The impact of flaxseed oil on lipid profiles, weight loss, and inflammatory markers in hemodialysis patients: A systematic review and meta-analysis of randomized controlled trials.

Studies investigating the effects of flaxseed oil on lipid profiles, weight loss, and inflammatory markers have produced inconsistent results. This systematic review and meta-analysis of randomized controlled trials (RCTs) aimed to explore the impact of flaxseed oil on these parameters in hemodialysis patients. The study protocol was registered online (PROSPERO number: CRD42023484076). The meta-analyses showed a significant decrease in triglyceride (TG) levels (WMD = -85.78 mg/dL, 95% CI: -155.24 to -16.32, I2 = 98.32%) and C-reactive protein (CRP) levels (WMD = -2.66 mg/L, 95% CI: -4.07 to -1.24, I2 = 92.26%) following consumption of flaxseed oil. Subgroup analyses revealed significant changes in LDL-C, HDL-C, and TC levels only in trials utilizing a dosage higher than 10 g per day and using ground flaxseed oil. Based on the results, flaxseed oil improves CRP and TG levels, and higher doses positively affect lipid profiles. However, it has no significant effect on anthropometric measures.

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.

Prevalence and factors associated with inter-arm systolic and diastolic blood pressure differences: results from the baseline Fasa Adult's Cohort Study (FACS).

BACKGROUND: One of the modifiable risk factors for cardiovascular diseases is the inter-arm blood pressure difference (IAD), which can be easily measured. This study aimed to determine the prevalence and factors related to the Iranian population's inter-arm differences in systolic and diastolic blood pressure. METHOD: This cross-sectional study was conducted on the baseline data of participants who had Iranian nationality, were at least 1 year of residence in the area, aged within the age range of 35-70 years, and willed to participate from the Fasa Persian Adult Cohort Study (FACS). IAD for systolic and diastolic blood pressure was measured and categorized into two groups of difference < 10 and ≥ 10 mmHg. Logistic regression was used to model the association between independent variables and IAD. RESULTS: The prevalence of systolic and diastolic IAD ≥ 10 mmHg was 16.34% and 10.2%, respectively, among 10,124 participants. According to the multivariable logistic regression models, age (adjusted odds ratio (aOR): 1.019 [95% CI: 1.013, 1.025]), body mass index (BMI) (aOR: 1.112 [95% CI: 1.016, 1.229]), having type 2 diabetes (aOR Yes/No: 1.172 [95% CI: 1.015, 1.368]), having chronic headaches (aOR Yes/No: 1.182 [95% CI: 1.024, 1.365]), and pulse rate (aOR: 1.019 [95% CI: 1.014, 1.024]) significantly increased the odds of systolic IAD ≥ 10 mmHg. Additionally, high socio-economic status decreased the odds of systolic IAD ≥ 10 mmHg (aOR High/Low: 0.854 [95% CI: 0.744, 0.979]). For diastolic IAD, age (aOR: 1.112 [95% CI: 1.015, 1.210]) and pulse rate (aOR: 1.021 [95% CI: 1.015, 1.027]) significantly increased the odds of diastolic IAD ≥ 10 mmHg. Moreover, high socioeconomic status decreased the odds of diastolic IAD ≥ 10 mmHg (aOR High/Low: 0.820 [95% CI: 0.698, 0.963]). CONCLUSION: The noticeable prevalence of systolic and diastolic IAD in general population exhibits health implications due to its' association with the risk of cardiovascular events. Sociodemographic and medical history assessments have potentials to be incorporated in IAD risk stratification and preventing programs.

Effects of flaxseed supplementation on weight loss, lipid profiles, glucose, and high-sensitivity C-reactive protein in patients with coronary artery disease: A systematic review and meta-analysis of randomized controlled trials.

This meta-analysis aimed to evaluate the effects of flaxseed supplementation on weight loss, lipid profiles, high-sensitivity C-reactive protein (hs-CRP), and glucose levels in patients with coronary artery disease (CAD). A systematic search was performed using various online databases, including Scopus, PubMed, Web of Science, EMBASE, and Cochrane Library, to identify relevant randomized controlled trials (RCTs) until June 2023. To evaluate heterogeneity among the selected studies, the Q-test and I2 statistics were employed. Data were combined using either a fixed- or random-effects model and presented as a weighted mean difference (WMD) with a 95% confidence interval (CI). Of the 428 citations, six RCTs were included. The pooled results did not show significant changes in the WMD of lipid factors (high-density lipoprotein cholesterol, triglycerides (TG), low-density lipoprotein cholesterol, and total cholesterol) following flaxseed intake. However, after performing a sensitivity analysis to determine the source of heterogeneity, flaxseed supplementation resulted in a significant decrease in TG levels (WMD = -18.39 mg/dL; 95% CI: -35.02, -1.75). Moreover, no significant differences were observed in either weight or BMI following flaxseed intake. However, the circulating levels of fasting blood glucose (WMD = -8.35 mg/dL; 95% CI: -15.01, -1.69, p = .01) and hs-CRP (WMD = -1.35 mg/L; 95% CI: -1.93, -0.77, p < .01) significantly decreased after the intervention. Flaxseed supplementation was associated with lowering FBS, hs-CRP, and TG levels but did not affect weight loss parameters and other lipid markers in CAD.

Volatile organic compounds analysis as promising biomarkers for Parkinson's disease diagnosis: A systematic review and meta-analysis.

OBJECTIVE: Researchers are investigating the potential of volatile organic compounds (VOCs) obtained from exhaled breath and sebum as non-invasive tools for early Parkinson's disease (PD) diagnosis. The present study aims to assess the feasibility of using VOC analysis for PD diagnosis and determine the overall diagnostic accuracy of the proposed tests. METHODS: We performed systematic searches based on the PRISMA guidelines to identify relevant studies on VOCs in PD diagnosis using exhaled breath or sebum samples. The selected articles were described, and meta-analysis was conducted on those that provided the sensitivity and specificity data. RESULTS: Out of 1268 articles initially identified, 8 met the inclusion criteria and provided specific sensitivity and specificity data for PD, which were included in the current meta-analysis. The pooled analysis of these findings showed a mean area under the receiver operating characteristic curve of 0.85, a sensitivity of 0.81 (95% confidence interval (CI): 0.72, 0.88), and a specificity of 0.76 (95% CI: 0.66, 0.84). CONCLUSION: The analysis of VOCs in exhaled breath and sebum has shown promise as a new avenue for non-invasive diagnosis of PD. VOCs' ability to distinguish PD from healthy controls suggests their potential clinical application in screening for the disease. Consequently, VOCs hold significant potential as biomarkers for PD diagnosis and offer a promising novel approach to identifying and diagnosing the condition.

The Therapeutic Values of IL-7/IL-7R and the Recombinant Derivatives in Glioma: A Narrative Review.

Interleukin-7 (IL-7) is essential for maintaining the immune system's defense functions by regulating the development and homeostasis of lymphocytes. Findings have shown the high efficacy of IL-7/IL-7 receptor (IL-7R)-based immunotherapy on various malignancies, with confirmation in both animal models and humans. In recent years, the progression-free survival and overall survival of patients suffering from gliomas significantly increased by introducing C7R-expressing chimeric antigen receptor (CAR)-T cells and long-acting IL-7 agonists such as NT-I7 (rhIL-7-hyFc, Efineptakin alfa). However, the effect of IL-7-based immunotherapies on the resistance of tumor cells to chemotherapy (when used simultaneously with chemotherapy agents) is still ambiguous and requires further studies. This article first reviews the pathophysiological roles of IL-7/IL-7R in tumors, focusing on gliomas. Subsequently, it discusses the therapeutic values of IL-7/IL-7R and the recombinant derivatives in gliomas.

Prediction of Mortality and Morbidity After Severe Traumatic Brain Injury: A Comparison Between Rotterdam and Richmond Computed Tomography Scan Scoring System.

OBJECTIVE: Accurate prediction of the morbidity and mortality outcomes of traumatic brain injury patients is still challenging. In the present study, we aimed to compare the predictive value of the Richmond and Rotterdam scoring systems as two novel computed tomography-based predictive models. METHODS: We retrospectively analyzed 1400 subjects who suffered from severe traumatic brain injury and were admitted to Emtiaz Hospital, a tertiary referral trauma center in Shiraz, south of Iran, from January 2018 to December 2019. We evaluated the 1-month results; considering two primary factors: mortality and morbidity. The patients' condition was the basis for this assessment. We conducted a logistic regression analysis to determine the association between scoring systems and outcomes. To determine the optimal threshold value, we utilized the receiver operating characteristic curve model. RESULTS: The mean age of participants was 36.61 ± 17.58 years, respectively. Concerning predicting the mortality rate, the area under the curve (AUC) for the Rotterdam score was relatively low 0.64 (95% confidence interval: 0.60, 0.67), while the Richmond score had a higher AUC 0.74 (0.71-0.77), which demonstrated the superiority of this scoring system. Moreover, the Richmond score was more accurate for predicting 1-month morbidity with AUC: 0.71 (0.69, 0.74) versus 0.62 (0.59, 0.65). CONCLUSIONS: The Richmond scoring system demonstrated more accurate predictions for the present outcomes. The simplicity and predictive value of the Richmond score make this system an ideal option for use in emergency settings and centers with high patient loads.

The association between nonalcoholic fatty liver disease and corrected QT interval prolongation among generally healthy Iranian population: Fasa Cohort Study (FACS).

BACKGROUND: There are limited studies about the association between nonalcoholic fatty liver disease (NAFLD) and corrected QT interval (QTc) prolongation worldwide. HYPOTHESIS: Therefore, we designed the current study to determine this association in a large cohort of a generally healthy population. METHODS: We analyzed the data of 4603 individuals aged 35-70 who participated in the Fasa Cohort Study (FACS). Based on 12-lead electrocardiograms, QT intervals were calculated and corrected by Bazzet's formula. A QTc interval of more than 430 ms in men and 450 ms in women was considered prolonged. The Fatty Liver Index was used to identify the participants with NAFLD. RESULTS: Of all participants, 1550 (33.6%) met the NAFLD criteria. In subjects of both genders with NAFLD, the mean values of the QTc interval were considerably higher than in those without NAFLD (p < .001). After adjusting for a wide range of confounders, including age, gender, smoking status, physical activity, total cholesterol, high-density lipoprotein-cholesterol levels, diabetes, and hypertension status, in linear regression analysis, the standardized ß coefficient of QTc interval among participants with NAFLD was 2.56 ms (95% confidence interval [CI]: 0.49-4.64). After controlling the same confounders, the odds ratio of NAFLD for a prolonged QTc interval in men was 1.47 (95% CI: 1.18-1.84; p < .001) and in women was 1.39 (95% CI: 1.15-1.68; p < .001) using logistic regression analysis. CONCLUSIONS: NAFLD was a risk factor for QTc interval prolongation. Awareness about the risk of NAFLD in increasing the potential cardiac arrhythmias should be raised to lower cardiac mortality.

Estimating the prevalence and incidence of treated type 2 diabetes using prescription data as a proxy: A stepwise approach on Iranian data.

AIMS: Type 2 diabetes is a serious health challenge, and large-scale studies on its prevalence in Iran are lacking. In pharmacoepidemiology, case-finding can be done by reviewing the prescription databases for specific drug(s) prescribed for a disease. We aimed to determine the prevalence and incidence of type 2 diabetes in Fars province, Iran, using prescription data and a stepwise approach to ascertain the results. METHODS: A dataset of 3,113 insured individuals aged ≥35 years were selected. Their Prescription Data Centre records were reviewed for all drugs frequently used in controlling type 2 diabetes available in the Iranian pharmacopeia. Then we used a stepwise method for case-finding. In step one, each individual with a positive drug history for type 2 diabetes was labeled as an individual with diabetes. The next two steps were implemented for ascertainment of step one estimations. RESULTS: Prevalence of type 2 diabetes based on prescription, internist opinion, and phone call verification in 2015 and 2016 was 9.3% and 10.3%, 8.5% and 9.8%, and 7.2% and 8.7%, respectively. An incidence of 1.9% was determined for 2016. CONCLUSIONS: We obtained a realistic estimation of prevalence and incidence of treated type 2 diabetes, using prescription data which are large-scale, low cost, and real-time.