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. 2019 Nov 5;23(9):1543–1554. doi: 10.1017/S136898001900301X

Diet quality is associated with reduced risk of hypertension among Inner Mongolia adults in northern China

Xuemei Wang 1,2, Aiping Liu 3, Maolin Du 2, Jing Wu 4, Wenrui Wang 5, Yonggang Qian 5, Huiqiu Zheng 2, Dan Liu 2, Xi Nan 2, Lu Jia 2, Ruier Song 2, Danyan Liang 2, Ruiqi Wang 2, Peiyu Wang 3,*
PMCID: PMC7196733  PMID: 31685051

Abstract

Objective:

The present study investigated the association between dietary patterns and hypertension applying the Chinese Dietary Balance Index-07 (DBI-07).

Design:

A cross-sectional study on adult nutrition and chronic disease in Inner Mongolia. Dietary data were collected using 24 h recall over three consecutive days and weighing method. Dietary patterns were identified using principal components analysis. Generalized linear models and multivariate logistic regression models were used to examine the associations between DBI-07 and dietary patterns, and between dietary patterns and hypertension.

Setting:

Inner Mongolia (n 1861).

Participants:

A representative sample of adults aged ≥18 years in Inner Mongolia.

Results:

Four major dietary patterns were identified: ‘high protein’, ‘traditional northern’, ‘modern’ and ‘condiments’. Generalized linear models showed higher factor scores in the ‘high protein’ pattern were associated with lower DBI-07 (βLBS = −1·993, βHBS = −0·206, βDQD = −2·199; all P < 0·001); the opposite in the ‘condiments’ pattern (βLBS = 0·967, βHBS = 0·751, βDQD = 1·718; all P < 0·001). OR for hypertension in the highest quartile of the ‘high protein’ pattern compared with the lowest was 0·374 (95 % CI 0·244, 0·573; P trend < 0·001) in males. OR for hypertension in the ‘condiments’ pattern was 1·663 (95 % CI 1·113, 2·483; P trend < 0·001) in males, 1·788 (95 % CI 1·155, 2·766; P trend < 0·001) in females.

Conclusions:

Our findings suggested a higher-quality dietary pattern evaluated by DBI-07 was related to decreased risk for hypertension, whereas a lower-quality dietary pattern was related to increased risk for hypertension in Inner Mongolia.

Keywords: Hypertension, Principal components analysis, Dietary patterns, Dietary quality, Chinese Diet Balance Index-07

Hypertension is a global public health issue. The total number of adults with hypertension reached 1·13 billion worldwide in 2015, up from 0·594 billion in 1975, and low- and middle-income countries contributed most to this increase(1). The hypertension prevalence rate in China is increasing. A survey by Wang et al. in 2014 showed that the national adjusted hypertension prevalence rate was 29·6 %(2), which was higher than that in 2002 (18 %)(3). Hypertension is an independent disease and an important risk factor for cardiovascular and cerebrovascular diseases. An investigation by the WHO on causes of death showed that about 17 million people die of CVD each year (which constitute about one-third of the total deaths); of all the cardiovascular causes of death, 47 % comprised heart disease and 54 % comprised stroke owing to hypertension(4,5).

Dietary factors are closely related to the development of hypertension. A diet containing vegetables, fruits and low amounts of salt and fat helps to prevent or reduce hypertension. Excessive Na consumption is a risk factor for hypertension. Researchers are paying increasing attention to the association between dietary patterns and disease. The Mediterranean dietary pattern can lower hypertension(6) and chronic heart failure(7). The Western dietary pattern is significantly associated with increased risk of maturity-onset CHD and stroke. Among different dietary patterns, the Dietary Approaches to Stop Hypertension (DASH) dietary pattern is generally recognized as an effective way to prevent and control hypertension. However, the DASH diet is not wholly appropriate for China, as it features fruits, vegetables, whole grains, other mineral-rich foods, low saturated fat consumption and low Na consumption(8). The traditional dietary pattern in China(9), particularly in northern China, is characterized by high consumption of wheat and starch and low consumption of protein products such as pork, beef, poultry, aquatic products and dairy products. This diet is very different from the DASH dietary pattern. In addition, the DASH dietary pattern focuses only on the effect of specific foods and nutrients on hypertension and does not consider the effect of overall dietary quality.

Located in northern China, Inner Mongolia is a minority community containing forty-nine ethnic groups, including the Han nationality and the Mongol nationality. Different ethnic groups have different genetic characteristics and food culture. Inner Mongolia has high prevalence rates for metabolic diseases related to nutrition, such as hypertension(10). To address the relationship between diet and hypertension in Inner Mongolia, the association between dietary patterns and the prevalent risks of hypertension was explored in the present study. Chinese Dietary Balance Index-07 (DBI-07) scores were used to evaluate the quality of the main dietary patterns.

Methods

Study design

The present study was a surveillance survey of chronic disease and nutrition in Chinese adults in Inner Mongolia in 2015. The survey was conducted across eight monitoring sites in Inner Mongolia. Participants comprised residents of urban, farming, pastoral and forest areas, and from different age and ethnic groups. The cross-sectional study investigated dietary and non-dietary factors: general demographics, lifestyle, hypertension prevalence rate, dietary behavioural habits and daily food intake, by multistage-stratified cluster-random sampling among those aged ≥18 years.

The survey was approved by the Ethical Committee of the National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention. All participants provided written informed consent before the start of the investigation.

Dietary data collection

A 24 h recall and weighing method over three consecutive days was used to collect dietary data. This dietary survey was recommended by the Chinese Dietary Guidelines for chronic disease and nutrition surveillance in Chinese adults, and aimed to know about the residents’ intakes of nutrients and foods. Every household member (aged 2 years or over) was investigated. In the 24 h recalls, participants recalled and described all food and alcohol consumption for three consecutive days. Information about consumption of condiments such as salt and soya sauce, and cooking oil, was collected using a weighing method. Condiments purchased and wasted were also recorded.

Drinking frequency, type (liquor with high alcohol content, liquor with low alcohol content, beer, yellow rice wine, rice wine, wine) and average drinking amount were measured. The average daily alcohol consumption was calculated according to the Manual of Chinese Chronic Disease and Nutrition Surveillance Survey(11). Participant height and weight were directly measured by trained and evaluated workers. Blood and urine samples were also collected. The laboratory director organized the quality-control sample assessment at a field laboratory.

Chinese Dietary Balance Index-07

The Chinese DBI-07 is a method for evaluating dietary structure and quality based on the Chinese Dietary Guidelines, which consists of seven components: (i) cereals; (ii) vegetables and fruits; (iii) dairy products, soyabeans and soyabean products; (iv) animal foods; (v) condiments and alcohol; (vi) dietary variety; and (vii) drinking-water(12,13). The DBI-07 evaluation of intake quality for different foods is based on the consumption patterns of individuals with different energy intakes. A score of 0 for each component indicates that the recommended intake has been met. Positive scores (0 to 12) are used to evaluate excessive intake of alcoholic beverages and condiments that should be reduced or limited according to the guidelines. Negative scores (−12 to 0) are used to evaluate insufficient intakes of vegetables and fruits, dairy products, soyabeans and soyabean products, food variety and drinking-water that should be consumed sufficiently or in quantity according to the guidelines. Both positive and negative scores are used to evaluate intake of cereals (−12 to 12) and animal foods (−12 to 8), which should be consumed in appropriate amounts according to the guidelines. To reflect the different nutritional needs of people with different energy consumption, the scores for cereals, vegetables, fruit, dairy products, soyabeans and soyabean products, animal foods and condiments are based on seven energy intake levels. Twelve food subgroups are used to evaluate the food variety recorded by the DBI-07: (i) rice and rice products; (ii) wheat and wheat products; (iii) corns, coarse grains, starchy roots and their products; (iv) dark-coloured vegetables; (v) light-coloured vegetables; (vi) fruits; (vii) soyabeans and soyabean products; (viii) dairy products; (ix) livestock meat and meat products; (x) poultry; (xi) eggs; and (xii) fish and shellfish. If food intake amounts meet the lowest recommended amounts, the score for this subgroup is 0; if not, −1. The lowest recommended intake amounts are 5 g for soyabeans and soyabean products and 25 g for other food subgroups. The score for food variety ranges from −12 to 0. An indicator of dietary quality is calculated from the scores on different parts of the DBI-07.

The higher-bound score (HBS) is calculated by adding all positive scores as an indicator of excessive food intake. The lower-bound score (LBS) is calculated by adding the absolute values of all negative scores as an indicator of insufficient food intake. Diet quality distance (DQD) is calculated by adding the absolute values of both positive and negative scores. The possible ranges for total score, HBS, LBS and DQD scores are −72 to 44, 0 to 32, 0 to 72 and 0 to 84, respectively. Each indicator is divided into five levels for convenience: (i) ‘no problem’ (a score of 0); (ii) ‘almost no problem’ (less than 20 % of the total score); (iii) ‘low level’ (20–40 % of the total score); (iv) ‘moderate level’ (40–60 % of the total score); and (v) ‘high level’ (>60 % of the total score). The total score of each DBI-07 component is divided by the total score of LBS, HBS and DQD to assess their contribution rate and how much each food subgroup affects dietary quality (see online supplementary material, Supplemental Table S1).

Definition of hypertension

The main outcome indicator was hypertension. Meeting one of the following conditions was considered to indicate hypertension. The first condition was self-reported hypertension; that is, having a diagnosis of hypertension and currently receiving hypertension treatment(14). The second condition was field-measured hypertension, assessed as the average of three blood pressure measurements carried out by trained investigators and defined as average systolic blood pressure ≥140 mmHg and/or average diastolic blood pressure ≥90 mmHg.

Other variables

Age was categorized as follows: <35, 35–44, 45–54, 55–64 and ≥65 years. Participant household registration place was categorized as urban or rural. Based on regional characteristics, ethnicity groups were categorized as Han, Mongolian or other minorities (i.e. all minorities living in Inner Mongolia except Han and Mongolian). Educational level was categorized as low (primary school or lower), medium (junior high school) or high (senior high school and above). Marital status was categorized as married, unmarried or widowed/divorced.

Smoking status was categorized as non-smoker (never having smoked previously), ex-smoker (previously smoked but has quit) or current smoker (has smoked at least 1 cigarette/d for more than 1 year and smokes now).

Physical activity was assessed by the questionnaire, which addressed three activity categories with twenty-six items: twenty items on physical activity state, four items on resting state and two items on sleeping state. The items asked participants what kind of activities they engaged in, the frequency of activities per week and the total time spent on activities per day. Physical activities were scored using the weighting procedure recommended by the Physical Activity Guidelines for Americans(15).

BMI was categorized as three groups according to the recommended standard issued by a working group on obesity in China(16). BMI was categorized as normal or underweight (BMI < 23·9 kg/m2), overweight (BMI = 24·0–27·9 kg/m2) or obese (BMI ≥ 28·0 kg/m2). The normal and underweight categories were combined into one as there were too few people in the standard underweight category.

Statistical analysis

Continuous variables were shown as means and standard deviations. ANOVA was used for group comparisons. Categorical variables were expressed as numbers and percentages and were analysed using the χ 2 test.

Principal components analysis was used to derive food patterns based on the twenty-nine food groups. The varimax rotation (orthogonal rotation) was used to extract factor loadings. Factors were selected based on their eigenvalues (>1·00). The number of dietary patterns was determined based on scree plots, reasonability of food combination and variance contribution rate. Factor scores for each pattern were calculated by adding the coefficient of the factor loading and the standardized daily intake amounts of every kind of food that was related to each pattern. Dietary patterns were defined according to absolute factor loading values >0·2 for each factor on different food types. Dietary patterns were named by combining the food composition characteristics of the dietary pattern with the main food types included. Based on quartiles, factor scores were classified into four groups, quartiles Q1, Q2, Q3 and Q4, in ascending order of factor scores. The higher the score, the more consistent the individual dietary intake condition and dietary pattern were; the lower the score, the less likely the individual dietary intake condition fitted the dietary pattern.

Using generalized linear models, LBS, HBS, DQD being the dependent variables and dietary patterns being the independent variable, the quality of dietary patterns was evaluated after adjusting for other confounders. Multivariate logistic regression models were used to examine the association between dietary patterns and hypertension. The ‘Forward: LR’ method was used to select independent variables. With α = 0·05 as the significance level, P ≤ 0·05 was considered statistically significant. The statistical software package IBM SPSS Statistics version 19.0 was used for all analyses.

Results

Participant characteristics

A total of 1861 participants were included in the present study: 889 (47·77 %) men and 972 (52·23 %) women. The mean age was 52·5 years. Of participants, 914 (49·11 %) were hypertension patients, 463 were male (52·08 %) and 451 were female (46·40 %). A total of 58·41 % of participants were from rural areas and 18·21 % were minorities; 45·89 % had primary school education or no formal education. Among the participants, the rate of excessive drinking was 3·39 %; 587 (31·54 %) and 109 (5·86 %) were identified as current smokers and ex-smokers, respectively; and 762 (41·41 %) were more likely partake in no exercise or inadequate exercise. The sex differences in marital status, weight control, salt control, dyslipidaemia and uric acid were significant (P < 0·05; see online supplementary material, Supplemental Table S2).

Dietary patterns

Four major dietary patterns, named the ‘high protein’ pattern, ‘traditional northern’ pattern, ‘modern’ pattern and ‘condiments’ pattern, were extracted (see online supplementary material, Supplemental Fig. S1). The total variance of these four dietary patterns was 27·754 %; the variance contribution rates were 9·017, 6·908, 6·645 and 5·184 %, respectively. Sex-specific dietary patterns are shown in Supplemental Tables S3 and S4 (Supplemental Figs S2 and S3). According to the variance, the order of the four major dietary patterns extracted was different between males and females, but the characteristics of food composition and the kinds of main foods in the four dietary patterns were basically the same. The ‘high protein’ pattern was characterized by milk tea and tea, fried wheat products, beef and mutton, milk and dairy products. The ‘traditional northern’ pattern represented a typical traditional diet: high intakes of starchy roots and products, pork, pickled vegetables/dried vegetables and corns. The ‘modern’ pattern featured the intake of various vegetables, fresh fruits, nuts and other foods. The ‘condiments’ pattern was characterized by high intakes of salt, animal oils, various condiments and various alcoholic beverages (Table 1).

Table 1.

Factor loadings of each dietary pattern found among Inner Mongolia adults (n 1861), northern China, 2015

Food group ‘High protein’ pattern ‘Traditional northern’ pattern ‘Modern’ pattern ‘Condiments’ pattern
Milk tea and tea 0·795 – – –
Fried wheat products 0·727 – – –
Beef/mutton 0·627 – – –
Dairy products 0·416 – – –
Milk 0·371 – – –
Potato – 0·780 – –
Dried vegetables, pickles – 0·729 – –
Pork – 0·651 – –
Corn and other cereals – 0·198 – –
Desserts, snacks, snack foods – 0·120 – –
Vegetables – – 0·562 –
Soyabeans – – 0·545 –
Wheat flour – – 0·495 –
Fresh fruits – – 0·458 –
Aquatic product – – 0·427 –
Eggs – – 0·425 –
Algae – – 0·346 –
Vegetable oil – – 0·254 –
Processed meat – – 0·241 –
Starch and sugar – – 0·236 –
Beverages – – 0·114 –
Nuts – – 0·105 –
Salt – – – 0·654
Animal oil – – – 0·607
Condiments – – – 0·528
Rice – – – 0·468
Alcohol – – – 0·318
Animal giblets – – – 0·180
Cake – – – −0·131
Variance (%) 9·017 6·908 6·465 5·184
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Characteristics of dietary patterns

Table 2 shows the demographic characteristics of the four dietary patterns. The sex difference was significant in the distribution of the percentage in Q1 and Q4 of the ‘high protein’ pattern scores. There was an ascending trend for males and a declining trend for females (P trend = 0·002). Of those showing the ‘high protein’ pattern, participants were younger, had higher educational levels, showed greater weight control and a higher percentage were in Q4. Most of the participants in Q4 (67·39 %) showed the ‘high protein’ pattern and engaged in adequate physical activity. Increasing factor scores were associated with a higher percentage of overweight and obese participants (P trend = 0·002) and a lower percentage of hypertension patients (P trend = 0·012).

Table 2.

Participant characteristics according to the lowest (Q1) and highest quartile (Q4) of each dietary pattern found among Inner Mongolia adults (n 1861), northern China, 2015

‘High protein’ pattern ‘Traditional northern’ pattern ‘Modern’ pattern ‘Condiments’ pattern
Q1 Q4 Q1 Q4 Q1 Q4 Q1 Q4
n % n % P trend n % n % P trend n % n % P trend n % n % Ptrend
Sex
  Men 195 21·93 237 26·66 0·002 205 23·06 253 28·46 0·001 204 22·95 245 27·56 0·004 226 25·42 246 27·67 0·175
  Women 270 27·78 228 23·46 261 26·85 212 21·81 262 26·95 221 22·74 240 24·69 220 22·63
Place of residence
  Urban 164 21·19 161 20·80 0·892 165 21·32 216 27·91 <0·001 117 15·12 304 39·28 <0·001 234 30·23 123 15·89 <0·001
  Rural 301 27·69 304 27·97 301 27·69 249 22·91 349 32·11 162 14·90 232 21·34 343 31·55
Age group (years)
  <35 53 27·89 42 22·11 <0·001 51 26·84 54 28·42 0·012 42 22·11 74 38·95 <0·001 51 26·84 51 26·84 <0·001
  35–44 75 23·73 124 39·24 78 24·68 88 27·85 67 21·20 102 32·28 64 20·25 92 29·11
  45–54 135 25·23 131 24·49 121 22·62 141 26·36 125 23·36 141 26·36 106 19·81 149 27·85
  55–64 126 24·51 114 22·18 134 26·07 133 25·88 145 28·21 103 20·40 142 27·63 123 23·93
  ≥65 76 24·84 54 17·65 82 26·80 49 16·01 87 28·43 46 15·03 103 33·66 51 16·67
Ethnicity
  Han 354 23·26 336 22·08 0·010 357 23·46 419 27·53 <0·001 360 23·65 407 26·74 <0·001 405 26·61 379 24·90 0·072
  Mongolian 88 32·47 115 42·44 92 33·95 37 13·65 97 35·79 31 11·44 44 16·24 74 27·31
  Other minority 23 33·82 14 20·59 17 25·00 9 13·24 9 13·24 28 41·18 17 25·00 13 19·12
Education level
  Low 248 29·04 198 23·19 0·001 217 25·41 234 27·40 0·553 301 35·25 123 14·40 <0·001 176 20·61 241 28·22 <0·001
  Medium 138 23·75 148 25·47 150 25·82 133 22·89 116 19·97 166 28·57 151 25·99 144 24·78
  High 79 18·54 119 27·93 99 23·24 98 23·00 49 11·50 177 41·55 139 32·63 81 19·01
Marital status
  Married 425 24·63 436 25·28 0·530 417 24·17 442 25·62 0·001 428 24·81 440 25·51 0·026 427 24·75 444 25·74 0·140
  Single 12 25·00 12 25·00 17 35·42 12 25·00 9 18·75 17 35·42 13 27·08 8 16·67
  Widowed/divorced 25 30·49 15 18·29 30 36·59 10 12·20 25 30·49 9 10·98 25 30·49 14 17·07
Physical activity
  None 46 10·02 21 4·57 0·003 71 15·37 25 5·46 <0·001 75 16·20 25 5·43 0·045 87 18·83 28 6·11 <0·001
  Inadequate 156 33·99 129 28·04 153 33·12 99 21·62 119 25·70 167 36·30 144 31·17 118 25·76
  Adequate 257 55·99 310 67·39 238 51·52 334 72·93 269 58·10 268 58·26 231 50·00 312 68·12
Smoking status
  Current smoker 147 25·04 138 23·51 0·943 137 23·34 180 30·66 0·003 154 26·24 145 24·7 0·292 136 23·17 176 29·98 <0·001
  Ex-smoker 26 23·85 26 23·85 21 19·27 33 30·28 26 23·85 34 31·19 23 21·10 36 33·03
  Non-smoker 292 25·06 301 25·84 308 26·44 252 21·63 286 24·55 287 24·64 307 26·35 254 21·80
Weight control
  No 425 25·98 403 24·63 0·026 416 25·43 406 24·82 0·276 443 27·08 378 23·11 <0·001 407 24·88 417 25·49 0·159
  Yes 465 17·86 465 27·68 50 22·32 59 26·34 22 9·82 88 39·29 58 25·89 49 21·88
Salt control
  No 306 25·93 316 26·78 0·740 339 28·73 260 22·03 <0·001 358 30·34 213 18·05 <0·001 284 24·07 290 24·58 0·596
  Yes 159 23·35 149 21·90 127 18·65 205 30·10 108 15·86 253 37·15 182 26·73 176 25·84
BMI
  Underweight/normal 207 28·67 158 21·88 0·002 182 25·21 183 25·35 0·962 202 27·98 157 21·75 0·004 179 24·79 195 27·01 0·024
  Overweight 176 23·25 204 26·95 188 24·83 195 25·76 187 24·70 205 27·08 182 24·04 195 25·76
  Obese 70 20·17 102 29·39 86 24·78 85 24·50 72 20·75 98 28·24 94 27·09 68 19·60
Hypertension
  No 206 21·75 250 26·40 0·012 230 24·29 261 27·56 0·022 229 24·18 260 27·46 0·070 255 26·93 225 23·76 0·046
  Yes 259 28·34 215 23·52 236 25·82 204 22·32 237 25·93 206 22·54 211 23·09 241 26·37
Diabetes
  No 419 24·62 433 25·44 0·249 421 24·74 431 25·32 0·173 431 25·32 419 24·62 0·281 423 24·85 436 25·62 0·082
  Yes 46 28·93 32 20·13 45 28·30 34 21·38 35 22·01 47 29·56 43 27·04 30 18·87
Dyslipidaemia
  No 295 27·39 253 23·49 0·009 275 25·53 270 25·07 0·655 285 26·46 243 22·56 0·005 257 23·86 284 26·37 0·055
  Yes 170 21·68 212 27·04 191 24·36 195 24·87 181 23·09 223 28·44 209 26·66 182 23·21
Uric acid
  No 414 25·17 410 24·92 0·563 402 24·44 409 24·86 0·658 420 25·53 401 24·38 0·105 408 24·80 414 25·17 0·440
  Yes 51 23·61 55 25·46 64 29·63 56 25·93 46 21·30 65 30·09 58 26·85 52 24·07
Hypertension history
  No 285 25·18 286 25·27 0·942 310 27·39 238 21·02 <0·001 310 27·39 241 21·29 <0·001 296 26·15 276 24·38 0·077
  Yes 180 24·69 179 24·55 156 21·40 227 31·14 156 21·40 225 30·86 170 23·32 190 26·06
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Participants with higher adherence to the ‘traditional northern’ pattern were from urban areas and were of Han nationality. Most were married, unlike those with low factor scores. There was a sex difference in the distribution of the percentages in Q1 and Q4 of the ‘traditional northern’ pattern; there was an ascending trend for males and a declining trend for females (P trend = 0·001). The proportion of participants in Q4 decreased with increasing age (P trend = 0·012); the ≥65 years group had the lowest percentage (16·01 %). Non-smokers showing the ‘traditional northern’ pattern were more likely to have low factor scores. There was an ascending trend from Q1 to Q4 in the percentages of participants who controlled their salt intake, but hypertension patients showed the opposite trend.

Participants with higher adherence to the ‘modern’ pattern were male and from urban areas. They were younger than those who had low factor scores on the ‘modern’ pattern. Fewer older participants were in Q4 (P < 0·01); those aged ≥65 years had the lowest percentage (15·03 %). Participants from other minorities had the highest percentage in Q4 (41·18 %). An ascending trend in the percentage in Q4 was observed as education level increased (P < 0·01). Participants with high factor scores on the ‘modern’ pattern engaged in adequate physical activity and many did not control their weight.

Participants with higher adherence to the ‘condiments’ pattern were ethnic Mongolian males from rural areas. They had lower education levels, most were aged 35–54 years and they were more likely to smoke. An ascending trend in the percentage in Q4 was associated with an increased level of physical activity (P trend < 0·001). The percentage of people who controlled their salt intake and the percentage of hypertension patients increased from Q1 to Q4.

Nutrient and energy intakes by dietary pattern

Table 3 compares nutrient intakes in the four dietary patterns by sex. In the ‘high protein’ pattern, the intake of Ca was highest in males and females. Additionally, the percentage of energy from protein was also higher. However, the percentage of energy from dinner was lowest. Among the four dietary patterns, intakes of nutrients such as total fat and K were highest in the ‘traditional northern’ pattern in both sexes. In the ‘modern’ pattern, the intake of carbohydrates was highest in both males and females. Additionally, the percentage of energy from carbohydrates was also higher. Among the four dietary patterns, Na intake and the percentage of the energy from dinner were highest in the ‘condiments’ pattern in both sexes. However, the intakes of Ca and dietary fibre were lowest. The food consumption in the four dietary patterns by sex is shown in the online supplementary material, Supplemental Table S5.

Table 3.

Sex-specific nutrient and energy intakes according to the lowest (Q1) and highest quartile (Q4) of each dietary pattern found among Inner Mongolia adults (n 1861), northern China, 2015

‘High protein’ pattern P ‘Traditional northern’ pattern P ‘Modern’ pattern P ‘Condiments’ pattern P
Q1 Q4 Q1 Q4 Q1 Q4 Q1 Q4
Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd
Males
  Energy (kJ/d) 6669·80 3765·14 9617·30 4629·93 <0·001 8535·99 7557·56 9725·12 3798·24 <0·001 6253·78 3226·95 11 208·81 6575·45 <0·001 6870·09 3148·29 10 783·61 7252·80 <0·001
  Energy (kcal/d) 1594·12 899·89 2298·59 1106·58 <0·001 2040·15 1806·30 2324·36 907·80 <0·001 1494·69 771·26 2678·97 1571·57 <0·001 1641·99 752·46 2566·59 1733·46 <0·001
  Energy intake from dinner (%) 40·00 12·77 33·72 9·79 <0·001 33·76 12·33 36·80 10·20 <0·001 33·92 12·52 34·98 10·15 0·460 30·78 10·86 38·26 11·46 <0·001
  Total fat (g/d) 54·89 38·40 83·71 53·88 <0·001 55·01 33·09 95·73 63·25 <0·001 61·56 58·52 81·85 47·26 <0·001 56·48 40·11 84·07 59·24 <0·001
  Carbohydrate (g/d) 197·25 75·75 285·83 118·53 <0·001 223·86 123·23 287·64 121·54 <0·001 186·74 65·74 321·37 134·02 <0·001 227·18 101·60 272·75 127·40 <0·001
  Protein (g/d) 36·57 16·68 75·40 31·31 <0·001 47·22 24·56 68·72 32·26 <0·001 41·91 26·13 73·17 29·73 <0·001 48·60 24·32 58·87 30·46 <0·001
  Energy intake from carbohydrates (%) 53·76 13·27 51·39 10·37* 0·109 51·09 13·58 50·79 11·85* <0·001 54·20 12·67 51·51 12·14* 0·099 56·65 9·91 47·57 13·81* <0·001
  Energy intake from fat (%) 30·71 14·08 32·44 10·32† 0·235 28·30 12·20 35·81 13·11† <0·001 32·93 13·40 29·37 10·58 0·017 29·89 10·17 31·68 14·42† 0·333
  Energy intake from protein (%) 9·90 2·92 13·61 3·72† <0·001 11·33 4·40 11·93 3·32 0·115 11·27 3·18 12·02 3·61† 0·082 11·95 2·50 10·41 4·16 <0·001
  Fibre (g/d) 5·97 3·78 9·00 4·98 <0·001 5·88 3·49 10·15 5·53 <0·001 5·59 4·12 11·22 4·89 <0·001 7·40 5·40 7·87 4·21 0·667
  Ca (mg/d) 186·77 93·70 390·28 192·71 <0·001 300·79 170·91 336·08 189·45 <0·001 236·84 203·18 385·88 145·10 <0·001 313·72 138·81 277·82 157·12 0·070
  K (g/d) 0·85 0·43 1·74 0·78 <0·001 1·04 0·55 1·86 0·75 <0·001 1·10 0·80 1·80 0·68 <0·001 1·19 0·66 1·44 0·76 0·001
  Na (g/d) 4·24 2·74 5·41 4·60 0·006 5·97 5·81 5·30 4·30 0·002 5·65 5·54 4·81 2·96 0·196 3·44 1·76 8·06 6·28 <0·001
  Na:K 6·08 4·63 3·42 2·85 <0·001 6·90 8·70 3·15 2·79 <0·001 6·86 8·58 2·92 1·85 <0·001 3·55 2·23 7·24 8·41 <0·001
Females
  Energy (kJ/d) 5520·37 2375·63 8099·43 3651·54 <0·001 5626·02 3011·85 9181·75 3556·48 <0·001 5600·74 3187·33 8669·46 3818·11 <0·001 5780·95 2266·05 8006·96 4017·94 <0·001
  Energy (kcal/d) 1319·40 567·79 1935·81 872·74 <0·001 1344·65 719·85 2194·49 850·02 <0·001 1338·61 761·79 2072·05 912·55 <0·001 1381·68 541·60 1913·71 960·31 <0·001
  Energy intake from dinner (%) 39·35 12·08 32·71 9·09 <0·001 33·36 11·61 36·44 10·12 0·018 34·21 12·77 35·17 9·59 0·589 29·03 10·00 36·54 9·64 <0·001
  Total fat (g/d) 55·67 41·63 79·10 63·83 <0·001 51·63 29·13 95·50 65·74 <0·001 59·23 65·47 75·06 38·69 <0·001 48·77 28·96 86·12 67·71 <0·001
  Carbohydrate (g/d) 166·41 62·57 239·01 112·34 <0·001 167·96 70·80 267·62 120·34 <0·001 161·72 60·61 270·38 125·94 <0·001 191·70 76·66 218·56 94·88 <0·001
  Protein (g/d) 33·71 15·44 62·93 28·95 <0·001 40·49 22·60 60·95 29·16 <0·001 36·49 20·78 64·35 29·68 <0·001 40·56 17·73 50·83 27·80 <0·001
  Energy intake from carbohydrates (%) 53·08 12·93 50·36 10·27* 0·002 52·15 11·31 50·11 12·73* <0·001 52·96 13·93 52·48 10·20* 0·892 56·37 9·74 48·37 12·66* <0·001
  Energy intake from fat (%) 35·19 14·09 35·33 11·01† 0·036 34·11 11·37 37·62 14·07† <0·001 34·79 14·78 33·11 10·60† 0·442 30·80 10·12 38·87 14·06† <0·001
  Energy intake from protein (%) 10·53 2·93 13·47 3·57† <0·001 12·28 3·56 11·31 3·18 0·007 11·32 3·24 12·65 3·04† <0·001 11·86 2·54 10·96 3·52 <0·001
  Fibre (g/d) 6·10 3·93 8·09 5·11 <0·001 5·68 3·96 9·79 5·95 <0·001 5·02 3·56 11·14 5·63 <0·001 6·87 4·95 7·69 4·83 0·014
  Ca (mg/d) 181·16 99·99 363·75 187·76 <0·001 282·30 177·55 327·07 203·76 <0·001 231·24 207·12 361·72 149·27 <0·001 282·41 135·15 279·57 189·57 0·030
  K (g/d) 0·85 0·45 1·55 0·77 <0·001 0·96 0·56 1·78 0·75 <0·001 0·98 0·68 1·70 0·71 <0·001 1·07 0·59 1·36 0·75 <0·001
  Na (g/d) 4·30 2·57 5·30 4·57 0·005 5·54 5·14 5·29 3·51 0·006 5·25 4·99 4·44 2·61 0·096 3·24 1·75 8·42 6·27 <0·001
  Na:K 6·22 4·87 3·80 3·02 <0·001 6·94 7·92 3·42 2·75 <0·001 6·81 7·72 2·83 1·71 <0·001 3·76 2·47 7·89 8·26 <0·001
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*

Compared with Q1, the mean ± sd is lower than the reference range.

†

Compared with Q1, the mean ± sd is higher than the reference range.

Quality evaluation of food intake using Dietary Balance Index-07 scores

The distribution of dietary quality among Inner Mongolian adults is shown in the online supplementary material, Supplemental Table S6. In the total population, moderate- and high-level dietary imbalance accounted for 79·64 %. Among them, the distribution of LBS indicated that 26·90 % of the participants had high level of inadequate intakes, and the distribution of HBS indicated that 1·10 % of the participants had high level of excessive intakes. The mean scores of LBS, HBS and DQD were 37·54, 4·73 and 42·26, respectively. Participants with hypertension had higher scores in LBS and DQD.

Quality evaluation of dietary patterns using Dietary Balance Index-07 scores

The quality of the ‘high protein’ pattern was higher as evaluated by DBI-07. Participants with higher factor scores in the ‘high protein’ pattern had lower DBI-07 scores: LBS (β = −1·993; 95 % CI −2·362, −1·625; P < 0·001), HBS (β =−0·206; 95 % CI −0·381, −0·030; P = 0·021) and DQD (β = −2·199; 95 % CI −2·598, −1·801; P < 0·001). The quality of the ‘condiments’ pattern was lower. Participants with higher factor scores in the ‘condiments pattern’ had higher DBI-07 scores: LBS (β = 0·967; 95 % CI 0·570, 1·364; P < 0·001), HBS (β = 0·751; 95 % CI 0·570, 0·933; P < 0·001) and DQD (β = 1·718; 95 % CI 1·293, 2·143; P < 0·001). Higher adherence to the ‘traditional northern’ pattern was with lower LBS and higher HBS and DQD, and higher adherence to the ‘modern’ pattern was with lower LBS, DQD and higher HBS, respectively, which indicates diet quality was lower (Table 4). The results of evaluation of dietary quality in the different dietary patterns are shown in the online supplementary material, Supplemental Table S7.

Table 4.

Generalized linear models * of dietary quality according to indicators of the Chinese Dietary Balance Index-07 (DBI-07) for each dietary pattern found among Inner Mongolia adults (n 1861), northern China, 2015

LBS HBS DQD
β 95 % CI P β 95 % CI P β 95 % CI P
‘High protein’ pattern −1·993 −2·362, −1·625 <0·001 −0·206 −0·381, −0·030 0·021 −2·199 −2·598, −1·801 <0·001
‘Traditional northern’ pattern −0·690 −1·084, −0·295 0·001 1·567 1·398, 1·735 <0·001 0·877 0·451, 1·308 <0·001
‘Modern’ pattern −4·882 −5·255, −4·508 <0·001 0·739 0·540, 0·937 <0·001 −4·143 −4·574, −3·712 <0·001
‘Condiments’ pattern 0·967 0·570, 1·364 <0·001 0·751 0·570, 0·933 <0·001 1·718 1·293, 2·143 <0·001
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LBS, lower-bound score; HBS, higher-bound score; DQD, diet quality distance.

*

Model adjusted for sex, age, place of residence, educational level, marital status, nationality, salt intake control, smoking status, weight control, BMI, hypertension, abnormal blood lipids and other variables.

Association between dietary patterns and hypertension

The ‘high protein’ pattern showed statistically significant inverse associations with hypertension in males, but no associations in females. In males, after adjusting for demographic and behavioural characteristics, the OR for hypertension in the highest quartile compared with the lowest was 0·406 (95 % CI 0·268, 0·615; P trend = 0·001); after further adjustment for BMI, the OR still less than 1, 0·374 (95 % CI 0·244, 0·573; P trend < 0·001). The ‘condiments’ pattern showed statistically significant positive associations with hypertension both in males and females. In males, after adjusting for demographic and behavioural characteristics and BMI, the OR for hypertension in the highest quartile compared with the lowest was 1·663 (95 % CI 1·113, 2·483; P trend = 0·005). In females, after adjusting for demographic and behavioural characteristics, the OR for hypertension in the highest quartile compared with the lowest quartile was 1·634 (95 % CI 1·067, 2·502; P trend = 0·037). After further adjustment for BMI, the OR increased to 1·788 (95 % CI 1·155, 2·766; P trend = 0·015). No associations between other dietary patterns and hypertension were observed (Tables 5 and 6).

Table 5.

Association of dietary patterns with hypertension across quartiles (Q) of dietary pattern scores in male Inner Mongolia adults (n 889), northern China, 2015

Q1 Q2 Q3 Q4 P trend
n, % or OR n, % or OR 95 % CI n, % or OR 95 % CI n, % or OR 95 % CI
‘High protein’ pattern Cases (n) 127 98 – 132 – 106 – –
Proportion (%) 65·13 45·58 – 54·55 – 44·73 – –
Model 1 1·000 0·448 0·301, 0·668 0·643 0·436, 0·947 0·433 0·293, 0·640 0·001
Model 2 1·000 0·384 0·250, 0·589 0·566 0·373, 0·859 0·391 0·257, 0·595 0·001
Model 3 1·000 0·380 0·248, 0·581 0·511 0·334, 0·781 0·406 0·268, 0·615 0·001
Model 4 1·000 0·408 0·264, 0·630 0·511 0·334, 0·781 0·374 0·244, 0·573 <0·001
‘Traditional northern’ pattern Cases (n) 110 112 – 129 – 112 – –
Proportion (%) 50·64 53·59 – 58·11 – 44·27 – –
Model 1 1·000 0·997 0·678, 1·467 1·198 0·817, 1·756 0·686 0·474, 0·993 0·081
Model 2 1·000 0·961 0·639, 1·445 1·215 0·812, 1·819 0·686 0·463, 1·016 0·066
Model 3 1·000 0·978 0·648, 1·475 1·217 0·811, 1·826 0·705 0·474, 1·048 0·153
Model 4 1·000 1·047 0·687, 1·598 1·263 0·834, 1·912 0·677 0·452, 1·014 0·094
‘Modern’ pattern Cases (n) 103 113 – 124 – 123 – –
Proportion (%) 50·49 53·30 – 54·39 – 50·20 – –
Model 1 1·000 1·119 0·762, 1·645 1·169 0·801, 1·707 0·989 0·682, 1·433 0·967
Model 2 1·000 1·220 0·812, 1·831 1·236 0·824, 1·852 1·185 0·789, 1·780 0·442
Model 3 1·000 1·206 0·803, 1·810 1·129 0·756, 1·687 1·074 0·722, 1·598 0·841
Model 4 1·000 1·139 0·752, 1·727 1·128 0·749, 1·701 0·956 0·635, 1·440 0·799
‘Condiments’ pattern Cases (n) 107 105 – 119 – 132 – –
Proportion (%) 47·35 50·97 – 56·40 – 53·66 – –
Model 1 1·000 1·156 0·792, 1·687 1·439 0·987, 2·098 1·288 0·897, 1·849 0·106
Model 2 1·000 1·150 0·769, 1·718 1·564 1·044, 2·344 1·584 1·070, 2·346 0·009
Model 3 1·000 1·205 0·806, 1·799 1·619 1·083, 2·421 1·718 1·160, 2·545 0·003
Model 4 1·000 1·102 0·729, 1·666 1·496 0·992, 2·254 1·663 1·113, 2·483 0·005
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Data were analysed using multivariable-adjusted logistic regression.

Model 1: crude model.

Model 2: adjusted for age place of residence, ethnicity, marital status, educational level, family history and dyslipidaemia.

Model 3: additionally adjusted for smoking status, physical activity, weight control and salt control.

Model 4: additionally adjusted for BMI.

Table 6.

Association of dietary patterns with hypertension across quartiles (Q) of dietary pattern scores in female Inner Mongolia adults (n 972), northern China, 2015

Q1 Q2 Q3 Q4 P trend
n, % or OR n, % or OR 95 % CI n, % or OR 95 % CI n, % or OR 95 % CI
‘High protein’ pattern Cases (n) 132 116 – 94 – 109 – –
Proportion (%) 48·89 46·40 – 41·96 – 47·81 – –
Model 1 1·000 0·905 0·641, 1·277 0·756 0·529, 1·080 0·958 0·673, 1·363 0·559
Model 2 1·000 0·746 0·502, 1·109 0·640 0·425, 0·963 1·136 0·755, 1·709 0·839
Model 3 1·000 0·746 0·502, 1·109 0·640 0·425, 0·963 1·136 0·755, 1·709 0·839
Model 4 1·000 0·675 0·448, 1·015 0·570 0·375, 0·868 0·992 0·654, 1·504 0·677
‘Traditional northern’ pattern Cases (n) 126 133 – 100 – 92 – –
Proportion (%) 48·28 51·95 – 41·15 – 43·40 – –
Model 1 1·000 1·159 0·820, 1·636 0·749 0·731, 1·223 0·821 0·571, 1·182 0·081
Model 2 1·000 0·957 0·647, 1·417 0·698 0·467, 1·041 0·749 0·493, 1·140 0·069
Model 3 1·000 0·957 0·647, 1·417 0·698 0·467, 1·041 0·749 0·493, 1·140 0·069
Model 4 1·000 1·002 0·670, 1·499 0·698 0·463, 1·051 0·782 0·511, 1·198 0·097
‘Modern’ pattern Cases (n) 134 120 – 114 – 83 – –
Proportion (%) 51·15 47·43 – 48·31 – 37·56 – –
Model 1 1·000 0·862 0·610, 1·218 0·893 0·628, 1·269 0·575 0·399, 0·827 0·007
Model 2 1·000 0·774 0·524, 1·142 0·867 0·573, 1·302 0·694 0·449, 1·072 0·159
Model 3 1·000 0·774 0·524, 1·142 0·867 0·573, 1·302 0·694 0·449, 1·072 0·159
Model 4 1·000 0·765 0·514, 1·137 0·845 0·558, 1·280 0·650 0·417, 1·015 0·097
‘Condiments’ pattern Cases (n) 104 123 – 115 – 109 – –
Proportion (%) 43·33 47·49 – 45·46 – 49·55 – –
Model 1 1·000 1·183 0·831, 1·168 1·109 0·764, 1·555 1·284 0·889, 1·854 0·106
Model 2 1·000 1·353 0·903, 2·026 1·312 0·871, 1·976 1·634 1·067, 2·502 0·037
Model 3 1·000 1·353 0·903, 2·026 1·312 0·871, 1·976 1·634 1·067, 2·502 0·037
Model 4 1·000 1·403 0·928, 2·120 1·382 0·910, 2·098 1·788 1·155, 2·766 0·015
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Data were analysed using multivariable-adjusted logistic regression.

Model 1: crude model.

Model 2: adjusted for age, place of residence, ethnicity, marital status, educational level, family history and dyslipidaemia.

Model 3: additionally adjusted for smoking status, physical activity, weight control and salt control.

Model 4: additionally adjusted for BMI.

Discussion

The prevalence of hypertension was 49·11 % in Inner Mongolia, which is higher than the rate reported in northern China(17) and a national survey(2). Lower diet quality as evaluated by DBI-07 was prominent in Inner Mongolia. Epidemiological evidence shows that the risk of hypertension is associated with sociodemographic, lifestyle, behavioural, genetic and dietary factors(18,19), especially closely with dietary quality. The present cross-sectional study aimed to explore the association between the risk of hypertension and dietary quality as evaluated by the Chinese DBI-07 in Inner Mongolia in 2015.

Many studies(20,21) have suggested that the DASH dietary pattern can prevent and control hypertension. The DASH diet mainly recommends the consumption of whole-wheat bread, spinach salad, olive oil, aquatic products and other foods. Inner Mongolia is located in northern China, and for geographical and economic reasons, residents consume small amounts of fresh vegetables and fruit. Although the consumption of fresh vegetables and fruit has increased in recent years, consumption remains lower than the recommended amount. A food culture that features meat as the main component still exists among local residents. Additionally, a large amount of alcohol consumption is associated with an increased risk of hypertension for males, particularly Asians, which is also a major feature in Inner Mongolia. Therefore, the DASH dietary pattern is not appropriate for evaluating the dietary quality of people in Inner Mongolia. Ultimately, we chose the Chinese DBI-07 to evaluate the dietary quality of residents in Inner Mongolia. Distinct from the DASH dietary pattern, the DBI-07 considers the effect of alcohol consumption. Moreover, the DBI-07 is based on the recommended amounts of various foods from the Dietary Guidelines for Chinese Residents. The DBI-07 evaluated the quality of different food intakes based on the consumption patterns of individuals with different energy intake needs. Compared with the recommended intakes of the Chinese Dietary Guidelines (2016)(22), the lower quality diet of Inner Mongolia residents was serious, especially the inadequate intakes.

The characteristics of food composition and the kinds of main foods in each of the four dietary patterns extracted were basically the same by sex. Therefore, we named them as ‘high protein’ pattern, ‘traditional northern’ pattern, ‘modern’ pattern and ‘condiments’ pattern in both sexes. And the cumulative variance of these four dietary patterns was 27·754 %, which is close to that found in other research(23,24).

The ‘high protein’ pattern was characterized by milk tea and tea, fried wheat products, beef and mutton, milk and dairy products. The LBS, HBS and DQD indicators of DBI-07 showed a descending trend as the factor scores increased in the ‘high protein’ pattern. The dietary intake of meat and dairy products, and the intakes of nutrients such as protein and Ca, were higher than in the other dietary patterns. The energy intake at dinner was lower than for the other patterns. Male participants who mainly adhered to the ‘high protein’ pattern had a lower risk of hypertension, while in females the ‘high protein’ pattern was not significantly associated with hypertension. The possible reason is that intakes of protein and Ca, which benefited to control blood pressure(25,26), were lower in females than in males. In addition, the ‘high protein’ pattern in the present study was not the main dietary pattern in females, but the ‘traditional northern’ pattern. Other authors(27,28) also showed that a dietary pattern high in protein was associated with a reduced risk of hypertension. Although the intake of meat is a risk factor for hypertension(29–31), our study did not show that the ‘high protein’ dietary pattern with consumption of beef and mutton was a risk factor for hypertension after adjusting for confounders. On one hand, the fat content of beef and mutton is lower than that of pork(32), and the other hand, dairy products are rich in protein and Ca. The high intake of protein makes people feel full quickly, which helps to eat less high-energy foods and improves the whole dietary quality to reduce blood pressure(25,33). Moreover, Ca contributes to reduce the incidence of obesity through shifting the energy balance, thereby reducing the risk of suffering hypertension(26). A notable feature of the ‘high protein’ dietary pattern was the high intake of milk tea and tea; of them, tea has a positive effect on blood pressure and blood lipids(34).

The ‘condiments’ pattern was characterized by high intakes of salt, animal oil, condiments and alcoholic beverages. The LBS, HBS and DQD indicators of DBI-07 showed an ascending trend as the factor scores increased. In the ‘condiments’ pattern, the dietary intake of oil and salt was higher than the recommended intake, and the dietary intake of vegetables, eggs and dairy was lower than the recommended intake. Participants had a higher risk of hypertension in the ‘condiments’ pattern. Factor score in the ‘condiments’ pattern was positively correlated with waist and lipid-related indicators, and thus it was also positively correlated with the prevalence of hypertension(35,36). It is worth noting that the intake of alcohol in the ‘condiments’ dietary pattern is much higher than in the other three dietary patterns. Long-term excessive intake of alcohol is an independent risk factor for hypertension(37,38). Participants with higher adherence to the ‘condiments’ pattern had the highest HBS for alcohol consumption and the highest risk of hypertension. Dietary salt and Na intakes were also the highest in the ‘condiments’ dietary pattern. Salt consumption is associated with hypertension(39,40). Long-term excessive intake of salt reduces the ability of the kidneys to process salt(41), leading to impairments of endothelial function, left ventricular relaxation, electric repolarization, endothelium dysfunction(42), and subsequent vascular sclerosis(43), resulting in primary hypertension by affecting Na loadings.

The ‘traditional northern’ pattern was characterized by high intakes of starch and sugar, pork, pickled vegetables/dried vegetables and corns; the dietary intake of cereals and meat was higher than the recommended intake, and the intakes of nutrients such as total fat and Na were also higher than recommended. The ‘modern’ pattern was characterized by the intakes of various vegetables, fresh fruits, nuts and other foods; the dietary intake of carbohydrates and Na was higher than the recommended intake, and intakes of the nutrients dietary fibre and Ca were lower than those recommended. In the ‘traditional northern’ pattern, the HBS and DQD indicators of DBI-07 ascended with increasing factor scores, although the LBS showed a descending trend. In the ‘modern’ pattern, HBS showed an ascending trend as the factor scores increased, even though LBS and DQD showed a descending trend. In our study, no association was found with hypertension.

Excessive intake of salt and alcohol is related to the development of hypertension(44–47). In our study, salt consumption was excessive in the four major dietary patterns, as assessed by DBI-07. For those participants with higher factor scores in the ‘traditional northern’ pattern and the ‘modern’ pattern, although they have no higher Na intake as factor scores increased compared with the ‘high protein’ pattern and the ‘condiments’ pattern, we did not find they were related to a lower risk of hypertension. The ‘condiments’ pattern had the highest intake of alcohol compared with the other three dietary patterns and showed a risk factor for hypertension. And the alcohol consumption in the ‘high protein’ pattern was higher than in the ‘traditional northern’ pattern. However, participants had a lower risk of hypertension if they mainly followed the ‘high protein’ pattern. Based on the excessive salt intake in the four major dietary patterns, and the higher alcohol consumption in the ‘high protein’ pattern than in the ‘traditional northern’ pattern, only the ‘high protein’ dietary pattern indicated a reduced hypertension risk, we suggest that the quality of dietary pattern evaluated by DBI-07 may explain the important part effect on hypertension. Evidence to support this assumption comes from a Japanese study(37), which indicated that the higher the dietary quality and the more high quality diet, the lower the hypertension risk.

There is a close relationship between BMI and hypertension(48). The present study focused on the relationship between the quality of dietary patterns and hypertension. In males, after adjusting for BMI, the OR of the ‘high protein’ pattern with higher quality and risk of hypertension changed from 0·406 (95 % CI 0·268, 0·615) to 0·374 (95 % CI 0·244, 0·573). In females, after adjusting for BMI, the OR of the ‘condiments’ pattern with lower quality and risk of hypertension changed from 1·634 (95 % CI 1·067, 2·502) to 1·788 (95 % CI 1·155, 2·766). After adjusting for BMI, the dietary quality was still the primary factor for hypertension.

Sociodemographic features generally affect people’s dietary pattern and quality(49). Our study mainly evaluated the relationship between the quality of current dietary patterns and hypertension. Therefore, demographic characteristics were only used as confounders for adjustment. We did not explore further their effect on the relationship between dietary patterns and hypertension in the present study.

The present study was cross-sectional. Despite effective methods of quality control, a certain amount of bias (including selection bias, information bias and other types of bias) is unavoidable and may have affected the representativeness of the sample and results. Although the study demonstrated a relationship between excessive/insufficient food intake and hypertension, and between dietary patterns and hypertension, causal relationships cannot be assumed. The findings offer some insights into the aetiology of the association between dietary patterns and hypertension; however, long-term follow-up studies are needed to determine if there is a causal relationship between these factors.

Conclusions

In summary, four major dietary patterns were identified; the ‘high protein’ pattern (a higher-quality dietary pattern as evaluated by DBI-07) was related to a decreased prevalent risk of hypertension, while the ‘condiments’ pattern (a lower-quality dietary pattern as evaluated by DBI-07) was related to an increased prevalent risk of hypertension.

Acknowledgements

Acknowledgements: The authors sincerely express appreciation to all the participants and to local colleagues. They thank Diane Williams, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. Financial support: This study was supported by Inner Mongolia Science and Technology Project (Intelligent Health Monitoring and Modern Medical Information System Development Based on Internet of Things Technology). The funder had no role in the design, analysis or writing of this article. Conflict of interest: None declared. Authorship: X.W. and P.W. conceived and designed the protocol. X.W. and A.L. conducted the statistical analysis and prepared the manuscript. M.D., J.W., W.W. and Y.Q. contributed to data collection. H.Z., D. Liu, X.N., L.J., R.S., D. Liang and R.W. participated in cleaning and analysing the data. X.W. and A.L. produced the final revised manuscript. P.W. reviewed the paper and approved its final version. All authors were involved in interpreting results, editing the manuscript for content and approved the final version of the manuscript. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Ethical Committee of the National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention. Written informed consent was provided by participants before the start of the investigation.

Supplementary material

For supplementary material accompanying this paper visit https://doi.org/10.1017/S136898001900301X.

S136898001900301Xsup001.doc (362.5KB, doc)

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References

  • 1. Zhou B, Bentham J, Cesare MD et al. (2017) Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet 389, 37–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Wang J, Zhang L, Wang F et al. (2014) Prevalence, awareness, treatment, and control of hypertension in China: results from a national survey. Am J Hypertens 27, 1355–1361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Wu Y, Huxley R, Li L et al. (2008) Prevalence, awareness, treatment, and control of hypertension in China: data from the China National Nutrition and Health Survey 2002. Circulation 118, 2679–2686. [DOI] [PubMed] [Google Scholar]
  • 4. World Health Organization (2013) A global brief on hypertension. https://www.who.int/cardiovascular_diseases/publications/global_brief_hypertension/en/ (accessed March 2013).
  • 5. Daskalopoulou SS, Rabi DM & Zarnke KB (2015) The 2015 Canadian Hypertension Education Program recommendations for blood pressure measurement, diagnosis, assessment of risk, prevention, and treatment of hypertension. Can J Cardiol 31, 549–568. [DOI] [PubMed]
  • 6. Kastorini CM & Panagiotakos DB (2010) Mediterranean diet and diabetes prevention: myth or fact? World J Diabetes 1, 65–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Chrysohoou C, Pitsavos C, Metallinos G et al. (2012) Cross-sectional relationship of a Mediterranean type diet to diastolic heart function in chronic heart failure patients. Heart Vessels 27, 576–584. [DOI] [PubMed] [Google Scholar]
  • 8. Sacks FM, Svetkey P, Vollmer WM et al. (2001) Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-Sodium Collaborative Research Group. N Engl J Med 344, 3–10. [DOI] [PubMed] [Google Scholar]
  • 9. Wang D, He Y, Li Y et al. (2011) Dietary patterns and hypertension among Chinese adults: a nationally representative cross-sectional study. BMC Public Health 11, 925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Li G, Wang H, Wang K et al. (2014) Prevalence, awareness, treatment, control and risk factors related to hypertension among urban adults in Inner Mongolia 2014: differences between Mongolian and Han populations. BMC Public Health 16, 294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Wang SS, Lay S, Yu HN et al. (2016) Dietary Guidelines for Chinese Residents (2016): comments and comparisons. J Zhejiang Univ Sci B 17, 649–656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Xu X, Hall J, Byles J et al. (2015) Assessing dietary quality of older Chinese people using the Chinese Diet Balance Index (DBI). PLoS One 10, e0121618. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Zang J, Yu H, Zhu Z et al. (2017) Does the dietary pattern of Shanghai residents change across seasons and area of residence: assessing dietary quality using the Chinese Diet Balance Index (DBI). Nutrients 9, 251.28282864 [Google Scholar]
  • 14. Du M, Yin S, Wang P et al. (2018) Self-reported hypertension in Northern China: a cross-sectional study of a risk prediction model and age trends. BMC Health Serv Res 18, 475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Ainsworth BE, Haskell WL, Whitt MC et al. (2000) Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 32, 9 Suppl., S498–S516. [DOI] [PubMed] [Google Scholar]
  • 16. Zhou B (2002) Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults – study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 15, 83–96. [PubMed] [Google Scholar]
  • 17. Yanlei P, Zengwu W, Xin W et al. (2015) A prevalence survey of cardiovascular risk factors and hypertension in northern rural areas of China. Chin J Dis Control 4, 327–329, 341. [Google Scholar]
  • 18. Horn LV, Tian L, Neuhouser ML et al. (2012) Dietary patterns are associated with disease risk among participants in the Women’s Health Initiative Observational Study. J Nutr 142, 284–291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Hong X, Li J-Q, Wang Z-Y et al. (2013) Dietary patterns and hyperglycemia in a follow-up study in Nanjing city. Chin J Prev Med 47, 135–141. [PubMed] [Google Scholar]
  • 20. Saneei P, Salehi-Abargouei A, Esmaillzadeh A et al. (2014) Influence of Dietary Approaches to Stop Hypertension (DASH) diet on blood pressure: a systematic review and meta-analysis on randomized controlled trials. Nutr Metab Cardiovasc Dis 24, 1253–1261. [DOI] [PubMed] [Google Scholar]
  • 21. Siervo M, Lara J, Chowdhury S et al. (2015) Effects of the Dietary Approach to Stop Hypertension (DASH) diet on cardiovascular risk factors: a systematic review and meta-analysis. Br J Nutr 113, 1–15. [DOI] [PubMed] [Google Scholar]
  • 22. Yang Y & Zhang H (2016) Introduction of Chinese residents’ dietary guidelines. Acta Nutrimenta Sinica 38, 209–217. [Google Scholar]
  • 23. Gao J (2012) Association of dietary patterns and physical activities with total body fat proportions and metabolic syndrome among middle-aged and elderly people: a cross-sectional study. PhD Thesis, Fudan University.
  • 24. Kim JH, Ji EL & Jung IK (2012) Dietary pattern classifications and the association with general obesity and abdominal obesity in Korean women. J Am Acad Nutr Diet 112, 1550–1559. [DOI] [PubMed] [Google Scholar]
  • 25. Buendia JR, Bradlee ML & Singer MR et al. (2015) Diets higher in protein predict lower high blood pressure risk in Framingham Offspring Study adults. Am J Hypertens 28, 372–379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Teegarden D (2003) Calcium intake and reduction in weight or fat mass. J Nutr 133, issue 1, 249S–251S. [DOI] [PubMed] [Google Scholar]
  • 27. Wang Y (2012) Dietary patterns and hypertension among adults in Taiwan. Health Res 41, 14–19. [PubMed] [Google Scholar]
  • 28. Zhao T, Chu Y, Zhao H et al. (2016) Survey analysis on relationship between dietary patterns and hypertension of residents in rural areas of Jilin Province. J Jilin Univ (Med Ed) 42, 400–404. [Google Scholar]
  • 29. Conlin PR, Chow D, Miller ER et al. (2000) The effect of dietary patterns on blood pressure control in hypertensive patients: results from the Dietary Approaches to Stop Hypertension (DASH) trial. Am J Hypertens 13, 949–955. [DOI] [PubMed] [Google Scholar]
  • 30. van den Brandt PA (2019) Red meat, processed meat, and other dietary protein sources and risk of overall and cause-specific mortality in the Netherlands Cohort Study. Eur J Epidemiol 34, 351–369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Rohrmann S, Overvad K, Bueno-de-Mesquita HB et al. (2013) Meat consumption and mortality – results from the European Prospective Investigation into Cancer and Nutrition. BMC Med 11, 63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Ekpo EB, Udofia O, Eshiet NF et al. (1992) Demographic, life style and anthropometric correlates of blood pressure of Nigerian urban civil servants, factory and plantation workers. J Hum Hypertens 6, 275–280. [PubMed] [Google Scholar]
  • 33. Martini LA & Wood RJ (2009) Milk intake and the risk of type 2 diabetes mellitus, hypertension and prostate cancer. Arq Bras Endocrinol Metab 53, 688–694. [DOI] [PubMed] [Google Scholar]
  • 34. Li Y, Li S, Wang L et al. Burden of disease attributable to main behavioral risk factor of chronic disease inactivity in China, 1990 and 2010. Chin J Prev Med 4, 303–308. [PubMed] [Google Scholar]
  • 35. Zhu T, Fang Z, Yang H et al. (2016) Application of factor analysis and structural equation model in the relationship between dietary patterns and hypertension. Chin J Health Stat 33, 16–19. [Google Scholar]
  • 36. Zhao T (2016) The investigation and analysis of relationship between dietary patterns and hypertension of the elderly Han nationality and Korean nationality residents in rural areas of Jilin Province. Master Thesis, Jilin University.
  • 37. Roerecke M, Tobe SW, Kaczorowski J et al. (2018) Sex-specific associations between alcohol consumption and incidence of hypertension: a systematic review and meta-analysis of cohort studies. J Am Heart Assoc 7, e008202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Wood AM, Kaptoge S, Butterworth AS et al. (2018) Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Lancet 391, 1513–1523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Lei L & Wang JG (2018) Dietary sodium intake and serum uric acid: a mini-review. Pulse (Basel) 6, 124–129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Campagnoli T, Gonzalez L & Santa Cruz F (2012) Salt intake and blood pressure in the University of Asuncion–Paraguay youths: a preliminary study. J Bras Nefrol 34, 361–368. [DOI] [PubMed] [Google Scholar]
  • 41. Meneton P, Jeunemaitre X, Wardener HE et al. (2005) Links between dietary salt intake, renal salt handling, blood pressure, and cardiovascular diseases. Physiol Rev 85, 679–715. [DOI] [PubMed] [Google Scholar]
  • 42. Tzemos N, Lim PO, Wong S et al. (2008) Adverse cardiovascular effects of acute salt loading in young normotensive individuals. Hypertension 51, 1525–1530. [DOI] [PubMed] [Google Scholar]
  • 43. Bragulat E, de la Sierra A, Antonio MT et al. (2001) Endothelial dysfunction in salt-sensitive essential hypertension. Hypertension 37, 444–448. [DOI] [PubMed] [Google Scholar]
  • 44. Silman AJ, Locke C, Mitchell P et al. (1983) Evaluation of the effectiveness of a low sodium diet in the treatment of mild to moderate hypertension. Lancet 1, 1179–1182. [DOI] [PubMed] [Google Scholar]
  • 45. Du S, Batis C, Wang H et al. (2014) Understanding the patterns and trends of sodium intake, potassium intake, and sodium to potassium ratio and their effect on hypertension in China. Am J Clin Nutr 99, 334–343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Ravi S, Bermudez OI, Harivanzan V et al. (2016) Sodium intake, blood pressure, and dietary sources of sodium in an adult South Indian population. Ann Glob Health 82, 234–242. [DOI] [PubMed] [Google Scholar]
  • 47. Kazim S, Ansari RA & Husain K (2016) Alcoholic beverages-induced hypertension and its management. World J Pharm Life Sci 2, 311–338. [Google Scholar]
  • 48. Rahman M, Williams G & Mamun AA (2017) Hypertension and diabetes prevalence among adults with moderately increased BMI (23.0–24.9 kg/m2): findings from a nationwide survey in Bangladesh. Public Health Nutr 20, 1343–1350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Darmon N & Drewnowski A (2008) Does social class predict diet quality? Am J Clin Nutr 87, 1107–1111. [DOI] [PubMed] [Google Scholar]

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