Gene expression biomarkers differentiate overall survival of colorectal cancer upon targeted therapies.

While monoclonal antibody-based targeted therapies have substantially improved progression-free survival in cancer patients, the variability in individual responses poses a significant challenge in patient care. Therefore, identifying cancer subtypes and their associated biomarkers is required for assigning effective treatment. In this study, we integrated genotype and pre-treatment tissue RNA-seq data and identified biomarkers causally associated with the overall survival (OS) of colorectal cancer (CRC) patients treated with either cetuximab or bevacizumab. We performed enrichment analysis for specific consensus molecular subtypes (CMS) of colorectal cancer and evaluated differential expression of identified genes using paired tumor and normal tissue from an external cohort. In addition, we replicated the causal effect of these genes on OS using validation cohort and assessed their association with the Cancer Genome Atlas Program data as an external cohort. One of the replicated findings was WDR62, whose overexpression shortened OS of patients treated with cetuximab. Enrichment of its over expression in CMS1 and low expression in CMS4 suggests that patients with CMS4 subtype may drive greater benefit from cetuximab. In summary, this study highlights the importance of integrating different omics data for identifying promising biomarkers specific to a treatment or a cancer subtype.

Comorbidities affecting re-admission and survival in COVID-19: Application of joint frailty model.

BACKGROUND: One of the common concerns of healthcare systems is the potential for re-admission of COVID-19 patients. In addition to adding costs to the healthcare system, re-admissions also endanger patient safety. Recognizing the factors that influence re-admission, can help provide appropriate and optimal health care. The aim of this study was to assess comorbidities that affect re-admission and survival in COVID-19 patients using a joint frailty model. METHODS: This historical cohort study was done using data of patients with COVID-19 who were re-hospitalized more than twice in a referral hospital in North of Iran. We used the joint frailty model to investigate prognostic factors of survival and recurrence, simultaneously using R version 3.5.1 (library "frailtypack"). P-values less than 0.05 were considered as statistically significant. RESULTS: A total of 112 patients with mean (SD) age of 63.76 (14.58) years old were recruited into the study. Forty-eight (42.9%) patients died in which 53.83% of them were re-admitted for a second time. Using adjusted joint model, the hazard of re-admission increased with cancer (Hazard ratio (HR) = 1.92) and hyperlipidemia (HR = 1.22). Furthermore, the hazard of death increased with hyperlipidemia (HR = 4.05) followed by age (HR = 1.76) and cancer (HR = 1.64). It Also decreased with lung disease (HR = 0.11), hypothyroidism (HR = 0.32), and hypertension (HR = 0.97). CONCLUSION: Considering the correlation between re-admission and mortality in the joint frailty model, malignancy and hyperlipidemia increased the risk of both re-admission and mortality. Moreover, lung disease probably due to the use of corticosteroids, was a protective factor against both mortality and re-admission.

Metabolomic profiles in Jamaican children with and without autism spectrum disorder.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, the underlying metabolic perturbations associated with ASD which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls. Objective: To elucidate potential metabolomic signatures associated with ASD in children and identify specific metabolites that may serve as biomarkers for the disorder. Methods: We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica (ERAJ-2), which was a 1:1 age (±6 months)-and sex-matched cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and imputation of missing values, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth. Results: Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three of these metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. Additionally, the amino acid sarcosine exhibited a significant association with ASD. Conclusions: These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions.

Broadcasters, receivers, functional groups of metabolites and the link to heart failure progression using polygenic factors.

In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids which are not synthesized in the human body and come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.

Alternate-day modified fasting diet improves weight loss, subjective sleep quality and daytime dysfunction in women with obesity or overweight: a randomized, controlled trial.

Background: Both sleep time and quality can be associated with overweight or obesity. In obese people, visceral fat tissue develops, which results in an increment in the production of cytokines. The increased production of inflammatory cytokines can disturb the sleep/wake cycle. Therefore, weight loss by reducing fat tissue can improve sleep disorders. Intermittent fasting diets are popular and effective diets that can decrease body weight and improve anthropometric data and body composition. The present study aimed to evaluate the effect of Alternate-day Modified Fasting (ADMF) on sleep quality, body weight, and daytime sleepiness. Methods: Classification of 56 obese or overweight women, based on age and body mass index (BMI), was done using stratified randomization. Then individuals were assigned to the ADMF group (intervention) or Daily Calorie Restriction (CR) group (control) using the random numbers table for 8 weeks. We measured the Pittsburgh sleep quality Index (PSQI), weight, BMI, and the Epworth sleepiness scale (ESS) as primary outcomes and assessed subjective sleep quality (SSQ), sleep latency, sleep disturbances, habitual sleep efficiency, daytime dysfunction, and sleep duration as secondary outcomes at baseline and after the study. Results: Following an ADMF diet resulted in a greater decrease in weight (kg) [-5.23 (1.73) vs. -3.15 (0.88); P < 0.001] and BMI (kg/m2) [-2.05 (0.66) vs. -1.17 (0.34); P < 0.001] compared to CR. No significant differences were found in the changes of PSQI [-0.39 (1.43) vs. -0.45 (1.88); P = 0.73] and ESS [-0.22 (1.24) vs. -0.54 (1.67); P = 0.43] between two groups. Also, following the ADMF diet led to significant changes in SSQ [-0.69 (0.47) vs. -0.08 (0.40); P = <0.001], and daytime dysfunction [-0.65 (0.57) vs. 0.04 (0.75); P: 0.001] in compare with CR diet. Conclusion: These results suggested that an ADMF could be a beneficial diet for controlling body weight and BMI. The ADMF diet didn't affect PSQI and ESS in women with overweight or obesity but significantly improved SSQ and daytime dysfunction. Clinical Trial Registration: The Iranian Registry of Clinical Trials (IRCT20220522054958N3), https://www.irct.ir/trial/64510.

Effect of modified alternate day fasting diet on the severity of premenstrual syndrome and health-related quality of life in women with overweight or obesity: a trial study protocol.

INTRODUCTION: Premenstrual syndrome (PMS) includes a range of physical, behavioural and psychological symptoms and decreases women's health-related quality of life (HRQoL). It has been proposed that increased body mass index (BMI) is associated with menstrual problems and decreased HRQoL. The body fat amount plays a role in menstrual cycles by altering the oestrogen/progesterone ratio. Alternate day fasting as an unusual diet results in the improvement of anthropometric indices and reduction of body weight. This study aims to investigate the effect of a daily calorie restriction diet and a modified alternate day fasting diet on PMS and HRQoL. METHODS AND ANALYSIS: This 8-week open-label parallel randomised controlled trial examines the impact of a modified alternate-day fasting diet and daily caloric restriction on the severity of PMS and HRQoL in obese or overweight women. Using simple random sampling, women between the ages of 18 years and 50 years and 25 ≤ BMI ˂ 40 who meet the inclusion and exclusion criteria will be chosen from the Kashan University of Medical Sciences Centre. Patients will be randomised, based on BMI and age through stratified randomisation. Then by the random numbers table, they are allocated to fasting (intervention) or daily calorie restriction (control) groups. Outcomes are chosen for the trial: the difference in the severity of PMS, HRQoL, BMI, body fat mass, fat-free mass, waist-to-hip ratio, waist circumference, hip circumference, per cent body fat, skeletal muscle mass and visceral fat area from baseline to 8 weeks. ETHICS AND DISSEMINATION: The Kashan University of Medical Sciences Ethics Committee has approved the trial (IR.KAUMS.MEDNT.REC.1401.003) (17 April 2022). Results will be published in peer-reviewed academic journals and the participants will be informed via phone calls. TRIAL REGISTRATION NUMBER: IRCT20220522054958N1.

Application of Frailty Quantile Regression Model to Investigate of Factors Survival Time in Breast Cancer: A Multi-Center Study.

Background: The prognostic factors of survival can be accurately identified using data from different health centers, but the structure of multi-center data is heterogeneous due to the treatment of patients in different centers or similar reasons. In survival analysis, the shared frailty model is a common way to analyze multi-center data that assumes all covariates have homogenous effects. We used a censored quantile regression model for clustered survival data to study the impact of prognostic factors on survival time. Methods: This multi-center historical cohort study included 1785 participants with breast cancer from four different medical centers. A censored quantile regression model with a gamma distribution for the frailty term was used, and p-value less than 0.05 considered significant. Results: The 10th and 50th percentiles (95% confidence interval) of survival time were 26.22 (23-28.77) and 235.07 (130-236.55) months, respectively. The effect of metastasis on the 10th and 50th percentiles of survival time was 20.67 and 69.73 months, respectively (all p-value < 0.05). In the examination of the tumor grade, the effect of grades 2 and 3 tumors compare with the grade 1 tumor on the 50th percentile of survival time were 22.84 and 35.89 months, respectively (all p-value < 0.05). The frailty variance was significant, which confirmed that, there was significant variability between the centers. Conclusions: This study confirmed the usefulness of a censored quantile regression model for cluster data in studying the impact of prognostic factors on survival time and the control effect of heterogeneity due to the treatment of patients in different centers.

Comparison of the effect of modified intermittent fasting and daily calorie restriction on sleep quality, anthropometric data, and body composition in women with obesity or overweight: study protocol of a randomized controlled trial.

BACKGROUND: Sleep disturbances are common in nearly one-third of adults. Both low quality of sleep and sleep time could be related to increased obesity. An increase in visceral adipose tissue can result in the secretion of inflammatory cytokines. Inflammatory cytokines can lead to a disturbance of the sleep-wake rhythm. Therefore, weight loss may improve sleep quality and duration. Intermittent fasting diet as a popular diet reduces body weight and improves anthropometric indices. This study is performed to further investigate the effect of a modified intermittent fasting diet on sleep quality and anthropometric indices. METHODS: This is an open-label randomized controlled trial to evaluate the effect of daily calorie restriction (control) and modified intermittent fasting (intervention) on sleep quality, anthropometric data, and body composition in women with obesity or overweight for 8 weeks. Fifty-six participants will be classified using stratified randomization based on body mass index (BMI) and age. Then, participants will be assigned to one of the two groups of intervention or control using the random numbers table. The sleep quality, daytime sleepiness, and insomnia will be evaluated by using the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Insomnia Severity Index respectively. The primary outcomes chosen for the study were as follows: the difference in sleep quality, daytime sleepiness, insomnia, BMI, fat-free mass (FFM), body fat mass, waist circumference, and waist-to-hip ratio from baseline to 8 weeks. Secondary outcomes chosen for the study were as follows: the difference in hip circumference, the visceral fat area, percent body fat, soft lean mass, skeletal muscle mass, extracellular water ratio, and total body water from baseline to 8 weeks. DISCUSSION: This study will investigate the effect of intermittent fasting intervention compared with daily calorie restriction on sleep quality and anthropometric indices. The information gained will enhance our understanding of fasting interventions, which can be used to improve clinical dietary recommendations. The findings will help to disclose as yet the unknown relationship between diet and sleep quality. TRIAL REGISTRATION: Iranian Registry of Clinical Trials IRCT20220522054958N3. Registered on 8 July 2022. https://www.irct.ir/trial/64510 .

Does an alternate-day modified fasting diet improve premenstrual syndrome symptoms and health-related quality of life in obese or overweight women with premenstrual syndrome? A randomized, controlled trial.

Background: Premenstrual syndrome disorder (PMS) is a condition that affects health-related quality of life (HRQoL) and encompasses a variety of symptoms, including psychological, physical, and behavioral symptoms. Some evidence suggests that an increase in body mass index (BMI) can reduce both HRQoL and menstrual quality. This is because the body fat tissue can affect menstrual cycles by changing the estrogen/progesterone ratio. This study investigated the impact of two diets alternate-day modified fasting (ADMF) and daily calorie restriction (DCR) - on PMS syndrome and HRQoL. Methods: The study was a randomized controlled, open-label trial that lasted for 8 weeks and involved 60 obese/overweight women. Participants were recruited from the Health Service Centers of Kashan University of Medical Sciences using simple random sampling. The study compared the impact of the ADMF and DCR diets on HRQoL and PMS symptoms. Patients were classified based on their BMI and age and then allocated to either the intervention (ADMF) or control (DCR) group using a random numbers table. The study measured HRQoL, PMS severity, weight, BMI, body fat mass, waist circumference, fat-free mass, and skeletal muscle mass before and after the study. The study had an almost 18% dropout rate. Results: Significant improvements were observed in mood lability (p = 0.044) and expressed anger (p < 0.001) in relation to PMS symptoms. However, no significant differences were detected in the changes of other COPE subscales. The ADMF diet had a significant impact on the 12-item Short-Form Health Survey (SF-12) total score (p < 0.001) and physical function subscales (p = 0.006) as well as mental health (p < 0.001) when compared to the control diet. This implies that the ADMF diet increased both SF-12 total score and its subscales. The intervention led to improvements in HRQoL, physical function, and mental health. Additionally, significant improvements in BMI and weight were observed between the two groups pre- and post-study (p < 0.001). Anthropometric data, including body fat mass and waist circumference, showed a significant improvement (p < 0.001 and p = 0.029, respectively) before and after the study. However, there were no significant changes in fat-free mass (p = 0.936) and skeletal muscle mass (p = 0.841) between the two groups. Conclusion: The study suggested that ADMF can improve HRQoL, mood lability, and expressed anger. It also showed that ADMF can reduce waist circumference, weight, and body fat mass in obese/overweight women. Clinical trial registration: The Iranian Registry of Clinical Trials (IRCT20220522054958N1).

Gene signatures derived from transcriptomic-causal networks stratified colorectal cancer patients for effective targeted therapy.

Predictive and prognostic gene signatures derived from interconnectivity among genes can tailor clinical care to patients in cancer treatment. We identified gene interconnectivity as the transcriptomic-causal network by integrating germline genotyping and tumor RNA-seq data from 1,165 patients with metastatic colorectal cancer (CRC). The patients were enrolled in a clinical trial with randomized treatment, either cetuximab or bevacizumab in combination with chemotherapy. We linked the network to overall survival (OS) and detected novel biomarkers by controlling for confounding genes. Our data-driven approach discerned sets of genes, each set collectively stratify patients based on OS. Two signatures under the cetuximab treatment were related to wound healing and macrophages. The signature under the bevacizumab treatment was related to cytotoxicity and we replicated its effect on OS using an external cohort. We also showed that the genes influencing OS within the signatures are downregulated in CRC tumor vs. normal tissue using another external cohort. Furthermore, the corresponding proteins encoded by the genes within the signatures interact each other and are functionally related. In conclusion, this study identified a group of genes that collectively stratified patients based on OS and uncovered promising novel prognostic biomarkers for personalized treatment of CRC using transcriptomic causal networks.

From classical mendelian randomization to causal networks for systematic integration of multi-omics.

The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data.

Determining Prognostic Factors of Disease-Free Survival in Breast Cancer Using Censored Quantile Regression.

Background: The analysis of disease-free survival and related factors leads to a better understanding of the patient's condition and recurrence-related characteristics and provides a basis for more appropriate treatment guidance. In this study, we aimed to investigate the role of prognostic factors on disease-free survival in breast cancer with a quantile regression model. Methods: This retrospective study was conducted by reviewing data obtained from 2056 breast cancer patients. Age at diagnosis and education status, tumor size, lymph node ratio, tumor grade, estrogen receptor and progesterone receptor, type of surgery, use of radiotherapy, chemotherapy, and hormone therapy were the prognosis factors considered in this study. A quantile regression model was used to investigate prognostic factors of disease-free survival in breast cancer. Results: Disease recurrence was verified in 251 (13.9%) women, and 39 (0.02%) women died before experience recurrence. The 10th percentile of disease-free survival for patients with the hormone therapy was 23.85 months greater than patients who did not receive this treatment (P value < .001). In the examination of the tumor size, the 10th and 20th percentiles of disease-free survival for patients with tumor size > 5 cm were 31.06 and 27 months less than patients with the tumor size < 2 cm, respectively (P value = .006 and .021, respectively). Compared with grade 1 tumors, the 10th and 20th percentiles of disease-free survival for patients with grade 3 tumors decreased 30.11 and 38.32 months, respectively (P value < .001 and .038, respectively). The 10th and 20th percentiles of disease-free survival decreased 28.16 and 45.32 months with a 1 unit increase in lymph node ratio, respectively (P value = .032 and .032, respectively). Conclusions: Among the prognostic factors, tumor size, grade, and lymph node ratio showed a close relationship with disease-free survival in breast cancer. The findings indicated that developing public screening and educational programs through the health care system with more emphasis on low-educated women is needed among Iranian women.

Tumor Immunogenomic Features Determine Outcomes in Patients with Metastatic Colorectal Cancer Treated with Standard-of-Care Combinations of Bevacizumab and Cetuximab.

PURPOSE: CALGB/SWOG 80405 was a randomized phase III trial in first-line patients with metastatic colorectal cancer treated with bevacizumab, cetuximab, or both, plus chemotherapy. We tested the effect of tumor immune features on overall survival (OS). EXPERIMENTAL DESIGN: Primary tumors (N = 554) were profiled by RNA sequencing. Immune signatures of macrophages, lymphocytes, TGFß, IFNγ, wound healing, and cytotoxicity were measured. CIBERSORTx scores of naive and memory B cells, plasma cells, CD8+ T cells, resting and activated memory CD4+ T cells, M0 and M2 macrophages, and activated mast cells were measured. RESULTS: Increased M2 macrophage score [HR, 6.30; 95% confidence interval (CI), 3.0-12.15] and TGFß signature expression (HR, 1.35; 95% CI, 1.05-1.77) were associated with shorter OS. Increased scores of plasma cells (HR, 0.55; 95% CI, 0.38-0.87) and activated memory CD4+ T cells (HR, 0.34; 95% CI, 0.16-0.65) were associated with longer OS. Using optimal cutoffs from these four features, patients were categorized as having either 4, 3, 2, or 0-1 beneficial features associated with longer OS, and the median (95% CI) OS decreased from 42.5 (35.8-47.8) to 31.0 (28.8-34.4), 25.2 (20.6-27.9), and 17.7 (13.5-20.4) months respectively (P = 3.48e-11). CONCLUSIONS: New immune features can be further evaluated to improve patient response. They provide the rationale for more effective immunotherapy strategies.

Chronic Kidney Disease and COVID-19 Infection: A Case-Control Study.

Background: To organize efforts to manage the coronavirus disease 2019 (COVID-19), it is necessary to understand which groups are at higher risk of infection. Kidney disease seems to be substantial in COVID-19 patients, but there are limited data on COVID-19 incidence and fatality among chronic kidney disease (CKD) patients. In this study, we intend to examine the association between CKD and susceptibility to COVID-19 infection. Materials and Methods: Participants were selected from those recruited in a population-based cross-sectional survey of CKD prevalence and associated risk factors in Iranian people 18 years and older. A three-part questionnaire was used for COVID-19 infection clinical symptoms and epidemiologic and hospitalization data. Results: A total of 962 individuals including 403 CKD patients and 559 healthy controls were recruited in this study. Healthy controls were suffering more from common cold signs, cough, fever, sore throat, headache, anosmia, dyspnea, and abdominal pain (all P < 0.05). Furthermore, the number of healthy individuals with myalgia was marginally higher compared to the CKD patients (P = 0.057). Data regarding the number of CKD patients with/without COVID-19 infection throughout different CKD stages revealed that there was no significant difference between the two groups in terms of COVID-19 infection in different stages of CKD (P = 0.956). Conclusion: We found that some of the clinical presentations of COVID-19 including common cold symptoms, cough, fever, sore throat, headache, anosmia, dyspnea, and abdominal pain were higher among healthy individuals compared to the CKD group. On the other hand, the susceptibility to COVID-19 infection was not significantly different in various early stages of CKD.

Plasma levels of angiopoietin-2, VEGF-A, and VCAM-1 as markers of bevacizumab-induced hypertension: CALGB 80303 and 90401 (Alliance).

Hypertension is a common toxicity induced by bevacizumab and other antiangiogenic drugs. There are no biomarkers to predict the risk of bevacizumab-induced hypertension. This study aimed to identify plasma proteins related to the function of the vasculature to predict the risk of severe bevacizumab-induced hypertension. Using pretreated plasma samples from 398 bevacizumab-treated patients in two clinical trials (CALGB 80303 and 90401), the levels of 17 proteins were measured via ELISA. The association between proteins and grade 3 bevacizumab-induced hypertension was performed by calculating the odds ratio (OR) from logistic regression adjusting for age, sex, and clinical trial. Using the optimal cut-point of each protein, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for hypertension were estimated. Five proteins showed no difference in levels between clinical trials and were used for analyses. Lower levels of angiopoietin-2 (p = 0.0013, OR 3.41, 95% CI 1.67-7.55), VEGF-A (p = 0.0008, OR 4.25, 95% CI 1.93-10.72), and VCAM-1 (p = 0.0067, OR 2.68, 95% CI 1.34-5.63) were associated with an increased risk of grade 3 hypertension. The multivariable model suggests independent effects of angiopoietin-2 (p = 0.0111, OR 2.71, 95% CI 1.29-6.10), VEGF-A (p = 0.0051, OR 3.66, 95% CI 1.54-9.73), and VCAM-1 (p = 0.0308, OR 2.27, 95% CI 1.10-4.92). The presence of low levels of 2-3 proteins had an OR of 10.06 (95% CI 3.92-34.18, p = 1.80 × 10-5) for the risk of hypertension, with sensitivity of 89.7%, specificity of 53.5%, PPV of 17.3%, and NPV of 97.9%. This is the first study providing evidence of plasma proteins with potential value to predict patients at risk of developing bevacizumab-induced hypertension.Clinical trial registration: ClinicalTrials.gov Identifier: NCT00088894 (CALGB 80303); and NCT00110214 (CALGB 90401).

The comparison of censored quantile regression methods in prognosis factors of breast cancer survival.

The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

Differential gene regulatory pattern in the human brain from schizophrenia using transcriptomic-causal network.

BACKGROUND: Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis. METHODS: Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication. RESULTS: Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network. CONCLUSIONS: Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.

Effect of Benson's Relaxation Technique on Propofol Consumption and Preoperative Anxiety of Patients Undergoing Cataract Surgery.

BACKGROUND: Benson's relaxation (BR) technique is a suitable non-pharmacological approach to reduce preoperative anxiety (PA). OBJECTIVES: This study aimed to investigate the effect of BR therapy on PA and the induction and maintenance dose of propofol during cataract surgery (CS). METHODS: Seventy-two patients were randomly divided into two experiments or BR and control groups. The Amsterdam and Spielberger State-Trait Anxiety inventory (STAI) scores were used to assess PA directly two days and a half-hour before the CS. The control group did not receive any preoperation intervention or relaxation. Benson's relaxation method was performed three times, each time for 20 minutes, including two days before surgery, a night before surgery, and an hour before the surgery in the presence of a researcher by an audio file. The induction and maintenance dose of anesthetic drug was recorded and compared between the two groups. RESULTS: The mean propofol consumption was significantly reduced during the induction of anesthesia in the intervention group compared to the control group (0.99 ± 0.29 versus 1.29 ± 0.49; P = 0.005) as well as the maintenance of anesthesia (84.66 ± 17.98 versus 108.33 ± 34.38, P = 0.001). The results of the post-intervention Amsterdam anxiety score showed a significant decrease in the intervention group compared to the control group (P = 0.032, F = 9.61, Eta2 = 0.12). The control group showed a higher Spielberger state score compared to the intervention group as well as the Spielberger trait (P < 0.001, F = 14.78, Eta2 = 0.18). CONCLUSIONS: The BR method effectively reduces the level of PA in patients undergoing CS. Moreover, it reduces the need for anesthetic drug, propofol, during surgery.

Factors associated with progression to pre-diabetes: a recurrent events analysis.

AIMS: Pre-diabetes is a strong risk factor for type 2diabetes (T2D). The aim of this study was to explore factors associated with normal glucose maintenance and pre-diabetes prevention or delay. METHODS: Data of 1016 first-degree relatives of T2D patients were retrieved from the Isfahan Diabetes Prevention Study (IDPS). Association of various variables including nutrients, serum tests and physical activity with the risk of pre-diabetes was assessed using recurrent events approach. RESULTS: Cumulative incidence of diabetes was 8.17, 9.44, and 4.91% for total sample and individuals with and without pre-diabetes experience in the follow-up. Risk of progression to pre-diabetes was higher in women and older people (p < 0.01). Additionally, BMI and blood pressure had significant association with the risk (p < 0.01) and individuals with higher intake of fat were at higher risk (HR = 2.26; 95% CI 1.66-3.07 for high-intake and HR = 1.52; 95% CI 1.27-1.83 for medium-intake compared to low-intake group). Carbohydrates and protein intake were positively associated with the risk of pre-diabetes with HR = 8.63 per 49 g extra carbohydrates per day and HR = 1.32 per 6 g extra protein per day (p < 0.01). The association was also significant for triglyceride (TG) with 7% risk increase per 1 SD = 1.14 increase in TG level. CONCLUSION: Despite frequent studies on lifestyle modification for pre-diabetes prevention, less information is available about the role of nutritional components. We observed direct effects for intake of macronutrients including fat, carbohydrates, and protein in first-degree relatives. Further research is warranted to assess these associations in general populations. LEVEL OF EVIDENCE: Level III: Evidence obtained from a single-center cohort study.

Investigation of Prognostic Factors of Survival in Breast Cancer Using a Frailty Model: A Multicenter Study.

BACKGROUND: Using data from different health centers can provide more accurate knowledge of the survival prognostic factors and their effect on the patient's survival. In this multicenter study, we aimed to investigate the role of prognostic factors on breast cancer survival with large data set. METHODS: This historical cohort study was carried out using data from 1785 participants with breast cancer. Data were gathered from medical records of patients referring to 4 breast cancer research centers in Tehran, Iran, between 1997 and 2013. Age at diagnosis (year), size of the tumor, involve lymph nodes, tumor grade, type of surgery, auxiliary treatment of chemotherapy, radiotherapy, recurrence, and metastasis were the prognosis factors considered in this study. A shared frailty model with a gamma distribution for frailty term was used. RESULTS: The median follow-up period was 29.71 months with the interquartile range of 19 to 61 months. During the follow-up period, 337 (18.9%) patients died from breast cancer and 1448 (81.1%) survived. The 1-, 3-, 5-, and 10-year survival rates were 96%, 84%, 76%, and 58%, respectively. In the Cox model by centers, in Center A, the type of surgery, number of nodes involved, and the grade 3 tumor; in center B, age, radiotherapy, metastasis, and between 1 and 3 involved nodes; in center C, age, radiotherapy, recurrence, metastasis, tumor size, and grade 3 tumor; and in center D, chemotherapy, metastasis, and lymph nodes involved were significant. Shared frailty model showed that type of surgery, number of lymph nodes involved, metastasis, radiotherapy, and the tumor grade are the prognostic factors survival in breast cancer. The frailty variance was significant, and it affirmed there was significant variability between centers. CONCLUSIONS: This study showed it is necessary to consider the frailty term in modeling multicenter survival studies and confirmed the importance of early diagnosis of cancer before the involvement of lymph nodes and the onset of metastasis and timely treatment could lead to longer life and increased quality of life for patients.