Study Design and Participants
For the purpose of this cross-sectional study,data from baseline phase of Ravansar Non-Communicable Disease(RaNCD) cohort study was used. Ravansar, a city in Kermanshah Province is located in the western part of Iran close to the border with Iraq with a population mainly comprised of Kurdish ethnicity. RaNCD cohort is part of the large PERSIAN (Prospective Epidemiological ReSearch in IrAN) study. The data used in this study pertained to more than 10000 participants aged 35 to 65 who had voluntarily entered the study and signed informed consent forms for participation. The study began in November 2014 and continues to date. The recruitment phase data of the participants has been collected for the cohort and includes general data , nutrition questionnaire and biological samples, More information is available in the cohort protocol [19-21].
Anthropometric indices were determined by bioelectric impedance device. The subjects’ heights were measured by a stadiometer with an accuracy of 1 cm. Body mass index (BMI) was calculated by dividing weight (kg) to squared height (m) . Participants were classified into 5 groups in terms of percent body fat (PBF): 5-10 (Essential Fat), 11-14 (Athletes), 15-20 (Fitness), 21-24 (Average), and 24> (Obese) for male and 8-15 (Essential Fat), 16-23 (Athletes), 24-30 (Fitness), 31-36 (Average), and 37> (Obese) for female.
According to the third report of the National Cholesterol Education Program (NCEP) on diagnosis, evaluation and treatment of high blood cholesterol in adults (Adult Treatment Panel III), waist to hip ratio (WHR) in female and in men were classified as abnormal. According to guidelines of international kidney foundation, CKD is defined as renal abnormalities or GFR<60 ml/min/1.73 (1.0 ml/s/1.73 ) present for more than three months. Renal abnormalities can be diagnosed by pathologic disorders or markers of dysfunction, including abnormalities in blood or urine test. In this study, Modification of Diet in Renal Disease (MDRD), and nonstandardardized equation ( our creatinine values were not standardized for the most part) were used for estimating GFR from age, sex, and creatinine level [26, 27]. Non-use of race variable is due to non-racial differences in this population (almost all participants are from Kurdish ethnicity.) eGFR=1.86 (0.742 if Female)
According to CKD Stage cut-point,eGFR was categorized into 4 groups of >90, 60-90, 30-59, <30 ml/min/1.73 .To analyze the structural part, eGFRwas used as quantitative variable in the model. Blood pressure was measured after 15 minute of rest twice from the right arm and twice from the left using a sphygmomanometer (RiesterDuplex 1948, Germany). The mean value of the two measurements was taken as the systolic and diastolic blood pressure. Given the criteria recommended by the Eighth Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure(JNC-8), people with systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg and/or a history of taking blood pressure-controlling medications were classified as hypertension. Diabetes mellitus (DM) was defined based on the American Diabetes Association's criteria for fasting blood sugar (FBS) ≤126 Mg /dl and/or patients who used insulin and/or glucose-lowering agents . Smoking was introduced as a self-reported variable (1- none smokers, 2- smokers, 3- former smokers). Blood urea nitrogen(BUN) was calculated based on the quantitative values. Plasma atherogenic index was calculated according to the following formula.
Atherogenic coefficient=[Total cholesterol]–[HDH cholesterol] / [HDL cholesterol]
Physical activity calculated according to individual’s activity per day was another variable introduced based on the 22-item questionnaire inRaNCD cohort study. Finally, METs (as an indicator for level and measure of physical activity) were extracted and entered the model. MET is the amount of oxygen consumed at rest (about 3.5 ml 02/kg/min) and equals to resting metabolic rate. MET for each activity was extracted using compendium of physical activities.
Nutritional status was determined according to a valid and reliablefood frequency questionnaire customized to the local culture . Consumption of red meat (including red meat, processed meat, liver, heart, gizzard) was another variable derived from food frequency questionnaire calculated based on grams of meat intake per day.
Spearman's rank correlation was applied, and stepwise linear regression was obtained to assess the associations between the study variables and to implement the conceptual framework. Then, structural equation modeling (SEM)was used with maximum likelihood estimation (MLE). SEM includes causal modeling, analysis of covariance structures, and latent variable models. This model is a generalization of multivariate regression that allows one to estimate the strength and sign of direct and indirect effects for complicated causal schemes with multiple dependent and independent variables .In order to create constructs (or factors), we applied confirmatory factor analysis (CFA). CFA is a multivariate statistical technique which is used to test consistency of measures of a construct with researcher's understanding of the nature of that construct (or factor). The objective of confirmatory factor analysis is to test whether the data fits a hypothesized measurement model. Path standardized coefficients (β) as the effect sizes of this model were calculated.CMIN/DF)Normed chi-square(,CFI)Comparative fit indices(, GFI)Goodness-of-fit indices(, RMSEA)Root mean squared error of approximation(, NFI)normed fit index(and AGFI)adjusted goodness-of-fitindex(were applied for assessing fitness of the model. Statistical analysis was performed using AMOS-SPSS 22 and STATA 14.0 (STATA Corp, College Station, TX). P-value less than 0.05 was considered as statistically significant.As the percentage of missing data was less than 2%, it was excluded from analysis.