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Purpose Bariatric surgery reduces mortality in patients with severe obesity and is predominantly performed in women. Therefore, an analysis of sex-specific differences after bariatric surgery in a population-based dataset from Austria was performed. The focus was on deceased patients after bariatric surgery. Materials and Methods The Austrian health insurance funds cover about 98% of the Austrian population. Medical health claims data of all Austrians who underwent bariatric surgery from 01/2010 to 12/2018 were analyzed. In total, 19,901 patients with 107,806 observed years postoperative were eligible for this analysis. Comorbidities based on International Classification of Diseases (ICD)-codes and drug intake documented by Anatomical Therapeutical Chemical (ATC)-codes were analyzed in patients deceased and grouped according to clinically relevant obesity-associated comorbidities: diabetes mellitus (DM), cardiovascular disease (CV), psychiatric disorder (PSY), and malignancy (M). Results In total, 367 deaths were observed (1.8%) within the observation period from 01/2010 to 04/2020. The overall mortality rate was 0.34% per year of observation and significantly higher in men compared to women (0.64 vs. 0.24%; p p p = 0.034), whereas malignant diseases (36%) were more frequent in women (30 vs. 41%; p = 0.025). Conclusion After bariatric surgery, short-term mortality as well as long-term mortality was higher in men compared to women. In deceased patients, diabetes was more common in men, whereas malignant diseases were more common in women. Graphical abstract

Objective Major depressive disorder (MDD) and anxiety disorders are both common and especially challenging during pregnancy. Considering possible risks of intrauterine drug exposure of the child, the role of psychopharmacological treatment is ambiguous and various negative obstetric outcomes were inconsistently associated with medication. Consequently, a critical examination of peri- and postnatal phenomena associated with intrauterine exposure to antidepressants based on serotonin-reuptake inhibition (SRI) and subsumed under the term "poor neonatal adaptation syndrome" (PNAS) is urgently called for. Methods A comprehensive literature search was conducted, revealing a total number of 33 relevant studies and 69 individual outcomes among 3025 screened studies. Seventeen outcomes allowed meta-analytic evaluation (random effects model). Measures for heterogeneity (I2 ) and contour-enhanced funnel plots were generated. Results Single studies showed increased risks for deficits in neurological functioning and autonomous adaptation in SRI exposed infants. Meta-analytical evaluation showed increased symptom occurrence or severity in exposed neonates for low APGAR scores, birth weight, size for gestational age, preterm delivery, neuromuscular and autonomous regulation, and higher rates of admission to specialized care. Mostly, increased risk after SRI exposure was supported by comparison to unexposed infants born to mothers diagnosed with depression. Conclusion Whereas statistically significant evidence for various effects of intrauterine exposure to SRI was found, the clinical relevance remains unresolved due to inherently low data quality in this research domain and insufficiently defined samples and outcomes. More systematic research under ethical considerations is required to improve multiprofessional counselling in the many women dealing with MDD during pregnancy and the peripartum.

Background: Sex-related effects on the evolution and phenotype of major depressive disorder (MDD) were reported previously. Methods: This European multicenter cross-sectional study compared sociodemographic, clinical, and treatment patterns between males and females in a real-world sample of 1410 in- and outpatients with current MDD. Results: Male MDD patients (33.1%) were rather inpatients, suffered from moderate to high suicidality levels, received noradrenergicand specific serotonergic antidepressants (ADs) as first-line AD treatment, generally higher mean AD daily doses, and showed a trend towards a more frequent administration of add-on treatments. Female MDD patients (66.9%) were rather outpatients, experienced lower suicidality levels, comorbid thyroid dysfunction, migraine, asthma, and a trend towards earlier disease onset. Conclusions: The identified divergencies may contribute to the concept of maleand female depressive syndromes and serve as predictors of disease severity and course, as they reflect phenomena that were repeatedly related to treatment-resistant depression (TRD). Especially the greater necessity of inpatient treatment and more complex psychopharmacotherapy in men may reflect increased therapeutic efforts undertaken to treat suicidality and to avoid TRD. Hence, considering sex may guide the diagnostic and treatment processes towards targeting challenging clinical manifestations including comorbidities and suicidality, and prevention of TRD and chronicity.

Background While the association between relationship status and the development of depressive symptoms in the general population were reported previously, its relation to the severity and the course of major depressive disorder (MDD) as well as the treatment patterns and response rates needs to be elucidated. Methods The present international multicenter cross-sectional study performed by the European Group for the Study of Resistant Depression (GSRD) investigated socio-demographic and clinical patterns of relationship status in a real-world sample of 1410 adult in- and outpatients with MDD as primary diagnosis. Results While 49.9% of all MDD patients were partnered, 25.4% were separated, and 24.8% were single. Single relationship status was linked to younger mean age, earlier mean age of onset, and current suicidal risk. Being separated was related to older mean age, unemployment, greater symptom severity, current suicidal risk, and add-on treatment strategies. Partnered relationship status was associated with less frequent current suicidal risk. Limitations The retrospective assessment of treatment response that was exclusively based on psychopharmacotherapeutic strategies should be critically considered and weighed while interpreting the present results providing novel insights into the complex interaction of relationship status with the clinical phenotype of MDD. Conclusions Although MDD patients living in relationships do not seem to be omitted from the evolution of MDD, they may be spared from chronicity and suicidality. Hence, being aware of the current relationship status might support clinicians in the diagnostic and therapeutic process towards optimized management of such challenging clinical phenomena and their negative consequences.

Objectives Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho‐sociodemographic variables and their predictive impact on treatment outcome phenotypes. Experimental Procedures Treatment response was defined by a change of HAM‐D 17‐item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross‐center validation design. In total, 88 predictors were implemented. Results Clustering revealed four distinct HAM‐D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability. Conclusion Our results support a decisive role for machine learning in the management of antidepressant treatment. Treatment‐ and symptom‐specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.

Context A substantial number of patients undergoing bariatric surgery are prescribed psychopharmacological medication. However, the impact of concomitant psychopharmacological medication on the frequency of relevant vitamin deficiencies in postoperative follow-up is not known. Methods Five hundred twenty-four patients with obesity who underwent bariatric surgery (January 2004 to September 2018) with follow-up of at least 12 months, were included in retrospective analysis. Postoperative follow-up visits between January 2015 and September 2019 were analyzed. Anthropometric and laboratory data were analyzed at the first documented follow-up visit after on average 39.5 ± 37.3 months and at every following visit during the observation period. Patients with prescribed psychopharmacological drugs (PD) were compared with patients without (control group, CON). Results Psychopharmacological medication was documented in 25% (132) of patients. In 59 patients documented prescription of more than one psychiatric drug was found, whereas psychopharmacological monotherapy was found in 73 patients. Frequencies of vitamin deficiencies were comparable between PD and CON (vitamin A: p = 0.852; vitamin D: p = 0.622; vitamin E: p = 0.901; folic acid: p = 0.941). Prevalence of vitamin B12 deficiency was rare (6% CON, 1% PD) but was significantly higher in CON (p = 0.023). A comparison of CON and POLY also showed no significant differences between the groups concerning prevalence of vitamin deficiencies. Conclusions Intake of psychopharmacological medication is highly prevalent in patients after bariatric surgery. Patients with psychopharmacological medication, who participate in structured follow-up care after bariatric surgery, are not at higher risk for vitamin deficiencies compared with controls.

About two thirds of the patients with major depressive disorder (MDD) do not sufficiently respond to monotherapy with antidepressants (ADs) which makes them reliant on further treatment approaches. Hereby, combination of different ADs and augmentation with second-generation antipsychotics (SGAs) are widely used and recommended psychopharmacotherapeutic strategies. The present secondary analyses are based on an international, naturalistic, cross-sectional multicenter study conducted by the European Group for the Study of Resistant Depression. Comparing socio-demographic and clinical characteristics of 436 adult MDD patients receiving either SGAs (N = 191, 43.8%) or ADs (N = 245, 56.2%), that were additionally administered to their first-line AD psychopharmacotherapy, we aimed to identify possible trajectories of decision-making for clinicians regarding which treatment option to prefer in individual patients. Our most robust findings represent an association of SGA augmentation with the presence of psychotic symptoms, longer mean duration of lifetime psychiatric hospitalizations, employment of further augmentation strategies with mood-stabilizers and benzodiazepines, and a trend towards higher mean daily dosages of their first-line ADs and current suicidal risk. Treatment outcome was not significantly different between patients receiving either SGA augmentation or AD combination. Being aware of limitations inherent to the cross-sectional study design and the lack of randomization, more severe and rather chronic conditions in MDD seemed to encourage clinicians to choose SGA augmentation over AD combination. The fact that mood-stabilizers and/or benzodiazepines were more frequently co-administered with SGAs may represent a requirement of an overall refined psychopharmacotherapy including additional fast-acting agents with potent AD, tranquilizing and anti-suicidal effects in MDD patients experiencing challenging clinical manifestations. New glutamatergic substances seem to be promising in this regard.

Background Augmentation with second-generation antipsychotics (SGAs) represents an evidence-based psychopharmacotherapeutic strategy recommended in case of insufficient response to the first-line antidepressant (AD) treatment in major depressive disorder (MDD). Comparative evidence regarding efficacy and prescription preferences of the individual SGAs is scarce. Methods In the scope of this European, multi-site, naturalistic cross-sectional investigation with retrospective assessment of treatment outcome, we compared sociodemographic and clinical characteristics of 187 MDD patients receiving either quetiapine (n = 150) or aripiprazole (n = 37) as augmentation of their first-line AD psychopharmacotherapy. Results Comorbid posttraumatic stress disorder and diabetes were significantly associated with aripiprazole augmentation in our primary and post-hoc binary logistic regression analyses. Furthermore, we identified an association between aripiprazole co-administration and the presence of additional psychotic features, higher rates of AD combination treatment, and a longer duration of psychiatric hospitalizations during the lifetime, which, however, lost significance after correcting for multiple comparisons. Regarding treatment outcome, we found a trend of higher response rates and greater reductions in severity of depressive symptoms in MDD patients dispensed quetiapine. Conclusions Factors associated with a more chronic and severe profile of MDD seem to encourage clinicians to choose aripiprazole over quetiapine, that was, however, administered in the majority of our MDD patients, which might reflect the current approval situation allowing to prescribe exclusively quetiapine as on-label augmentation in MDD in Europe. Given the retrospective assessment of treatment response, the markedly smaller proportion of patients receiving aripiprazole augmentation generally showing an unfavorable disease profile, and the partially heterogeneous statistical robustness of our findings, further studies are required to elaborate on our observation and to generate unambiguous recommendations regarding the choice of first-line SGA augmentation in MDD.

Objective: Patients with type 2 diabetes mellitus (T2DM) are at an increased risk of developing infectious diseases such as pneumonia. Hitherto, there has been uncertainty as to whether there is a relationship between different antidiabetic drug combinations and development of pneumonia in this specific cohort. Research Design and Methods: In this longitudinal retrospective study we used multiple logistic regression analysis to assess the odds ratios (ORs) of pneumonia during an observational period of 2 years in 31,397 patients with T2DM under previously prescribed stable antidiabetic drug combinations over a duration of 4 years in comparison to 6568 T2DM patients without drug therapy over 4 years adjusted for age, sex and hospitalization duration. Results: Of the 37,965 patients with T2DM, 3720 patients underwent stable monotherapy treatment with insulin (mean age: 66.57 ± 9.72 years), 2939 individuals (mean age: 70.62 ± 8.95 y) received stable statin and insulin therapy, and 1596 patients were treated with a stable combination therapy of metformin, insulin and statins (mean age: 68.27 ± 8.86 y). In comparison to the control group without antidiabetic drugs (mean age: 72.83 ± 9.96 y), individuals undergoing insulin monotherapy (OR: 2.07, CI: 1.54–2.79, p &lt, 0.001), insulin and statin combination therapy (OR: 2.24, CI: 1.68–3.00, p &lt, metformin, insulin and statin combination therapy (OR: 2.27, CI: 1.55–3.31, p &lt, statin, insulin and dipeptidyl peptidase-4 inhibitor (DPP-IV inhibitor) combination therapy (OR: 4.31, CI: 1.80–10.33, p = 0.001), as well as individuals treated with metformin and sulfonylureas (OR: 1.70, CI: 1.08–2.69, p = 0.02) were at increased risk of receiving a diagnosis of pneumonia. Conclusions: Stable monotherapy with insulin, but also in combination with other antidiabetic drugs, is related to an increased risk of being diagnosed with pneumonia during hospital stays in patients with type 2 diabetes mellitus compared to untreated controls.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with a robust genetic influence. The norepinephrine transporter (NET) is of particular interest as it is one of the main targets in treatment of the disorder. As ADHD is a complex and polygenetic condition, the possible regulation by epigenetic processes has received increased attention. We sought to determine possible differences in NET promoter DNA methylation between patients with ADHD and healthy controls. DNA methylation levels in the promoter region of the NET were determined in 23 adult patients with ADHD and 23 healthy controls. A subgroup of 18 patients with ADHD and 18 healthy controls underwent positron emission tomography (PET) with the radioligand (S,S)-[18F]FMeNER-D2 to quantify the NET in several brain areas in vivo. Analyses revealed significant differences in NET methylation levels at several cytosine–phosphate–guanine (CpG) sites between groups. A defined segment of the NET promoter (“region 1”) was hypermethylated in patients in comparison with controls. In ADHD patients, a negative correlation between methylation of a CpG site in this region and NET distribution in the thalamus, locus coeruleus, and the raphe nuclei was detected. Furthermore, methylation of several sites in region 1 was negatively associated with the severity of hyperactivity–impulsivity symptoms. Our results point to an epigenetic dysregulation in ADHD, possibly due to a compensatory mechanisms or additional factors involved in transcriptional processing.

The objective of the present multicenter study was to elucidate relevant associations between major depressive disorder (MDD) and comorbid hypertension that are known for their frequent co-occurrence and interaction with regard to functional disability. Demographic and clinical information of altogether 1410 patients were retrieved cross-sectionally. Consecutively, a comparison of patient characteristics between MDD subjects with and without comorbid hypertension were conducted by descriptive statistics, analyses of covariance (ANCOVA) and binary logistic regression analyses. The point prevalence rate for comorbid hypertension was 18.9%. Patients with MDD+comorbid hypertension were significantly older, heavier, more likely to be in a relationship, inpatient and diagnosed with further comorbid chronic somatic diseases including heart disease, diabetes and thyroid dysfunction. In addition, individuals with MDD and comorbid hypertension exhibited a higher score at the Montgomery and Åsberg Depression Rating Scale (MADRS) at onset of the current depressive episode. Melancholic features of depression showed a higher probability. The first line antidepressant treatment did not differ significantly between MDD subjects with versus without comorbid hypertension. Augmentation with pregabalin and combination with one additional antidepressant, however, were more common in the MDD+hypertension group. In conclusion, high blood pressure may influence illness severity and is associated with a distinct psychopathology in MDD patients. Patients with MDD and comorbid hypertension, that seems to be underdiagnosed in MDD patients compared to the general population, are subject to additional somatic diseases in almost 100 percent of the cases and hence, need to be screened and treated accordingly.

Diabetes mellitus ist haufig mit psychischen Erkrankungen assoziiert. Depressive Storungen kommen bei diabetischen PatientInnen doppelt so haufig vor wie in der nicht-diabetischen Population. Andere psychische Erkrankungen, die gehauft mit Pradiabetes und Diabetes mellitus vorkommen, sind kognitive Dysfunktionen bis zur Demenz, auffalliges Essverhalten, Angststorungen, Schizophrenie, bipolare Storungen und Borderline-Personlichkeitsstorungen. Die ungunstigen Auswirkungen dieser Koinzidenz auf den Stoffwechsel sind nachhaltig und manifestieren als schlechtere metabolische Kontrolle und vermehrte mikro- und makroangiopathische Komplikationen. Ziel dieses Positionspapieres ist sowohl die Sensibilisierung aller involvierten medizinischen FachkollegInnen sowie sonstiger mit dem Thema befasster Berufsgruppen und Organisationen als auch die Intensivierung der komplexen therapeutischen Interventionen bei PatientInnen. Positive Auswirkungen waren die Verringerung der Inzidenz von Diabetes mellitus bei PatientInnen mit psychischen Erkrankungen, die konsekutive Reduktion von Spatfolgen, insbesondere der kardiovaskularen Morbiditat und Mortalitat sowie eine verbesserte Lebensqualitat der Betroffenen.

Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.

Objective: To examine the dose-dependent relationship of different types of statins with the occurrence of major depressive disorder (MDD) and prescription of antidepressant medication.Methods: This cross-sectional study used medical claims data for the general Austrian population (n = 7,481,168) to identify all statin-treated patients. We analyzed all patients with MDD undergoing statin treatment and calculated the average defined daily dose for six different types of statins. In a sub-analysis conducted independently of inpatient care, we investigated all patients on antidepressant medication (statin-treated patients: n = 98,913; non-statin-treated patients: n = 789,683). Multivariate logistic regression analyses were conducted to calculate the risk of diagnosed MDD and prescription of antidepressant medication in patients treated with different types of statins and dosages compared to non-statin-treated patients.Results: In this study, there was an overrepresentation of MDD in statin-treated patients when compared to non-statin-treated patients (OR: 1.22, 95% CI: 1.20–1.25). However, there was a dose dependent relationship between statins and diagnosis of MDD. Compared to controls, the ORs of MDD were lower for low-dose statin-treated patients (simvastatin>0– < =10 mg:OR: 0.59, 95% CI: 0.54–0.64; atorvastatin>0– < =10 mg:OR:0.65, 95%CI: 0.59–0.70; rosuvastatin>0– < =10 mg:OR: 0.68, 95% CI: 0.53–0.85). In higher statin dosages there was an overrepresentation of MDD (simvastatin>40– < =60 mg:OR: 2.42, 95% CI: 2.18–2.70, >60–80 mg:OR: 5.27, 95% CI: 4.21–6.60; atorvastatin>40– < =60 mg:OR: 2.71, 95% CI: 1.98–3.72, >60– < =80 mg:OR: 3.73, 95% CI: 2.22–6.28; rosuvastatin>20– < =40 mg:OR: 2.09, 95% CI: 1.31–3.34). The results were confirmed in a sex-specific analysis and in a cohort of patients taking antidepressants, prescribed independently of inpatient care.Conclusions: This study shows that it is important to carefully re-investigate the relationship between statins and MDD. High-dose statin treatment was related to an overrepresentation, low-dose statin treatment to an underrepresentation of MDD.

Treatment-resistant depression (TRD) occurs in ~30% of patients with major depressive disorder (MDD) but the genetics of TRD was previously poorly investigated. Whole exome sequencing and genome-wide genotyping were available in 1209 MDD patients after quality control. Antidepressant response was compared to non-response to one treatment and non-response to two or more treatments (TRD). Differences in the risk of carrying damaging variants were tested. A score expressing the burden of variants in genes and pathways was calculated weighting each variant for its functional (Eigen) score and frequency. Gene-based and pathway-based scores were used to develop predictive models of TRD and non-response using gradient boosting in 70% of the sample (training) which were tested in the remaining 30% (testing), evaluating also the addition of clinical predictors. Independent replication was tested in STAR*D and GENDEP using exome array-based data. TRD and non-responders did not show higher risk to carry damaging variants compared to responders. Genes/pathways associated with TRD included those modulating cell survival and proliferation, neurodegeneration, and immune response. Genetic models showed significant prediction of TRD vs. response and they were improved by the addition of clinical predictors, but they were not significantly better than clinical predictors alone. Replication results were driven by clinical factors, except for a model developed in subjects treated with serotonergic antidepressants, which showed a clear improvement in prediction at the extremes of the genetic score distribution in STAR*D. These results suggested relevant biological mechanisms implicated in TRD and a new methodological approach to the prediction of TRD., SCOPUS: ar.j, info:eu-repo/semantics/published

Objectives: Psychological stress affects central as well as peripheral metabolism and hormone trafficking via the hypothalamic-pituitary-adrenal axis. Stress thereby plays a decisive role in the etiology and progression of overweight and obesity, leading to several chronic diseases, such as diabetes, and mental health disorders. The interplay of biological and psychometric correlates of stress, anthropometric, immunological, and metabolic parameters and psychosocial factors such as gender roles, however, remains poorly understood. Methods: In this exploratory study, 43 healthy women were assessed for glucose metabolism by an oral glucose tolerance test and computation of functional parameters for insulin secretion, sensitivity, and resistance. Further, the fatty liver index (FLI) and anthropometric parameters body mass index (BMI), waist-to-hip ratio, body fat, and lean mass were assessed. Psychological stress assessment included the “Brief Symptom Inventory” (BSI), the “Burnout Dimensions Inventory” (BODI), and Perceived Stress Scale (PSS). Biological stress response was evaluated with heart rate variability and cortisol levels. Finally, gender role self-identification was assessed with the “Bem Sex-Role Inventory” (BSRI). Generalized linear models were computed for exploratory association with psychometric outcome. Uncorrected p values are reported. Results: Burnout and PSS scores were associated with insulin secretion, sputum cortisol, thyroid-stimulating hormone, anthropometric measures, and gender role. BSI ratings for psychiatric symptom dimensions were associated with insulin resistance, sex hormones, anthropometric measures, and gender role. Female self-identification was associated with higher BMI as well as body fat and a higher FLI. Conclusions: Considering the increased risk of unfavorable metabolic, cardiovascular, and also mental health outcome in obese women, a higher BMI in women with predominant female gender self-identification may be relevant for clinical risk assessment. The broad range of interacting biological, psychological, and gender-related parameters calls for an integrative management of both mental and endocrinological health. However, the exploratory nature of the study requires replication in larger samples before definite conclusion can be drawn.

There is still a debate, if melancholic symptoms can be seen rather as a more severe subtype of major depressive disorder (MDD) or as a separate diagnostic entity. The present European multicenter study comprising altogether 1410 MDD in- and outpatients sought to investigate the influence of the presence of melancholic features in MDD patients. Analyses of covariance, chi-squared tests, and binary logistic regression analyses were accomplished to determine differences in socio-demographic and clinical variables between MDD patients with and without melancholia. We found a prevalence rate of 60.71% for melancholic features in MDD. Compared to non-melancholic MDD patients, they were characterized by a significantly higher likelihood for higher weight, unemployment, psychotic features, suicide risk, inpatient treatment, severe depressive symptoms, receiving add-on medication strategies in general, and adjunctive treatment with antidepressants, antipsychotics, benzodiazepine (BZD)/BZD-like drugs, low-potency antipsychotics, and pregabalin in particular. With regard to the antidepressant pharmacotherapy, we found a less frequent prescription of selective serotonin reuptake inhibitors (SSRIs) in melancholic MDD. No significant between-group differences were found for treatment response, non-response, and resistance. In summary, we explored primarily variables to be associated with melancholia which can be regarded as parameters for the presence of severe/difficult-to treat MDD conditions. Even if there is no evidence to realize any specific treatment strategy in melancholic MDD patients, their prescribed medication strategies were different from those for patients without melancholia.

IntroductionBoth diabetes mellitus and being female significantly increase the risk of being diagnosed with major depressive disorder (MDD). The diagnosis of MDD, combined with diabetes mellitus, can be detrimental in terms of mortality and morbidity. We aimed at investigating the impact of diabetes mellitus on the gender gap in MDD over the course of a human lifetime.Research design and methodsIn a cross-sectional study over the course of 17 years, medical claims data of the general Austrian population (n=8 996 916) between 1997 and 2014 was analyzed. Of these, 123 232 patients with diabetes mellitus were extracted and compared with non-diabetic controls.ResultsIn a cohort of 123 232 patients with diabetes mellitus and 1 933 218 controls (52% females, 48% males), women with diabetes had 2.55 times increased ORs to be diagnosed with MDD compared with women without diabetes (95% CI 2.48 to 2.62, pConclusionsDiabetes mellitus is a stronger risk factor for MDD in women than in men, with the greatest width of the gender gap between 40 and 49 years. High-risk patients for MDD, such as overweight female patients with diabetes, should be more carefully assessed and monitored.

Background Selective serotonin reuptake inhibitors (SSRIs) are predominantly prescribed for people suffering from major depressive disorder. These antidepressants exert their effects by blocking the serotonin transporter (SERT), leading to increased levels of serotonin in the synaptic cleft and subsequently to an attenuation of depressive symptoms and elevation in mood. Although long-term studies investigating white matter (WM) alterations after exposure to antidepressant treatment exist, results on the acute effects on the brain’s WM microstructure are lacking. Methods In this interventional longitudinal study, 81 participants were included (33 patients and 48 healthy controls). All participants underwent diffusion weighted imaging on 2 separate days, receiving either citalopram or placebo using a randomized, double-blind, cross-over design. Fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were calculated within the FMRIB software library and analyzed using tract-based spatial statistics. Results The repeated-measures ANOVA model revealed significant decreases after SSRI administration in mean diffusivity, axial diffusivity, and radial diffusivity regardless of the group (P Conclusions Our investigation provides the first evidence, to our knowledge, that fast WM microstructure adaptations within 1 hour after i.v. SSRI administration precede elevations in mood due to SSRI treatment. These results add a new facet to the complex mode of action of antidepressant therapy. This study was registered at clinicaltrials.gov with the identifier NCT02711215.

This cross-sectional European multicenter study with retrospective assessment of treatment response sought to determine variables associated with the administration of augmentation strategies with second-generation antipsychotics (SGAs) and lithium in the pharmacotherapy of major depressive disorder (MDD). In 349 DSM-IV-TR MDD patients, differences in socio-demographic, clinical, treatment, and pharmacological features between participants receiving add-on treatment of their antidepressants with either SGAs (n = 318) or lithium (n = 31) were investigated using analyses of covariance, chi-squared tests, and binary logistic regression analyses. As only significant between-group difference, we found SGA augmentation (compared with lithium augmentation) to be associated with high depressive symptom severity expressed by a higher mean Montgomery and Åsberg Depression Rating (MADRS) total score (27.19 ± 11.35 vs 18.87 ± 12.88, F = 14.82, p = .0001) and a higher mean 21-item Hamilton Rating Scale for Depression (HAM-D) total score (21.27 ± 9.30 vs 13.74 ± 9.11, F = 18.60, p = .0001). No significant differences for socio-demographic features, psychotic symptoms, suicidality, psychiatric and somatic comorbidities, antidepressant pharmacotherapy, and other add-on medications could be seen. Even if there was no significant superiority of one augmentation strategy with regard to treatment response pattern, a trend whereupon adjunctive SGAs were more likely dispensed in treatment-resistant and difficult-to-treat MDD conditions could be observed. In terms of the prescription pattern, we could demonstrate that lithium is less frequently used than SGAs in the clinical routine care which may reflect the need of continuous plasma level determinations and the anticipation of adverse effects.

Objectives: Clinical variables were investigated in the ‘treatment resistant depression (TRD)- III’ sample to replicate earlier findings by the European research consortium ‘Group for the Study of Resistant Depression’ (GSRD) and enable cross-sample prediction of treatment outcome in TRD. Experimental procedures: TRD was defined by a Montgomery and Åsberg Depression Rating Scale (MADRS) score ≥22 after at least two antidepressive trials. Response was defined by a decline in MADRS score by ≥50% and below a threshold of 22. Logistic regression was applied to replicate predictors for TRD among 16 clinical variables in 916 patients. Elastic net regression was applied for prediction of treatment outcome. Results: Symptom severity (odds ratio (OR) = 3.31), psychotic symptoms (OR = 2.52), suicidal risk (OR = 1.74), generalized anxiety disorder (OR = 1.68), inpatient status (OR = 1.65), higher number of antidepressants administered previously (OR = 1.23), and lifetime depressive episodes (OR = 1.15) as well as longer duration of the current episode (OR = 1.022) increased the risk of TRD. Prediction of TRD reached an accuracy of 0.86 in the independent validation set, TRD-I. Conclusion: Symptom severity, suicidal risk, higher number of lifetime depressive episodes, and comorbid anxiety disorder were replicated as the most prominent risk factors for TRD. Significant predictors in TRD-III enabled robust prediction of treatment outcome in TRD-I., SCOPUS: ar.j, info:eu-repo/semantics/published

Background: This European multicenter study aimed to elucidate suicidality in major depressive disorder. Previous surveys suggest a prevalence of suicidality in major depressive disorder of ≥50%, but little is known about the association of different degrees of suicidality with socio-demographic, psychosocial, and clinical characteristics. Methods: We stratified 1410 major depressive disorder patients into 3 categories of suicidality based on the Hamilton Rating Scale for Depression item 3 (suicidality) ratings (0 = no suicidality; 1–2 = mild/moderate suicidality; 3–4 = severe suicidality). Chi-squared tests, analyses of covariance, and Spearman correlation analyses were applied for the data analyses. Results: The prevalence rate of suicidality in major depressive disorder amounted to 46.67% (Hamilton Rating Scale for Depression item 3 score ≥1). 53.33% were allocated into the no, 38.44% into the mild/moderate, and 8.23% into the severe suicidality patient group. Due to the stratification of our major depressive disorder patient sample according to different levels of suicidality, we identified some socio-demographic, psychosocial, and clinical variables differentiating from the patient group without suicidality already in presence of mild/moderate suicidality (depressive symptom severity, treatment resistance, psychotic features, add-on medications in general), whereas others separated only when severe suicidality was manifest (inpatient treatment, augmentation with antipsychotics and benzodiazepines, melancholic features, somatic comorbidities). Conclusions: As even mild/moderate suicidality is associated with a failure of achieving treatment response, adequate recognition of this condition should be ensured in the clinical practice., SCOPUS: ar.j, info:eu-repo/semantics/published

Obesity is highly prevalent, causing substantial cardiovascular and mental health morbidity. Women show increased risk for mental health disorders, that is multiplied in obesity and related to cellular and psychological stress that can be targeted by non-pharmacological interventions. A total of 43 women underwent two weeks of caloric restriction, half of which also received 7 h of individualized clinical psychological intervention including psychoeducation, mindfulness, and heart-rate-variability biofeedback. Effects on body mass index (BMI), fatty liver index (FLI), bioimpedance measures, serum parameters, perceived stress (PSS), burn-out susceptibility (burn out diagnostic inventory) and dimensional psychiatric symptom load (brief symptom inventory, BSI) were analyzed with linear mixed effects models. Caloric restriction led to a reduction in BMI, body fat and FLI, decreased serum concentrations of leptin, PSS score, BSI dimensions and global severity index (all p ≤ 0.0001, withstanding Bonferroni–Holm correction). Benefits of add-on biofeedback were observed for BMI reduction (p = 0.041). Caloric restriction was effective in ameliorating both psychological wellbeing and metabolic functions following a BMI reduction. Biofeedback boosted effects on BMI reduction and the combinative therapy may be protective against common progression to mental health and cardiovascular disorders in overweight women while comparing favorably to pharmacological interventions in terms of side-effects and acceptability.

This international, multicenter, cross-sectional study comprising 1346 adult in- and outpatients with major depressive disorder (MDD) investigated the association between MDD as primary diagnosis and comorbid post-traumatic stress disorder (PTSD). In a cross-sectional data collection process, the presence of comorbid PTSD was determined by the Mini International Neuropsychiatric Interview (MINI) and the patients’ socio-demographic, clinical, psychopharmacological, and response information were obtained. Clinical features between MDD with and without concurrent PTSD were compared using descriptive statistics, analyses of covariance (ANCOVA), and binary logistic regression analyses. 1.49% of the MDD patients suffered from comorbid PTSD. Significantly more MDD + comorbid PTSD patients exhibited atypical features, comorbid anxiety disorders (any comorbid anxiety disorder, panic disorder, agoraphobia, and social phobia), comorbid bulimia nervosa, current suicide risk, and augmentation treatment with low-dose antipsychotic drugs. In the binary logistic regression analyses, the presence of atypical features (odds ratio (OR) = 4.49, 95%CI:1.01–20.12; p≤.05), any comorbid anxiety disorder (OR = 3.89, 95%CI:1.60-9.44; p = .003), comorbid panic disorder (OR = 6.45, 95%CI:2.52–16.51; p = .001), comorbid agoraphobia (OR = 6.51, 95%CI:2.54-16.68; p≤.001), comorbid social phobia (OR = 6.16, 95%CI:1.71–22.17; p≤.001), comorbid bulimia nervosa (OR = 10.39, 95%CI:1.21–88.64; p = .03), current suicide risk (OR = 3.58, 95%CI:1.30–9.91; p = .01), and augmentation with low-potency antipsychotics (OR = 6.66, 95%CI:2.50–17.77; p

Since many patients with major depressive disorder (MDD) do not satisfactorily respond to initial antidepressant monotherapy, add-on treatment strategies with other psychiatric compounds are often established. The present European multicenter cross-sectional study comprising 1410 MDD in- and outpatients investigated the prescription pattern of benzodiazepines as add-on treatment in the psychopharmacotherapy of MDD. Analyses of variance, chi-squared tests, and logistic regression analyses were conducted to examine differences in socio-demographic, clinical, and treatment characteristics between benzodiazepine users and non-users. The prescription rate for adjunctive benzodiazepine treatment amounted to 31.35%. The most often administered benzodiazepines were lorazepam (11.13%), clonazepam (6.74%), and alprazolam (6.60%). Benzodiazepine users exhibited more severe depressive symptoms expressed by a higher mean Montgomery and Åsberg Depression Rating Scale total score at study entry (26.92 ± 11.07 vs 23.55 ± 11.23, p

BackgroundUp to 60% of patients with major depressive disorder (MDD) do not respond to the first treatment with antidepressants. Response to antidepressants is a polygenic trait, although its underpinning genetics has not been fully clarified. This study aimed to investigate if Polygenic Risk Scores (PRSs) for major psychiatric disorders and neuroticism were associated with non-response or resistance to antidepressants in MDD.MethodsPRSs for bipolar disorder, MDD, neuroticism, and schizophrenia (SCZ) were computed in 1148 MDD patients recruited by the European Group for the Study of Resistant Depression. Summary statistics from largest meta-analyses of genome-wide association studies were used as base data. Patients were classified as responders, non-responders to one treatment, non-responders to two or more treatments (treatment-resistant depression or TRD). Regression analyses were adjusted for population stratification and recruitment sites.ResultsPRSs did not predict either non-response or TRD after Bonferroni correction. However, SCZ-PRS was nominally associated with non-response (p=0.003). Patients in the highest SCZ-PRS quintile were more likely to be non-responders than those in the lowest quintile (OR=2.23, 95% CI=1.21-4.10, p=0.02). Patients in the lowest SCZ-PRS quintile showed higher response rates when they did not receive augmentation with second-generation antipsychotics (SGAs), while those in the highest SCZ-PRS quintile had a poor response independently from the treatment strategy (p=0.009).ConclusionsA higher genetic liability to SCZ may reduce responsiveness to pharmacological treatment in MDD. From a clinical point of view, our results suggest that MDD patients with low SCZ-PRS do not benefit from augmentation with SGAs.

Sex-steroid hormones are primarily involved in sexual differentiation and development and are thought to underlie processes related to cognition and emotion. However, divergent results have been reported concerning the effects of hormone administration on brain structure including side effects like brain atrophy and dementia. Cross-sex hormone therapy in transgender subjects offers a unique model for studying the effects of sex hormones on the living human brain. In this study, 25 Female-to-Male (FtM) and 14 Male-to-Female (MtF) subjects underwent MRI examinations at baseline and after a period of at least 4-months of continuous cross-sex hormone administration. While MtFs received estradiol and anti-androgens, FtM subjects underwent high-dose testosterone treatment. The longitudinal processing stream of the FreeSurfer software suite was used for the automated assessment and delineation of brain volumes to assess the structural changes over the treatment period of cross-sex hormone administration. Most prominent results were found for MtFs receiving estradiol and anti-androgens in the form of significant decreases in the hippocampal region. Further analysis revealed that these decreases were reflected by increases in the ventricles. Additionally, changes in progesterone levels correlated with changes in gray matter structures in MtF subjects. In line with prior studies, our results indicate hormonal influences on subcortical structures related to memory and emotional processing. Additionally, this study adds valuable knowledge that progesterone may play an important role in this process.

Altered serotonergic neurotransmission has been found to cause impulsive and aggressive behavior, as well as increased motor activity, all exemplifying key symptoms of ADHD. The main objectives of this positron emission tomography (PET) study were to investigate the serotonin transporter binding potential (SERT BPND ) in patients with ADHD and to assess associations of SERT BPND between the brain regions. 25 medication-free patients with ADHD (age ± SD; 32.39 ± 10.15; 10 females) without any psychiatric comorbidity and 25 age and sex matched healthy control subjects (33.74 ± 10.20) were measured once with PET and the highly selective and specific radioligand [11 C]DASB. SERT BPND maps in nine a priori defined ROIs exhibiting high SERT binding were compared between groups by means of a linear mixed model. Finally, adopted from structural and functional connectivity analyses, we performed correlational analyses using regional SERT binding potentials to examine molecular interregional associations between all selected ROIs. We observed significant differences in the interregional correlations between the precuneus and the hippocampus in patients with ADHD compared to healthy controls, using SERT BPND of the investigated ROIs (P

Big data analytics and advanced statistical learning methods held an auspicious entry into neuropsychiatric research over the last decade. Especially for common multifactorial diseases as major depressive disorder (MDD), decisive advantages for diagnostics and prediction of treatment outcome phenotypes were both promised and expected. While a substantial amount of research was brought forward over the last years that already acknowledged the high potential of big data analytics for precision medicine in psychiatry, these expectations have so far been curbed by data management and methodological issues as well as difficulties inherent to the heterogeneous nature of neuropsychiatric disorders.

Background: Alzheimer's disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur. Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for Aβ-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging - Alzheimer's Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 ± 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported. Results: The most informative ROIs included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROIs anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change. Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry.

This multicenter study of the European Group for the Study of Resistant Depression (GSRD) aimed to explore the association between major depressive disorder (MDD) and comorbid thyroid disease. A total number of 1410 patients` characteristics in terms of demographic and clinical information were compared between MDD subjects with and without concurrent thyroid disease using descriptive statistics, analyses of covariance (ANCOVA) and binary logistic regression analyses. We determined a point prevalence rate for comorbid hypothyroidism of 13.2% and 1.6% for comorbid hyperthyroidism respectively. Patients with MDD+comorbid hypothyroidism were significantly older, more likely to be female, inpatient and suffering from other comorbid chronic somatic conditions. Furthermore, MADRS score at onset of the current depressive episode was significantly higher, psychotic features of depression were more likely pronounced. Overall, patients in the MDD+comorbid hypothyroidism group were rather treated with a combination of drugs, for example, pregabalin, antipsychotic drugs and mood stabilizers. In the MDD+comorbid hyperthyroidism group patients were significantly older, of Caucasian origin and diagnosed with other somatic comorbidities. In conclusion, our analyses suggest that abnormal thyroid function, especially hypothyroidism, is linked to depression severity and associated with distinct psychopathologic features of depression. However, comorbid thyroid disease has no influence on treatment response. A combination or augmentation of psychopharmacological drugs, especially with antipsychotics, mood stabilizers and pregabalin is more likely in patients with hypothyroid conditions. Thyroid disorder is frequently found in combination with other chronic somatic diseases including hypertension and heart disease.

Objective The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome. Method 552 Patients diagnosed with major depressive disorder (MDD) according to DSM-IV criteria were enrolled between 2011 and 2016. TRD was defined as failure to reach response to antidepressant treatment, characterized by a Montgomery-Asberg Depression Rating Scale (MADRS) score below 22 after at least 2 antidepressant trials of adequate length and dosage were administered. RandomForest (RF) was used for predicting treatment outcome phenotypes in a 10-fold cross-validation. Results The full model with 47 predictors yielded an accuracy of 75.0%. When the number of predictors was reduced to 15, accuracies between 67.6% and 71.0% were attained for different test sets. The most informative predictors of treatment outcome were baseline MADRS score for the current episode; impairment of family, social, and work life; the timespan between first and last depressive episode; severity; suicidal risk; age; body mass index; and the number of lifetime depressive episodes as well as lifetime duration of hospitalization. Conclusions With the application of the machine learning algorithm RF, an efficient prediction model with an accuracy of 75.0% for forecasting treatment outcome could be generated, thus surpassing the predictive capabilities of clinical evaluation. We also supply a simplified algorithm of 15 easily collected clinical and sociodemographic predictors that can be obtained within approximately 10 minutes, which reached an accuracy of 70.6%. Thus, we are confident that our model will be validated within other samples to advance an accurate prediction model fit for clinical usage in TRD.

Objective: To elucidate the impact of the presence of psychotic features in patients diagnosed with major depressive disorder (MDD) on sociodemographic, psychosocial, clinical, and response characteristics. Methods: A total of 1, 410 DSM-IV-TR MDD patients were included in the present European multicenter study, which was conducted between 2011 and 2016. Analyses of covariance, ?2 tests, and binary logistic regression analyses were performed to explore differences in sociodemographic and clinical variables between MDD patients with and without psychotic symptoms. Results: A prevalence rate of 10.92% for psychotic features was found in MDD. Compared to nonpsychotic MDD patients, those with psychotic features were characterized by a higher likelihood for melancholic characteristics (73.38% vs 59.16%, P = .0006), a higher rate of current suicide risk (60.39% vs 44.27%, P = .0002), greater likelihood of receiving inpatient treatment (55.84% vs 32.01%, P < .0001), greater depressive symptom severity (measured by various rating scales), and more often receiving augmentation/combination treatment strategies in general (81.17% vs 58.12%, P < .0001) and add-on therapy with antipsychotics (50.00% vs 22.69%, P < .0001) and benzodiazepines (47.40% vs 31.29%, P = .0001) in particular. Moreover, psychotic symptoms in MDD were highly predictive of treatment resistance, expressed by a more than 2.2-fold higher likelihood for resistance compared to nonpsychotic MDD patients (79.87% vs 35.75%, P < .0001). Only 3.25% of the patients with psychotic MDD achieved treatment response (vs 27.15% of those with nonpsychotic MDD, P < .0001). Conclusions: These findings suggest that adequate diagnosis of psychotic features in MDD should be ensured in routine clinical care. As a combination of antipsychotics and antidepressants represents the first-line treatment option in psychotic MDD, the finding of a 2-fold higher prescription rate for antipsychotic drugs in psychotic versus nonpsychotic MDD patients reflects the current evidence.

Background Treatment Resistant Depression (TRD) is a common complication (~1/3 of patients) of Major Depressive Disorder (MDD) that contributes to the huge personal and socio-economic burden of the disease. Genetic variants are key modulators of antidepressant efficacy, but previous Genome-Wide Association Studies (GWAS) have generally failed to identify the polymorphisms involved. Whole exome sequencing and network analysis represent promising and innovative strategies. Methods 47 patients with diagnosis of MDD with current episode of at least moderate severity were selected if they satisfied the definition of TRD or were optimal antidepressant responders. TRD was defined as lack of response to at least two antidepressant trials while optimal responders satisfied remission criteria after the first antidepressant trial. Next generation exome sequencing was performed from whole blood. After quality control (fastqc) and alignment to the reference human genome (ENSEMBL, release 83), gene-based tests were performed by PlinkSeq and enrichment analysis was performed using the Cytoscape GeneMania plugin to identify networks involved in TRD. Replication of results in the STAR⁎D study was attempted using a comparable phenotype (remission to the first antidepressant trial (n=583) vs. non-remission to the fourth antidepressant trial (n=48)). Results 24 TRD patients and 23 good responders were included in the study. 967,208 variants were available. Initial pathway analysis based on known gene product interactions identified some networks that were associated with TRD status, including the neurotrophin signalling pathway and other pathways involved in cell growth. Particularly a group of genes belonging to a network involved in metaphase-anaphase transition of mitotic cell cycle was replicated in STAR⁎D. Final analyses will be presented at the meeting. Discussion Genes involved in cell growth and in metaphase-anaphase transition of mitotic cell cycle may have a relevant role in the risk of TRD. Components of this network (the CDC20/APC complex) were demonstrated to have a pivotal role in controlling dendrite growth in post-mitotic neurons, a process that is essential for antidepressant response. Future directions include whole exome sequencing in a larger sample to replicate these findings.

Background Major depressive disorder is a high-prevalence disease associated with a heavy burden on both personal well-being and socio-economical welfare, partly as a result of lacking tailored treatment options [1]. Common single nucleotide polymorphisms (SNPs) were estimated to account for 0.42 of the variance in antidepressant response [2], confirming the hypothesis that genetic polymorphisms may be used as effective markers to provide tailored antidepressant treatments. Candidate gene and genome-wide analyses can provide a complementary strategy, since the former can be applied to clarify the role of SNPs with high pre-test probability of association with the trait and the latter is useful to study the joint effects of a number of SNPs in a gene or a set of genes [3]. Aim We applied such a complementary strategy to the study of eight genes that are very strong candidates with previous evidence of pleiotropic effect across psychiatric traits. The genes of interest are involved in the regulation of neurotransmission (CACNA1C, CACNB2, ANK3), neural differentiation, synaptic plasticity, adhesion processes and structural organization (GRM7, TCF4, ITIH3, SYNE1) and glucocorticoid signaling (FKBP5). Methods Three samples with major depressive disorder (total n=671) were genotyped for 44 SNPs in strong candidate genes based on biological function and previous genome-wide association studies (CACNA1C, CACNB2, ANK3, GRM7, TCF4, ITIH3, SYNE1, FKBP5). Phenotypes were response/remission after 4 weeks of treatment and treatment-resistant depression (TRD: non response/non remission to at least two antidepressant treatments). Genome-wide data from STAR*D were used to replicate findings for response/remission (Level 1, n=1409) and TRD (Level 2, n=620). Pathways including the most promising candidate genes for involvement in TRD were investigated in STAR*D Level 2. Top pathway(s) were investigated using machine learning models. Results FKBP5 rs3800373, rs1360780 and rs9470080 showed replicated associations with response, remission or TRD. CACNA1C SNPs showed contradictory direction of association across samples. ANK3 rs1049862 AA genotype showed a replicated association with better outcome. In STAR*D the best pathway associated with TRD included CACNA1C (GO:0006942, permutated p=0.15). Neural networks and gradient boosted machine showed that independent SNPs in this pathway predicted TRD with a mean sensitivity of 0.83 and specificity of 0.56 after 10-fold cross validation repeated 100 times. Conclusions FKBP5 polymorphisms should be considered for inclusion in antidepressant pharmacogenetic tests. CACNA1C is a good candidate and GO:0006942 includes several genes coding for ion channels expressed in the central nervous system and other genes relevant for excitatory mechanisms. CACNB2 and ANK3 showed replicated associations with phenotypes and further investigations could help in clarifying their role. This study may pave the way to the identification of sets of genetic predictors in specific pathways able to predict the risk of TRD. It is reasonable to hypothesize a certain degree of variability in the genetic variants involved in TRD across different patients, but the involved pathways are expected to be more stable. Validated genetic markers of TRD could have a pivotal role in the implementation of targeted antidepressant treatments.

Objective The major aim of this multicenter study of the European Group for the Study of Resistant Depression (GSRD) was to elucidate associations between major depressive disorder (MDD) and comorbid diabetes. Methods Demographic and clinical information of 1410 patients with a primary MDD diagnosis according to DSM-IV were retrieved cross-sectionally between 2012 and 2016. By applying descriptive statistics, analyses of covariance (ANCOVA) and binary logistic regression analyses, a comparison between patient characteristics with and without comorbid diabetes was performed. Results The point prevalence rate for comorbid diabetes across MDD patients was 6%. Individuals with MDD + comorbid diabetes were significantly older, heavier, more likely to be inpatient and diagnosed with additional comorbid chronic somatic diseases. In addition, current suicide risk was significantly increased and melancholic features were more likely pronounced. In general, patients in the MDD + diabetes group received a combination therapy with at least one additional antidepressant rather than various other augmentation strategies. Conclusion Our analyses depict a lower prevalence rate of diabetes in MDD patients than previous studies. However, in light of the prevalence of diabetes in the geographical area of the study, we found an increased risk for individuals with depression compared to the general population. Current suicide risk is markedly elevated and has to be thoroughly assessed in every patient with comorbid diabetes. Depression severity and treatment response remained unaffected by concurrent diabetes in MDD.

Objective: Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. Method: Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV. Treatment-resistant depression (TRD) was defined by a 17-item Hamilton Depression Rating Scale (HDRS) score ≥17 after at least 2 antidepressant trials of adequate dosage and length. Remission was defined by an HDRS score < 8. Stepwise predictor reduction using randomForest was performed to find the optimal number for classification of treatment outcome. After importance values were generated, prediction for remission and resistance was performed in a training sample of 400 patients. For prediction, we used a set of 80 patients not featured in the training sample and computed receiver operating characteristics. Results: The most useful predictors for treatment outcome were the timespan between first and last depressive episode, age at first antidepressant treatment, response to first antidepressant treatment, severity, suicidality, melancholia, number of lifetime depressive episodes, patients' admittance type, education, occupation, and comorbid diabetes, panic, and thyroid disorder. While single predictors could not reach a prediction accuracy much different from random guessing, by combining all predictors, we could detect resistance with an accuracy of 0.737 and remission with an accuracy of 0.850. Consequently, 65.5% of predictions for TRD and 77.7% for remission can be expected to be accurate. Conclusions: Using machine learning algorithms, we could demonstrate success rates of 0.737 for predicting TRD and 0.850 for predicting remission, surpassing predictive capabilities of clinicians. Our results strengthen data mining and suggest the benefit of focus on interaction-based statistics. Considering that all predictors can easily be obtained in a clinical setting, we hope that our model can be tested by other research groups.

Background The substantial contribution of genetic variants to inter-individual variance in antidepressant response makes genetic polymorphisms a unique opportunity to provide tailored antidepressant treatments. The present study combined a candidate approach of variants with strong previous evidence and a genome-wide approach in order to investigate the role of eight genes that appear optimal candidates for involvement in antidepressant response on the basis of their biological function and previous literature suggesting a pleiotropic effect across several psychiatric traits. Methods Three samples of patients with major depressive disorder (n=357, 218 and 96) were genotyped for 44 SNPs in genes involved in neurotransmission (CACNA1C, CACNB2, ANK3), neural differentiation, synaptic plasticity, adhesion processes, structural organization (GRM7, TCF4, ITIH3, SYNE1) and glucocorticoid signaling (FKBP5). Phenotypes were response/remission at week 4 and Treatment-Resistant Depression (TRD: non response/non remission to at least two antidepressants). STAR⁎D genome-wide data were used to replicate findings for response/remission (Level 1, n=1409) and TRD (Level 2, n=620). Standard quality control and genotype imputation were performed and pathways including the most promising candidate genes for involvement in TRD were investigated in STAR⁎D Level 2. Top pathway(s) were investigated also using machine learning (R cran Caret package). At SNP-level results with p Results Several FKBP5 SNPs (rs3800373, rs1360780, rs9470080) showed consistent associations with response, remission or TRD across at least two samples. Results involving CACNA1C SNPs (rs2283326, rs10848635, rs1006737) had contradictory direction of association across samples. ANK3 rs1049862 AA genotype showed a consistent association with a more favorable outcome in two samples. In STAR⁎D pathway analysis for the top candidate genes did not identify significant results after permutation. The best pathway associated with TRD included the CACNA1C gene (GO:0006942, regulation of striated muscle contraction, corrected p=0.15). Neural networks and gradient boosted machine showed that independent SNPs in this pathway predicted TRD with a mean accuracy of 0.73, sensitivity of 0.83 and specificity of 0.56. Discussion According to the present results and previous literature, FKBP5 polymorphisms should be considered for inclusion in pharmacogenetic tests for the prediction of antidepressant response and TRD. The contribution of CACNA1C polymorphisms was unclear, but further investigation is supported by previous literature and interesting findings for GO:0006942 pathway that includes several genes coding for ion channels that are expressed in the central nervous system as well as other genes relevant for excitatory mechanisms.

Background: Few studies investigated socio-demographic and clinical predictors of non response and remission in treatment resistant depression (TRD) in the case of failure of more than two adequate antidepressant (AD) trial. The primary aim of this study was to investigate socio-demographic and clinical predictors of TRD defined as the lack of response to at least three adequate AD treatments, two of which prospectively evaluated. As secondary aims, we also investigated predictors of non response and remission to: (1) at least two adequate AD treatment (one of which prospectively assessed); (2) at least one adequate and retrospectively assessed AD treatment. Methods: In the context of a European multicenter project, 407 major depressive disorder (MDD) patients who failed to respond to a previous AD treatment were recruited for a 2 stage trial, firstly receiving venlafaxine and then escitalopram. MINI, HRSD, MADRS, UKU, CGI-S and CGI-I were administered. Results: Ninety eight subjects (27.61%) were considered as resistant to three AD treatments. Clinical predictors were: longer duration and higher severity of the current episode (p = 0.004; ES = 0.24; p = 0.01; RR=1.41, respectively), outpatient status (p =0.04; RR =1.58), higher suicidal risk level (p =0.02; RR= 1.49), higher rate of the first/second degree psychiatric antecedents (MDD and others) (p =0.04; RR = 1.31, p = 0.03; RR = 1.32 respectively) and side effects during treatments (p = 0.002; RR = 2.82). Multivariate analyses underlined the association between TRD and the severity of the current episode (p =0.04). As for secondary outcomes, predicting factors were partially overlapping. Limitations: The limited sample size and specific drugs used limit present findings. Conclusion: Subjects with a high degree of resistance to AD treatments show specific features which may guide the clinicians to the choice of more appropriate therapies at baseline. (C) 2015 Elsevier B.V. All rights reserved.

This multicenter, cross-sectional study with retrospective assessment of treatment response evaluated the current prescription trends and pharmacological treatment strategies applied in European university/academic psychiatric centers in unipolar depression. Altogether, 1181 adult in- and outpatients with major depressive disorder (MDD) were enrolled in 9 academic sites in 8 European countries. Socio-demographic, clinical, and medication information were retrieved and the present symptom severity was assessed by the Montgomery and Asberg Depression Rating Scale (MADRS). The symptom improvement during the current MDD episode was covered by retrospective MADRS measurements. Beyond descriptive statistics, analyses of variance (ANOVA) and Spearman correlation analyses were accomplished to determine the influence of symptom severity and treatment response on the prescription patterns. 53.4% of all MDD patients received a selective serotonin reuptake inhibitor (SSRI) and 23.6% a serotonin–norepinephrine reuptake inhibitor (SNRI) as first-line treatment. The majority of participants (59.4%) were treated with polypharmaceutical strategies (median: 2 psychiatric compounds per patient) and for the number of individual drugs we found a significant correlation with the current MADRS total score and the MADRS total score change during the present depressive episode. Benzodiazepines (33.2% of patients), antidepressants (29.0%), antipsychotics (24.2%), and mood stabilizers (10.1%) were the most frequently prescribed adjunctive agents. There were no significant differences between the different psychopharmacological classes used as augmentors in terms of symptom severity and treatment response. In summary, this international cross-sectional study revealed the widespread use of polypharmaceutical treatment strategies in European tertiary psychiatric treatment centers for the management of MDD.

Major depressive disorder (MDD) is the most common neuropsychiatric disease and despite extensive research, its genetic substrate is still not sufficiently understood. The common polymorphism rs6295 of the serotonin-1A receptor gene (HTR1A) is affecting the transcriptional regulation of the 5-HT1A receptor and has been closely linked to MDD. Here, we used positron emission tomography (PET) exploiting advances in data mining and statistics by using machine learning in 62 healthy subjects and 19 patients with MDD, which were scanned with PET using the radioligand [carbonyl-11C]WAY-100635. All the subjects were genotyped for rs6295 and genotype was grouped in GG vs C allele carriers. Mixed model was applied in a ROI-based (region of interest) approach. ROI binding potential (BPND) was divided by dorsal raphe BPND as a specific measure to highlight rs6295 effects (BPDiv). Mixed model produced an interaction effect of ROI and genotype in the patients’ group but no effects in healthy controls. Differences of BPDiv was demonstrated in seven ROIs; parahippocampus, hippocampus, fusiform gyrus, gyrus rectus, supplementary motor area, inferior frontal occipital gyrus and lingual gyrus. For classification of genotype, ‘RandomForest’ and Support Vector Machines were used, however, no model with sufficient predictive capability could be computed. Our results are in line with preclinical data, mouse model knockout studies as well as previous clinical analyses, demonstrating the two-pronged effect of the G allele on 5-HT1A BPND for, we believe, the first time. Future endeavors should address epigenetic effects and allosteric heteroreceptor complexes. Replication in larger samples of MDD patients is necessary to substantiate our findings.

IntroductionThe multicenter, cross-sectional survey summarizes the current prescription patterns of psychopharmacological medications in patients with major depressive disorder (MDD) treated in European university psychiatric centers.MethodsThe study included a total of 1181 MDD patients who were recruited in 9 academic sites across 8 European countries. Socio-demographic, clinical, and psychopharmacological characteristics were collected within a detailed clinical interview and the current depressive symptom severity was measured by the Montgomery and Åsberg Depression Rating Scale (MADRS). Symptom reduction during the present MDD episode was analyzed by calculating retrospective MADRS scores. Descriptive statistics, analyses of variance (ANOVAs), and Spearman correlation analyses were performed to examine the impact of various features on the applied pharmacological strategies.ResultsRegarding first-line antidepressant medication, the most frequently prescribed drug classes were selective serotonin reuptake inhibitors (SSRIs) (53.4%), serotonin-norepinephrine reuptake inhibitors (SNRIs) (23.6%), noradrenergic and specific serotonergic antidepressants (NaSSAs) (8.2%), tricyclic antidepressants (TCA) (5.1%), and the melatonergic antidepressant agomelatine (5.0%). The most commonly used individual antidepressants were escitalopram (18.4%), venlafaxine (15.2%), sertraline (12.9%), paroxetine (9.1%), mirtazapine (8.2%), duloxetine (7.0%), and fluoxetine (6.5%). Among the patients, 59.4% were treated with polypsychopharmaceutical medications (mean: 2 drugs) and for the number of individual drugs, we found a significant correlation with the present MADRS total score and the MADRS total score change during the current depressive episode.ConclusionConsistent with surveys investigating primarily municipal psychiatric treatment centers, we could replicate the observation that SSRIs are the most commonly used antidepressants in MDD for the first time for European university centers.Disclosure of interestThe authors have not supplied their declaration of competing interest.

For over a decade, the European Group for the Study of Resistant Depression (GSRD) has examined single nucleotide polymorphisms (SNP) and clinical parameters in regard to treatment outcome. However, an interaction based model combining these factors has not been established yet. Regarding the low effect of individual SNPs, a model investigating the interactive role of SNPs and clinical variables in treatment-resistant depression (TRD) seems auspicious. Thus 225 patients featured in previous work of the GSRD were enrolled in this investigation. According to data availability and previous positive results, 12 SNPs in HTR2A, COMT, ST8SIA2, PPP3CC and BDNF as well as 8 clinical variables featured in other GSRD studies were chosen for this investigation. Random forests algorithm were used for variable shrinkage and k-means clustering for surfacing variable characteristics determining treatment outcome. Using these machine learning and clustering algorithms, we detected a set of 3 SNPs and a clinical variable that was significantly associated with treatment response. About 62% of patients exhibiting the allelic combination of GG-GG-TT for rs6265, rs7430 and rs6313 of the BDNF, PPP3CC and HTR2A genes, respectively, and without melancholia showed a HAM-D decline under 17 compared to about 34% of the whole study sample. Our random forests prediction model for treatment outcome showed that combining clinical and genetic variables gradually increased the prediction performance recognizing correctly 25% of responders using all 4 factors. Thus, we could confirm our previous findings and furthermore show the strength of an interaction-based model combining statistical algorithms in identifying and operating treatment predictors.