The association between dietary patterns and metabolic syndrome among Iranian adults, a cross-sectional population-based study (findings from Bandare-Kong non-communicable disease cohort study).

BACKGROUND: Metabolic syndrome is a cluster of metabolic disorders increasing the risk of cardiovascular disease and diabetes. Dietary patterns are supposed to be important and controllable factors in developing metabolic syndrome. The purpose of this study was to investigate the association of dietary patterns with metabolic syndrome and its components. SUBJECTS/METHODS: Cross-sectional data were extracted from the Bandare-Kong cohort study conducted on 4063 people aged 35 to 70. Dietary patterns were extracted using principal component analysis based on thirty-eight pre-defined food groups. Multivariable logistic regression was conducted to investigate the association between metabolic syndrome and its components with quintiles of dietary patterns in crude and adjusted models. RESULTS: Three major dietary patterns were identified (healthy, western, and traditional) in the final analysis of 2823 eligible individuals. After adjusting for covariates, the odds of metabolic syndrome were significantly decreased by 46% in subjects with the highest adherence to the healthy dietary pattern compared to those with the lowest adherence quintile. Results from fully adjusted models on individual metabolic syndrome components showed an inverse association between higher adherence to the healthy dietary pattern and the odds of increased blood glucose, high waist circumference, and elevated blood pressure. However, in fully adjusted models, no significant association was observed between the western and traditional dietary patterns with odds of metabolic syndrome and its components. CONCLUSIONS: Adherence to a healthy dietary pattern containing high amounts of fruits, vegetables, nuts, low-fat dairy products, and legumes, could be recommended to prevent and control metabolic syndrome.

Development of a Modified Friedewald's Formula to Calculate Low-Density Lipoprotein in an Iranian Population.

Background: Elevated low-density lipoprotein cholesterol (LDL-C) is a significant risk factor for cardiovascular diseases. LDL-C can be directly measured using various methods, but this requires expensive equipment. Currently, clinical laboratories estimate LDL-C based on Friedewald's formula (FF). We aimed to develop a modified formula based on directly measured LDL-C (D-LDL-C) values in a large population in Southern Iran and compare the results with various other estimation formulas. Methods: The participants of this cross-sectional study were adults aged >18 years living in Southern Iran. Blood samples from 15,200 individuals were collected, and the measured lipid parameters were randomly divided into training (n=10,184) and validation (n=5,016) datasets. A new formula was developed using a linear regression model, and its accuracy was validated. Pearson's correlation and Cohen's kappa were used to determin the relationship between D-LDL-C and calculated LDL-C (C-LDL-C). Results: The developed formula for the estimation of LDL-C was 0.857 total cholesterol (TC)-0.915 high-density lipoprotein cholesterol (HDL-C)-0.115 triglycerides (TG). Based on our proposed formula, for TG<150 and TG≥150 mg/dL, there was a significant correlation between mean values of D-LDL-C and C-LDL-C (r=0.985 and r=0.974, respectively). Compared to other formulas, C-LDL-C obtained from the proposed formula had the highest correlation with D-LDL-C. The agreement between D-LDL-C and C-LDL-C for TC<200, 200-239, and ≥240 mg/dL was 80.8%, 63.2%, and 67.4%, respectively, indicating a higher level of agreement than other formulas. Conclusion: The new formula appears to be more accurate than FF when applied to the population of Southern Iran.

Cut-off points for anthropometric indices to screen for hypertension among Iranian adults of the Bandare-Kong cohort: a cross-sectional study.

BACKGROUND: Obesity is one of the major determinants of blood pressure. This study aimed to determine the optimal sex- and age-specific cut-off points of anthropometric indices, including body mass index (BMI), waist circumference (WC), hip circumference (HC), wrist circumference (WrC), waist-hip ratio (WHR), and waist-height ratio (WHtR), to screen for hypertension (HTN) in a cohort of Iranian adults aged 35 to 70 years, and to compare the predictive performance of the indices based on receiver operating characteristic (ROC) curves. METHODS: This population-based study was carried out on the participants aged 35 to 70 years of the Bandare-Kong Non-Communicable Diseases (BKNCD) Cohort Study, a part of the Prospective Epidemiological Research Studies in IrAN (PERSIAN). Using the area under the receiver operating characteristic curve (AUC) and Youden's J index, optimal sex- and age-specific cut-off points of the anthropometric indices in association with HTN were calculated. RESULTS: This study included a total of 2256 females and 1722 males. HTN was diagnosed in 736 females (32.6%) and 544 males (31.6%). The optimal cut-off of WC for HTN was 90 cm in males and 95 cm in females, with an area under the ROC curve (AUROC) of 0.60 and 0.64, respectively. For HC, the optimal cut-off was 95 cm for males and 108 cm for females (AUROC = 0.54 for both). Moreover, WrC optimal cut-offs were 17 cm for males (AUROC = 0.56) and 15 cm for females (AUROC = 0.57). As for BMI, the optimal cut-off was 25 kg/m2 in males and 27 kg/m2 in females (AUROC of 0.59 and 0.60, respectively). Also, a cut-off of 0.92 was optimal for WHR in males (AUROC = 0.64) and 0.96 in females (AUROC = 0.67). On the other hand, WHtR optimal cut-offs were 0.52 for males and 0.60 for females (AUROC of 0.63 and 0.65, respectively). CONCLUSIONS: WHR and WHtR, as anthropometric indices of obesity, were demonstrated to be significant predictors of HTN. Further, we suggest using WHR (cut-off point of 0.92 for males and 0.96 for females) and WHtR (cut-off point of 0.52 for males and 0.60 for females) as measures of preference to predict HTN among the southern Iranian population. Further multicenter longitudinal studies are recommended for a more accurate prediction of HTN.

Metabolic syndrome severity score in the middle-aged and elderly Iranian population: A cross-sectional survey of Bandare-Kong Cohort Study (the findings of PERSIAN Cohort Study).

Background: Metabolic syndrome (MetS) is defined as the presence of several metabolic risk factors. The traditional MetS criteria have been considered insufficient for evaluating individuals at risk. MetS has always been categorized using binary criteria, which deny that the risk associated with MetS is likely to exist as a continuum. Also, MetS may present differently depending on age, sex, race, or ethnicity. We aimed to derive age-sex-specific equations for MetS severity scores within a southern Iranian population. Methods: This study used first-phase data from the Bandare-Kong Non-Communicable Diseases (BKNCD) Cohort Study as part of the Prospective Epidemiological Research Studies in IrAN (PERSIAN). After exclusion of the pregnant women, diabetic patients, and individuals taking antihypertensive, antihyperlipidemic, and antidiabetic medications, 2,735 individuals aged 35 to 70 years were selected for analysis. The diagnosis of MetS was based on the National Cholesterol Education Program (NCEP) criteria for the Iranian population. Confirmatory factor analysis (CFA) was performed to formulate MetS severity scores. The receiver operating characteristic (ROC) analysis was performed to validate MetS severity score equations for age-sex-specific categories. Results: Triglyceride had the highest factor loading range in all age-sex categories for determining the MetS severity score. Conversely, systolic blood pressure and fasting plasma glucose (FPG) exhibited the lowest factor loadings across all age-sex groups. In both sexes, when age was considered, systolic blood pressure and FPG factor loadings were less significant among subjects aged ≥45 and 35-44 years, respectively. Conclusion: MetS severity scores might be more applicable than the current criteria of MetS. Prospective population-based studies should be conducted to assess the accuracy and validity of the MetS severity score for predicting cardiometabolic diseases.

The association of sleep duration and metabolic syndrome in the Bandare-Kong cohort study, a cross-sectional survey (finding from PERSIAN cohort study).

BACKGROUND: A variety of health problems, such as metabolic syndrome (MetS), have been linked to sleep disorders. While numerous epidemiological studies have shown a U-shaped relationship between sleep duration and poor health outcomes, the results were limited and inconsistent. This study was designed to evaluate the relationship between sleep duration and MetS. METHODS: This population-based study was conducted on the participants aged 35-70 of Bandare-Kong Non-Communicable Diseases (BKNCD) Cohort Study, a part of Prospective Epidemiological Research Studies in IrAN (PERSIAN). MetS was diagnosed according to the National Cholesterol Education Program (NCEP) criteria and the Iranian-specific cut-off for waist circumference (≥ 95 cm). Sleep information was extracted through a standard questionnaire based on self-reported information. Data were analyzed by R software using generalized additive models (GAMs). A statistically significant level was considered as P < 0.05. RESULTS: A total of 3695 participants were included in the analyses. The mean age was 48.05 years (SD 9.36), and 2067 (55.9%) were female. The estimated Prevalence of MetS was 35.9%, and women appeared to be more likely to have MetS than men (P < 0.001). There was a non-linear and linear association between sleep duration and the risk of MetS in women and men, respectively. The lowest risk was observed among those with 7-7.5 h of sleep duration per night. CONCLUSION: Long sleep duration was associated with increased risk of MetS and higher MetS severity score in both genders, while the short sleep duration increased the risk of Mets as well as MetS severity score just in women. The longitudinal studies would be suggested to assess the relationship between sleep quality and quantity components and MetS.