Chronic kidney disease (CKD) is recognized a public health problem with poor clinical outcomes, affecting 8–16 % of the population worldwide(Reference Jha, Garcia-Garcia and Iseki1). The burden of CKD has implications for the demand for renal replacement therapy and is associated with increased risks of morbidity, mortality and hospitalization(Reference Jha, Garcia-Garcia and Iseki1,Reference Ayodele and Alebiosu2) . In the USA, CKD is a common chronic disease, with an estimated 26 million adults affected in 2007(Reference Coresh, Selvin and Stevens3). In Taiwan, the average prevalence of CKD was 11·9 % from 1994 to 2006, and it was higher in patients with low socio-economic status (19·9 %)(Reference Wen, Cheng and Tsai4). Zhang et al. reported that the overall prevalence of CKD surpassed 119·5 million (approximately 10·8 % of the general population) in a nationally representative sample of Chinese adults(Reference Zhang, Wang and Wang5). Known risk factors for CKD include obesity, hypertension, diabetes, smoking and use of nephrotoxic medications(Reference Jha, Garcia-Garcia and Iseki1). Moreover, dietary factors have also been recognized as important risk factors for CKD(Reference Banerjee, Liu and Crews6). For example, high intakes of refined grains, high-fat dairy, red meat, processed meat and refined sugars are associated with increased risk of CKD(Reference Khatri, Moon and Scarmeas7).
In recent decades, many epidemiological studies have focused on diet as an important pathogenic factor for the development of CKD(Reference Khatri, Moon and Scarmeas7). Many of these studies have focused on the effects of individual nutrients or foods and food groups(Reference Strippoli, Craig and Rochtchina8–Reference Jankowska, Szupryczyńska and Dębska-Ślizień10). However, in reality, people generally consume a combination of various foods and nutrients in each meal, not individual foods and nutrients. Besides, individual foods and nutrients do not show the interactive and synergistic effects of different foods and nutrients in a whole diet(Reference Hu11). In this context, dietary pattern analysis has emerged in nutritional epidemiology as an alternative approach for assessing the association between diet and chronic non-communicable diseases, since it considers the complexity of the overall diet and may provide guidance for nutrition intervention and education(Reference Shu, Shen and Li12).
Several past studies have explored the association between dietary pattern and CKD risk(Reference Banerjee, Liu and Crews6,Reference Mazidi, Gao and Kengne13–Reference Gutiérrez, Muntner and Rizk18) . However, almost all of these studies have been conducted in Western and Korean populations(Reference Mazidi, Gao and Kengne13,Reference Lee, Lee and Hyun15–Reference Gutiérrez, Muntner and Rizk18) , while research in the Chinese population has been very limited(Reference Shi, Taylor and Riley14). Only one published study has investigated dietary patterns in relation to CKD risk in the Chinese population(Reference Shi, Taylor and Riley14). Furthermore, to the authors’ knowledge, no previous study has assessed the association between overall dietary patterns and the risk of CKD among a middle-aged Chinese population. To fill such gaps in the literature, the present study aimed to identify the major dietary patterns existing in a middle-aged Chinese population and to evaluate their association with the risk of CKD.
Materials and methods
Study population
The present cross-sectional study was conducted in Hangzhou city, the capital of Zhejiang Province, eastern China, from June 2015 to December 2016. The details of the study and the recruitment procedures are described elsewhere(Reference Shu, Shen and Li12). The sample was taken from ten areas (Xihu, Gongshu, Shangcheng, Xiacheng, Bingjiang, Jianggan, Xiaoshan, Yuhang, Fuyang and Linan) and three counties (Tonglu, Chunan and Jiande) by a stratified cluster random-sampling method. We chose two residential villages or communities from every county or area randomly, according to resident health records, with participants being individuals aged 45–59 years residing in the selected villages or communities. A total of 2437 eligible participants (1143 males, 1294 females; aged between 45 and 59 years) were invited to attend a health examination at the Medical Center for Physical Examination, Zhejiang Hospital. Each individual participated in a face-to-face interview with a trained interviewer using written questionnaires. Of the 2437 participants in the original study, we excluded 213 participants who reported implausible energy intakes (<2510 or >16 736 kJ (<600 or >4000 kcal for males); <2092 or >12 552 kJ (<500 or >3000 kcal) for females). Additionally, twenty-five participants were excluded due to missing serum creatinine or urine albumin values; and 195 were excluded because of incomplete anthropometric information and/or missing or incomplete information on their dietary intake. This resulted in a total of 2004 participants (1003 males and 1001 females) for inclusion in the present study.
Assessment of dietary intake
Dietary intake was assessed using a validated semi-quantitative FFQ containing 138 food items. This FFQ was based on the FFQ used in the 2010 China National Nutrition and Health Survey (CNNHS), which has been validated among a middle-aged Chinese population(Reference Shu, Shen and Li12). Before that study, a pilot survey on the validity of the FFQ was performed in this population by comparison with three 24 h dietary recalls. Thus, the validity and reliability of this FFQ in terms of food consumption have been documented elsewhere(Reference Shu, Shen and Li12). Participants were asked to recall the consumption frequency of each food item in the previous 12 months (never, less than 1 time/month, 1–3 times/month, 1–2 times/week, 3–4 times/week, 5–6 times/week, 1 time/d, 2 times/d, 3 times/d) and the estimated portion size, using local weight units (1 Liang = 50 g) or natural units (cups). Data from the FFQ were then converted to grams or millilitres per day and used in the following analyses.
Identification of dietary patterns
Individual food items from this FFQ were aggregated into thirty predefined food groups based on similarity of type of food and nutrient composition (see online supplementary material, Supplemental Table S1). The Kaiser–Meyer–Olkin measure of sample adequacy and Bartlett’s test of sphericity were applied to assess the sample adequacy for factor analysis. Subsequently, we conducted factor analysis (principal component) to derive major dietary patterns. The number of factors retained was based on the following criteria: components with an eigenvalue ≥2·0; scree plot test; and interpretability of the factors. The factors identified were rotated by orthogonal transformation (varimax rotation), which maintains the uncorrelated factors and obtains a simpler structure with greater interpretability(Reference Zheng, Shu and Zhang19). Food groups with a factor loading ≥|0·4| were considered to be the major contributors to each pattern.
The labelling of dietary patterns was based on the interpretation of foods with high factor loadings on each pattern(Reference Wang, Yang and Wang20). The factor scores for each dietary pattern were calculated by summing observed intakes of food groups weighted by their factor loadings. In our analyses, factor scores for each pattern were categorized into quartiles (Q1 represents a low intake of the food pattern; Q4 represents a high intake of the food pattern). The first quartile of each pattern was used as the reference group.
Assessment of anthropometric variables
Body height and weight were measured with participants standing without shoes and wearing light clothing. Height was measured to the nearest 0·1 cm and weight was measured to the nearest 0·1 kg using a balance-beam scale. BMI was calculated as weight in kilograms divided by the square of height in metres. Waist circumference (WC) was measured at the midpoint between the lower rib edge and the upper iliac crest by means of a metric measure with an accuracy of 1 mm, and hip circumference was measured at the maximum level over light clothing by using an inelastic plastic tape(Reference Esmaillzadeh, Kimiaqar and Mehrabi21). All measurements were carried out by trained researchers.
Assessment of other variables
Information on physical activity was collected using the International Physical Activity Questionnaire (IPAQ)(Reference Yang, Shu and Wang22) and expressed as metabolic equivalent hours per week (MET × h/week), in which different activities were ranked on a scale from sleeping (0·9 MET) to high-intensity physical activities (>6 MET). The questionnaire also collected data on participants’ smoking habit and level of education. Smoking data classified participants as ‘never smokers’, ‘former smokers’ and ‘current smokers’. Educational level was classified into three categories (<high school (primary school or below); high school (middle and high school); >high school (junior college or above)). Total dietary energy intake for each participant was estimated through the semi-quantitative FFQ and expressed as kilocalories per day.
Assessment of biomarkers
Blood samples were obtained from participants between 07.00 and 09.00 hours after an overnight fast (12 h) and stored temporarily at –20°C until analysis. After clotting, serum was separated by centrifugation for 15 min at 3000 rpm. The samples were then analysed in the Medical Center for Physical Examination, Zhejiang Hospital for fasting plasma glucose, total cholesterol, TAG, HDL-cholesterol, LDL-cholesterol, serum uric acid, serum creatinine, alanine aminotransferase and aspartate aminotransferase using the Hitachi 7180 automatic biochemical analyser (Tokyo, Japan).
Assessment of blood pressure
Each participant’s resting blood pressure (resting in a seated position for at least 5 min) was taken twice by a trained nurse using a standard mercury sphygmomanometer. The mean of the two measurements was utilized as the final blood pressure value for the study(Reference Chobanian, Bakris and Black23).
Definition of variables
The CKD Epidemiology Collaboration (CKD-EPI) equation was used to estimate glomerular filtration rate (eGFR, in ml/min per 1·73 m2); those with eGFR <60 ml/min per 1·73 m2 or with the presence of albuminuria were classified as having CKD(Reference Chavers, Simonson and Michael24). Serum creatinine was measured using Jaffe’s kinetic methods. Urinary albumin was measured by the immunoturbidimetric method in a spot morning urine sample. Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or the use of antihypertensive medication(Reference Chobanian, Bakris and Black23). Obesity was defined as BMI ≥ 28 kg/m2(Reference Wang, Wang and Yu25) and abdominal obesity was defined as WC ≥ 85 cm for males and WC ≥ 80 cm for females in the Chinese population(Reference Yu, Shu and Shen26).
Statistical analyses
Data were calculated across quartiles of each dietary pattern score, and presented as mean and standard deviation for continuous variables or as number and percentage for categorical variables. If data were normally distributed variables, we used the independent-samples t test to assess the significant differences in continuous variables. If not, the Mann–Whitney U test was applied. A χ 2 test was also used to examine the difference between categorical variables. Logistic regression analysis models were used to examine the associations between dietary patterns and the risk of CKD with adjustment of potential confounding variables. In model 1, we adjusted for age (continuous) and gender. In model 2, we additionally adjusted for income (continuous), education level (<high school, high school, >high school), physical activity level (light, moderate, heavy), smoking status (never, current, former), BMI (continuous), type 2 diabetes mellitus (T2DM; yes/no) and hypertension (yes/no). In model 3, we further adjusted for total energy intake (continuous). All statistical analyses were performed using the statistical software package IBM SPSS Statistics version 22.0 and a two-sided P < 0·05 was considered statistically significant.
Results
The overall prevalence of CKD in our study population was 7·3 %. The demographic characteristics of participants based on CKD status are shown in Table 1 (n 2004). There were significant differences between participants with and without CKD by age, gender, smoking status, education, monthly income and the prevalence of obesity, hypertension and T2DM (P < 0·05).
Table 1 Demographic characteristics of the study participants by chronic kidney disease (CKD) status: Chinese adults aged 45–59 years (n 2004) from Hangzhou, Zhejiang Province, eastern China, June 2015–December 2016
Continuous variables are presented as mean and standard deviation, categorical variables are presented as number and percentage.
*P value for continuous variables from the independent-samples t test or the Mann–Whitney U test, and for categorical variables from the χ 2 test.
The Kaiser–Meyer–Olkin index in the present study was 0·704, with a Bartlett’s test value of P < 0·001. These results indicated that the correlation between different dietary variables was strong enough for performing a factor analysis(Reference Wai, Kelly and Johnson16).
Three distinct dietary patterns were identified by factor analysis. These were labelled as: the ‘traditional southern Chinese’ pattern (higher intakes of refined grains, vegetables, fruits, pickled vegetables, fish and shrimp, bacon and salted fish, salted and preserved eggs, milk, soyabean and its products, miscellaneous bean, fats, drinks); the ‘Western’ pattern (higher intakes of red meats, poultry and organs, processed and cooked meat, eggs, seafood, cheese, fast foods, snacks, chocolates, alcoholic beverages, coffee); and the ‘grains–vegetables’ pattern (higher intakes of whole grains, tubers, vegetables, mushrooms, vegetable oil, nuts, honey, tea). These patterns explained 10·3, 8·5 and 6·8 % of the dietary intake variance, respectively. The factor loading matrices for the dietary patterns are provided in Table 2.
Table 2 Factor loading matrix for the three dietary patterns* found in the study participants: Chinese adults aged 45–59 years (n 2004) from Hangzhou, Zhejiang Province, eastern China, June 2015–December 2016
*Absolute values < 0·4 were excluded for simplicity.
The characteristics of the study participants by quartiles of dietary pattern scores are shown in Table 3. Participants within the top quartile of the traditional southern Chinese pattern were older, smokers and had a higher prevalence of CKD compared with participants in the lowest quartile. Compared with those in the lowest quartile, participants in the highest quartile of the Western pattern were younger, male, smokers, undertook light physical activity, had higher BMI, WC, waist-to-hip ratio and monthly income, and had higher prevalence of obesity, hypertension and CKD. Furthermore, we also found that participants in the highest quartile of the grains–vegetables pattern were older, female, never smokers, with lower BMI, WC, waist-to-hip ratio and total energy intake, had lower prevalence of obesity, hypertension and CKD, and had higher monthly income and physical activity level than those in the lowest quartile.
Table 3 Characteristics of the study participants by quartile (Q) categories of dietary pattern scores: Chinese adults aged 45–59 years (n 2004) from Hangzhou, Zhejiang Province, eastern China, June 2015–December 2016
WC, waist circumference; WHR, waist-to-hip rate; CKD, chronic kidney disease.
Continuous variables are presented as mean and standard deviation, categorical variables are presented as number and percentage.
*P value for continuous variables from the independent-samples t test or the Mann–Whitney U test, and for categorical variables from the χ 2 test.
The major food consumption of the study participants by quartiles of dietary pattern scores is shown in Table 4. Compared with participants in the lowest quartile, those in the highest quartile of the traditional southern Chinese dietary pattern had higher intakes of refined grains, tubers, fruits, fish and shrimp, and eggs. In comparison with the participants from the lowest quartile of the Western dietary pattern, those in the highest quartile had higher intakes of refined grains, red meat, fish and shrimp, and drinks, and lower intake of whole grains. There were also significant differences in the consumption of whole grains, tubers, vegetables, fruits, red meat, fish and shrimp, eggs and drinks across quartiles of the grains–vegetables dietary pattern (P < 0·05).
Table 4 Major food consumption of the study participants by quartile (Q) categories of dietary pattern scores: Chinese adults aged 45–59 years (n 2004) from Hangzhou, Zhejiang Province, eastern China, June 2015–December 2016
The associations between dietary patterns and the risk of CKD by logistic regression analysis are presented in Table 5. After adjusting for potential confounding variables, participants in the highest quartile of the Western dietary pattern had greater odds of CKD (OR = 1·83, 95 % CI 1·21, 2·81; P < 0·05) than those in the lowest quartile; while those in the highest quartile of the grains–vegetables dietary pattern had lower odds of CKD (OR = 0·84, 95 % CI 0·77, 0·93; P < 0·05) than those in the lowest quartile. In addition, we observed no significant association between the traditional southern Chinese pattern and the risk of CKD (P > 0·05).
Table 5 Multivariable-adjusted OR and 95 % CI for chronic kidney disease across quartile (Q) categories of dietary pattern scores: Chinese adults aged 45–59 years (n 2004) from Hangzhou, Zhejiang Province, eastern China, June 2015–December 2016
Q1, lowest quartile of dietary patterns (reference); Q4: highest quartile of dietary patterns.
Model 1: adjusted for gender and age (continuous).
Model 2: further adjusted for income (continuous), education level (<high school, high school, >high school), physical activity level (light, moderate, heavy), smoking status (never, current, former), BMI (continuous), type 2 diabetes mellitus (yes/no) and hypertension (yes/no).
Model 3: further adjusted for total energy intake.
Discussion
In our study of a Chinese population aged 45–59 years, the prevalence of CKD was 7·3 % and we identified three major dietary patterns in the population: traditional southern Chinese, Western and grains–vegetables patterns. The main findings were that the Western dietary pattern was positively associated with the risk of CKD, whereas the grains–vegetables dietary pattern was inversely associated with the risk of CKD. To the best of our knowledge, the present study is the first to examine the relationship of major dietary patterns with the risk of CKD in a middle-aged Chinese population.
It is well known that CKD is a multifactorial chronic disease that may be associated with some aetiologies, including diabetes, obesity, hypertension and dietary factors(Reference Jha, Garcia-Garcia and Iseki1,Reference Banerjee, Liu and Crews6) . In our previous study, findings suggested that higher consumption of whole grains, tubers and vegetables is likely beneficial for the prevention of T2DM, while the higher consumption of red meats, poultry and organs, processed and cooked meat may increase the risk of T2DM(Reference Shu, Shen and Li12). In China, diabetes is one of the main contributors to CKD(Reference Farhadnejad, Asghari and Mirmiran27). Besides, a substantial body of evidence has also demonstrated that diet plays a key role in the development of obesity and hypertension(Reference Lee, Cai and Yang28,Reference Reddy and Katan29) . Jha et al. reported that hypertension and diabetes are the leading causes of CKD worldwide(Reference Jha, Garcia-Garcia and Iseki1). In statistical analyses, there are two main approaches (a priori approach and a posteriori approach) to define dietary patterns. The a priori approach to evaluate dietary patterns uses previously defined scores to assess the overall diet quality, based on prevailing dietary recommendations. So far, most studies focused on a priori dietary indices, such as the Mediterranean Diet Score(Reference Chrysohoou, Panagiotakos and Pitsavos30), the Healthy Eating Index (HEI)(Reference Gopinath, Harris and Flood31) and the Dietary Approaches to Stop Hypertension(Reference Lee, Lee and Hyun15), have found them to be associated with the risk of CKD. In contrast to the a priori approach, the a posteriori approach to identifying dietary patterns is derived from factor analysis, using data from dietary records or FFQ. Several studies have also reported the significant relationship between adherence to a posteriori dietary patterns and the risk of kidney function in the USA(Reference Lin, Fung and Hu17), Europe(Reference Nettleton, Steffen and Palmas32) and China(Reference Shi, Taylor and Riley14). In the China Health and Nutrition Survey, Shi et al. observed that the traditional Southern pattern characterized by high consumption of fruit, soya milk, egg and deep fried products was associated with a 50 % decreased risk of CKD(Reference Shi, Taylor and Riley14). Thus, studies based on different cultures and populations may facilitate a better understanding of the importance of overall diet in the development of CKD.
In the present study, the traditional southern Chinese pattern was characterized by a high consumption of refined grains, vegetables, fruits, pickled vegetables, fish and shrimp, bacon and salted fish, salted and preserved eggs, milk, soyabean and its products, miscellaneous beans, fats and drinks. In our data, no significant association was observed between this pattern and the risk of CKD. Our findings are inconsistent with the previous study(Reference Shi, Taylor and Riley14), which reported that the traditional southern dietary pattern was positively associated with the risk of CKD. The complex nature of this pattern may explain the inconsistent findings to some extent. First, high consumption of refined grains has been found to increase the risk of diabetes, a risk factor for CKD(Reference Hu, Pan and Malik33). Second, some foods (e.g. bacon and salted fish, salted and preserved eggs) have a high content of salt, which can increase the risk of hypertension, an important component of strategies to prevent CKD(Reference Jha, Garcia-Garcia and Iseki1). A recent study by Cheung et al. reported that high sodium intake might increase the risk of CKD in the Third National Health and Nutrition Examination Survey(Reference Cheung, Sahni and Cheung9). Third, drinks contain large amounts of fructose. Previous studies have shown that sugar consumption, especially in the form of fructose, is associated with an increased risk of kidney disease(Reference Karalius and Shoham34). In contrast, vegetables and fruits also contain large amounts of dietary fibre. A previous study demonstrated that consumption of a diet rich in dietary fibre is inversely associated with the risk of CKD(Reference Farhadnejad, Asghari and Mirmiran27). Besides, vegetables, fruits, fish and shrimp, milk, soyabean and its products in this pattern are rich in protective nutrients such as antioxidant vitamins, potassium, magnesium, calcium and phytochemicals. Yang et al. reported that these nutrients might affect kidney function and decrease the risk of CKD(Reference Yang, Fox and Vassalotti35). Meanwhile, the n-3 PUFA abundant in fish and shrimp have also been reported to be associated with decreased risk of obesity and hypertension(Reference Zheng, Shu and Zhang19), which are known to be risk factors of CKD(Reference Huang, Jiménez-Moleón and Lindholm36). These possibilities cannot be excluded in the present study.
In the present study, we found a positive association between the Western dietary pattern and the risk of CKD. Our finding is line with a previous study(Reference Odermatt37), which showed that the Western-style diet was a major risk factor for impaired kidney function and CKD. Several plausible explanations have been proposed to elucidate the detrimental association of this pattern with CKD risk. First, high intake of meat, containing large amounts of saturated fat and cholesterol, is strongly associated with the development of CKD(Reference Lin, Hu and Curhan38). Second, processed and cooked meat, seafood and fast foods in the Western pattern often contain a lot of salt. As already discussed(Reference Jha, Garcia-Garcia and Iseki1), high salt consumption is positively associated with the development of CKD. Third, in our previous study, the Western dietary pattern was found to be significantly associated with increased risk of obesity, hypertension and T2DM, and all of these factors have been documented to be important risk factors for CKD(Reference Jha, Garcia-Garcia and Iseki1,Reference Farhadnejad, Asghari and Mirmiran27) .
The grains–vegetables dietary pattern was characterized by high consumption of whole grains, tubers, vegetables, mushrooms, vegetable oil, nuts, honey and tea. In our analyses, we observed an inverse association between the grains–vegetables pattern and the risk of CKD. These results are in agreement with prior studies(Reference Lin, Fung and Hu17,Reference Nettleton, Steffen and Palmas32,Reference Hsu, Jhang and Chang39) which demonstrated that a diet with higher intakes of whole grains, vegetables and fruits is inversely associated with the risk of CKD. The possible mechanism for the protective effect of the grains–vegetables pattern on CKD may be due to the content of whole grains, vegetables, mushrooms and tea, which are rich in fibres, folate and antioxidants (e.g. vitamin C, vitamin E and other carotenoid compounds). First, as mentioned above, high consumption of dietary fibre can play a protective role in the risk of CKD by lowering the levels of inflammatory markers including IL6, total homocysteine and C-reactive protein(Reference Farhadnejad, Asghari and Mirmiran27,Reference Wannamethee, Lowe and Rumley40) . Second, high consumption of vegetables, containing amounts of folate, is associated with a decreased risk of CKD(Reference Mazidi, Gao and Kengne13,Reference Yang, Fox and Vassalotti35) . Carney reported that after a median follow-up of 4·4 years, combined daily treatment with enalapril (10 mg) and folic acid (0·8 mg), compared with daily enalapril alone, reduced the risk of CKD progression by 21 % and the rate of eGFR decline by 10 % among 15 104 Chinese adults (aged 45–75 years) with hypertension and eGFR > 30 ml/min per 1·73 m2 at baseline(Reference Carney41). Finally, vegetables and tea that provide large amounts of antioxidant substances (e.g. vitamin C, vitamin E, other carotenoid compounds) may contribute to reducing the risk of CKD(Reference Yang, Fox and Vassalotti35). A recent study by Jankowska et al. investigated the status of dietary intake of vitamins in patients with CKD, reporting an inverse relationship between vitamin intake and risk of CKD(Reference Jankowska, Szupryczyńska and Dębska-Ślizień10).
Strengths and limitations
The present study has both strengths and limitations. First, to our knowledge, it is the first study to examine the association between empirically derived dietary patterns and the risk of CKD in a middle-aged Chinese population. Second, dietary information was collected by well-trained dietitians using a validated semi-quantitative FFQ. This ensured that the dietary data we collected are accurate. Third, the present findings are reliable because we have controlled for several potential confounders in the final analysis. Nevertheless, some limitations of the present study need to be acknowledged. First, because of the cross-sectional design of the study, we could not assess the causal association between dietary patterns and the risk of CKD. Therefore, further prospective studies are needed to confirm this finding. Second, statistical methods used to derive dietary patterns, including food grouping and principal component analysis, involve several subjective decisions(Reference Htun, Suga and Imai42). Third, because of the nature of the self-reporting questionnaire, recall bias exists and the estimates of food frequency may be not accurate in FFQ. Fourth, the study participants with previous diagnosed CKD may have been advised to change their diet as part of a strategy to slow the progression of the disease, which in turn could affect our findings. Finally, participants in our study were predominately recruited in the city of Hangzhou and are not a random sample of the general population. Therefore, the present findings may not be extrapolated to the general population.
Conclusions
In conclusion, our study suggested that the Western pattern was associated with higher risk, whereas the grains–vegetables pattern was associated with lower risk of CKD. These findings provide further insight to better understand the association between dietary patterns and CKD risk. Given the cross-sectional nature of our study, future prospective studies are warranted to confirm the causal association between dietary patterns and the risk of CKD.
Acknowledgements
Acknowledgements: The authors thank all participants from the School of Medicine, Huainan Union University and the Department of Nutrition, Zhejiang Hospital for their assistance and support. They also acknowledge staff of the Medical Center for Physical Examination, Zhejiang Hospital for their important contribution to collection of data in this study. Financial support: This study was supported by Natural Science Foundation of Zhejiang (grant number LY17H030008) and Natural Science Research Project of Anhui University (grant number KJ2019A1003). The Natural Science Foundation of Zhejiang and Natural Science Research Project of Anhui University had no role in the design, analysis or writing of this article. Conflict of interest: The authors declare that there is no conflict of interests. Authorship: S.-S.X. and L.S. contributed to the study design. J.H. and Y.-Q.H. performed the statistical analysis for the manuscript and S.-S.X. drafted the paper. All authors contributed to a critical review of the manuscript during the writing process. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the institutional review and ethics committee of Zhejiang Hospital. Written informed consent was obtained from all participants.
Supplementary material
To view supplementary material for this article, please visit https://doi.org/10.1017/S1368980019002805
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