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).

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.

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.

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics.

BACKGROUND: Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. RESULTS: The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate (p-value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. CONCLUSION: Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.

Identification of Rare Variants in Metabolites of the Carnitine Pathway by Whole Genome Sequencing Analysis.

We use whole genome sequence data and rare variant analysis methods to investigate a subset of the human serum metabolome, including 16 carnitine-related metabolites that are important components of mammalian energy metabolism. Medium pass sequence data consisting of 12,820,347 rare variants and serum metabolomics data were available on 1,456 individuals. By applying a penalization method, we identified two genes FGF8 and MDGA2 with significant effects on lysine and cis-4-decenoylcarnitine, respectively, using Δ-AIC and likelihood ratio test statistics. Single variant analyses in these regions did not identify a single low-frequency variant (minor allele count > 3) responsible for the underlying signal. The results demonstrate the utility of whole genome sequence and innovative analyses for identifying candidate regions influencing complex phenotypes.

Comprehensive maternal characteristics associated with birth weight: Bayesian modeling in a prospective cohort study from Iran.

BACKGROUND: In this study, we aimed to determine comprehensive maternal characteristics associated with birth weight using Bayesian modeling. MATERIALS AND METHODS: A total of 526 participants were included in this prospective study. Nutritional status, supplement consumption during the pregnancy, demographic and socioeconomic characteristics, anthropometric measures, physical activity, and pregnancy outcomes were considered as effective variables on the birth weight. Bayesian approach of complex statistical models using Markov chain Monte Carlo approach was used for modeling the data considering the real distribution of the response variable. RESULTS: There was strong positive correlation between infant birth weight and the maternal intake of Vitamin C, folic acid, Vitamin B3, Vitamin A, selenium, calcium, iron, phosphorus, potassium, magnesium as micronutrients, and fiber and protein as macronutrients based on the 95% high posterior density regions for parameters in the Bayesian model. None of the maternal characteristics had statistical association with birth weight. CONCLUSION: Higher maternal macro- and micro-nutrient intake during pregnancy was associated with a lower risk of delivering low birth weight infants. These findings support recommendations to expand intake of nutrients during pregnancy to high level.

Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data.

Understanding causal relationships among large numbers of variables is a fundamental goal of biomedical sciences and can be facilitated by Directed Acyclic Graphs (DAGs) where directed edges between nodes represent the influence of components of the system on each other. In an observational setting, some of the directions are often unidentifiable because of Markov equivalency. Additional exogenous information, such as expert knowledge or genotype data can help establish directionality among the endogenous variables. In this study, we use the method of principle component analysis to extract information across the genome in order to generate a robust statistical causal network among phenotypes, the variables of primary interest. The method is applied to 590,020 SNP genotypes measured on 1596 individuals to generate the statistical causal network of 13 cardiovascular disease risk factor phenotypes. First, principal component analysis was used to capture information across the genome. The principal components were then used to identify a robust causal network structure, GDAG, among the phenotypes. Analyzing a robust causal network over risk factors reveals the flow of information in direct and alternative paths, as well as determining predictors and good targets for intervention. For example, the analysis identified BMI as influencing multiple other risk factor phenotypes and a good target for intervention to lower disease risk.

Identification, analysis, and interpretation of a human serum metabolomics causal network in an observational study.

Untargeted metabolomics, measurement of large numbers of metabolites irrespective of their chemical or biologic characteristics, has proven useful for identifying novel biomarkers of health and disease. Of particular importance is the analysis of networks of metabolites, as opposed to the level of an individual metabolite. The aim of this study is to achieve causal inference among serum metabolites in an observational setting. A metabolomics causal network is identified using the genome granularity directed acyclic graph (GDAG) algorithm where information across the genome in a deeper level of granularity is extracted to create strong instrumental variables and identify causal relationships among metabolites in an upper level of granularity. Information from 1,034,945 genetic variants distributed across the genome was used to identify a metabolomics causal network among 122 serum metabolites. We introduce individual properties within the network, such as strength of a metabolite. Based on these properties, hypothesized targets for intervention and prediction are identified. Four nodes corresponding to the metabolites leucine, arichidonoyl-glycerophosphocholine, N-acyelyalanine, and glutarylcarnitine had high impact on the entire network by virtue of having multiple arrows pointing out, which propagated long distances. Five modules, largely corresponding to functional metabolite categories (e.g. amino acids), were identified over the network and module boundaries were determined using directionality and causal effect sizes. Two families, each consists of a triangular motif identified in the network had essential roles in the network by virtue of influencing a large number of other nodes. We discuss causal effect measurement while confounders and mediators are identified graphically.

Rare variants analysis using penalization methods for whole genome sequence data.

BACKGROUND: Availability of affordable and accessible whole genome sequencing for biomedical applications poses a number of statistical challenges and opportunities, particularly related to the analysis of rare variants and sparseness of the data. Although efforts have been devoted to address these challenges, the performance of statistical methods for rare variants analysis still needs further consideration. RESULT: We introduce a new approach that applies restricted principal component analysis with convex penalization and then selects the best predictors of a phenotype by a concave penalized regression model, while estimating the impact of each genomic region on the phenotype. Using simulated data, we show that the proposed method maintains good power for association testing while keeping the false discovery rate low under a verity of genetic architectures. Illustrative data analyses reveal encouraging result of this method in comparison with other commonly applied methods for rare variants analysis. CONCLUSION: By taking into account linkage disequilibrium and sparseness of the data, the proposed method improves power and controls the false discovery rate compared to other commonly applied methods for rare variant analyses.

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.

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.

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.

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 .

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.

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).

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.

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.

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.