Factors Predicting Ictal Quality in Bilateral Electroconvulsive Therapy Sessions

In electroconvulsive therapy (ECT), ictal characteristics predict treatment response and can be modified by changes in seizure threshold and in the ECT technique. We aimed to study the impact of ECT procedure-related variables that interact during each session and might influence the seizure results. Two hundred and fifty sessions of bilateral ECT in forty-seven subjects were included. Seizure results were evaluated by two different scales of combined ictal EEG parameters (seizure quality index (SQI) and seizure adequacy markers sum (SAMS) scores) and postictal suppression rating. Repeated measurement regression analyses were performed to identify predictors of each session's three outcome variables. Univariate models identified age, physical status, hyperventilation, basal oxygen saturation, days between sessions, benzodiazepines, lithium, and tricyclic antidepressants as predictors of seizure quality. Days elapsed between sessions, higher oxygen saturation and protocolized hyperventilation application were significant predictors of better seizure quality in both scales used in multivariate models. Additionally, lower ASA classification influenced SQI scores as well as benzodiazepine use and lithium daily doses were predictors of SAMS scores. Higher muscle relaxant doses and lower applied stimulus intensities significantly influenced the postictal suppression rating. The study found several modifiable procedural factors that impacted the obtained seizure characteristics; they could be adjusted to optimize ECT session results

BDNF genetic variants and methylation: effects on cognition in major depressive disorder

Brain-derived neurotrophic factor (BDNF) gene regulation has been linked to the pathophysiology of major depressive disorder (MDD). MDD patients show cognitive deficits, and altered BDNF regulation has a relevant role in neurocognitive functions. Our goal was to explore the association between BDNF genetic and epigenetic variations with neurocognitive performance in a group of MDD patients and healthy controls considering possible modulating factors. The sample included 134 subjects, 64 MDD patients, and 70 healthy controls. Clinical data, childhood maltreatment, and neurocognitive performance were assessed in all participants. Eleven single nucleotide polymorphisms (SNPs) and two promoter regions in the BDNF gene were selected for genotype and methylation analysis. The role of interactions between BDNF genetic and epigenetic variations with MDD diagnosis, sex, and Childhood Trauma Questionnaire (CTQ) scores was also explored. We observed significant associations between neurocognitive performance and two BDNF SNPs (rs908867 and rs925946), an effect that was significantly mediated by methylation values at specific promoter I sites. We identified significant associations between neurocognitive results and methylation status as well as its interactions with MDD diagnosis, sex, and CTQ scores. Our results support the hypothesis that BDNF gene SNPs and methylation status, as well as their interactions with modulating factors, can influence cognition. Further studies are required to confirm the effect of BDNF variations and cognitive function in larger samples.This study was supported in part by grants from the Carlos III Health Institute through the Ministry of Economy and Competitiveness (PI10/01753, PIE14/00034 and PI15/00662), co-funded by the European Regional Development Fund (ERDF) “A way to build Europe”, CIBERSAM, and the Catalan Agency for the Management of University and Research Grants (AGAUR 2017 SGR 1247). We also thank CERCA Programme/ Generalitat de Catalunya for institutional support. The genotyping and methylation services were carried out at CEGEN-PRB3-ISCIII and were supported by grants PT13/0001 and PT17/0019, ISCIII-SGEFI/FEDER. Dr. Labad received an Intensification of the Research Activity Grant (SLT006/17/00012). Dr. Costas was supported by a Miguel Servet II contract from the Carlos III Health Institute (CPII16/00019). Dr. Soriano-Mas was supported by a Miguel Servet contract from the Carlos III Health Institute (CPII16/00048)S

Childhood maltreatment interacts with hypothalamic-pituitary-adrenal axis negative feedback and major depression: effects on cognitive performance

Background: Childhood maltreatment (CM) is associated with impaired hypothalamic-pituitary-adrenal (HPA) axis negative feedback and cognitive dysfunction, resembling those abnormalities linked to major depressive disorder (MDD). Objectives: We aimed to assess the potential modulating effects of MDD diagnosis or HPA axis function in the association between different types of CM and cognitive performance in adulthood. Methods: Sixty-eight MDD patients and 87 healthy controls were recruited. CM was assessed with the Childhood Trauma Questionnaire. We obtained three latent variables for neuropsychological performance (verbal memory, visual memory and executive function/processing speed) after running a confirmatory factor analysis with cognitive tests applied. Dexamethasone suppression test ratio (DSTR) was performed using dexamethasone 0.25 mg. Results: Different types of CM had different effects on cognition, modulated by MDD diagnosis and HPA axis function. Individuals with physical maltreatment and MDD presented with enhanced cognition in certain domains. The DSTR differentially modulated the association between visual memory and physical neglect or sexual abuse. Conclusions: HPA axis-related neurobiological mechanisms leading to cognitive impairment might differ depending upon the type of CM. Our results suggest a need for early assessment and intervention on cognition and resilience mechanisms in individuals exposed to CM to minimize its deleterious and lasting effects

BDNF genetic variants and methylation: effects on cognition in major depressive disorder

Brain-derived neurotrophic factor (BDNF) gene regulation has been linked to the pathophysiology of major depressive disorder (MDD). MDD patients show cognitive deficits, and altered BDNF regulation has a relevant role in neurocognitive functions. Our goal was to explore the association between BDNF genetic and epigenetic variations with neurocognitive performance in a group of MDD patients and healthy controls considering possible modulating factors. The sample included 134 subjects, 64 MDD patients, and 70 healthy controls. Clinical data, childhood maltreatment, and neurocognitive performance were assessed in all participants. Eleven single nucleotide polymorphisms (SNPs) and two promoter regions in the BDNF gene were selected for genotype and methylation analysis. The role of interactions between BDNF genetic and epigenetic variations with MDD diagnosis, sex, and Childhood Trauma Questionnaire (CTQ) scores was also explored. We observed significant associations between neurocognitive performance and two BDNF SNPs (rs908867 and rs925946), an effect that was significantly mediated by methylation values at specific promoter I sites. We identified significant associations between neurocognitive results and methylation status as well as its interactions with MDD diagnosis, sex, and CTQ scores. Our results support the hypothesis that BDNF gene SNPs and methylation status, as well as their interactions with modulating factors, can influence cognition. Further studies are required to confirm the effect of BDNF variations and cognitive function in larger samples

Childhood Maltreatment and Its Interaction with Hypothalamic–Pituitary–Adrenal Axis Activity and the Remission Status of Major Depression: Effects on Functionality and Quality of Life

Relationships among childhood maltreatment (CM), hypothalamic-pituitary-adrenal (HPA) axis disturbances, major depressive disorder (MDD), poor functionality, and lower quality of life (QoL) in adulthood have been described. We aimed to study the roles of the remission status of depression and HPA axis function in the relationships between CM and functionality and QoL. Ninety-seven patients with MDD and 97 healthy controls were included. The cortisol awakening response, cortisol suppression ratio in the dexamethasone suppression test, and diurnal cortisol slope were assessed. Participants completed measures of psychopathology, CM, functionality, and QoL. Multiple linear regression analyses were performed to study the relationships between CM and functionality and QoL. Only non-remitted MDD patients showed lower functionality and QoL than controls, indicating that depressive symptoms may partly predict functionality and QoL. Cortisol measures did not differ between remitted and non-remitted patients. Although neither HPA axis measures nor depression remission status were consistently associated with functionality or QoL, these factors moderated the effects of CM on functionality and QoL. In conclusion, subtle neurobiological dysfunctions in stress-related systems could help to explain diminished functionality and QoL in individuals with CM and MDD and contribute to the persistence of these impairments even after the remission of depressive symptoms

The Role of Sleep Quality, Trait Anxiety and Hypothalamic-Pituitary-Adrenal Axis Measures in Cognitive Abilities of Healthy Individuals

Altres ajuts: This study was supported in part by grants from the European Regional Development Fund (ERDF) "A way to build Europe", the Fundació La Marató de TV3 (092230/092231), CIBERSAM. Samples from patients included in this study were processed and preserved by the Biobank IISPV and the Biobank HUB-ICO-IDIBELL, integrated in the Spanish National Biobanks Network (PT17/0015/0024) and Xarxa Banc de Tumors. The funders had no role in the study design,data collection and analysis, decision to publish, or preparation of the manuscript.Sleep plays a crucial role in cognitive processes. Sleep and wake memory consolidation seem to be regulated by glucocorticoids, pointing out the potential role of the hypothalamic-pituitary-adrenal (HPA) axis in the relationship between sleep quality and cognitive abilities. Trait anxiety is another factor that is likely to moderate the relationship between sleep and cognition, because poorer sleep quality and subtle HPA axis abnormalities have been reported in people with high trait anxiety. The current study aimed to explore whether HPA axis activity or trait anxiety moderate the relationship between sleep quality and cognitive abilities in healthy individuals. We studied 203 healthy individuals. We measured verbal and visual memory, working memory, processing speed, attention and executive function. Sleep quality was assessed with the Pittsburgh Sleep Quality Index. Trait anxiety was assessed with the State-Trait Anxiety Inventory. HPA axis measures included the cortisol awakening response (CAR), diurnal cortisol slope and cortisol levels during the day. Multiple linear regression analyses explored the relationship between sleep quality and cognition and tested potential moderating effects by HPA axis measures and trait anxiety. Poor sleep quality was associated with poorer performance in memory, processing speed and executive function tasks. In people with poorer sleep quality, a blunted CAR was associated with poorer verbal and visualmemory and executive functions, and higher cortisol levels during the day were associated with poorer processing speed. Trait anxiety was a moderator of visual memory and executive functioning. These results suggest that subtle abnormalities in the HPA axis and higher trait anxiety contribute to the relationship between lower sleep quality and poorer cognitive functioning in healthy individuals

Relationship between immunometabolic status and cognitive performance among major depression disorder patients

Background: Alterations in cognitive performance have been described in patients with major depressive disorder (MDD). However, the specific risk factors of these changes are not yet known. This study aimed to explore whether inmunometabolic parameters are related to cognitive performance in MDD in comparison to healthy controls (HC) METHODS: Sample consisted of 84 MDD patients and 78 HC. Both groups were compared on the results of cognitive performance measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB), the presence of metabolic syndrome (MetS) and an inflammatory/oxidative index calculated by a principal component analysis of peripheral biomarkers (tumor necrosis factor, C-reactive protein and 4-hydroxynonenal). A multiple linear regression was carried out, to study the relationship between inmunometabolic variables and the global cognitive performance, being the latter the dependent variable. Results: Significant differences were obtained in the inflammatory/oxidative index between both groups (F(1157)= 12.93; p < .001), also in cognitive performance (F(1157)= 56.75; p < .001). The inmunometabolic covariate regression model (i.e., condition (HC/MDD), sex, age and medication loading, MetS, inflammatory/oxidative index and the interaction between MetS and inflammatory/oxidative index) was statistically significant (F(7157)= 11.24; p < .01) and explained 31% of variance. The condition, being either MDD or HD, (B=-0.97; p < .001), age (B=-0.28; p < .001) and the interaction between inflammatory/oxidative index and MetS (B=-0.38; p = .02) were factors associated to cognitive performance. Limitations: Sample size was relatively small. The cross-sectional design of the study limits the possibilities of analysis. Conclusions: Our results provide evidence on the conjoint influence of metabolic and inflammatory dysregulation on cognitive dysfunction in MDD patients. In this way, our study opens a line of research in immunometabolic agents to deal with cognitive decline associated with MDD

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algo-rithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits.Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12).The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non -treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables.Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified.The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-alpha levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58).In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-alpha (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption.Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity

The Role of Sleep Quality, Trait Anxiety and Hypothalamic-Pituitary-Adrenal Axis Measures in Cognitive Abilities of Healthy Individuals

Sleep plays a crucial role in cognitive processes. Sleep and wake memory consolidation seem to be regulated by glucocorticoids, pointing out the potential role of the hypothalamic-pituitary-adrenal (HPA) axis in the relationship between sleep quality and cognitive abilities. Trait anxiety is another factor that is likely to moderate the relationship between sleep and cognition, because poorer sleep quality and subtle HPA axis abnormalities have been reported in people with high trait anxiety. The current study aimed to explore whether HPA axis activity or trait anxiety moderate the relationship between sleep quality and cognitive abilities in healthy individuals. We studied 203 healthy individuals. We measured verbal and visual memory, working memory, processing speed, attention and executive function. Sleep quality was assessed with the Pittsburgh Sleep Quality Index. Trait anxiety was assessed with the State-Trait Anxiety Inventory. HPA axis measures included the cortisol awakening response (CAR), diurnal cortisol slope and cortisol levels during the day. Multiple linear regression analyses explored the relationship between sleep quality and cognition and tested potential moderating effects by HPA axis measures and trait anxiety. Poor sleep quality was associated with poorer performance in memory, processing speed and executive function tasks. In people with poorer sleep quality, a blunted CAR was associated with poorer verbal and visual memory and executive functions, and higher cortisol levels during the day were associated with poorer processing speed. Trait anxiety was a moderator of visual memory and executive functioning. These results suggest that subtle abnormalities in the HPA axis and higher trait anxiety contribute to the relationship between lower sleep quality and poorer cognitive functioning in healthy individuals

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algorithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits. Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12). The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non-treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables. Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified. The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-α levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58). In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-α (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption. Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity.This study was supported in part by grants from the Carlos III Health Institute through the Ministry of Science, Innovation and Universities (PI15/00662, PI15/0039, PI15/00204, PI19/01040), co-funded by the European Regional Development Fund (ERDF) “A way to build Europe”, CIBERSAM, and the Catalan Agency for the Management of University and Research Grants (AGAUR 2017 SGR 1247). We also thank CERCA Programme/Generalitat de Catalunya for institutional support. Work partially supported by Biobank HUB-ICO-IDIBELL, integrated in the Spanish Biobank Network and funded by Instituto de Salud Carlos III (PT17/0015/0024) and by Xarxa Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. YSC work is supported by the FPI predoctoral grant (FPI 2016/17) from Universidad Autonoma de Madrid. VS received an Intensification of the Research Activity Grant from the Instituto de Salud Carlos III (INT21/00055) during 202