Study population
Participants for this study were recruited from the TLGS, a large-scale population-based cohort study performed to determine risk factors for non-communicable diseases in a representative sample of residents of Tehran. At first phase of the study (1999–2001), 15005 individuals aged ≥ 3 years were selected using multistage stratified cluster random sampling, and follow-up examinations were conducted in five consecutive phases: Phase 2 (2002–2005), Phase 3 (2005–2008), Phase 4 (2008–2011), Phase 5 (2012–2015) and Phase 6 (2015–2018). The details of the study have been published elsewhere (11). Of 8843 individuals aged ≥ 18 years who participated in Phase 4, a total of 6791 subjects (3016 men) completed the dietary assessment. We selected these subjects as baseline population in this study and followed them at next Phases (Phases 5 and 6). We compared characteristics of adult participants who had dietary data (respondents, n = 6791) and those who did not have (non-respondents, n = 2052). Among respondents, 44.4% were male, 22.6% were current smoker and 5.5% had family history of CVD (FH-CVD) compared with 44.8%, 20.6% and 4.7%, respectively, in non-respondents (P > 0.05). The mean (SD) of age and BMI were 40.8 (14.1) and 27.3 (4.9), respectively, in respondents vs. 44.8 (17.1) and 27.7 (5.2) in non-respondents (P < 0.001). The mean (SD) of systolic (SBP) and diastolic (DBP) blood pressure among respondents were 114 (16.7) and 75.5 (11.1), respectively, compared with 118 (19.7) and 77.2 (11.5) mmHg in non-respondents (P < 0.001). The level of physical activity did not differ in two groups.
Of 6791 participants, we excluded under- or over-reporters of energy intake (< 800 or ≥ 4200 kcal/day, n = 457), those with prevalent hypertension at baseline (n = 1116) and subjects with missing data on hypertension status at baseline (n = 19). Finally, after excluding participants without any follow up data (n = 406), 4793 subjects (1986 men) were remained and entered in the analysis.
Covariates Measurements
At baseline and next phases, information on the age, sex, smoking status, medical history and medication use was obtained through a personal interview using a standardized questionnaire. Body weight was measured using a calibrated digital scale (Seca 707). Height was measured using a portable stadiometer. Blood pressure was measured two times with the subjects seated after they had had 15 min rest before the first measurement; the mean of the two measurements was considered as the participant’s blood pressure (11). The physical activity level (PAL) was assessed using the Persian-translated modifiable activity questionnaire (MAQ) with high reliability and relative validity (12). Blood samples were taken after a 12-h overnight fast to determine the fasting plasma glucose (FPG), 2-hour post load plasma glucose (2 h-PLPG) and triglyceride (11).
2.3.Dietary assessments and dietary patterns
Dietary data were collected at baseline through face-to-face personal interviews with the use of a valid and reliable 168-items semi-quantitative food frequency questionnaire (FFQ). Participants used the standard serving sizes to report the usual frequency of consumption of individual food items on a daily, weekly, or monthly basis during the last year. The usual food intakes were then converted to daily intake (in grams) and were also calculated in energy-adjusted terms (serving per 1000 kcal/day). Because the Iranian food composition table (FCT) is incomplete, the United States Department of Agriculture (USDA) FCT was used to analyze foods (13). Foods listed in the FFQ were collapsed into 20 mutually exclusive food categories based on the similarity of type of food and nutrient composition.
2.3.2. The Dietary Approaches to Stop Hypertension (DASH) score
The DASHis a dietary index that have been used to measure the diet quality and originally developed to prevent and control hypertension(14).We computed a 40-points DASH score which includes 8 dietary components(15).All components were computed per 1000 kcal and were then divided into quintiles. Each quintile intake received 1 point. For fruits, vegetables, whole grains, low-fat dairy, and nuts and legumes, a score 5 was given to those in the top quintile. For sodium, red and processed meats, and sweetened beverages, the lowest quintile was given a score of 5, and the top quintile was given a score of one. The overall DASH score was then obtained by adding the component scores rangingfrom 8 to 40. A higher DASH score indicates better adherence.
2.3.3. Healthy Eating Index (HEI) score
The healthy eating index (HEI) is based on key recommendations of the 2015-2020dietary guidelines for Americans (DGA). It is comprised of 13 dietary components. Nine adequacy components include total fruits, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, and fatty acids. Four moderation components (those that should be limited) include refined grains, sodium, added sugars, and saturated fats (16). The HEI scoring is based on density (amount per 1000 kcal, ratio of fatty acids) and recommendations in the range of 1200-2400 kcal dietary patterns. To compute the score of HEI, six components from nine adequacy components (total fruit, whole fruit, total vegetables, greens and beans, total protein foods and seafood and plant proteins) each received a score of 0 and 5 respectively for the lowest and highest consumption. The other three adequacy components (whole grains, dairy and fatty acids) were scored from 0 to 10 for the lowest and highest consumption, respectively.The four moderation components (refined grains, sodium, added sugars, and saturated fats) received a score of 10 and 0 for the lowest and highest intakes, respectively. Intermediate intakes between the minimum and maximum were prorated. The scores from the 13 components were added for a total HEIscore ranging from 0 to 100. Higher totalHEIscores indicate greater adherence to DGA recommendations(16).
2.4. Definition of terms and outcome
Smoking status was categorized as smoker (current smokers) versus non-smoker (including past and never-smokers). A current smoker was defined as a person who smokes cigarettes or other smoking implements daily or occasionally. A positive FH-CVDwas defined as diagnosis of CVD in a male first degree relative <55 or in a female first degree relative <65 years. Individuals were considered physically active when they achieved a minimum of at least 600 MET (metabolic equivalent task)- minutes per week (17). Type 2 diabetes mellitus (DM) was defined as FPG ≥7 mmol/L or 2 h-PLPG ≥11.1 mmol/L (18) or using glucose-lowering treatment. Hypertension was defined as a SBP ≥140 mmHg or a DBP ≥90 mmHg or taking antihypertensive medications (19).
2.5. Statistical Methods
Missing data among total population (after applying the exclusion criteria) were 1.1, 1.1, 0.1, 2.4 and %9.6 for baseline covariates including smoking status, BMI, TC, DM status and PAL, respectively. Thus, multivariate imputations by chained equations (MICE) (mice package in R software) (20)were used to impute missing values at baseline.The PCA was used as a posteriori method with orthogonal rotation to identify dietary patterns on 20 food groups (as servings per 1000 kcal/day). Eigenvalues >1 derived from the correlation matrix, scree plots, factor interpretability and variance explained >5% were used to extract key dietary patterns. Food groups with absolute factor loadings values >0.2 were considered as contributing highly to the extracted pattern. Each person received a factor score for each dietary pattern by summing intakes of food groups weighted by the loadings generated by the PCA.The posteriori and priori dietary patterns(DASH and HEI) scoreswere then stratifiedinto quartiles.The baseline characteristics of the study population were compared across quartile categories of each dietary patternusing descriptive analysis. To test linear trend for categorical and continuous variables across quartiles of the dietary patterns score, weused logistic and linear regression tests, respectively, with the use of quartiles of dietary patterns scores as a continuous variable and represented theP value associated with the regression coefficient. The incidence density rate of hypertension was calculated by dividing the number of events by the person-years at risk.
The association betweendifferent dietary patterns and incidence ofhypertensionwas analyzed using time-dependentCox proportional hazard (PH) regression. All covariates (excluding sex and dietary patterns) were included in the models as time-dependent variables. Missing data on time-dependent variables was imputed by the last observation carried forward (LOCF) approach.For these analyses, the lowest quartiles of the different dietary patterns were considered as the reference category. Time to event was defined as the time between baseline and the event date (for event cases) or the last follow-up (for censored cases), whichever occurred first. The event date was defined as the mid-time between the date of the follow-up visit at which hypertension was detected for the first time, and the most recent follow-up visit prior to the diagnosis. Study participants were censored due to death, loss to follow-up or non-occurrence of hypertension before the end of the follow-up (18th April 2018). Two models were developed; model 1 was adjusted for the age and sex. Model 2 was further adjusted for time dependent BMI, smoking, DM status, PAL, TG, FH-CVD, total energy and salt intakes as the most important confounders.The PH assumption was verified using Schoenfeld residuals test and plot oflog [−log (survival)] versus log (time) to see if they are parallel.We conducted tests for linear trends with the use of quartiles of dietary patterns as a continuous variable and modeled this variable in separate Cox proportional hazards models. Analyses were conducted with R software (version 3.6.2) and the Statistical Package for Social Sciences (version 21.0; SPSS), and a two sides P values <0.05 were considered statistically significant.