Long Short-term Memory-Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation.

BACKGROUND: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. OBJECTIVE: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. METHODS: The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer. RESULTS: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). CONCLUSIONS: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.

A candidate-gene association study of topiramate-induced weight loss in obese patients with and without type 2 diabetes mellitus.

OBJECTIVE: Clinical response to topiramate can vary greatly in obese patients. Identifying genetic variants associated with treatment response could help gain insight into the mechanism of action of topiramate. Little is known about the relationship between genetic variability and topiramate treatment response. We performed a large-scale candidate-gene study to identify genetic risk factors predictive of topiramate-induced weight loss. METHODS: We collected DNA samples from patients who had previously participated in clinical trials to assess the efficacy of topiramate for the treatment of obesity. A custom chip containing single nucleotide polymorphisms from ∼ 480 candidate genes was utilized to genotype a discovery cohort of 445 obese patients from a clinical study. Variants predictive of topiramate-induced weight loss were identified and further tested in an independent replication cohort of drug-naive, obese patients with type 2 diabetes (N=139). RESULTS: We identified a haplotype in INSR that may contribute to differential topiramate-induced weight loss. Carriers and noncarriers of an INSR haplotype lost 9.1 and 7.0% of body weight, respectively (P = 6.5 × 10(-6), P adj = 0.001). This finding was replicated, with carriers and noncarriers losing 9.5 and 7.3% of body weight, respectively (P Bonf=0.02), in the independent replication cohort. We also identified an SNP in HNF1A that may be associated with topiramate response and an SNP in GRIA3 that may be associated with nonpharmacologic treatment response. CONCLUSION: According to our preliminary findings, genetic variation in the INSR and HNF1A genes may differentially affect weight loss in obese individuals treated with topiramate and genes related to insulin action are implicated in modulating topiramate response. However, these findings need to be further replicated in additional larger samples.

Utilizing nullomers in cell-free RNA for early cancer detection.

Early detection of cancer can significantly improve patient outcomes; however, sensitive and highly specific biomarkers for cancer detection are currently missing. Nullomers are the shortest sequences that are absent from the human genome but can emerge due to somatic mutations in cancer. We examine over 10,000 whole exome sequencing matched tumor-normal samples to characterize nullomer emergence across exonic regions of the genome. We also identify nullomer emerging mutational hotspots within tumor genes. Finally, we provide evidence for the identification of nullomers in cell-free RNA from peripheral blood samples, enabling detection of multiple tumor types. We show multiple tumor classification models with an AUC greater than 0.9, including a hepatocellular carcinoma classifier with an AUC greater than 0.99.

Parenting Styles and Psychosocial Factors of Mother-Child Dyads Participating in the ENDORSE Digital Weight Management Program for Children and Adolescents during the COVID-19 Pandemic.

Childhood obesity is a complex disease with multiple biological and psychosocial risk factors. Recently, novel digital programs were developed with growing evidence for their effectiveness in pediatric weight management studies. The ENDORSE platform consists of mobile applications, wearables, and serious games for the remote management of childhood obesity. The pilot studies included 50 mothers and their children aged 6-14 years and resulted in a clinically significant BMI z-score reduction over 4 to 5 months. This secondary analysis of the ENDORSE study focuses on parenting styles and psychosocial factors. METHODOLOGY: Semi-structured clinical interviews were conducted with all participating mothers pre-and post-intervention. The Parenting Styles and Dimensions Questionnaire (PSDQ) evaluated the mothers' parenting styles. The psychosocial functioning of the participating children was assessed with the parental version of the Strengths and Difficulties Questionnaire (SDQ). The relationship between parenting styles, psychosocial parameters, and weight outcomes was investigated using a linear regression analysis. RESULTS: Weight-related stigma at school (56%), body image concerns (66%), and difficulties in family relationships (48%) were the main concerns documented during the initial psychological interviews. According to the SDQ, there was a significant decrease in children's conduct problems during the study's initial phase (pre-pilot group). A decrease in maternal demandingness (i.e., strict parenting style) was associated with a decrease in BMI z-score (beta coefficient = 0.314, p-value = 0.003). CONCLUSION: Decreasing parental demandingness was associated with better weight outcomes, highlighting the importance of assessing parenting factors in pediatric weight management programs.

The ENDORSE Feasibility Study: Exploring the Use of M-Health, Artificial Intelligence and Serious Games for the Management of Childhood Obesity.

Childhood obesity constitutes a major risk factor for future adverse health conditions. Multicomponent parent-child interventions are considered effective in controlling weight. Τhe ENDORSE platform utilizes m-health technologies, Artificial Intelligence (AI), and serious games (SG) toward the creation of an innovative software ecosystem connecting healthcare professionals, children, and their parents in order to deliver coordinated services to combat childhood obesity. It consists of activity trackers, a mobile SG for children, and mobile apps for parents and healthcare professionals. The heterogeneous dataset gathered through the interaction of the end-users with the platform composes the unique user profile. Part of it feeds an AI-based model that enables personalized messages. A feasibility pilot trial was conducted involving 50 overweight and obese children (mean age 10.5 years, 52% girls, 58% pubertal, median baseline BMI z-score 2.85) in a 3-month intervention. Adherence was measured by means of frequency of usage based on the data records. Overall, a clinically and statistically significant BMI z-score reduction was achieved (mean BMI z-score reduction -0.21 ± 0.26, p-value < 0.001). A statistically significant correlation was revealed between the level of activity tracker usage and the improvement of BMI z-score (-0.355, p = 0.017), highlighting the potential of the ENDORSE platform.

Subphenotypes in Non-Syndromic Orofacial Cleft Patients Based on the Tooth Agenesis Code (TAC).

Background: It was the aim of this study to investigate tooth agenesis patterns, which are expressed to different subphenotypes according to the TAC method in a spectrum of non-syndromic orofacial cleft patients. Methods: A total of 183 orofacial cleft patient records were assessed for tooth agenesis and TAC patterns. The association between TAC and sex, and cleft type was examined, and logistic regression models were additionally applied. Additionally, the distribution of missing teeth by cleft type and the tooth agenesis inter-quadrant association were examined. Results: The most frequent cleft type was CLPL (n = 72; 39.3%), while the maxillary left lateral incisor was the most frequently missing tooth that was strongly dependent on the cleft type (29.5%, p < 0.001). Of the 31 TAC patterns identified, four were the most prevalent and occurred in 80.8% of the sample, while 20 TAC patterns were unique. Cleft type contrary to sex (p = 0.405) was found to play a significant role in TAC distribution (p = 0.001). The logistic regression's results suggested that overall, neither sex nor cleft type were associated with tooth agenesis. Prevalence of tooth agenesis in each quadrant clearly depended on cleft type; and there was a strong association found between tooth agenesis in different quadrants. Conclusions: Thirty-one different subphenotypes were identified in TAC patterns. The first four TAC patterns accounted for the 80.8% of the sample's variability while twenty of the patterns were unique. A strong association was present between TAC pattern and cleft type. No association was found between the sex of the patient, tooth agenesis and TAC patterns. Tooth agenesis depended strongly on the cleft type, and the most frequently missing tooth was the maxillary left lateral incisor. The interquadrant association for tooth agenesis found suggests a genetic link in the etiology of clefts.

Orofacial Muscle Weakening in Facioscapulohumeral Muscular Dystrophy (FSHD) Patients.

BACKGROUND: Facioscapulohumeral muscular dystrophy is the third most commonly found type of muscular dystrophy. The aim of this study was to correlate the D4Z4 repeat array fragment size to the orofacial muscle weakening exhibited in a group of patients with a genetically supported diagnosis of FSHD. METHODS: Molecular genetic analysis was performed for 52 patients (27 female and 25 male) from a group that consisted of 36 patients with autosomal dominant pedigrees and 16 patients with either sporadic or unknown family status. The patients were tested with the southern blotting technique, using EcoRI/Avrll double digestion, and fragments were detected by a p13E-11 telomeric probe. Spearman's correlation was used to compare the fragment size with the degree of muscle weakening found in the forehead, periocular and perioral muscles. RESULTS: A positive non-significant correlation between the DNA fragment size and severity of muscle weakness was found for the forehead (r = 0.27; p = 0187), the periocular (r = 0.24; p = 0.232) and the left and right perioral (r = 0.29; p = 0.122), (r = 0.32; p = 0.085) muscles. CONCLUSIONS: Although FSHD patients exhibited a decrease in muscular activity related to the forehead, perioral, and periocular muscles the genotype-phenotype associations confirmed a weak to moderate non-significant correlation between repeat size and the severity of muscle weakness. Orofacial muscle weakening and its association with a D4Z4 contraction alone may not have the significance to serve as a prognostic biomarker, due to the weak to moderate association. Further studies with larger sample sizes are needed to determine the degree of genetic involvement in the facial growth in FSHD patients.

Outbreak of West Nile virus infection in Greece, 2010.

During 2010, an outbreak of West Nile virus infection occurred in Greece. A total of 197 patients with neuroinvasive disease were reported, of whom 33 (17%) died. Advanced age and a history of heart disease were independently associated with death, emphasizing the need for prevention of this infection in persons with these risk factors.

Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images.

Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. Early diagnosis of such cases is of great importance, since it enables clinicians to apply a more effective treatment strategy. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque. To address the highly imbalanced distribution of patients between the symptomatic and asymptomatic classes (16 vs 58, respectively), an ensemble learning scheme based on a sub-sampling approach was applied along with a two-phase, cost-sensitive strategy of learning, that uses the original and a resampled data set. Convolutional Neural Networks (CNNs) were utilized for building the primary models of the ensemble. A six-layer deep CNN was used to automatically extract features from the images, followed by a classification stage of two fully connected layers. The obtained results (Area Under the ROC Curve (AUC): 73%, sensitivity: 75%, specificity: 70%) indicate that the proposed approach achieved acceptable discrimination performance. Finally, interpretability methods were applied on the model's predictions in order to reveal insights on the model's decision process as well as to enable the identification of novel image biomarkers for the stratification of patients with carotid atheromatous plaque.Clinical Relevance-The integration of interpretability methods with deep learning strategies can facilitate the identification of novel ultrasound image biomarkers for the stratification of patients with carotid atheromatous plaque.

Refugee camp health services utilisation by non-camp residents as an indicator of unaddressed health needs of surrounding populations: a perspective from Mae La refugee camp in Thailand during 2006 and 2007.

INTRODUCTION: This study explored the differences on the level of medical care required by camp and non-camp resident patients during utilisation of the health services in Mae La refugee camp, Tak province, Thailand during the years 2006 and 2007. METHODS: Data were extracted from camp registers and the Health Information System used during the years 2006 and 2007 and statistical analysis was performed. RESULTS: The analysis showed that during 2006 and 2007 non-camp resident patients, coming from Thailand as well as Myanmar, who sought care in the outpatient department (OPD) of the camp required at a significantly higher proportion admission to the inpatient department (IPD) or referral to the district hospital compared to camp resident patients. Although there was a statistically significant increased mortality of the non-camp resident patients admitted in the IPD compared to camp resident patients, there was no significant difference in mortality among these two groups when the referrals to the district hospital were analysed. CONCLUSION: Non-camp resident patients tended to need a more advanced level of medical care compared to camp resident patients. Provided that this it is further validated, the above observed pattern might be potentially useful as an indirect indicator of unaddressed health needs of populations surrounding a refugee camp.

An Ontology-Based Serious Game Design for the Development of Nutrition and Food Literacy Skills.

Unhealthy dietary habits constitute a major risk factor for the onset of chronic diseases, such as cardiovascular diseases, cancer, diabetes and other conditions linked to obesity. Effective dietary changes are of paramount importance and can be promoted through empowering individuals with Nutrition Literacy (NL) and Food Literacy (FL) skills. This paper presents a novel serious game aiming at building NL and FL skills in adolescents and young adults. It is based on an innovative conceptual framework, incorporating a recipe ontology and a theory driven game design approach maximizing user attractiveness and promoting sustainable effective dietary changes. The ontological modeling of recipes offers game experience personalization while providing a realistic and diverse simulation environment. Modern game design techniques from three game genres (cooking, roguelike, puzzle) are employed along with a compelling plot for engagement purposes.

The role of pharmacogenetics in treating central nervous system disorders.

Symptoms of central nervous system (CNS) disorders include abnormalities in both physical and psychological domains. Many drugs indicated for the treatment of CNS disorders are fraught with side effects and/or poor efficacy which impact patients' quality of life and drives non-compliance. Moreover, for many CNS drugs such as antidepressants and antipsychotics, it takes time to determine whether a particular drug is efficacious in an individual patient. To optimize drug treatment for each patient, prescribing physicians often need to raise or lower doses, switch drug classes, or prescribe additional drugs to mitigate side effects, often in a "trial and error" fashion. Pharmacogenetic (PGx) testing, particularly in the realm of CNS therapy, can reduce the unpredictability of this process. By determining a patient's genetic profile, individual therapy parameters may be predicted pre-treatment for drug efficacy, optimal drug dose, and the risk of adverse drug reactions (ADRs). The intent of this review is to highlight the power of PGx testing to predict the likelihood of ADRs and efficacy during the treatment of the following CNS disorders: epilepsy, bipolar disorder, schizophrenia and depression.

Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication.

The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques toward the development of personalized models able to predict the risk of fatal or nonfatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a subsampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.

High-flow nasal cannula oxygen and nasal continuous positive airway pressure and full oral feeding in infants with bronchopulmonary dysplasia.

OBJECTIVE: To determine whether the time to achieve full oral feeding differed between infants with bronchopulmonary dysplasia (BPD) supported by nasal continuous positive airway pressure (nCPAP) compared with those supported by nCPAP and subsequently transferred to heated, humidified, high-flow nasal cannula oxygen (HHFNC). DESIGN: Two-cohort comparison. SETTING: Tertiary neonatal unit. PATIENTS: -72 infants, median gestational age 27 (range 24-32) weeks in the nCPAP group, and 44 infants, median gestational age 27 (range 24-31) weeks in the nCPAP/HHFNC group. INTERVENTIONS: Between 2011 and 2013, infants post extubation were supported by nCPAP and from 2013 infants were supported by nCPAP and then HHFNC. MAIN OUTCOME MEASURES: The postnatal age at which oral feeds were first trialled and full oral feeds established. The length of respiratory support as either nCPAP or nCPAP/HHFNC and the total length of respiratory support and hospital stay were also determined. Subanalysis was undertaken of infants requiring respiratory support beyond 34 weeks postmenstrual age (PMA). RESULTS: The postnatal age at trial of first oral feeds was earlier in the nCPAP/HHFNC group (p=0.012), but infants were a shorter time on nCPAP compared with nCPAP/HHFNC (p=0.003). On subgroup analysis, the age to achieve full oral feeds was earlier in the nCPAP/HHFNC group (p<0.001). CONCLUSIONS: In infants with BPD who required respiratory support beyond 34 weeks PMA, use of nCPAP then HHFNC was associated with earlier establishment of full oral feeds. Consideration should be given to assessing stable BPD infants with regard to oral feeding while on CPAP.

Discovery and utilization of haplotypes for pharmacogenetic studies of psychotropic drug response.

The treatment of seriously mentally ill patients is complicated by variability in individual response to psychotropic drugs. Some patients remain treatment refractory even after two to three therapeutic modalities. Other patients experience adverse events that range from mild discomfort, to poor compliance, to life threatening. Genaissance Pharmaceuticals is actively engaged in a candidate gene-based haplotype (HAP Marker) approach to the pharmacogenetics of drug response and adverse events. In the present article, we review reasons why HAP Markers are more useful than single nucleotide polymorphisms (SNPs) for discovering genetic correlations to clinical response. In addition, we review our approach to HAP Marker discovery, which involves discovering SNPs in the functional regions of genes by sequencing, organizing these SNPs into HAP Markers for an index population of ethnically diverse individuals and calculating population frequencies for these HAP Markers. For clinical correlations, HAP Markers are defined and correlated to clinical data using the in-house DecoGen Informatics System. This approach has clear implications for the discovery of psychiatric disease-associated genes as well as for the development of safer, more efficacious psychiatric drugs.

Electrophysiological evidence for rapid 5-HT1A autoreceptor inhibition by vilazodone, a 5-HT1A receptor partial agonist and 5-HT reuptake inhibitor.

This study examined the effect of vilazodone, a combined serotonin (5-HT) reuptake inhibitor and 5-HT(1A) receptor partial agonist, paroxetine and fluoxetine on the sensitivity of 5-HT(1A) autoreceptors of serotonergic dorsal raphe nucleus neurons in rats. These effects were assessed by determining the intravenous dose of (±)-8-hydroxy-2-(di-n-propylamino)-tetralin (8-OH-DPAT) required to suppress the basal firing rate of these neurons by 50% (ID50) in anesthetized rats using in vivo electrophysiology. 5-HT uptake inhibition was determined by the ability of the compounds to reverse (±)-p-chloroamphetamine (PCA)-induced rat hypothalamic 5-HT depletion ex vivo. Acute vilazodone administration (0.63 and 2.1 µmol/kg, s.c.), compared with vehicle, significantly increased (2-3-fold) the ID50 of 8-OH-DPAT at 4 h, but not 24h after administration. Subchronic administration (3 days) significantly increased the ID50 value at 4 h (3-4-fold) and at 24 h (~2-fold). In contrast, paroxetine and fluoxetine at doses that were supramaximal for 5-HT uptake inhibition did not significantly alter the ID50 value of 8-OH-DPAT after acute or subchronic administration. Vilazodone antagonized the action of PCA 3.5 h and 5 h after a single dose (ID50 1.49 and 0.46 µmol/kg, s.c., respectively), but was inactive 18 h post-administration, corroborating the electrophysiological results at 24 h following acute administration. The results are consistent with the concept of rapid and, following repeated treatment, prolonged inhibition of 5-HT(1A) autoreceptors by vilazodone. This effect could occur by either direct interaction with, or desensitization of, these receptors, an effect which cannot be ascribed to vilazodone's 5-HT reuptake inhibiting properties.

The efficacy profile of vilazodone, a novel antidepressant for the treatment of major depressive disorder.

OBJECTIVE: Vilazodone is a novel serotonin reuptake inhibitor and serotonin 1A receptor partial agonist approved for the treatment of major depressive disorder (MDD). This evaluation presents side-by-side efficacy data from two randomized, double-blind, placebo-controlled, short-term 8-week trials (referred to as randomized controlled trial [RCT]-1 [N = 410] and RCT-2 [N = 481]); efficacy data for demographic and clinical subgroups (derived from pooled RCT data); and effectiveness data from a 52-week, open-label, long-term study (N = 616). The objective is to summarize the efficacy profile of vilazodone at its approved dose of 40 mg/day. METHODS: The main assessment in individual pivotal trials and pooled subgroup analyses was the change from baseline to end of treatment (EOT, 8 weeks) in the Montgomery-Åsberg Depression Rating Scale (MADRS) total score. Mixed-effects repeated-measures analyses were conducted in the placebo-controlled trials. Effectiveness analyses in the long-term study included mean MADRS score change over time. RESULTS: Vilazodone-treated patients in both short-term studies showed greater improvement from baseline to EOT in mean MADRS scores than placebo-treated patients (least-squares mean [LSM] treatment difference: -3.2 [p = 0.001], RCT-1; -2.5 [p = 0.009], RCT-2). Clinical Global Impressions-Improvement mean scores at EOT reflected greater improvement with vilazodone compared with placebo in both studies (LSM treatment difference: -0.4 [p = 0.001], RCT-1; -0.3 [p = 0.004], RCT-2). MADRS response rates were significantly greater among patients receiving vilazodone versus those receiving placebo (RCT-1: 40.4% versus 28.1%, respectively [p = 0.007]; RCT-2: 43.7% versus 30.3%, respectively [p = 0.002]). The greater efficacy of vilazodone versus placebo was consistent for the majority of demographic and MDD characteristic subgroups. In the long-term study, the mean MADRS score improved from 29.9 (baseline) to 11.4 (week 8), 8.2 (week 24), and 7.1 (week 52). CONCLUSION: Vilazodone 40 mg/day resulted in clinically meaningful, statistically significant improvement in MDD symptoms in two placebo-controlled, 8-week studies. Findings are supported by subgroup analysis and open-label, long-term effectiveness data. TRIAL REGISTRATION: Randomized controlled trial 1: ClinicalTrials.gov identifier: NCT00285376, http://ClinicalTrials.gov/ct2/show/NCT00285376 ; randomized controlled trial 2: ClinicalTrials.gov identifier: NCT00683592, http://ClinicalTrials.gov/ct2/show/NCT00683592 ; open-label, long-term study: ClinicalTrials.gov identifier: NCT00644358, http://ClinicalTrials.gov/ct2/show/NCT00644358 .

A randomized, double-blind, placebo-controlled, 8-week study of vilazodone, a serotonergic agent for the treatment of major depressive disorder.

OBJECTIVE: To evaluate the efficacy, and further establish the safety profile, of oral once-daily vilazodone, a potent and selective serotonin 1A receptor partial agonist and reuptake inhibitor, in the treatment of major depressive disorder (MDD). METHOD: This phase 3, randomized, double-blind, placebo-controlled, 8-week study (conducted March 2008-February 2009) enrolled 481 adults with DSM-IV-TR-defined MDD. Patients received vilazodone (titrated to 40 mg/d) or placebo. The primary efficacy endpoint was change in Montgomery-Asberg Depression Rating Scale (MADRS) total score from baseline to end of treatment. Secondary efficacy measures included MADRS and 17-item Hamilton Depression Rating Scale (HDRS-17) response and change in HDRS-17, HDRS-21, Hamilton Anxiety Rating Scale (HARS), Clinical Global Impressions-Severity of Illness (CGI-S), and Clinical Global Impressions-Improvement (CGI-I) scores. The Changes in Sexual Functioning Questionnaire (CSFQ) was administered at baseline and week 8. RESULTS: Vilazodone-treated patients had significantly greater improvement (P = .009) according to the MADRS than placebo patients (intent-to-treat; least-squares mean changes: -13.3, -10.8). MADRS response rates were significantly higher with vilazodone than placebo (44% vs 30%, P = .002). Remission rates for vilazodone were not significantly different based on the MADRS (vilazodone, 27.3% vs placebo, 20.3%; P = .066) or HDRS-17 (vilazodone, 24.2% vs placebo, 17.7%; P = .088). Vilazodone-treated patients had significantly greater improvements from baseline in HDRS-17 (P = .026), HDRS-21 (P = .029), HARS (P = .037), CGI-S (P = .004), and CGI-I (P = .004) scores than placebo patients. Rates of discontinuation due to adverse events were 5.1% (vilazodone) and 1.7% (placebo). The most common adverse events (vilazodone vs placebo) were diarrhea (31% vs 11%), nausea (26% vs 6%), and headache (13% vs 10%). Treatment-related effects on sexual function as measured by the CSFQ were small and similar to placebo. Effects on weight were no different from placebo. CONCLUSIONS: Vilazodone 40 mg/d was well tolerated and effective in adult patients with MDD. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00683592.

The Safety and Tolerability Profile of Vilazodone, A Novel Antidepressant for the Treatment of Major Depressive Disorder.

OBJECTIVE: Vilazodone is a selective serotonin reuptake inhibitor and 5-HT1A receptor partial agonist approved for the treatment of major depressive disorder (MDD). This report summarizes the safety and tolerability of vilazodone 40 mg/day during short- and long-term treatment of adult MDD. METHODS: Pooled data from two 8-week, double-blind studies of vilazodone (n = 436) vs placebo (n = 433) and data from one 52-week, open-label study (n = 616, vilazodone only) were analyzed. Patients aged 18-70 with DSM-IV-TR-defined MDD received vilazodone or placebo (8-week studies only) once daily, with food, titrated to 40 mg/day over 2 weeks. Safety and tolerability assessments included adverse events (AEs), laboratory tests, vital signs, electrocardiograms, and weight. RESULTS: The most common AEs in all studies were diarrhea, nausea, and headache. Vilazodone-associated AEs in the two 8-week studies, defined as an incidence rate of ≥5% in the vilazodone group and at least twice that for placebo, were diarrhea (28.0% vs 9.2%), nausea (23.4% vs 5.1%), and insomnia (6.0% vs 2.1%), with the majority reported as mild to moderate and <5% of those patients requiring concomitant (directed) treatment for these conditions. Discontinuation rates due to AEs were 7.1% (vilazodone) and 3.2% (placebo) in the 8-week studies and 20.7% in the 52-week study. Vilazodone had no clinically significant effects on vital signs, laboratory tests, or electrocardiograms. CONCLUSION: Vilazodone 40 mg/day was well tolerated during short- and long-term MDD treatment in these trials. Safety profiles associated with 8- and 52-week exposure were consistent.

Candidate gene analysis identifies a polymorphism in HLA-DQB1 associated with clozapine-induced agranulocytosis.

OBJECTIVE: Clozapine is considered to be the most efficacious drug to treat schizophrenia, although it is underutilized, partially due to a side effect of agranulocytosis. This analysis of 74 candidate genes was designed to identify an association between sequence variants and clozapine-induced agranulocytosis (CIA). METHOD: Blood and medical history were collected for 33 CIA cases and 54 clozapine-treated controls enrolled between April 2002 and December 2003. Significant markers from 4 genes were then assessed in an independently collected case-control cohort (49 CIA cases, 78 controls). RESULTS: Sequence variants in 5 genes were found to be associated with CIA in the first cohort: HLA-DQB1, HLA-C, DRD1, NTSR1, and CSF2RB. Sequence variants in HLA-DQB1 were also found to be associated with CIA in the second cohort. After refinement analyses of sequence variants in HLA-DQB1, a single SNP (single nucleotide polymorphism), 6672G>C, was found to be associated with risk for CIA; the odds of CIA are 16.9 times greater in patients who carry this marker compared to those who do not. CONCLUSIONS: A sequence variant (6672G>C) in HLA-DQB1 is associated with increased risk for CIA. This marker identifies a subset of patients with an exceptionally high risk of CIA, 1,175% higher than the overall clozapine-treated population under the current blood-monitoring system. Assessing risk for CIA by testing for this and other genetic variants yet to be determined may be clinically useful when deciding whether to begin or continue treatment with clozapine.