A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning.

Purpose: We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms. Methods: We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan). The following features were input to our machine learning model: fat-free mass, fat percentage, basal metabolic rate, total body water, right arm fat-free mass, right leg fat-free mass, trunk fat-free mass, trunk fat percentage, sex, age, right leg fat percentage, and right arm fat percentage. We performed classification into diabetes vs. no diabetes classes using linear support vector machine, decision tree, stochastic gradient descent, logistic regression, Gaussian naïve Bayes, k-nearest neighbors (k = 3 and k = 4), and multi-layer perceptron, as well as ensemble learning using random forest, gradient boosting, adaptive boosting, XGBoost, and ensemble voting classifiers with Top3 and Top4 algorithms. 4661 subjects (mean age 47.64 ± 9.37 years, range 35 to 70 years; 2155 male, 2506 female) were analyzed and stratified into 571 and 4090 subjects with and without a self-declared history of diabetes, respectively. Results: Age, fat mass, and fat percentages in the legs, arms, and trunk were positively associated with diabetes; fat-free mass in the legs, arms, and trunk, were negatively associated. Using XGBoost, our model attained the best excellent accuracy, precision, recall, and F1-score of 89.96%, 90.20%, 89.65%, and 89.91%, respectively. Conclusions: Our machine learning model showed that regional body fat compositions were predictive of diabetes status.

Framingham risk scores for determination the 10-year risk of cardiovascular disease in participants with and without the metabolic syndrome: results of the Fasa Persian cohort study.

BACKGROUND: Metabolic syndrome (MetS) is a cluster of risk factors and the Framingham risk score (FRS) is a useful metric for measuring the 10-year cardiovascular disease (CVD) risk of the population. The present study aimed to determine the 10-year risk of cardiovascular disease using the Framingham risk score in people with and without MetS in a large Iranian cohort study. METHODS: This cross-sectional study was done using the Fasa cohort. Participants aged ≥ 35 years old were recruited to the study from 2015 to 2016. The FRS was calculated using age, sex, current smoking, diabetes, systolic blood pressure (SBP), total cholesterol, and high-density lipoprotein (HDL) cholesterol. MetS was defined as the presence of three or more of the MetS risk factors including triglyceride (TG) level ≥ 150 mg dl- 1, HDL level < 40 mg dl- 1 in men and < 50 mg dl- 1 in women, systolic/diastolic blood pressure ≥ 130/≥85 mmHg or using medicine for hypertension, fasting blood sugar (FBS) level ≥ 100 mg dl- 1 or using diabetes medication and abdominal obesity considered as waist circumference (WC) ≥ 88 cm for women and ≥ 102 cm for men. Multiple logistic regressions were applied to estimate the 10- year CVD risk among people with and without MetS. RESULTS: Of 8949 participants, 1928 people (21.6%) had MetS. The mean age of the participants with and without Mets was 50.4 ± 9.2 years and 46.9 ± 9.1 years respectively. In total 15.3% of participants with MetS and 8.0% of participants without MetS were in the high-risk category of 10-year CVD risk. Among participants with MetS gender, TG, SBP, FBS and in people without MetS gender, TG, SBP, FBS, and HDL showed strong associations with the predicted 10-year CVD risk. CONCLUSION: Male sex and increased SBP, TG, and FBS parameters were strongly associated with increased 10-year risk of CVD in people with and without MetS. In people without MetS, reduced HDL-cholestrol was strongly associated with increased 10-year risk of CVD. The recognition of participant's TG, blood pressure (BP), FBS and planning appropriate lifestyle interventions related to these characteristics is an important step towards prevention of CVD.

CircRNA-associated ceRNA networks (circCeNETs) in chronic obstructive pulmonary disease (COPD).

Chronic obstructive pulmonary disease (COPD), a chronic airway disorder, which is mostly brought on by cigarette smoke extract (CSE), is a leading cause of death which has a high frequency. In COPD patients, smoking cigarette could also trigger the epithelial-mesenchymal transition (EMT) of airway remodeling. One of the most significant elements of environmental contaminants that is linked to pulmonary damage is fine particulate matter (PM2.5). However, the basic processes of lung injury brought on by environmental contaminants and cigarette smoke are poorly understood, particularly the molecular pathways involved in inflammation. For the clinical management of COPD, investigating the molecular process and identifying workable biomarkers will be important. According to newly available research, circular RNAs (circRNAs) are aberrantly produced and serve as important regulators in the pathological processes of COPD. This class of non-coding RNAs (ncRNAs) functions as microRNA (miRNA) sponges to control the levels of gene expression, changing cellular phenotypes and advancing disease. These findings led us to concentrate our attention in this review on new studies about the regulatory mechanism and potential roles of circRNA-associated ceRNA networks (circCeNETs) in COPD.

Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up.

BACKGROUND: Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area. METHODS: This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35-70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP). RESULTS: Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth. CONCLUSION: ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension.

Evaluation of the Expression of Infection-Related Long Noncoding RNAs among COVID-19 Patients: A Case-Control Study.

Background and Aims: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a worldwide pandemic, activates signaling cascades and leads to innate immune responses and secretion of multiple chemokines and cytokines. Long noncoding RNAs (lncRNAs) have a crucial role in inflammatory pathways. Through our search on the PubMed database, we discovered that existing research has primarily focused on examining the regulatory impacts of five lncRNAs in the context of viral infections. However, their role in regulating other conditions, including SARS-CoV-2, has not been explored. Therefore, this study aimed to investigate the expression pattern of lncRNAs in the peripheral blood mononuclear cells (PBMC) and their potential roles in SARS-CoV-2 infection. Potentially significant competing endogenous RNA (ceRNA) networks of these five lncRNAs were found using online in-silico techniques. Methods: Ethylenediaminetetraacetic acid (EDTA) blood samples of the control group consisted of 45 healthy people, and a total of 53 COVID-19-infected patients in case group, with a written informed consent, was collected. PBMCs were extracted, and then, the RNA extraction and complementary DNA (cDNA) synthesis was performed. The expression of five lncRNAs (lnc ISR, lnc ATV, lnc PAAN, lnc SG20, and lnc HEAL) was assessed by real-time PCR. In order to evaluate the biomarker roles of genes, receiver operating characteristic (ROC) curve was drawn. Results: Twenty-four (53.3%) and 29 (54.7%) of healthy and COVID-19-infected participants were male, respectively. The most prevalent symptoms were as follows: cough, general weakness, contusion, headache, and sore throat. The results showed that three lncRNAs, including lnc ISR, lnc ATV, and lnc HEAL, were expressed dramatically higher in the case group compared to healthy controls. According to ROC curve analysis, lnc ATV has a higher AUC and is a better biomarker to differentiate COVID-19 patients from the healthy controls. Then, using bioinformatics methods, the ceRNA network of these lncRNAs enabled the identification of mRNAs and miRNAs with crucial functions in COVID-19. Conclusion: The considerable higher expression of ISR, ATV, and HEAL lncRNAs and the significant area under curve (AUC) in ROC curve demonstrate that these RNAs probably have a potential role in controlling the host innate immune responses and regulate the viral replication of SARS-CoV-2. However, these assumptions need further in vitro and in vivo investigations to be confirmed.

Machine-learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study.

INTRODUCTION: The application of machine learning (ML) is increasingly growing in biomedical sciences. This study aimed to evaluate factors associated with type 2 diabetes mellitus (T2DM) and compare the performance of ML methods in identifying individuals with the disease in an Iranian setting. METHODS: Using the baseline data from Fasa Adult Cohort Study (FACS) and in a sex-stratified manner, we studied factors associated with T2DM by applying seven different ML methods including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbours (KNN), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB) and Bagging classifier (BAG). We further compared the performance of these methods; for each algorithm, accuracy, precision, sensitivity, specificity, F1 score, and Area Under Curve (AUC) were calculated. RESULTS: 10,112 participants were recruited between 2014 and 2016, of whom 1246 had T2DM at baseline. 4566 (45%) participants were males, aged between 35 and 70 years. For males, age, sugar consumption, and history of hospitalization were the most weighted variables regarding their importance in screening for T2DM using the GBM model, respectively; these variables were sugar consumption, urine blood, and age for females. GBM outperformed other models for both males and females with AUC of 0.75 (0.69-0.82) and 0.76 (0.71-0.80), and F1 score of 0.33 (0.27-0.39) and 0.42 (0.38-0.46), respectively. GBM also showed a sensitivity of 0.24 (0.19-0.29) and a specificity of 0.98 (0.96-1.0) in males and a sensitivity of 0.38 (0.34-0.42) and specificity of 0.92 (0.89-0.95) in females. Notably, close performance characteristics were detected among other ML models. CONCLUSIONS: GBM model might achieve better performance in screening for T2DM in a south Iranian population.

Long non-coding RNAs and JAK/STAT signaling pathway regulation in colorectal cancer development.

Colorectal cancer (CRC) is one of the main fatal cancers. Cell signaling such as Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling substantially influences the process of gene expression and cell growth. Long non-coding RNAs (lncRNAs) play regulatory roles in cell signaling, cell proliferation, and cancer fate. Hence, lncRNAs can be considered biomarkers in cancers. The inhibitory or activating effects of different lncRNAs on the JAK/STAT pathway regulate cancer cell proliferation or tumor suppression. Additionally, lncRNAs regulate immune responses which play a role in immunotherapy. Mechanisms of lncRNAs in CRC via JAK/STAT regulation mainly include cell proliferation, invasion, metastasis, apoptosis, adhesion, and control of inflammation. More profound findings are warranted to specifically target the lncRNAs in terms of activation or suppression in hindering CRC cell proliferation. Here, to understand the lncRNA cross-talk in CRC through the JAK/STAT signaling pathway, we collected the related in vitro and in vivo data. Future insights may pave the way for the development of novel diagnostic tools, therapeutic interventions, and personalized treatment strategies for CRC patients.

Thyroid function and opium use disorder: a cross-sectional study on the Fasa adults cohort study (FACS), 2017.

BACKGROUND: Addiction increases the risk of different lifelong disorders. However, there are limited studies evaluating the effects of opioid use disorder (OUD) on thyroid function. The present study aimed to compare the thyroid function of individuals with and without OUD. METHODS: This cross-sectional study was conducted on 700 eligible participants of the Persian Cohort of Fasa, Iran. Pregnant women and participants with false or missing data were excluded from the study. Remained participants were divided into case and control groups based on the recorded history of OUD. Frozen plasma samples of the cohort bank were used to determine the levels of T3, T4, and thyroid-stimulating hormone (TSH). The thyroid function was compared between the two groups using the Mann-Whitney test (P < 0.05). RESULTS: The mean age of the final studied population (n = 648) was 54.0 ± 9.8 years, including 336 men (49.1%) and 197 participants with OUD (28.8%). The median levels of TSH, T4, and T3 were 2.91 ± 4.61, 9.26 ± 3.65, and 1.22 ± 0.49, respectively. The case group had significantly higher TSH (3.72 ± 6.2 vs. 2.58 ± 3.75, P < 0.001) and lower T4 (8 ± 3.6 vs. 9.8 ± 3.5, P < 0.001). Also, T3 was slightly lower in the case group (1.1 ± 0.5 vs. 1.3 ± 0.5; P = 0.369), although this association was only significant in female opium users (P < 0.001). CONCLUSIONS: The present findings revealed that OUD caused a reduction in T4 while increasing TSH. Therefore, OUD may lead to the development of primary hypothyroidism, which needs to be investigated in future studies.

The potential roles of Th17 cells in the pathogenesis of oral lichen planus.

BACKGROUND: Oral lichen planus (OLP) is a T cell-mediated chronic autoimmune disease, whose pathogenesis and etiology are not entirely understood. OLP is characterized by subepithelial lymphocyte infiltration and elevated intra-epithelial lymphocytes. The majority of lamina propria lymphocytes are CD4+ T cells. CD4+ helper T (Th) cells play a crucial role in activating CD8+ cytotoxic T cells (CTLs) through interactions and cytokine production. Th1 and Th2 cells are well-accepted to be associated with OLP pathogenesis. However, OLP treatment is challenging yet, the more information we have about the pathology of OLP, the easier it will be treated. With the discovery of Th17 cells in recent years and the demonstration of their role in autoimmune disease, many researchers started to investigate the role of Th17 in the pathogenesis of OLP. METHODS: To make up this review, studies covering the role of TH17 in different types of lichen planus were selected from major databases. RESULTS: As we review in this article, Th17 cells and their signature cytokines play an important role in OLP pathogenesis. As well, utilizing some anti-IL-17 antibodies showed promising results in improving the disease; however, more studies are still needed to better understand and treat OLP.

Predictive value of triglycerides to high-density lipoprotein cholesterol and triglyceride glycemic index for diabetes incidence in pre-diabetes patients: a prospective cohort study.

BACKGROUND: The triglyceride glucose (TyG) and triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-c) are the indices that can predict the progression of pre-diabetes to type 2 diabetes mellitus (T2DM). This study aimed to examine the relationship between TyG and TG/HDL-c indices with the incidence of T2DM in pre-diabetes patients. METHODS: A total of 758 pre-diabetic patients aged 35-70 years who were enrolled in a prospective Fasa Persian Adult Cohort were followed up for 60 months. TyG and TG/HDL-C indices were obtained at baseline data and divided into quartiles. The 5-year cumulative incidence of T2DM was analyzed by Cox proportional hazards regression analysis while controlling for baseline covariates. RESULTS: During 5 years of follow-up, there were 95 incident cases of T2DM, with an overall incidence rate of 12.53%. After adjusting for age, sex, smoking, marital status, socioeconomic status, body mass index, waist circumference, hip circumference, hypertension, total cholesterol, and dyslipidemia, the multivariate-adjusted hazard ratios (HRs) demonstrated that patients with the highest TyG and TG/HDL-C indices quartile were at higher risk of T2DM (HR = 4.42, 95%CI 1.75-11.21) and (HR = 2.15, 95%CI 1.04-4.47), respectively, compared to participants in the lowest quartile. As the quantiles of these indices increase, the HR value shows a significant increment (P < 0.05). CONCLUSION: The results of our study showed that the TyG and TG/HDL-C indices can be important independent predictors for the progression of pre-diabetes to T2DM. Therefore, controlling the components of these indicators in pre-diabetes patients can prevent developing T2DM or delay its occurrence.

The Effect of Educational Intervention Based on Health Belief Model on Eye Care Practice of Type II Diabetic Patients in Southern Iran.

Background: The ocular complication caused by diabetes is one of the most common reasons of blindness in the world. This study aimed to investigate the effect of educational intervention on eye care practice of type II diabetic patients based on health belief model (HBM) in Fasa city. Methods: This study was a quasi-experimental study on 100 patients with type II diabetes referred to the diabetes center in Fasa city, Fars province, Iran, in 2019. Data were collected using a valid self-reported questionnaire including demographic variables, knowledge, and HBM (perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action), and eye care performance (based on self-report) and the level of HbA1cof both groups were measured before and three months after the educational intervention. The experimental group received training in eight sessions; each session lasted for 50 to 55 minutes. In order to analyze the studied data, SPSS 22 software (SPSS Inc., IBM, Chicago, IL, USA), Chi-square, independent t-test, and paired t-test have been used. P < 0.05 was considered as statistically significant. Results: The results showed that the mean scores of knowledge (P < 0.001) and HBM components (P < 0.001) in the experimental and control groups after intervention have a significant difference. After the training program, eye care performance in the experimental group was better than that in the control group (P < 0.001). Furthermore, HbA1c (P < 0.001) improved significantly in the experimental group compared to the control group. Conclusions: Planning and implementing education using the HBM to improve eye care performance in diabetic patients are very effective and beneficial. Moreover, educational programs based on health education and health promotion models for diabetic patients for preventing side effects caused by diabetes should be performed.

Nickel Nanoparticles: Applications and Antimicrobial Role against Methicillin-Resistant Staphylococcus aureus Infections.

Methicillin-resistant Staphylococcus aureus (MRSA) has evolved vast antibiotic resistance. These strains contain numerous virulence factors facilitating the development of severe infections. Considering the costs, side effects, and time duration needed for the synthesis of novel drugs, seeking efficient alternative approaches for the eradication of drug-resistant bacterial agents seems to be an unmet requirement. Nickel nanoparticles (NiNPs) have been applied as prognostic and therapeutic cheap agents to various aspects of biomedical sciences. Their antibacterial effects are exerted via the disruption of the cell membrane, the deformation of proteins, and the inhibition of DNA replication. NiNPs proper traits include high-level chemical stability and binding affinity, ferromagnetic properties, ecofriendliness, and cost-effectiveness. They have outlined pleomorphic and cubic structures. The combined application of NiNPs with CuO, ZnO, and CdO has enhanced their anti-MRSA effects. The NiNPs at an approximate size of around 50 nm have exerted efficient anti-MRSA effects, particularly at higher concentrations. NiNPs have conferred higher antibacterial effects against MRSA than other nosocomial bacterial pathogens. The application of green synthesis and low-cost materials such as albumin and chitosan enhance the efficacy of NPs for therapeutic purposes.

Upregulation of hsa_circ_0004812 promotes COVID-19 cytokine storm via hsa-miR-1287-5p/IL6R, RIG-I axis.

BACKGROUND: SARS-CoV-2 is one of the most contagious viruses in the Coronaviridae (CoV) family, which has become a pandemic. The aim of this study is to understand more about the role of hsa_circ_0004812 in the SARS-CoV-2 related cytokine storm and its associated molecular mechanisms. MATERIALS AND METHODS: cDNA synthesis was performed after total RNA was extracted from the peripheral blood mononuclear cells (PBMC) of 46 patients with symptomatic COVID-19, 46 patients with asymptomatic COVID-19, and 46 healthy controls. The expression levels of hsa_circ_0004812, hsa-miR-1287-5p, IL6R, and RIG-I were determined using qRT-PCR, and the potential interaction between these molecules was confirmed by bioinformatics tools and correlation analysis. RESULTS: hsa_circ_0004812, IL6R, and RIG-I are expressed higher in the severe symptom group compared with the negative control group. Also, the relative expression of these genes in the asymptomatic group is lower than in the severe symptom group. The expression level of hsa-miR-1287-5p was positively correlated with symptoms in patients. The results of the bioinformatics analysis predicted the sponging effect of hsa_circ_0004812 as a competing endogenous RNA on hsa-miR-1287-5p. Moreover, there was a significant positive correlation between hsa_circ_0004812, RIG-I, and IL-6R expressions, and also a negative expression correlation between hsa_circ_0004812 and hsa-miR-1287-5p and between hsa-miR-1287-5p, RIG-I, and IL-6R. CONCLUSION: The results of this in-vitro and in silico study show that hsa_circ_0004812/hsa-miR-1287-5p/IL6R, RIG-I can play an important role in the outcome of COVID-19.

Individual genetic variability mainly of Proinflammatory cytokines, cytokine receptors, and toll-like receptors dictates pathophysiology of COVID-19 disease.

Innate and acquired immunity responses are crucial for viral infection elimination. However, genetic variations in coding genes may exacerbate the inflammation or initiate devastating cytokine storms which poses severe respiratory conditions in coronavirus disease-19 (COVID-19). Host genetic variations in particular those related to the immune responses determine the patients' susceptibility and COVID-19 severity and pathophysiology. Gene polymorphisms such as single nucleotide polymorphisms (SNPs) of interferons, TNF, IL1, IL4, IL6, IL7, IL10, and IL17 predispose patients to the severe form of COVID-19 or severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). These variations mainly alter the gene expression and cause a severe response by B cells, T cells, monocytes, neutrophils, and natural killer cells participating in a cytokine storm. Moreover, cytokines and chemokines SNPs are associated with the severity of COVID-19 and clinical outcomes depending on the corresponding effect. Additionally, genetic variations in genes encoding toll-like receptors (TLRs) mainly TLR3, TLR7, and TLR9 have been related to the COVID-19 severe respiratory symptoms. The specific relation of these mutations with the novel variants of concern (VOCs) infection remains to be elucidated. Genetic variations mainly within genes encoding proinflammatory cytokines, cytokine receptors, and TLRs predispose patients to COVID-19 disease severity. Understanding host immune gene variations associated with the SARS-COV-2 infection opens insights to control the pathophysiology of emerging viral infections.

The Effect of Vitamin D Replacement on Patient with Subclinical Hypothyroidism: A Pilot Randomized Clinical Trial.

BACKGROUND: Subclinical hypothyroidism (SCH) is characterized by an elevated Thyroid Stimulating Hormone (TSH) with a normal T4. The prevalence of Vitamin D deficiency in patients SCH is high. Some studies suggested that Vitamin D supplements could be reduced serum concentration of TSH so improve SCH and prevent overt hypothyroidism. This study aims to explore the effect of vitamin D replacement on subclinical hypothyroidism. MATERIALS AND METHODS: Fifty-nine patients, diagnosed with both subclinical hypothyroidism and Vitamin D deficiency by the Endocrinology outpatient clinics between January 2018 and March 2019, were included in this trial. The patients with overt hypothyroidism, cardiovascular risk factors, or positive TPO antibody, abnormal T4, and pregnant women were excluded from this study. The 40 subjects were investigated who received vitamin D supplements for two months. Analyses were conducted through paired-samples t-test and independent-samples t-test using SPSS 24 (Armonk, NY: IBM Corp). RESULTS: The mean serum levels of TSH was decreased from 6.89 mIU/l in the pre-test to 3.34 mIU/l in the post-test, and the difference was found to be statistically significant at P<0.001. CONCLUSION: We found that the TSH mean level significantly dropped through the use of vitamin D supplements. Thus, it is recommended that all the patients with subclinical hypothyroidism be screened and treated with vitamin D supplements.

Comparison of Formulas for Low-Density Lipoprotein (LDL) Calculation for Predicting the Risk of Metabolic Syndrome.

BACKGROUND: The correlation between serum cholesterol level and the risk of developing atherosclerosis and metabolic syndrome has been well established in previous studies. Serum low-density lipoprotein (LDL-C) measurement is conducted using different methods which are generally divided into two groups, namely direct and indirect. Using indirect methods or calculations such as the Friedewald or Iranian formula for measuring LDL, particularly in developing countries, is quite common. The present study has stepped in to compare the robustness of the extant formulas in prognosticating and determining the incidence of metabolic syndrome. MATERIALS AND METHODS: In this cross-sectional study, the target population was the community of Fasa cohort study. According to the views of the statistical advisor, 9530 people were included in the study and clinical laboratory examinations were done for each person. Their serum LDL level was measured using the existing formulas. Then, the results of the serum LDL level that was computed with different formulas, were compared with both the status of metabolic syndrome and laboratory tests of individuals. RESULTS: The Iranian formula has the highest area under curve, the sensitivity of 0.73, and specificity of 0.77, higher positive and negative predictive values among other formulas. In Friedewald formula, for example, sensitivity and specificity equal 0.28 and 0.80, respectively. After further analysis, two new models proposed for predicting metabolic syndrome. The results revealed that these two models even outperform the Iranian formula. CONCLUSION: The Iranian formula for plasma LDL calculation has higher precision and application for predicting and measuring the metabolic syndrome in the Iranian population due to its considerable features. It is required to develop a new formula for each population and even for each sex, if possible.

Early screening of gestational diabetes mellitus using hemoglobin A1C: Revising current screening guidelines.

BACKGROUND: HbA1C has been a known predictor and diagnostic test for diabetes type 2. However, this test has not yet been widely studied in GDM and more importantly no cutoff point has been defined for HbA1C in GDM. We investigated the efficacy of screening during first and second trimester of pregnancy and defined appropriate cutoff points according to HbA1C and FBS for predicting maternal GDM among women with none to minimal previously known risk factors. METHODS: This is a prospective multi-centered cohort study. Individuals were evaluated at first trimester, at 20-24 weeks and 24-28 weeks of gestation. GDM tests were done during visits and accuracy of each of these measurements was evaluated. RESULTS: Overall, 356 entered the study, among which 25 individuals and 30 individuals developed GDM during 20-24 and 24-28 weeks of gestation, respectively. HbA1C measured during first trimester at a cut-off of 5.35% predicted GDM of 24-28 weeks with an accuracy of 85.6%, sensitivity of 80%, and specificity of 80%. Furthermore, at a cut-off of 5.75% measured at 20-24 weeks, HbA1C predicted GDM with an accuracy of 94.5%, sensitivity of 97%, and specificity of 96%. CONCLUSION: Screening programs during first trimester and at 20-24 weeks of pregnancy, using HbA1C, can significantly aid in the early prediction of GDM, even among women with no to minimal previously known risk factors, which shows a need for revision in current guidelines.

Impact of Physical Activity on the Incidence of Vascular Diseases in Adults with Type 2 Diabetes Mellitus.

BACKGROUND: Diabetes mellitus (DM) is a common metabolic disease worldwide and has many complications. Vascular events are the major complication of DM, which have an important effect on mortality and disability. Physical activity (PA) enhances the vascular function by several pathways. The aim of this study was to evaluate the relationship between PA and vascular diseases in patients with DM. MATERIALS AND METHODS: This study was performed as a case-control study extracted from a prospective epidemiological research study in Iran. Patients with type 2 DM for more than six months as a case group were compared to sex- and age-matched healthy control subjects. The metabolic equivalent of task score was used to evaluate the level of PA and blood glucose, lipid profile, body mass index, overweight, dyslipidemia, glomerular filtration rate, myocardial infarction, unstable angina, and stroke. RESULTS: Overall, 1242 patients with DM were extracted, and 2484 non-DM subjects were investigated. In the case group, 355 (28.6%) and 887 (71.4%) subjects were men and women, respectively, and 710 (28.6%) men and 1774 (71.4%) women were in the control group. The mean metabolic equivalent of task score was 30 and 40.97 in the DM and non-DM groups, respectively (P˂0.001). The frequency of myocardial infarction, stroke, and cardiac ischemia was 44 (3.5%), 37 (3%), and 267 (21.5%) in the DM group, and 54 (2.2%), 43 (1.7%), and 389 (15.7%) in the non-DM group, respectively. CONCLUSION: The incidence of vascular events associated with PA level in patients with DM and adherence to regular PA reduced vascular events and DM complications.

Do site and type of metastasis in breast cancer show a changing pattern with increased age? A cross comparison of clinicopathological characteristics between age groups.

BACKGROUND: In here, we evaluated pattern of metastasis and cross-compared clinicopathological features between different age groups with breast cancer (BC). METHODS: This study was conducted in the Shiraz Breast Cancer Registry (largest BC registry in Iran). Patients were classified as < 30 years old (group 1), 30-60 years old (group 2), and > 60 years old (group 3). The three age groups were compared regarding clinical and baseline characteristics. RESULTS: Overall, 564 individuals entered group 1, 4519 group 2, and 670 group 3. Group 1 had lower rates of tumor necrosis (p < 0.001), higher lymphatic or vascular invasion (p = 0.002), estrogen receptor-negative individuals, and HER2-positive individuals (p ≤ 0.001). Younger groups had more stage 3 BC (31.1, 25.6, and 19.7% for groups 1, 2, and 3, respectively) (p = 0.016), grade 3 BC (27.4, 20.6, and 16.5% for groups 1, 2, and 3, respectively) (p = 0.001), and grade 3 nucleus (43.1, 34.5, and 27.6% for groups 1, 2, and 3, respectively) (p < 0.001). Group 1 had higher rates of regional metastasis (4.7 vs. 1.5 and 2.1% for groups 2 and 3, respectively). Younger individuals had higher rates of brain metastasis (13.3, 5.4, and 1.1% for groups 1, 2, and 3, respectively). Moreover, those > 60 years old had more lung metastasis (33 vs. 12.6 and 6.7% for groups 2 and 1, respectively) (p < 0.001). Younger groups had more < 5-year recurrence (16.3, 11.7, and 8.9%, for groups 1, 2, and 3, respectively) (p = 0.023). CONCLUSION: Pattern and site of recurrence changes according to age in BC. This brings up the question whether age is an independent predictor of organ of metastasis or is site of metastasis the result of other clinicopathological determinants which differ between age groups.