Published
2019

45. P.4.12 Classification of mood disorders using a multiple kernel approach on multimodal neuroimaging data

Author

Cristina Colombo, Elisa M T Melloni, Francesco Benedetti, S. Poletti, Benedetta Vai, C. Bertocchi, Irene Bollettini, Bertocchi, C., Vai, B., Poletti, S., Bollettini, I., Melloni, E., Colombo, C., and Benedetti, F.

Subjects
Pharmacology, Computer science, business.industry, Multimodal neuroimaging, Pattern recognition, medicine.disease, Psychiatry and Mental health, Neurology, Mood disorders, Kernel (statistics), medicine, Pharmacology (medical), Neurology (clinical), Artificial intelligence, business, Biological Psychiatry
Published
2019

46. SUBSISTEMAS DE GESTÃO DE PESSOAS: olhares múltiplos (Volume II)

Author

C. R. de. SOUSA, E. B. F. GOMES, H. S. CARVALHO, Roberta de Sousa ALMEIDA, W. F. SANTOS, BRUNNO PATERLINE FIGUEIRA, C. G. QUEIROZ, CHRISTIANE RUFINO MOTTA PIONA, D. P. T. BERMOND, DIONE VANELLI GRASSI, FÁBIO LUCHI VALIN, F. S. POLETTI, G. S. ROCHA, GEOVANE BERMOND MARQUES, H. J. ABREU, J. C. LISBOA, KATHYÚCIA CARDOSO VALLE ROSSI, Maicon Richard TABELINI, MARCOS RODRIGUES DO CARMO, M. C. S. MARTINS, M. J. A. CARDOSO, MAYARA ALLOCHIO, MESSIAS VETTORACI PASSAMANI, PAMELLA ROSSE, P. V. C. SILVA, T.O CLOSEL, V. M. MORAES, V. PETRI, and FELIPE TASCA GOMES

Published
2017

47. Excitatory amino acid transporters 1 affects corticolimbic circuitry during implicit processing of negative emotional stimuli in bipolar disorder

Author

S. Brioschi, Francesco Benedetti, M. Riberto, S. Poletti, Benedetta Vai, Cristina Lorenzi, Riberto, M, Poletti, S, Lorenzi, C, Vai, B, Brioschi, S, and Benedetti, F

Subjects
Pharmacology, chemistry.chemical_classification, Emotional stimuli, Transporter, medicine.disease, 030227 psychiatry, Amino acid, 03 medical and health sciences, Psychiatry and Mental health, 0302 clinical medicine, Neurology, chemistry, Excitatory postsynaptic potential, medicine, Pharmacology (medical), Neurology (clinical), Bipolar disorder, Psychology, Neuroscience, 030217 neurology & neurosurgery, Biological Psychiatry
Published
2017

48. P.3.032 A European multicentric voxel based morphometry (VBM) study on schizophrenia and bipolar disorder

Author

E. Maggioni, B. Crespo-Facorro, I. Nenadic, F. Benedetti, C. Gaser, H. Sauer, R. Roiz-Santiañez, S. Poletti, G. Rambaldelli, V. Marinelli, M. Bellani, C. Perlini, M. Ruggeri, A.C. Altamura, V.A. Diwadkar, P. Brambilla, Maggioni, E., Crespo-Facorro, B., Nenadic, I., Benedetti, F., Gaser, C., Sauer, H., Roiz-Santiañez, R., Poletti, S., Rambaldelli, G., Marinelli, V., Bellani, M., Perlini, C., Ruggeri, M., Altamura, A. C., Diwadkar, V. A., and Brambilla, P.

Subjects
Pharmacology, Psychiatry and Mental health, Neurology, Pharmacology (medical), Neurology (clinical), Biological Psychiatry
Published
2016

49. P.3.025 Brain-derived neurotrophic factor associates with gray matter volumes and early adverse experiences in bipolar disorder

Author

Veronica Aggio, Francesco Benedetti, Volker Arolt, Oliver Ambrée, S. Poletti, Cristina Colombo, Aggio, V., Poletti, S., Ambrée, O., Colombo, C., Arolt, V., and Benedetti, F.

Subjects
Pharmacology, Brain-derived neurotrophic factor, business.industry, medicine.disease, Gray (unit), Psychiatry and Mental health, Neurology, Medicine, Pharmacology (medical), Neurology (clinical), Bipolar disorder, business, Neuroscience, Biological Psychiatry
Published
2016

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