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Abstract Background Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions. Methods We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication. Results We identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1, are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related. Conclusions This study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab.

Abstract Background The overuse of supplements among athletes is a widespread issue affecting the health of both male and female athletes. However, research on supplements usage among female fitness athletes is limited, and there is little information on the feeding behavior of fitness athletes who use supplements. This study aimed to fill the gap in knowledge by examining the prevalence of supplement usage and its related attitudes and reasons among fitness athletes in the gyms of Kashan. It further aimed to investigate the correlation between supplements usage and the feeding behavior of fitness athletes. Methods For these purposes, in this cross-sectional study, 433 fitness athletes (15‒46 years old) in 28 gyms in the city of Kashan were surveyed using a researcher-made questionnaire in 2023. Five experts confirmed the validity of the questionnaire. The present study considered the supplements based on the Australian Institute of Sport position statement. A Chi-square analysis was conducted to examine the relationship between the study variables and supplement usage. Results Overall, 272 male and 161 female fitness athletes participated in this study. The results revealed that 57.9% of participants used supplements, most commonly vitamin C, vitamin D, omega-3 fatty acids, and whey protein. The main reason for using supplements was to speed up body repair after exercise (69.5%). Additionally, 41.8% of these athletes believed that using supplements improves their overall performance, and 21.9% thought that supplements do not harm the body. Moreover, a correlation was observed between feeding behavior and the consumption of supplements. It was found that athletes who use supplements tend to eat more white meat, seeds, and nuts and fewer high-fat dairy products than those who do not consume them. Conclusion Using supplements among fitness athletes in the gyms of Kashan is common. The main reason for using these substances was to speed up body repair after exercise, and nearly half of the athletes believed that supplements improved their performance. In addition, it was revealed that athletes who take supplements have healthier feeding behaviors than those who do not. Thus, these findings confirm the necessity of informing fitness athletes about using supplements.

Background & Aims One of the goals of providing training to the anesthesia care team is to train staff with theoretical and functional abilities to deal with clinical challenges. However, there is a gap between the existing and desired performance levels of personnel in anesthesia procedures. This study aims to assess the competence of anesthesia personnel in Kashan, Iran, and find its related factors. Materials & Methods This descriptive-analytical study was conducted on 95 anesthesia personnel of hospitals in Kashan, Iran, in 2023, who were selected using a census method. The data collection tools were a demographic form and the Anesthesia Nursing Competence Scale. Data were analyzed in SPSS v.22 software using descriptive statistics (frequency, mean, standard deviation), Mann-Whitney U test, Kruskal-Wallis test, Spearman’s correlation test, Point-Biserial correlation test, and regression analysis. Results The mean age of the participants was 34.57±10.1 years. The overall competence level of anesthesia personnel was at a high level (84.84±53.11). Their competence had a significant relationship with their sex and marital status (P

BackgroundPremenstrual 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.MethodsThe 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.ResultsSignificant improvements were observed in mood lability (p = 0.044) and expressed anger (p

BackgroundOne 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.MethodsThis 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.ResultsA 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).ConclusionConsidering 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.

Abstract 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 .

Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regression to censored data, particularly for upper quantiles. The minimum distance approach, utilizing dual-kernel estimation and the inverse cumulative distribution function, shows promise in addressing these challenges, especially with Methods: This study contrasts two methods within the realm of quantile linear regression for survival analysis: check-based modeling and the minimum distance approach. Effectiveness is assessed across various scenarios through comprehensive simulation. Results: The simulation results showed that using the quantile regression model with the minimum distance approach reduces the percentage of root mean square error in parameter estimation compared to the quantile regression models based on the check loss function. Additionally, a larger sample size and reduced censoring percentage led to decreased root mean square error in parameter estimation. Conclusion: The research highlights the benefits of using the minimum distance approach for quantile regression. It reduces errors, improves model predictions, captures patterns, and optimizes parameters even with complete data. However, this approach has limitations. The accuracy of estimated quantiles can be influenced by the choice of distance metric and weighting scheme. The assumption of independence between censoring mechanism and survival time may not hold in real-world scenarios. Additionally, dealing with large datasets can be computationally complex.

BackgroundBoth 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.MethodsClassification 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.ResultsFollowing an ADMF diet resulted in a greater decrease in weight (kg) [−5.23 (1.73) vs. −3.15 (0.88); P

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.

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 10 th and 50 th 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 10 th and 50 th percentiles of survival time was 20.67 and 69.73 months, respectively (all p -value

Abstract 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.

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.

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 5 cm were 31.06 and 27 months less than patients with the tumor size

Abstract 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.

Abstract 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.

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.

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.

Objective: Bone is the most common site of metastasis in breast cancer. Prognostic factors for predicting bone metastases in breast cancer are controversial yet. In this study, we investigated clinical factors associated with secondary bone metastasis of breast cancer. Methods: In total, 1690 patients with breast cancer recorded between 2002 and 2012 in Motamed Cancer Institute, Tehran, Iran entered in the retrospective study. We studied age, menopausal status, histologic type, tumor size, number of cancerous axillary lymph nodes, serum concentrations of alkaline phosphatase (ALP), carcinogenicity antigen (CEA), cancer antigen (CA)-153, and hemoglobin (HB) in 2 groups with bone metastases (n = 123) and without it, respectively. We applied logistic regression to identify bone metastasis prognostic factors in breast cancer patients and calculated the cut-off value, sensitivity, and characteristics of independent prognostic factors using receiver operating characteristic (ROC) curve analysis. Results: Menopause, larger tumor size, and the greater number of cancerous axillary lymph nodes increased the chance of bone metastases significantly ( P .05). Logistic regression showed that age (odds ratio (OR) = 1.021), menopausal status (OR = 1.854), number of cancerous axillary lymph nodes (OR = 1.065), a tumor size between 2 and 5 cm diameter (OR = 2.002) and more than 5 cm diameter (OR = 4.009), and ALP (OR = 1.005) are independent prognostic factors associated with bone metastases. The ROC curve showed that the abovementioned factors have comparable predictive accuracy for bone metastases. Conclusions: Age, menopausal status, number of axillary lymph node metastases, tumor size, and ALP were identified as prognostic factors for bone metastasis in patients with breast cancer. So patients with these characteristics should be monitored more precisely with regular follow-ups.

مقدمه: مطالعات نشان داده‌اند که قبل از بروز دیابت نوع 2، همواره یک دوره پره‌دیابت روی می‌دهد. اصلاح شیوه‌ی زندگی به منظور پیشگیری یا به تأخیر انداختن این دوره پیشنهاد شده است. عملکرد تغذیه‌ای از اجزای شیوه‌ی زندگی است. مصرف درشت مغذی‌ها می‌تواند بر روی متابولیسم چربی و گلوكز تأثیر بگذارد و از این طریق، موجب تغییرات افزایش مقاومت به انسولین شود. روش‌ها: مطالعه‌ی حاضر یک مطالعه‌ی هم‌گروهی 9 ساله از بستگان درجه‌ی اول بیماران مبتلا به دیابت نوع 2 بود. اطلاعات تغذیه‌ای، عوامل خونی، شاخص‌های دموگرافیک و شاخص‌های آنتروپومتریک بیماران ثبت ‌شد. برای بررسی ارتباط بین مصرف درشت مغذی‌ها و متغیر پاسخ وضعیت پره‌دیابت، از مدل شکنندگی در بقا استفاده گردید. یافته‌ها: خطر تکرار پره‌دیابت در افراد با مصرف کربوهیدرات با حذف اثر سایر مخدوشگرها، به طور معنی‌داری افزایش می‌یابد (نسبت خطر = 04/1). همچنین خطر تکرار پره‌دیابت برای افرادی با چربی دریافتی بین 35-25 واحد، 52/1 بیشتر از افرادی با چربی دریافتی کمتر از 25 واحد بود و خطر برای افراد با چربی دریافتی بیش از 35 واحد، 22/2 بیشتر از افراد با چربی کمتر از 25 بود. خطر تکرار پره‌دیابت در افراد مصرف‌ کننده‌ی پروتئین نیز با تعدیل سایر متغیرهای مخدوشگر، به طور معنی‌داری افزایش یافت (نسبت خطر = 05/1). نتیجه‌گیری: مصرف درشت مغذی‌ها می‌تواند بر روی متابولیسم چربی و گلوكز تأثیر بگذارد و از این طریق، موجب تغییرات افزایش مقاومت به انسولین شود. مصرف این عامل، از عوامل تأثیرگذار در کنترل تکرار وضعیت پره‌دیابت می‌باشد.

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.