A cohort study protocol to analyze the predisposing factors to common chronic non-communicable diseases in rural areas: Fasa Cohort Study

Association between body composition components and electrocardiogram parameters: results from the Fasa Adults Cohort Study (FACS)

OBJECTIVE

Cardiovascular diseases as the leading cause of death and disability are increasing worldwide. The present study aimed to investigate the association between total body water, total fat percent, and trunk fat percent with electrocardiogram (ECG) parameters in the Persian Fasa Adult Cohort Study (FACS).

METHODS

This cross-sectional study was performed on the baseline information obtained from 3217 35-70-year-old participants of the FACS, southern Iran. Various ECG and body composition parameters were obtained from the FACS baseline database. Independent t-test, Chi-square test and linear regression were used for data analysis.

RESULTS

Total body water (TBW) was positively associated with QRS length in men and women, and with PD and PR interval in women, exclusively. Additionally, in participants without chronic diseases (healthy group), TBW was shown to be positively associated with QRS length and PD; however, it was associated with QRS length and S wave in V3 lead (SV3) among those with type two diabetes, hypertension or ischemic heart disease (defined as unhealthy group). Total body fat percent (TBFP) was linearly associated with SV3 in unhealthy group. None of the studied ECG parameters was associated with TBFP in men, women and healthy group. Moreover, trunk fat percent was shown to be positive associated with R wave in aVL lead in women and unhealthy groups.

CONCLUSION

Body fat percentage and fat distribution may affect ECG parameters, and subsequently associated with cardiovascular diseases, such as arrhythmias. Thus, these indicators may carry the potential for screening high-risk individuals.

Recurrent Aphthous Stomatitis and Sleep Quality Factors: A Comprehensive Analysis

BACKGROUND

Due to the unclear etiology of recurrent aphthous stomatitis (RAS), a painful and distressing condition with a high prevalence, the researchers have hypothesized a connection between sleep quality and RAS.

METHODS

The cross-sectional study enrolled 10,138 Fasa Cohort Study participants aged 35-70 years. Various sleep quality factors were calculated and categorized based on the Pittsburgh Sleep Questionnaire.

RESULTS

Among 9030 subjects finally included with RAS prevalence of 20.2%, adjusted logistic regression showed significant odds ratios (ORs) in subjects who sleep < 5 h (OR = 1.44, 95%CI 1.25, 1.66), have sleep latencies of more than 60 min (OR = 1.37, 95%CI 1.11, 1.69), have sleep efficiencies of 65%-75% (OR = 1.55, 95%CI 1.21, 1.98), or regularly go to bed after 11 p.m. (OR = 1.23, 95%CI 1.11, 1.37). Subgroup analyses indicated no significant associations between RAS and various sleep factors in individuals who worked night shifts, and stronger associations were observed in men than women.

CONCLUSION

The study found a significant positive association between RAS and sleep quality factors such as shorter duration, lower efficiency, longer latency, later bedtime, and regular sleeping pill use. Establishing early and sufficient sleep and addressing sleep onset disturbances by adhering to sleep hygiene principles should be prioritized in individuals with RAS.

A Nonlinear Association of Body Mass Index and Fasting Blood Glucose: A Dose-Response Analysis From Fasa Adults Cohort Study (FACS)

Background and Aims

Obesity is associated with diabetes; however, the dose-response association between body mass index (BMI) and fasting blood glucose (FBG) remains unclear. This study aims to evaluate the dose-response relationship between BMI and the risk of prediabetes and diabetes among adults.

Methods

A total of 10,135 participants were enrolled in this study from the baseline profiles of the Fasa Adults Cohort Study (FACS) conducted between October 2014 and September 2016. Multivariable logistic regression model and restricted cubic spline (RCS) were applied to evaluate the dose-response relationship between BMI and the risk of prediabetes and ‎diabetes. Statistical ‎ analyses were performed using the software R (4.3.1), taking the significance level at 0.05.

Results

Findings indicated that after adjusting the confounding variables, the risk of diabetes was increased by increasing BMI (overweight: OR = 1.67, 95% CI = 1.36-2.06; obese: OR = 1.76, 95% CI = 1.37-2.26). The results of dose-response analysis displayed a nonlinear J-shaped association between BMI and the risk of diabetes (p trend < 0.001, nonlinear p < 0.001) and prediabetes (p trend < 0.001, nonlinear p < 0.049).

Conclusion

Based on our research, a higher BMI is a dose-dependent, independent risk factor for diabetes. As a result, prevention initiatives should think about emphasizing ongoing BMI modifications.

The influence of gender and waist circumference in the association of body fat with cardiometabolic diseases

BACKGROUND

The link between obesity and cardiometabolic risk has been well recognized. We investigated the association between body fat percentage (BF%), as an appropriate indicator of obesity, and prevalence of cardiometabolic diseases using baseline data of Fasa PERSIAN cohort study.

METHODS

The cross-sectional study was performed on data obtained at the first phase of the Fasa cohort study in Iran (n = 4658: M/F: 2154/2504). Anthropometric characteristics, blood pressure, cardiometabolic biomarkers, and body fat content were measured. Information on demographic and lifestyle factors, and history of cardiometabolic diseases (metabolic syndrome, type 2 diabetes, non-alcoholic fatty liver disease (NAFLD), and myocardial infarction) was obtained.

RESULTS

Cardiometabolic risk factors (body mass index, waist circumference, blood pressure, blood glucose and lipids) had an inverse association with BF% tertiles in both sexes. Women had a higher BF% and prevalence of metabolic diseases than men, but men demonstrated stronger associations between BF% and cardiometabolic diseases. In both sexes, the association between BF% and metabolic syndrome and NAFLD was stronger than that between BF% and diabetes and myocardial infarction. Addition of waist circumference to the confounders either weakened (for NAFLD and metabolic syndrome) or faded (for type 2 diabetes and myocardial infarction) the relationship of BF% and cardiometabolic diseases.

CONCLUSION

Strategies to decrease body fat may be effective in ameliorating the risk of milder metabolic diseases, such as metabolic syndrome, but interventions to decrease abdominal fat (estimated by waist circumference) may be more effective in preventing more serious metabolic disorders such as type 2 diabetes and myocardial infarction.

Socioeconomic inequality in hypertension and its determinants in people over 60 years in Fasa, southern Iran: a Blinder-Oaxaca decomposition

BACKGROUND

Hypertension (HTN) is well-known as a major risk factor for various noncommunicable diseases. Evidence indicates a link between socioeconomic status and the likelihood of developing HTN. A thorough comprehension of the inequalities in HTN is crucial for implementing evidence-based interventions. This study aimed to assess the socioeconomic disparities in HTN among people aged 60 and older in Fasa County, located in southern Iran.

METHODS

A total of 1,632 seniors, aged 60 and above, were included in the analysis, with data obtained from the Fasa Adults Cohort Study (FACS). Initially, we utilized both simple and multiple logistic regression models to investigate the associations between HTN and the determinant variables. The Blinder‒Oaxaca decomposition method was used to decompose the disparity between the impoverished and the wealthy.

RESULTS

The research indicated that elderly individuals with a higher risk of developing HTN were notably linked to factors such as socioeconomic status, education level, a history of coronary artery disease, employment status, smoking habits, body mass index (BMI), waist‒hip ratio (WHR), and physical activity (p < 0.05). The decomposition model revealed a significant gap in HTN rate between rich and poor individuals, with those in the lower income bracket having a 7.59% higher rate of HTN (p = 0.001). Additionally, the leading factors contributing to the greatest disparities in HTN among older adults from different socioeconomic backgrounds include education level (33.07%), employment status (12.78%), BMI (12.25%), physical activity (-15.02%), and WHR (-9.22%).

CONCLUSIONS

The decomposition model illustrated a significant disparity in HTN rates among various socioeconomic groups, with a higher rate observed in the lower-income demographic. The analysis revealed that a substantial part of the explained gap can be attributed to factors including education level, employment status, WHR, BMI, and level of physical activity. Nonetheless, a considerable portion of HTN inequality among older adults remains unexplained by the model, highlighting the necessity for additional research that includes a wider variety of factors and variables to gain a deeper insight into the root causes of these disparities.

Comparison of 10-year atherosclerotic cardiovascular disease (ASCVD) risk in metropolitan and rural areas of South of Iran

The first step to reducing the growing burden of cardiovascular disease (CVD) is to find modifiable risk factors with the highest burden in each population. Urban and rural citizens may have different priorities in this regard. This study aimed to compare the 10-year incidence probability of CVD events and its associated risk factors between rural and urban areas in Iran. Data was extracted from two big cohorts, Fasa Adults Cohort Study (FACS) and Shiraz Heart Study (SHS), with participation of over 12,000 general population. Linear regression models were used to test the difference in CVD risk between two populations. Totally, 6,258 FACS and 6,101 SHS participants entered the study. Urban participants had a significantly higher mean ASCVD score (4.43% vs. 5.51%, p-value < 0.001). Also, they significantly showed higher body mass index, waist circumference, cholesterol level, fasting blood glucose level, systolic blood pressure, educational attainment, and occupational status. However, the prevalence of smoking was higher in rural areas. Notably, socioeconomic parameters including marital, occupational, and educational statuses seem to have strong impact on cardiovascular risk factors. After adjustment for all confounders, living in the urban areas seemed to be associated with higher atherosclerotic CVD risk (β = 0.78, 95%CI: [0.69-1.05]), which was consistent across both sexes. Given the higher risk of cardiovascular events in urban areas and different profiles of risk factors between these two regions, preventive strategies should be precisely and separately designed for each population by the health authorities and policymakers in order to reduce the CVD toll efficiently.

Dietary antioxidant index and hypertension in the Iranian population: a nested case-control study within the Fasa adults cohort study

Introduction

Hypertension (HTN) is a prevalent condition associated with numerous cardiovascular and non-cardiac complications. Lifestyle interventions, including dietary adjustments, offer promising avenues for hypertension management. However, the precise relationship between dietary antioxidants and hypertension risk necessitates further investigation. This study aimed to elucidate the association between the Dietary Antioxidant Index (DAI) and hypertension risk using a nested case-control design.

Method

A matched nested case-control study was conducted within the Fasa Adult Cohort Study (FACS), comprising 975 participants aged 35-70 years. Cases (n = 325) were hypertensive patients, while controls (n = 650) were individuals without hypertension, matched for sex and age. Dietary intake was assessed using a validated food frequency questionnaire, and DAI was computed based on standardized intake of antioxidants. Conditional logistic regression models were employed to evaluate the association between DAI and hypertension risk, adjusting for confounding variables.

Results

A significant inverse correlation was observed between DAI and hypertension risk across all models (OR = 0.89; 95% CI = 0.86-0.93, p < 0.001). This association remained robust after adjusting for potential confounders, including BMI, smoking, lipid profile, blood glucose levels, and educational status. In conclusion, higher DAI values were associated with a reduced risk of hypertension, highlighting the potential benefits of antioxidant-rich diets in hypertension prevention.

Conclusion

These findings underscore the importance of dietary interventions as a complementary approach to hypertension management.

Determining the sensitivity and specificity of the calculated fatty liver index in comparison with ultrasound

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease in human history and it is expected to surpass other causes of liver disease mortality by 2030. Therefore, finding an alternative way to diagnose steatosis in the early stage when imaging modalities are not available is crucial. This study decided to validate the optimal cut-off points and the sensitivity and specificity of the Fatty Liver Index (FLI) based on the Iranian population compared to ultrasonography.

METHODS

The data of 367 individuals, 108 males and 259 females over 35, were analyzed. Hepatic steatosis was identified by ultrasound. FLI was determined from waist circumference, gamma-glutamyl transferase, triglyceride, and body mass index data. The receiver operating characteristic curve (ROC) was used to determine the best FLI index cut point for diagnosing nonalcoholic fatty liver. The sensitivity and specificity indices were calculated for the determined cut point.

RESULTS

The AUC of the FLI index in diagnosing NAFLD in the total population was 0.733 (95% CI: 0.68-0.77, specificity = 0.6705, sensitivity = 0.7320) with the optimal COP of 40.6. There was a statistically significant association between non-alcoholic liver disease and FLI-based ultrasound (p < 0.0001). Furthermore, the sex-specific optimal COPs of FLI was 33.4, specificity = 0.6071, sensitivity = 0.8462 in men vs. 27.8, sensitivity = 0.8233, specificity = 0.7655 in women.

CONCLUSION

FLI is a reliable tool for identifying individuals with NAFLD. It has the potential to aid in detecting and managing this condition in large-scale populations while other methods are not available. We also determine an optimal COP of 40.6 with sensitivity and specificity of 73.20% and 67.05% in the general population, respectively.

Association between diet protein score comprising plants to animal protein ratio and body composition in an Iranian population

Obesity is increasingly recognized as a leading cause of death and is associated with various comorbidities. This study evaluates the relationship between protein score, characterized by the plant-to-animal protein ratio (PAR) and total protein per calorie (Pro%), and body composition: fat percentage (FATP), fat mass (FATM), and fat-free mass (FFM). We categorized 4512 individuals (55.2% female) into tertiles based on their protein score and its components. Male participants in the highest and middle protein score tertiles exhibited significantly greater FFM in both adjusted and crude models, and lower FATP and FATM in adjusted model 2. FFM was elevated in the top (P < 0.001) and middle (P = 0.002) Pro% tertiles in males in both adjusted models and only in the top tertile of all models in females (P = 0.003). The analysis of male participants revealed significantly lower FATP and FATM in the highest tertiles of Pro% in adjusted models. Among female participants, only the highest PAR tertile was associated with significantly lower FATM in adjusted model 1 (P = 0.042). Our findings indicate that protein score and its components are associated with favorable body composition differences. Health administrators may leverage these insights to refine dietary guidelines.

The association between low carbohydrate dietary score (LCDS) and cardiovascular risk factors: results from the Shiraz Heart Study (SHS)

BACKGROUND

Cardiovascular disease (CVD) is a major cause of death and recent studies have highlighted the potential role of dietary carbohydrate indices in cardiovascular health. Given the controversial results in this field, the aim of this study was to investigate the association between low carbohydrate dietary score (LCDS) and CVD risk factors in a population of Iranian adults.

METHODS

This cross-sectional study was conducted within the framework of the Shiraz Heart Study (SHS) including 1982 adults. The validated 168 items food frequency questionnaire (FFQ) was used to assess participants' dietary intakes. To investigate the association between LCDS and cardiometabolic risk factors, logistic regression, was conducted.

RESULTS

During 5 years of follow-up, a total of 1982 adults, with a mean age of 53.07 ± 8.38 years, were included to the analysis. The adjusted model based on known confounding factors (age, sex, smoking, physical activity, energy intake and body mass index) revealed a significant decrease in a body shape index (ABSI) (OR = 0.70, 95% CI= (0.50 to 0.98), P = 0.038) comparing highest LCDS tertile vs. the reference. In contrast, risk of hypertension (HTN), body mass index (BMI), waist-hip ratio (WHR), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), atherogenic index of plasma (AIP), and fasting blood sugar (FBS) were not significantly associated with LCDS.

CONCLUSION

Current findings suggest that adherence to a low carbohydrate diet reduces ABSI, a main indicator of central obesity.

Office-based risk equation of Globorisk for prediction of ten-years cardiovascular risk among Iranian population: findings from Fasa PERSIAN cohort study

BACKGROUND

Globorisk is one of the prediction tools for 10-year risk assessment of cardiovascular disease, featuring an office-based (non-laboratory-based) version. This version does not require laboratory tests for determining the CVD risk. The present study aims to determine the 10-year CVD risk using the office-based Globorisk model and factors associated with the 10-year CVD risk.

METHODS

In this study, baseline data from 6810 individuals participating in the Fasa cohort study, with no history of CVD or stroke, were utilized. The risk equation of the office-based Globorisk model incorporates age, sex, systolic blood pressure (SBP), body mass index (BMI), and smoking status. The Globorisk model categorizes the risk into three groups: low risk (< 10%), moderate risk (10% to < 20%), and high risk (≥ 20%). To identify factors associated with the 10-year CVD risk, the predicted risk was categorized into two groups: <10% and ≥ 10%. Multivariable logistic regression analysis was employed to determine factors associated with an increased CVD risk.

RESULTS

According to the 10-year CVD risk categorization, 78.3%, 16.4%, and 5.3% of men were in the low, moderate, and high risk groups, respectively, while 85.8%, 10.0%, and 4.2%, of women were in the respective risk groups. Multivariable logistic regression results indicated that in men, the 10-year CVD risk decreases with being an opium user, and increases with being illiterate, having abdominal obesity, and low or moderate physical activity compared to high physical activity. In women, being married, and higher fiber consumption decrease the 10-year CVD risk, while being illiterate, low or moderate physical activity compared to high physical activity, having abdominal obesity, opium use, and being in wealth quintiles 1 to 4 compared to quintile 5 increase the risk.

CONCLUSIONS

Considering the factors associated with increased CVD risk, there is a need to enhance awareness and modify lifestyle to mitigate and reduce the risk of CVD. Additionally, early identification of individuals at moderate to high risk is essential for preventing disease progression. The use of the office-based Globorisk model can be beneficial in settings where resources are limited for determining the 10-year CVD risk.

Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population.

METHODS

Data from the prospective Fasa Cohort Study, initiated in rural Fars province, Iran (March 2014), were employed for this purpose. The required data were collected using blood tests, questionnaires, liver ultrasonography, and physical examinations. A two-step approach identified key predictors from over 100 variables: (1) statistical selection using mean decrease Gini in random forest and (2) incorporation of clinical expertise for alignment with known MASLD risk factors. The hold-out validation approach (with a 70/30 train/validation split) was utilized, along with 5-fold cross-validation on the validation set. Logistic regression, Naïve Bayes, support vector machine, and light gradient-boosting machine (LightGBM) algorithms were compared for model construction with the same input variables based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.

RESULTS

A total of 6,180 adults (52.7% female) were included in the study, categorized into 4816 non-MASLD and 1364 MASLD cases with a mean age (±standard deviation [SD]) of 48.12 (±9.61) and 49.47 (±9.15) years, respectively. Logistic regression outperformed other ML algorithms, achieving an accuracy of 0.88 (95% confidence interval [CI]: 0.86-0.89) and an AUC of 0.92 (95% CI: 0.90-0.93). Among more than 100 variables, the key predictors included waist circumference, body mass index (BMI), hip circumference, wrist circumference, alanine aminotransferase levels, cholesterol, glucose, high-density lipoprotein, and blood pressure.

CONCLUSION

Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies.

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

BACKGROUND

Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes.

METHODS

We employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN, Random Over Sampling and KMeansSMOTE, paired with Random Forest, Gradient Boosting, Decision Tree and Multi-Layer Perceptron (MLP) classifier. We evaluated model performance using F1 score, AUC, and G-means-metrics chosen to provide a comprehensive assessment of model accuracy, discrimination ability, and overall balance in performance, particularly in the context of imbalanced datasets.

RESULTS

our study uncovered key factors influencing diabetes risk and evaluated the performance of various machine learning models. Feature importance analysis revealed that the most influential predictors of diabetes differ between males and females. For females, the most important factors are triglyceride (TG), basal metabolic rate (BMR), and total cholesterol (CHOL), whereas for males, the key predictors are body Mass Index (BMI), serum glutamate Oxaloacetate Transaminase (SGOT), and Gamma-Glutamyl (GGT). Across the entire dataset, BMI remains the most important variable, followed by SGOT, BMR, and energy intake. These insights suggest that gender-specific risk profiles should be considered in diabetes prevention and management strategies. In terms of model performance, our results show that ADASYN with MLP classifier achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28.

CONCLUSION

These combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.

Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms

Cardiovascular disease (CVD) can often lead to serious consequences such as death or disability. This study aims to identify a tree-based machine learning method with the best performance criteria for the detection of CVD. This study analyzed data collected from 9,499 participants, with a focus on 38 different variables. The target variable was the presence of cardiovascular disease (CVD) and the villages were considered as the cluster variable. The standard tree, random forest, Generalized Linear Mixed Model tree (GLMM tree), and Generalized Mixed Effect random forest (GMERF) were fitted to the data and the estimated prediction power indices were compared to identify the best approach. According to the analysis of important variables in all models, five variables (age, LDL, history of cardiac disease in first-degree relatives, physical activity level, and presence of hypertension) were identified as the most influential in predicting CVD. Fitting the decision tree, random forest, GLMM tree, and GMERF, respectively, resulted in an area under the ROC curve of 0.56, 0.73, 0.78, and 0.80. The GMERF model demonstrated the best predictive performance among the fitted models based on evaluation criteria. Regarding the clustered structure of the data, using relevant machine-learning approaches that account for this clustering may result in more accurate predicting indices and targeted prevention frameworks.

High dietary antioxidant intake linked to lower risk of myocardial infarction: a nested case-control study

BACKGROUND

In developing nations, myocardial infarction (MI) remains a significant contributor to deaths from sudden cardiac arrest, with diet playing a key role in its incidence through oxidative stress mechanisms. Although the connection between the Dietary Antioxidant Index (DAI) and cardiovascular diseases has been demonstrated in some studies, the relationship between DAI and MI has not been extensively explored. Therefore, this research aims to investigate this association.

METHODS

We conducted a nested case-control study involving 156 MI cases and 312 healthy controls, utilizing data from the Fasa Adults Cohort Study (FACS), a population-based study of individuals aged 35-70 residing in Fasa, Iran, with 11,097 participants included at baseline. The DAI was determined by normalizing the intake values of six dietary vitamins and minerals, adjusting by subtracting the global mean, and then dividing by the global standard deviation. MI diagnosis was established by an experienced cardiologist using electronic medical records. Conditional logistic regression was employed to examine the association between DAI and MI.

RESULTS

There were no significant differences between the case and control groups in terms of age (P = 0.96), gender distribution (P = 0.98), and education level (P = 0.38). In a multiple conditional logistic regression analysis, after adjusting for key variables-including body mass index (BMI), smoking status, education level, and serum levels of triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), fasting blood sugar (FBS), saturated fatty acids (SFA), and polyunsaturated fatty acids (PUFA)-an inverse association was found between DAI and the risk of myocardial infarction (MI) [adjusted Odds Ratio (Adj OR) = 0.88, 95% Confidence Interval (CI): 0.85-0.92; P < 0.001].

CONCLUSIONS

This study highlights the crucial role of the DAI in reducing the risk of myocardial infarction. Promoting diets rich in antioxidants presents a straightforward and effective strategy for MI prevention and the promotion of cardiovascular health, underscoring the novelty and significance of this research in dietary approaches to disease prevention.

Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method

BACKGROUND

The worldwide prevalence of type 2 diabetes mellitus in adults is experiencing a rapid increase. This study aimed to identify the factors affecting the survival of prediabetic patients using a comparison of the Cox proportional hazards model (CPH) and the Random survival forest (RSF).

METHOD

This prospective cohort study was performed on 746 prediabetics in southwest Iran. The demographic, lifestyle, and clinical data of the participants were recorded. The CPH and RSF models were used to determine the patients' survival. Furthermore, the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curve were employed to compare the performance of the Cox proportional hazards (CPH) model and the random survival forest (RSF) model.

RESULTS

The 5-year cumulative T2DM incidence was 12.73%. Based on the results of the CPH model, NAFLD (HR = 1.74, 95% CI: 1.06, 2.85), FBS (HR = 1.008, 95% CI: 1.005, 1.012) and increased abdominal fat (HR = 1.02, 95% CI: 1.01, 1.04) were directly associated with diabetes occurrence in prediabetic patients. The RSF model suggests that factors including FBS, waist circumference, depression, NAFLD, afternoon sleep, and female gender are the most important variables that predict diabetes. The C-index indicated that the RSF model has a higher percentage of agreement than the CPH model, and in the weighted Brier Score index, the RSF model had less error than the Kaplan-Meier and CPH model.

CONCLUSION

Our findings show that the incidence of diabetes was alarmingly high in Iran. The results suggested that several demographic and clinical factors are associated with diabetes occurrence in prediabetic patients. The high-risk population needs special measures for screening and care programs.

Androgenic alopecia is associated with higher dietary inflammatory index and lower antioxidant index scores

Background

Androgenic alopecia (AGA), the most prevalent hair loss type, causes major psychological distress and reduced quality of life. A definite and safe cure/prevention for this condition is still lacking. The role of oxidative stress and inflammation in AGA pathogenesis prompted us to investigate the association between dietary antioxidant index (DAI) and energy-adjusted dietary inflammatory index (E-DII) with AGA.

Methods

The investigation was designed based on data from 10,138 participants from the Fasa Adult Cohort Study (FACS). DAI and energy-adjusted DII (E-DII) were calculated utilizing a validated 125-item food frequency questionnaire (FFQ). A physician diagnosed AGA. Logistic regression models were utilized to evaluate the association of DAI and E-DII with AGA.

Results

After exclusion, 9,647 participants (44.0% men, mean age: 48.6 ± 9.5 years) consisting of 7,348 participants with AGA entered the analyses. Higher DAI was associated with 10% lower AGA odds, while higher E-DII showed 4% higher AGA odds after adjusting for various confounding variables. However, significant associations were found only among women, and adjusting for metabolic syndrome (MetS) made the E-DII-AGA association insignificant.

Conclusion

Antioxidant-rich diets protect against AGA, while pro-inflammatory diets increase the risk, likely through developing MetS. Patient nutrition is frequently overlooked in clinical practice, yet it plays a crucial role, especially for women genetically predisposed to androgenetic alopecia. Dietary changes, such as reducing pro-inflammatory foods (like trans and saturated fats) and increasing anti-inflammatory options (fruits and vegetables), can help prevent hair loss and mitigate its psychological impacts, ultimately lowering future treatment costs.

Hypertension among persons with type 2 diabetes and its related demographic, socioeconomic and lifestyle factors in the Fasa cohort study

Hypertension is a very common comorbidity in type 2 diabetes patients, which leads to important health and treatment challenges. The present study was conducted with the aim of determining the prevalence of hypertension and its risk factors in type 2 diabetes patients. This study was conducted using cross-sectional data from 1245 participants aged between 35 and 70 years and diagnosed with type 2 diabetes at baseline in the Fasa cohort study. The prevalence hypertension was determined and multivariate logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between various risk factors and hypertension prevalence. The average age of the participants was 53.5 ± 8.7 years and 71.7% (n = 893) were female and 28.3% (n = 352) were male. The prevalence of hypertension in people with type 2 diabetes was 45.5% (n = 566). Higher age (AOR, 95% CI: 8.1, 4.6-14.3), female gender (OR, 95% CI: 1.8, 1.2-2.5), Fars (AOR, 95% CI: 1.6, 1.1-2.4) and Turk (AOR, 95% CI: 1.6, 1.1-2.5) vs. other ethnicity, and overweight (AOR, 95% CI: 1.8, 1.38-2.38) and obesity (AOR, 95% CI: 2.7, 2.0-3.8) vs. BMI < 25 was associated with a higher prevalence of hypertension, while higher physical activity (AOR, 95% CI: 0.57, 0.42-0.78) was associated with lower prevalence of hypertension in the multivariable model. The prevalence of hypertension in persons with type 2 diabetes was high and increased with greater age, in some ethnic groups, and with higher BMI and low physical activity. Further prospective studies are needed to investigate these associations in this population.

The association between energy-adjusted dietary inflammatory index and metabolic syndrome and its mediatory role for cardiometabolic diseases: a prospective cohort study

Background

Metabolic syndrome (MetS) is a collection of medical conditions that elevate the chance of cardiovascular disease. An unhealthy diet is a major risk factors for MetS through different mechanisms, especially systemic chronic inflammation.

Objective

This study aimed to investigate the effect of dietary inflammatory potential on MetS incidence and the role of MetS in the association between Energy-adjusted dietary inflammatory index (E-DII) and cardiometabolic diseases.

Methods

In this prospective cohort study, 10,138 participants were recruited. All participants were divided into MetS or non-MetS groups based on the Adult Treatment Panel III criteria. The E-DII was used to assess the inflammatory potential of diet. After excluding the participants with MetS at baseline, 2252 individuals were followed for 5 years (longitudinal phase), and the effect of E-DII on MetS incidence was investigated using logistic regression models (p-value <0.05).

Results

The cohort's mean age (45.1% men) was 48.6 ± 10.0 years. E-DII ranged from -6.5 to 5.6 (mean: -0.278 ± 2.07). Higher E-DII score had a 29% (95%CI: 1.22-1.36) increased risk for incidence of MetS and its components during five-year follow-up. Also, E-DII was significantly associated with the prevalence of MetS (OR = 1.55, 95%CI: 1.51-1.59). Among MetS components, E-DII had the strongest association with waist circumference in the cross-sectional study (OR = 2.17, 95%CI: 2.08-2.25) and triglyceride in the longitudinal study (OR = 1.19, 95%CI: 1.13-1.25). The association between E-DII and MetS was consistent in both obese (OR = 1.13, 95%CI:1.05-1.21) and non-obese (OR = 1.42, 95%CI: 1.27-1.60) individuals and stronger among non-obese participants. Additionally, MetS mediated the association between E-DII and hypertension, diabetes, and myocardial infarction.

Conclusion

In conclusion, a pro-inflammatory diet consumption is associated with a higher risk of MetS and its components. Furthermore, a pro-inflammatory diet increases the risk of cardiometabolic diseases. The higher E-DII had a stronger association with MetS, even among normal-weight individuals.

The association between energy-adjusted dietary inflammatory index and 10-year cardiovascular risk: Fasa adult cohort study

A healthy diet is dominant in cardiovascular disease (CVD) prevention. Inflammation is pivotal for CVD development. This study aimed to evaluate the association between the pro-inflammatory diet and the CVD risk. This cross-sectional study involved 10,138 Fasa adult cohort study participants. After excluding participants with missing data, the Energy-Adjusted Dietary Inflammatory Index (E-DII) was calculated to assess the inflammatory potential of diet using the recorded Food Frequency Questionnaire. Framingham risk score (FRS) was used to predict the 10-year risk of CVD. The association between E-DII and high risk for CVD was investigated using multinominal regression. After exclusion, the mean age of studied individuals (n = 10,030) was 48.6 ± 9.6 years, including 4522 men. Most participants were low risk (FRS <10%) for CVD (87.6%), while 2.7% of them were high risk (FRS ≥20%). The median FRS was 2.80 (1.70, 6.30). The E-DII ranged from -4.22 to 4.49 (mean E-DII = 0.880 ± 1.127). E-DII was significantly associated with FRS. This result persisted after adjusting for confounding factors and in both genders. This study revealed that the pro-inflammatory diet significantly increases the CVD risk. Consequently, reducing the inflammatory potential of diet should be considered an effective dietary intervention in CVD prevention.

Reply to Ghobadi and Jafari: diet quality in relation to the risk of hypertension among Iranian adults: cross-sectional analysis of Fasa PERSIAN cohort study

Ghobadi and Jafari have mentioned some points about our article titled "Diet quality in relation to the risk of hypertension among Iranian adults: cross-sectional analysis of Fasa PERSIAN cohort study" which was published in the Nutrition Journal. Thanks for their consideration, the following is provided as a response to their comments.

The High-Dietary Insulin Load Score Is Associated With Elevated Level of Fasting Blood Sugar in Iranian Adult Men: Results From Fasa PERSIAN Cohort Study

Aim: The potential of different foods to induce postprandial hyperinsulinemia may be involved in the development of metabolic syndrome (MetS). We aimed to evaluate the association between dietary insulin indices and MetS in a large population of adults in Iran. Methods: A total of 6356 adults aged 35-70 years were included in the present cross-sectional study. A validated block-format 125-item semiquantitative food frequency questionnaire (FFQ) was used to obtain usual food intakes, and MetS was defined according to the International Diabetes Federation (IDF) and American Heart Association (AHA)/National Heart, Lung, and Blood Institute (NHLBI) criteria. Results: MetS was prevalent in 13.8% of participants. Mean age of the study participants was 46.58 ± 8.82 years, and mean body mass index (BMI) was 25.02 ± 4.60 kg/m2. Mean dietary insulin index (DII) and dietary insulin load (DIL) were 63.15 ± 7.57 and 168.253 ± 52.09, respectively. In the crude model, men in the highest DIL quartile were more likely to have hyperglycemia than those in the lowest quartile (OR: 1.75, 95% CI: 1.12-2.73, p trend = 0.04). This association remained significant and was even stronger after adjusting for potential confounders in model I (OR: 3.64, 95% CI: 1.57-8.47, p trend = 0.005) and further adjustment for BMI in model II (OR: 3.61, 95% CI: 1.55-8.44, p trend = 0.006). Conclusions: In healthy men, adherence to a high-DIL diet may be associated with a greater likelihood of having hyperglycemia. No statistically significant association was observed between insulin indices and the odds of having MetS.

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.

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.

Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree

BACKGROUND

Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators.

METHODS

We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software.

RESULTS

Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD (P<0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models.

CONCLUSION

Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning.

Association between dietary total antioxidant capacity and the risk of stroke: a nested case-control study

BACKGROUND

Oxidative stress after ischemic stroke contribute to neuronal cell injury. Unhealthy and unbalanced dietary patterns can increase the risk of several diseases, including stroke and cardiometabolic ones. However, the association between dietary total antioxidant capacity (DTAC) of antioxidant and stroke is controversial. Our study aimed to establish a correlation between DTAC and its impact on the occurrence of stroke.

METHODS

This nested case-control study included 79 stroke cases and 158 healthy controls. We used data from the Fasa Adults Cohort Study (FACS) comprising 10,035 individuals at baseline. To assess the nutritional status of each individual, a 125-item food frequency questionnaire (FFQ) has been used to evaluate their dietary habits and intakes over the past year. DTAC was calculated using the ferric-reducing antioxidant power (FRAP) international databases. The stroke was confirmed by an experienced neurologist using standard imaging methods. Conditional logistic regression analyses were performed to evaluate the association between DTAC and stroke.

RESULTS

The assessment of DTAC revealed that there was no statistically significant distinction between cases (mean ± SD: 5.31 ± 2.65) and controls (5.16 ± 2.80) with a p-value of 0.95. Even after adjusting for the potentially important confounding factors such as age, sex, event time, energy intake, smoking, hypertension, and diabetes, the association remains non-significant (adjusted odds ratio (OR) = 1.06, 95% CI: 0.94, 1.20, p-value = 0.33).

CONCLUSIONS

Our results did not confirm a significant link between DTAC and stroke risk. These findings emphasize the intricate interplay of factors influencing stroke risk and highlight the need for further research to unravel these relationships more comprehensively.

Cohort profile: the PERSIAN Dena Cohort Study (PDCS) of non-communicable diseases in Southwest Iran

PURPOSE

This study conducted in Dena County is a population-based cohort study as part of the Prospective Epidemiological Research Studies in Iran (PERSIAN). The specific objectives of this study were to estimate the prevalence of region-specific modifiable risk factors and their associations with the incidence of major non-communicable diseases (NCDs).

PARTICIPANTS

This PERSIAN Dena Cohort Study (PDCS) was conducted on 1561 men and 2069 women aged 35-70 years from October 2016 in Dena County, Kohgiluyeh and Boyer-Ahmad Province, Southwest Iran. The overall participation rate was 82.7%.

FINDINGS TO DATE

Out of 3630 participants, the mean age was 50.16 years, 2069 (56.9%) were women and 2092 (57.6%) were rural residents. Females exhibited higher prevalence rates of diabetes, hypertension, fatty liver, psychiatric disorders, thyroiditis, kidney stones, gallstones, rheumatic disease, chronic lung disease, depression and osteoporosis compared with males (p<0.05). Furthermore, the urban population showed elevated rates of diabetes, thyroiditis, kidney stones and epilepsy, whereas psychiatric disorders and lupus were more prevalent in rural areas (p<0.05). According to laboratory findings, 418 (13.0%), 1536 (48.1%) and 626 (19.3%) of the participants had fasting blood sugar >126 mg/dL, low-density lipoprotein >100 mg/dL and haematuria, respectively; most of them were female and urban people (p<0.05).

FUTURE PLANS

PDCS will be planned to re-evaluate NCD-related incidence, all-cause and cause-specific mortality every 5 years, along with annual follow-up for 15 years. Some examples of additional planned studies are evaluation of genetic, environmental risk, spirometry and ECG tests.

Consumption of ultra-processed foods could influence the metabolic syndrome odds: A cross-sectional study

Metabolic syndrome (MetS) prevalence has augmented globally during recent decades. Over the past years, the consumption of ultra-processed foods (UPFs) has grown significantly worldwide. So, the present research investigated the association between UPFs and MetS in an Iranian sample. This cross-sectional research was conducted on people (n = 8841) in the Fasa cohort study, Fars province, Iran. The participants' dietary consumption over a year, UPF consumption, and MetS diagnosis were evaluated through a 125-item modified food frequency questionnaire, the NOVA food group classification, and the Adult Treatment Panel III of the National Cholesterol Education Program, respectively. The association between the quartiles (Q) of UPF intake and the odds of MetS was estimated using the backward LR method of multivariate analysis. In the multivariate model, after adjusting potential confounders, the association between UPF intake and the odds of MetS was significant (Q4: odds ratio (OR = 3.27; 95% confidence interval (CI): 2.76-3.89). Also, the odds of increasing triglycerides (TG), blood pressure, and fasting blood sugar (FBS) and decreasing high-density lipoprotein cholesterol (HDL-C) were significantly higher in the last quartile compared to the first quartile of UPFs (TG: OR = 1.71; 95% CI: 1.49-1.97, blood pressure: OR = 1.53; 95% CI: 1.30-1.79, FBS: OR = 1.30; 95% CI: 1.10-1.54, and HDL-C: OR = 1.22; 95% CI: 1.08-1.39). The current research found a relationship between UPF intake and MetS and its components, indicating a diet-containing UPFs can be related to the occurrence of noncommunicable diseases.

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.

Smoking, alcohol consumption, drug abuse, and osteoporosis among older adults: a cross-sectional study on PERSIAN cohort study in Fasa

BACKGROUND

With increasing life expectancy and a growing population of older adults, the prevalence of osteoporosis has risen, resulting in a higher incidence of bone fractures, which necessitate extended treatment and specialized medical care. This study investigates the relationship between smoking, alcohol consumption, drug abuse, and osteoporosis among older adults in southern Iran, utilizing cohort data.

METHODS

This cross-sectional study is derived from the Fasa Adult Cohort Study (FACS), which included 10,133 individuals. From this cohort, we selected 1,631 older adults using census sampling methods. Our study aimed to explore the correlation between smoking, alcohol consumption, and drug abuse among older adults and the incidence of osteoporosis. We collected demographic information, nutritional indexes, medical history, glucocorticoid usage, and self-reported data on smoking, alcohol consumption, drug abuse, and osteoporosis through questionnaires. To investigate the relationship between smoking, alcohol, and drug use with osteoporosis while accounting for confounding factors, we employed logistic regression analysis.

RESULTS

The average age of the study participants was 64.09 ± 3.8 years, with a majority (898 (55.1%)) being female. Osteoporosis prevalence among the subjects was 25.20%. The results did not reveal a significant correlation between smoking, alcohol consumption, drug abuse, and osteoporosis (p > 0.05). Regression analysis identified gender, recent history of fractures within the past five years, history of using glucocorticoids, and physical activity as significant predictive risk factors for osteoporosis within the study population (p < 0.05).

CONCLUSION

The study underscores the significance of addressing osteoporosis risk factors in older adults. Healthcare policymakers and administrators can use these findings to identify and mitigate influential factors contributing to osteoporosis in this demographic.

Association of Dietary Acid Load With Metabolic Syndrome and Its Components in Iranian Adults: A Cross-Sectional Study

Introduction Metabolic syndrome (MetS) remains one of the leading health challenges worldwide. A combination of genetic and environmental factors has been implicated in the etiology of MetS. Diet is a changeable environmental risk factor, and dietary modifications could significantly reduce the incidence and mortality of numerous diseases, including MetS. Certain dietary factors may contribute to MetS by affecting the acid-base balance within the body. This study examined the association of dietary acid load (DAL) with MetS and its components in Iranian adults. Materials and methods This cross-sectional study was conducted in 2022 on 6356 Iranian adults aged 35-70 years. Potential renal acid load (PRAL) and net endogenous acid production (NEAP) as two indicators of DAL were calculated based on nutrient intake data from validated food frequency questionnaires. MetS and its components were defined according to the Adult Treatment Panel III criteria. Logistic regression analysis was used to explore the associations between DAL and MetS and its components. Age, energy intake, physical activity, education, marital status, home ownership, socioeconomic status, history of obesity-related disease, and calcium supplements were included in model I. Further adjustment in model II was made for body mass index. Results Higher NEAP scores were associated with increased odds of low high-density lipoprotein cholesterol (HDL-C) in the crude model (OR: 1.26, 95% CI: 1.01-2.56, p trend = 0.06) in women, which was confirmed in the adjusted models. In model I, women in the last quintile of NEAP had 54% greater odds of having hypertriglyceridemia compared to the first quintile (OR: 1.54, 95% CI: 1.007-2.36, p trend = 0.02). This association was still significant and even stronger after further adjustment for BMI (OR: 1.55, 95% CI: 1.01-2.40, p trend = 0.01). In addition, in model I, men in the fourth quintile of NEAP had 5.68-fold greater odds of hyperglycemia compared to the first quintile (OR: 5.68, 95% CI: 1.18-27.25, p trend = 0.11). Similar results were found in the fully adjusted model (OR: 5.89, 95% CI: 1.19-28.99, p trend = 0.54). Conclusion There was no significant association between DAL and MetS. DAL was positively associated with the odds of low HDL-C and hypertriglyceridemia in women. Moreover, moderate DAL (NEAP) was associated with an increased odds of hyperglycemia in men.

Association between latent profile of dietary intake and cardiovascular diseases (CVDs): Results from Fasa Adults Cohort Study (FACS)

Cardiovascular diseases (CVDs) have been among the most significant non-communicable diseases. Dietary risks account for the most cause of CVDs mortalities. Evaluating overall dietary patterns (through the Latent profile of dietary intake) can provide a more accurate prediction regarding the prevalence of CVDs. The present cross-sectional study aimed to investigate the relationship between the latent profile of dietary intake and CVDs prevalence. The population of the Fasa Adults Cohort Study was employed to gather the data (n = 8319). A modified FFQ was employed to assess eating behaviors. Minerals, as well as the energy intake and total fiber, were measured using Nutritionist IV software (version 7.0). To estimate the prevalence of CVDs, accurate records of patients' histories were made. Individuals were clustered according to their dietary intake using latent profile analysis. The mean age was 48.75 ± 9.59 years, and 53.28% (4430) were women. 63.9% of participants with low Socioeconomic Status (SES) were in the low-intake profile (P < 0.001), and high SES increases the odds of being in the high-intake profile (ORhigh/low = 2.87, 95% CI 2.55-3.24). The low-intake group had the lowest amount of physical activity (Met) (P < 0.001). The result of multivariate logistic regression revealed that categorized in the low-intake group significantly increased the development of CVDs (OR = 1.32, 95% CI 1.07-1.63, P = 0.010). The mean micronutrients and total fiber, in individuals with a low intake profile, were significantly lower than other groups (P < 0.001). Overall, we estimated that a low intake of all food groups increases the odds of developing CVDs significantly.

A comparison of laboratory-based and office-based Framingham risk scores to predict 10-year risk of cardiovascular diseases: a population-based study

BACKGROUND

Two versions of Framingham's 10-year risk score are defined for cardiovascular diseases, namely laboratory-based and office-based models. The former is mainly employed in high-income countries, but unfortunately, it is not cost-effective or practical to utilize it in countries with poor facilities. Therefore, the present study aims to identify the agreement and correlation between laboratory-based and office-based Framingham models.

METHODS

Using laboratory-based and office-based Framingham models, this cross-sectional study used data from 8944 participants without a history of CVDs and stroke at baseline in the Fasa cohort study to predict the 10-year risk of CVDs. The laboratory-based model included age, sex, diabetes, smoking status, systolic blood pressure (SBP), treatment of hypertension, total cholesterol, and high-density lipoprotein (HDL); and the office-based model included age, sex, diabetes, smoking status, SBP, treatment of hypertension, and body mass index (BMI). The agreement between risk categories of laboratory-based and office-based Framingham models (low [< 10%], moderate [from 10 to < 20%], high [≥ 20%]) was assessed by kappa coefficients and percent agreement. Then, the correlation between the risk scores was estimated using correlation coefficients and illustrated using scatter plots. Finally, agreements, correlation coefficient, and scatter plots for laboratory-based and office-based Framingham models were analyzed by stratified Framingham risk score factors including sex, age, BMI categories, hypertension, smoking, and diabetes status.

RESULTS

The two models showed substantial agreement at 89.40% with a kappa coefficient of 0.75. The agreement was substantial in all men (kappa = 0.73) and women (kappa = 0.72), people aged < 60 years (kappa = 0.73) and aged ≥ 60 years (kappa = 0.69), smokers (kappa = 0.70) and non-smokers (kappa = 0.75), people with hypertension (kappa = 0.73) and without hypertension (kappa = 0.75), diabetics (kappa = 0.71) and non-diabetics (kappa = 0.75), people with normal BMI (kappa = 0.75) and people with overweight and obesity (kappa = 0.76). There was also a very strong positive correlation (r ≥ 0.92) between laboratory-based and office-based models in terms of age, sex, BMI, hypertension, smoking status and diabetes status.

CONCLUSIONS

The current study showed that there was a substantial agreement between the office-based and laboratory-based models, and there was a very strong positive correlation between the risk scores in the entire population as well across subgroups. Although differences were observed in some subgroups, these differences were small and not clinically relevant. Therefore, office-based models are suitable in low-middle-income countries (LMICs) with limited laboratory resources and facilities because they are more convenient and accessible. However, the validity of the office-based model must be assessed in longitudinal studies in LMICs.

Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling

Many factors can lead to an increase in the prevalence of metabolic syndrome (MetS) in different populations. Using an advanced structural equation model (SEM), this study is aimed to determine the most important risk factors of MetS, as a continuous latent variable, using a large number of males and females. We also aimed to evaluate the interrelations among the associated factors involved in the development of MetS. This study used data derived from the Fasa PERSIAN cohort study, a branch of the PERSIAN cohort study, for participants aged 35 to 70 years with 10,138 males and females. SEM was used to evaluate the direct and indirect effects, as well as gender effects of influencing factors. Results from the SEM showed that in females most changes in MetS are described by waist circumference (WC), followed by hypertension (HP) and triglyceride (TG), while in males most changes in MetS are described by WC, followed by TG then fasting blood glucose (FBG). Results from the SEM confirmed the gender effects of social status on MetS, mediated by sleep and controlled by age, BMI, ethnicity and physical activity. This study also shows that the integration of TG and WC within genders could be useful as a screening criterion for MetS in our study population.

A comparative assessment between Globorisk and WHO cardiovascular disease risk scores: a population-based study

The Globorisk and WHO cardiovascular risk prediction models are country-specific and region-specific, respectively. The goal of this study was to assess the agreement and correlation between the WHO and Globorisk 10-year cardiovascular disease risk prediction models. The baseline data of 6796 individuals aged 40-74 years who participated in the Fasa cohort study without a history of cardiovascular disease or stroke at baseline were included. In the WHO and Globorisk models scores were calculated using age, sex, systolic blood pressure (SBP), current smoking, diabetes, and total cholesterol for laboratory-based risk and age, sex, SBP, current smoking, and body mass index (BMI) for non-laboratory-based risk (office-based or BMI-based). In Globorisk and WHO risk agreement across risk categories (low, moderate, and high) was examined using the kappa statistic. Also, Pearson correlation coefficients and scatter plots were used to assess the correlation between Globorisk and WHO models. Bland-Altman plots were presented for determination agreement between Globorisk and WHO risk scores in individual's level. In laboratory-based models, agreement across categories was substantial in the overall population (kappa values: 0.75) and also for females (kappa values: 0.74) and males (kappa values: 0.76), when evaluated separately. In non-laboratory-based models, agreement across categories was substantial for the whole population (kappa values: 0.78), and almost perfect for among males (kappa values: 0.82) and substantial for females (kappa values: 0.73). The results showed a very strong positive correlation (r ≥ 0.95) between WHO and Globorisk laboratory-based scores for the whole population, males, and females and also a very strong positive correlation (r > 0.95) between WHO and Globorisk non-laboratory-based scores for the whole population, males, and females. In the laboratory-based models, the limit of agreements was better in males (95%CI 2.1 to - 4.2%) than females (95%CI 4.3 to - 7.3%). Also, in the non-laboratory-based models, the limit of agreements was better in males (95%CI 2.9 to - 4.0%) than females (95%CI 3.2 to - 6.1%). There was a good agreement between both the laboratory-based and the non-laboratory-based WHO models and the Globorisk models. The correlation between two models was very strongly positive. However, in the Globorisk models, more people were in high-risk group than in the WHO models. The scatter plots and Bland-Altman plots showed systematic differences between the two scores that vary according to the level of risk. So, for these models may be necessary to modify the cut points of risk groups. The validity of these models must be determined for this population.

Healthy beverage index could decrease odds of metabolic syndrome: A cross-sectional study

Some studies indicated that sugar-sweetened beverages (SSBs) were related to MetS risk, and others found no relationship between MetS and SSBs. Therefore, the present study aimed to investigate the relationship between healthy beverage index (HBI) and MetS in Iranian adults. This cross-sectional study was performed on baseline data FASA cohort. Out of 10,127 participants in the FASA cohort study, 8838 participants were included in this study. The National Cholesterol Education Program's Adult Treatment Panel (ATP) III was used for MetS definition. The HBI was calculated by a 125-item food frequency questionnaire with standard criteria. The association between HBI and MetS and its components was evaluated by univariate regression. Multivariate regression with the backward method was used for adjusting confounders. p < .05 was considered as statistically significant. Compared to the first quartile, it was observed that HBI in the last quartile was significantly related to lower odds of MetS in the multivariate analysis (odds ratio [OR] = 0.72; 95% confidence interval [CI]: 0.60-0.87, p < .001). Also, we observed a significant association between the last quartile of HBI with lower odds of high waist circumference (WC) (OR = 0.55; 95% CI: 0.45-0.67, p < .002). Our findings showed that the higher HBI score reduced MetS odds and WC. Therefore, to reduce the odds of MetS, a healthy pattern of beverage consumption, including drinking water, low-fat milk, unsweetened tea, and coffee, and reducing the consumption of SSB are recommended. More studies are needed to confirm the findings.

The relationship between pregnancy count and duration of breast-feeding with metabolic syndrome (Fasa Persian cohort study)

BACKGROUND

Changes that occur during pregnancy and after that during breastfeeding induce some symptoms similar to metabolic syndrome (MetS) risk factors. This study aims to determine the relationship between pregnancy, as well as the duration of breastfeeding and MetS controlling the effect of other risk factors like hypertension, glucose intolerance, triglyceride, central obesity, and reduction of high-density lipoprotein in women of Fasa Persian Cohort Study.

MATERIALS AND METHODS

In this cross-sectional study, 5015 women aged 35-70 years were investigated in the Sheshdeh region from 2016 to 2021, and the information related to the disease symptoms was collected through questionnaires, examinations, and laboratory tests. MetS was calculated based on two guidelines according to adult treatment panel III (ATP III) and international diabetes federation (IDF) methods. For reporting the data, the odds ratio with its 95% confidence interval was used. In order to eliminate the effect of confounders, logistic regression was used.

RESULTS

Prevalence of MetS showed a descending trend in women with up to two pregnancies and it reached 22.6% and 22.4% using ATPIII and IDF methods respectively, while with an increase in the number of pregnancies of more than two, MetS prevalence was ascending. The prevalence of MetS did not have any specific trend across various breastfeeding duration groups. Multivariate analysis approved that the odds ratio of developing MetS in comparison with women who had two pregnancies was significantly increasing trend when the pregnancy counts increased.

CONCLUSION

The chance of developing MetS based on both IDF and ATP III methods after adjustment for confounding effects would grow with an increase in the number of pregnancies to more than two and breast-feeding of more than seven years. It is recommended that women with more than two pregnancies or the long duration of breast-feeding women undergo a specialized examination to investigate and control MetS problems so that future diseases could be prevented.

Dietary inflammatory index (DII) is correlated with the incidence of non-alcoholic fatty liver disease (NAFLD): Fasa PERSIAN cohort study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a prevalent liver disease predisposing patients to life-threatening conditions, including cirrhosis. There is evidence that the incidence of NAFLD is related to the individuals' dietary patterns; however, it is still remaining unknown whether the inflammatory potential of various foods/dietary patterns can directly predict a higher incidence of NAFLD.

METHODS

In this cross-sectional cohort study, we investigated the relationship between the inflammatory potential of various food items and the incidence/odds of NAFLD. We used data from Fasa PERSIAN Cohort Study comprising 10,035 individuals. To measure the inflammatory potential of diet, we used the dietary inflammatory index (DII®). Fatty liver index (FLI) was also calculated for each individual to identify the presence of NAFLD (cut-off = 60).

RESULTS

Our findings showed that higher DII is significantly associated with increased incidence/odds of NAFLD (OR = 1.254, 95% CI: 1.178-1.334). Additionally, we found out that higher age, female gender, diabetes mellitus, hypertriglyceridemia, hypercholesterolemia, and hypertension are other predictors of developing NAFLD.

CONCLUSIONS

It can be concluded that consuming foods with a higher inflammatory potential is associated with a greater risk of developing NAFLD. Additionally, metabolic diseases, including dyslipidemia, diabetes mellitus, and hypertension, can also predict the incidence of NAFLD.

Body composition predicts hypertension using machine learning methods: a cohort study

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.

Diet quality and dietary acid load in relation to cardiovascular disease mortality: Results from Fasa PERSIAN cohort study

Dietary intake is a determining factor in the morbidity and mortality of chronic disorders. However, not many documents have investigated this relationship. The aim of this study was to evaluate the associations of the Mediterranean dietary score (MDS), Alternative Healthy Eating Index (AHEI), Dietary Inflammatory Index (DII), DASH score, and dietary acid load with cardiovascular disease (CVD) mortality. A total of 2158 CVD patients (mean age of 54.73 ± 8.62 years) from the Fasa cohort study, Iran, participated in the current study. Diet quality indices including DII, AHEI, MDS, DASH, and dietary acid load (NEAP score) were computed using a validated 125-item Food Frequency Questionnaire (FFQ). Cox regression analyses were used to determine HRs and 95% CIs. During a follow-up of 3 years, we documented 59 CVD deaths. After adjusting for relevant confounders (age, gender, family history of CVD, smoking, physical activity, alcohol intake, and HTN) in the final model, we found that higher DII scores and dietary acid load were significantly related to increased mortality due to CVD (HR = 1.11; 95% CI = 1.01-1.24; and HR = 1.02; 95% CI = 1.01-1.03). However, the DASH score was insignificantly associated with decreased CVD mortality by 20.4% (HR = 0.79; 95% CI = 0.57-1.09). There was no significant relationship among AHEI score, MDS, and CVD mortality. This study showed that increasing dietary acidity and the use of inflammatory food compounds could contribute to CVD mortality. Also, adherence to the DASH diet may be associated with reduced CVD mortality.

Laboratory-based and office-based Globorisk scores to predict 10-year risk of cardiovascular diseases among Iranians: results from the Fasa PERSIAN cohort

Background

Globorisk is a novel risk prediction model for predicting cardiovascular disease (CVD). Globorisk is a country-specific risk prediction model that determines CVD risk for all countries. This model has two versions; laboratory-based and office-based. This study aimed to determine the agreement between laboratory-based and office-based models in a large sample of the general population.

Methods

Baseline data from the Fasa cohort study was used for the current study. In total, 6810 participants ≥ 40 years without any history of cardiovascular disease or stroke were included in the study. To determine the laboratory-based risk model, factors include age, sex, current smoking status, history of diabetes, systolic blood pressure (SBP), and total cholesterol. To estimate the office-based risk model, factors were age, sex, current smoking status, SBP, and body mass index (BMI). Kappa statistics was used to distinguish the agreement between grouped scores in these two models. Additionally, correlation coefficients and scatter plots were used to determine the linear correlation between the two models.

Results

In this study 46.53% of the participants were men. The mean age (SD) of participants was 51.08 (7.88) years. Agreements between the two models were moderate and substantial in all women and all men, respectively. The agreement between the two CVD risk groups was 90.15% (kappa = 0.717) in all men, 92.94% (kappa = 0.571) among men aged < 60 years and 77.60% (kappa = 0.645) in men aged ≥ 60 years. The agreement between the two CVD risk groups was 86.68% (kappa = 0.572) among all women, 93.96% (kappa = 0.274) among women aged < 60 years and 62.46% (kappa = 0.422) among women aged ≥ 60 years. A very strong positive correlation (r = 0.94) was found between the two risk scores in all men, and it was similar among men aged < 60 years (r = 0.84) and men aged > 60 years (r = 0.94). Among all women, there was a very strong positive correlation (r = 0.87), and the strong positive correlation remained among < 60 years old (r = 0.76) and women > 60 years old (r = 0.76).

Conclusion

The Globorisk office-based model which is easier to use as it does not require blood testing can determine the risk groups in this population. The Globorisk office-based model may be used for CVD risk screening in low-middle income countries where resources are limited.

Interleukin-33 and Soluble ST2 as Potential Biomarkers of Cancer in Opium Users: A Nested Case-Control Study

BACKGROUND

Opium abuse is one of the social hazards in the Middle Eastern countries. Opium consumption attributes to various malignancies. However, the exact molecular mechanism of this correlation still remains unclear. Cancer and inflammation are closely correlated. Interleukin-33 (IL-33) and its receptors, transmembrane ST2 (ST2L) and soluble ST2 (sST2), have been significantly associated with tumorigenicity. The present study aimed to investigate whether IL-33 and sST2 levels serve as cancer biomarkers in opium users.

METHODS

Serum samples were collected from 100 opium users and 100 healthy non-opium users in a nested case-control design. The subjects with over five years of history of opium abuse were enrolled. To assess the incidence of malignancies, the opium users were followed up from 2014 to 2019. Serum levels of IL-33 and sST2 were measured using an ELISA kit. For comparison of IL-33 and sST2 levels between the groups, two-tailed Student's t test and Mann-Whitney U test were utilized, accordingly. Logistic regression analysis was performed to evaluate the influence of confounders on the incidence of cancer.

RESULTS

During the five-year follow-up, eight opium users were diagnosed with cancer. Cancer was developed by 9.3 folds in the individuals abusing opium compared to that in the non-opium users (P=0.040, OR=9.3; 95%CI [1.1-79.4]). Serum levels of IL-33 were found to be significantly higher in the opium users than those in the healthy control group (P=0.001). The sST2 levels were significantly lower in the opium users (P=0.001). The opium users with cancer exhibited significantly higher levels of IL-33 and lower levels of sST2 than the cancer-free ones (P=0.001).

CONCLUSION

Decline in sST2 levels and rise in the level of IL-33 are valuable biomarkers in predicting cancers. Regarding the significant alterations in the levels of these biomarkers in the opium users, as well as those in the opium users diagnosed with cancer, IL-33 and sST2 may serve as potential biomarkers in the early prediction of cancer.

Non-alcoholic fatty liver disease and dairy products consumption: Results from FASA Persian cohort study

Background/objectives

There are limited data on the association between dairy products consumption and nonalcoholic fatty liver disease (NAFLD). This study was conducted to evaluate the association between total intake of different dairy products and fatty liver index (FLI), a marker of subclinical fatty liver.

Methods

A total of 7,540 adults were included in this population-based cohort study. Dairy products consumption was evaluated by a validated interview questionnaire for food intake frequency. The FLI was calculated using the standard formula. Liver enzyme levels, lipid profiles, glycemic profiles and demographic characteristics were recorded for all participants. Univariate and multiple logistic regression models were used to respectively assess the mean percentage difference of mean FLI and odds ratios (ORs) for subclinical NAFLD across quantiles of dairy consumption.

Results

The mean age of all participants was 48.81 ± 9.631 years. FLI measurements for men and women were 26.71 ± 23.39 and 39.99 ± 26.64 respectively, which was significantly higher in women (P < 0.05). Multiple logistic regression analysis demonstrated that the amount of milk consumption was an independent preventive predictor of FLI (OR = 0.96; 95% CI: 0.94–0.99), conversely, it did not predict higher levels of liver enzymes. In term of cheese intake, participants in the third tertile of cheese intake had significantly lower FLI than lower tertiles (P = 0.01). However, there wasn't any significant association between cheese intake and the odds of FLI in the multivariate model (P > 0.05). We didn't find any significant association between yogurt consumption and NAFLD indicators (P > 0.05).

Conclusion

Higher milk consumption was inversely associated with FLI. However, there wasn't any significant association between other types of dairy products and NAFLD indicators.

Prevalence of dyslipidemia and its association with opium consumption in the Rafsanjan cohort study

The potential effects of opium consumption on lipid profile remain unquantified. We considered the association between opium use and dyslipidemia. In this cross-sectional study, we used data obtained from the Rafsanjan cohort study, as a part of the prospective epidemiological research studies in IrAN (PERSIAN) with detailed and validated data on opium consumption and selected other exposures. A total of 9932 adults were included in the study. Logistic regression models were used to assess the relationships of opium consumption with the prevalence of dyslipidemia and lipid disorders. In this population, 73.33% had dyslipidemia and the prevalence rates of high TC, high TG, high LDL and low HDL were 54.24%, 47.45%, 34.43% and 11.91% respectively. After adjustment for all confounders, opium users compared with non-users had lower odds ratios (OR) of high TC and high LDL [0.81 (95% confidence interval, CI 0.71–0.92) and 0.80 (95% CI 0.69–0.93) respectively] and greater OR of low HDL [1.30 (95% CI 1.04–1.62)]. Longer duration of opium consumption resulted in lower ORs of high TC, 0.68 (95% CI 0.55–0.84) and high LDL, 0.82 (95% CI 0.67–0.99), and shorter duration of opium consumption resulted in increased odds of low HDL, 1.30 (95% CI 1.02–1.66). High dose of opium consumption was associated with an OR of dyslipidemia of 0.80 (95% CI 0.65–0.97), high TC of 0.80 (95% CI 0.67–0.95), and high LDL of 0.78 (95% CI 0.64–0.96) and low dose of opium consumption, with an OR of low HDL of 1.30 (95% CI 1.02–1.65). In relation to route of consumption, opium smoking was a risk factor for low HDL with an adjusted odds ratio of 1.31 (1.04–1.63). Opium use was associated with selected changes on serum lipid levels, but opium users had higher frequency of cardiovascular disease history.

Evaluating type and amount of dietary protein in relation to metabolic syndrome among Iranian adults: cross-sectional analysis of Fasa Persian cohort study

BACKGROUND AND AIM

Metabolic syndrome is characterized by several conditions including abdominal obesity, dyslipidemia, impaired glucose levels, and hypertension; which all are directly associated with an increased risk of cardiovascular disease and type 2 diabetes mellitus. This study aimed to evaluate the association of the amount and source of dietary protein with the risk of metabolic syndrome and its components in adult men and women.

MATERIAL AND METHOD

This study was performed using Persian cohort data, Fasa branch, which included 3660 men and 5262 women. Individuals were divided into five groups for total dietary proteins, plant proteins, and animal proteins using the quintiles, and the odds of incidence for metabolic syndrome and each component was evaluated.

RESULTS

A higher intake of total protein was associated with lower odds of having metabolic syndrome (OR: 0.24 95% CI 0.18-0.33, P-trend < 0.001 in men, OR: 0.42 95% CI 0.34-0.51, P-trend < 0.001 in women) and all of its components. men (OR: 0.35 95% CI 0.25-0.48, P-trend < 0.001) and women (OR: 0.41 95% CI 0.33-0.52, P-trend < 0.001) in the highest quintile of plant protein intake had a reduced prevalence of metabolic syndrome and its components. An increased animal protein intake was associated with a lower prevalence of metabolic syndrome (P-trend < 0.001), a declined risk of having elevated triglycerides (P-trend = 0.016) in men, and a reduced risk of having abdominal obesity in men (P-trend < 0.001) and women (P-trend < 0.001).

CONCLUSION

A higher consumption of total protein and plant protein was associated with a lower prevalence of metabolic syndrome and its components. Increased consumption of animal protein seemed to be related to a lower prevalence abdominal obesity. Also, only in men, animal protein was associated with decreased risk of having metabolic syndrome.

The best QT correction formula in a non-hospitalized population: the Fasa PERSIAN cohort study

Background

QT interval as an indicator of ventricular repolarization is a clinically important parameter on an electrocardiogram (ECG). QT prolongation predisposes individuals to different ventricular arrhythmias and sudden cardiac death. The current study aimed to identify the best heart rate corrected QT interval for a non-hospitalized Iranian population based on cardiovascular mortality.

Methods

Using Fasa PERSIAN cohort study data, this study enrolled 7071 subjects aged 35–70 years. Corrected QT intervals (QTc) were calculated by the QT interval measured by Cardiax® software from ECGs and 6 different correction formulas (Bazett, Fridericia, Dmitrienko, Framingham, Hodges, and Rautaharju). Mortality status was checked using an annual telephone-based follow-up and a minimum 3-year follow-up for each participant. Bland–Altman, QTc/RR regression, sensitivity analysis, and Cox regression were performed in IBM SPSS Statistics v23 to find the best QT. Also, for calculating the upper and lower limits of normal of different QT correction formulas, 3952 healthy subjects were selected.

Results

In this study, 56.4% of participants were female, and the mean age was 48.60 ± 9.35 years. Age, heart rate in females, and QT interval in males were significantly higher. The smallest slopes of QTc/RR analysis were related to Fridericia in males and Rautaharju followed by Fridericia in females. Thus, Fridericia’s formula was identified as the best mathematical formula and Bazett’s as the worst in males. In the sensitivity analysis, however, Bazett’s formula had the highest sensitivity (23.07%) among all others in cardiac mortality. Also, in the Cox regression analysis, Bazett’s formula was better than Fridericia’s and was identified as the best significant cardiac mortality predictor (Hazard ratio: 4.31, 95% CI 1.73–10.74, p value = 0.002).

Conclusion

Fridericia was the best correction formula based on mathematical methods. Bazett’s formula despite its poorest performance in mathematical methods, was the best one for cardiac mortality prediction. Practically, it is suggested that physicians use QTcB for a better evaluation of cardiac mortality risk. However, in population-based studies, QTcFri might be the one to be used by researchers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02502-2.

Oral contraceptive pill and metabolic syndrome: Fasa Persian Cohort Study (Descriptive study)

BACKGROUND AND AIMS

The contraceptive use is 56% and prevalence of metabolic syndrome (MetS) is 30% in Iran. The aim of this study investigates relationship between oral contraceptive pills (OCP) use and MetS in women in the cohort population of Fasa city.

METHODS

In a cross-sectional study, 5489 women aged 35-70 years were studied for 5 years in the Sheshdeh area in 2016. MetS were calculated using adult treatment panel III (ATP III) and international diabetes federation (IDF) methods. The odds ratio (OR) with a 95% confidence interval (CI) was reported. Linear regression was used to eliminate the confounding effect.

RESULTS

The OR of developing MetS in the OCP recipients was estimated as higher than the non-recipients. The OR of developing MetS using IDF criteria in OCP recipients was (OR = 0.896,95% CI:0.800-1.004). that increased to (OR = 1.230,95% CI:1.084-1.395) after adjusting for confounding variables. Also, the odds ratio using ATP (III) criteria was (OR = 0.900,95% CI:0.804-1.009). that increased to (OR = 1.245,95% CI:1.098-1.413) after adjusting for confounding variables. Also, the OR of developing MetS in OCP recipients decreased with increasing the number of MetS components from 1.199 to 0.812,95% CI:0.771-1.864,0.467-1.413, but after adjustment, increased from 1.151 to 1.747,95% CI:0.733-1.805,0.815-3.746.

CONCLUSION

The results of the present study showed that the OR of developing MetS in OCP recipients using both IDF and ATP (III) methods was higher after adjusting by confounder effects so it is recommended monitoring by physicians.

Examining the association between opium use, cigarette smoking and alcohol consumption with the liver enzyme levels in a population-based study: Fasa Persian cohort data

Objectives

Opium use, cigarette smoking, and alcohol consumption are serious health problems in many countries including Iran. The present study aimed to examine the association between the opium use, cigarette smoking and alcohol consumption with liver enzyme levels in Southern Iran. This analytical cross-sectional study was conducted in 2020. The entire population of the Fasa Persian cohort study in the southern region of Iran was selected as the sample. Accordingly, 10,145 people participated in the study.

Results

Results indicated that there was a significant relationship between cigarette smoking and alcohol consumption with liver enzymes (AST, ALT, and ALP). There was also a significant relationship between inhaled opium and liver enzymes, but oral opium revealed no significant relationship with the activity of liver enzymes. Accordingly, policymakers of the health care system are recommended to hold educational programs to improve the health literacy level of the society and take effective preventative strategies in reducing the use of these substances.