Journal of Nephrology

, Volume 28, Issue 2, pp 173–180

Associations of dietary macronutrients with glomerular filtration rate and kidney dysfunction: Tehran lipid and glucose study

Authors

  • Emad Yuzbashian
    • Nutrition and Endocrine Research Center, Obesity Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical Sciences
  • Golaleh Asghari
    • Nutrition and Endocrine Research Center, Obesity Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical Sciences
    • Department of Clinical Nutrition and Dietetics, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research InstituteShahid Beheshti University of Medical Sciences
  • Fahimeh-Sadat Hosseini
    • Nutrition and Endocrine Research Center, Obesity Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical Sciences
  • Fereidoun Azizi
    • Endocrine Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical Sciences
Original Article

DOI: 10.1007/s40620-014-0095-7

Cite this article as:
Yuzbashian, E., Asghari, G., Mirmiran, P. et al. J Nephrol (2015) 28: 173. doi:10.1007/s40620-014-0095-7

Abstract

Background

Although dietary components may play a role in the development of chronic kidney disease (CKD), data on this topic are scarce. The objective of this study was to investigate the association between macronutrient intakes and CKD in a large non-diabetic adult population-based study.

Methods

This cross-sectional study recruited 5,316 participants aged ≥27 years without diabetes within the framework of the Tehran lipid and glucose study. Dietary intake was collected using a validated food-frequency questionnaire. Macronutrients intake including total-, animal-, and plant-protein, carbohydrate, simple sugar, fructose, total fat, saturated fatty acids, poly- and monounsaturated-fatty acids (PUFA and MUFA), and n-3 and n-6 fatty acids was categorized into quartiles. Anthropometrics, blood pressure, serum creatinine, and fasting plasma glucose and lipids were measured. Estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease Study equation. CKD was defined as eGFR <60 ml/min/1.73 m2.

Result

Mean age of participants was 45.0 ± 12.2 years. Mean eGFR was 71.9 ± 11.1 ml/min/1.73 m2, and 13 % had CKD. After adjustment for serum triglycerides and cholesterol, body mass index, and hypertension, the risk of CKD decreased in the highest quartile compared to lowest quartile of plant protein (OR, 95 % CI) (0.70, 0.51–0.97), PUFA (0.73, 0.55–0.99), and n-6 fatty acids (0.75, 0.57–0.97). However, the risk of CKD increased in the highest quartile of animal protein (1.37, 1.05–1.79) compared to the lowest.

Conclusion

Plant protein, PUFA, and n-6 fatty acids are associated with a lower risk of CKD, independently of hypertension and diabetic mellitus, while animal protein may be a risk factor for CKD in adults.

Keywords

Chronic kidney diseaseGlomerular filtration rateProteinFatty acids

Introduction

Chronic kidney disease (CKD) is defined as abnormal kidney function with or without reduced glomerular filtration rate (GFR). People with CKD may have abnormal pathologic factors or renal failure markers (such as abnormal xerographic, albuminurea, and increased urinary sodium) or GFR lower than 60 ml/min/1.73 m2 for 3 months [1]. Increased risk of cardiovascular disease (CVD) and cerebral vascular accident (CVA) are noticeable complications of CKD [2]. A high prevalence of CKD was reported in 2009 in more than 871,000 American adults [3]. Hence, in recent years, CKD has received considerable attention, and primarily diet is one of the most important factors linked to CKD [4], but significant controversy still exists in the literature, in particular with respect to the association of nutrient and food intakes with the risk of CKD [46].

Dietary protein has been studied more than other nutrients [7, 8] with controversial results [9]. In one study, compared to participants receiving a normal protein diet, individuals with a low-protein diet showed a significantly lower increase in urinary albumin excretion rate, and a lower decline in GFR or creatinine clearance during the intervention [8, 10]; there was also concern about high-protein diet in the elderly regarding its negative effects on kidney function and reduced GFR. Walrand et al. [11] however found no relationship between protein intake and estimated GFR (eGFR) among the elderly. In contrast, in a cross-over study, a high-protein diet increased eGFR among young healthy males [12].

Findings on dietary fat and carbohydrate have led to confusion and serious questions about the influence of simple sugar, fiber, and type of fat on CKD and GFR. High consumption of long-chain n-3 fatty acids (polyunsaturated fatty acids, PUFA) and n-6 fatty acids was associated with reduced and increased risk of CKD, respectively [13]. No significant association was reported between carbohydrates or simple sugar intake and CKD [9, 14]. However, dietary fiber was related to a lower risk of CKD [9].

Considering the lack of studies carried out in Middle-East populations, whose dietary patterns and GFR and incidence of CKD may be different from those of western countries, and inconsistencies in the available data, we aimed to examine the association between macronutrient intakes and CKD among Tehranian adults.

Materials and methods

Subjects

This cross-sectional study was conducted within the framework of the Tehran lipid and glucose study (TLGS), which is an ongoing community-based prospective investigation, aimed at preventing non-communicable diseases (NCD) through developing programs promoting healthy lifestyles and reducing NCD risk factors. This study was conducted on a sample of residents under the coverage of three medical health centers in District No. 13 of Tehran, the capital city of Iran. Briefly, using multistage cluster random sampling methods, 15,005 people, aged ≥3 years, were selected. The first phase of the TLGS began in March 1999 and data collection, at 3-year intervals, is ongoing [15, 16]. During the fourth examination survey of the TLGS (2009–2012), of 12,523 participants who had complete data on their medical histories and underwent physical examinations, a representative sample of participants of 7,956 subjects, was randomly selected for dietary assessment. For the current study, 6,777 participants aged ≥27 years were selected. Subjects with a history of CVD or stroke were excluded because of possible changes in diet. We also excluded 1,329 individuals who had diabetes on the basis self-report of clinical diagnosis or diabetes medication use. In addition, also excluded were subjects who reported daily energy intakes outside the range of 800–4,200 kcal/day (n = 132), or had missing information on systolic blood pressure (SBP) and diastolic blood pressure (DBP), triglycerides, cholesterol, or serum creatinine (n = 1,102). Some individuals fell into more than one exclusion category; eventually 5,316 participants remained for the final analysis.

The protocol of this study was approved by the institutional ethics committee of the Research Institute for Endocrine Sciences, affiliated to the Shahid Beheshti University of Medical Sciences.

Dietary assessment

Dietary data were collected using a using a validated and reliable 147-item food frequency questionnaire (FFQ). Trained dietitians with at least 5 years of experience in the TLGS survey asked participants to designate their consumption frequency for each food item consumed during the previous year on a daily, weekly, or monthly basis. Portion sizes of consumed foods that were reported in household measures were then converted to grams [1719].

For calculation of energy and nutrient intakes, we used the USDA Food Composition Table (FCT) [20]. The Iranian Food Composition Table [21] was used for national foods not listed in the USDA FCT. The reliability and validity of the FFQ were assessed in a random sample (based on sex and age groups), by comparing the data from two FFQs completed 1 year apart and comparing the data from the FFQ and multiple 24-h dietary recalls, respectively. The validity and reliability of the FFQ for dietary intakes were acceptable; the correlation coefficients between the FFQ and multiple 24-h recalls were 0.59 and 0.38 and those between the two FFQs were 0.43 and 0.42 in male and female participants, respectively.

In the current study, the protein content of animal foods (meat, beef, meat products, organ meats, ground meat, poultry, fish, tuna, milk, yogurt, cheese, other dairy products, and egg) and plant foods (cereal and grain products, legumes, nuts, and vegetables) was calculated. Finally, total, animal, and plant protein, animal to plant protein ratio, carbohydrate, simple sugar, fructose, fiber, fatty acids, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), PUFA, and n-3 and n-6 fatty acids were analyzed.

Measurement of clinical and laboratory covariates

Subjects were interviewed by trained interviewers using pretested questionnaires, following which demographic and anthropometric data were collected, as well as medical history of CVD and medication use by trained general physicians. Weight was measured while the subjects were minimally clothed without shoes using digital scales and recorded to the nearest 100 g. Height was measured in standing position, without shoes, using a tape measure while the shoulders were in a normal position. Body mass index (BMI) was calculated as weight in kilograms, divided by height in meters squared. SBP and DBP were measured twice with the subject in seated position after a 15-min rest period using a standard mercury sphygmomanometer. Blood samples were taken after a 12- to 14-h overnight fast. All blood analyses were done at the TLGS research laboratory on the day of blood collection [16]. Plasma total cholesterol (TC) and triglyceride (TG) levels were measured using enzymatic colorimetric kits (Pars Azmoon, Tehran, Iran); serum creatinine (mg/dl) was measured according to the standard colorimetric Jaffe_Kinetic reaction method (Pars Azmon).

Definitions

Hypertension was defined as SBP/DBP ≥140/90 mmHg or current therapy for a definite diagnosis of hypertension [22]. We used the Modification of Diet in Renal Disease (MDRD) equation to express eGFR in ml/min/1.73 m2 of body surface area [23]. The abbreviated MDRD study equation is as follows:
$$ {\text{eGFR }} = { 186 } \times \, \left( {\text{serum creatinine}} \right)^{ - 1. 1 5 4} \times \, \left( {\text{age}} \right)^{ - 0. 20 3} \times \, \left( {0. 7 4 2 {\text{ if female}}} \right) $$

Patients were classified as having CKD based on their eGFR levels according to the National Kidney Foundation guidelines [1], i.e. if eGFR was <60 ml/min/1.73 m2.

Statistical analysis

Statistical analysis was performed using the Statistical Package for Social Sciences (version 15.0; SPSS Inc., Chicago, IL, USA). Continuous variables in the text and tables are reported as mean ± standard deviation (SD) and categorical variables as percentages. The normality of distribution was checked for all variables. Differences between subjects with vs. without CKD were tested using the paired t test, and variables not normally distributed were tested using the Mann–Whitney test.

Dietary intakes were divided into quartiles. The association between macronutrient intake quartiles and the risk of CKD was assessed by multiple logistic regression, and odds ratios (OR) with 95 % confidence intervals (CI) were reported. Furthermore, a general linear model was used to assess differences in mean eGFR among the quartiles of macronutrient intakes. The P value versus the first quartile was also reported for all models.

The initial model was adjusted for age, gender, and total calorie intake. In multivariate models, we further adjusted for serum triglycerides, serum cholesterol, BMI, and hypertension. Tests of linear trend (P for trend) were conducted by assigning the medians of intakes in quartiles treated as a continuous variable. Statistical significance was set at P < 0.05.

Results

Mean (SD) age and BMI were 45.0 (12.2) years and 27.9 (4.7) kg/m2, respectively. Of the 5,316 participants (2,351 men and 2,965 women) who met the inclusion criteria, 700 (13 %) had CKD according to eGFR values. The clinical and biochemical characteristics of participants are shown in Table 1. Participants with CKD were more likely to be female, older, have a higher BMI, and have hypertension. They also had higher serum concentrations of triglycerides and cholesterol and a lower eGFR than those without CKD.
Table 1

Characteristics of participants with and without chronic kidney disease (CKD)

 

Without CKD (n = 4,616)

With CKD (n = 700)

P

Age (years)

42.4 ± 11.1

53.6 ± 11.6

<0.01

Men (%)

47.2

25.7

<0.01

Energy intake (kcal)

2427 ± 758

2284 ± 709

<0.03

BMI (kg/m2)

27.6 ± 15.5

29.0 ± 4.5

<0.01

Systolic blood pressure (mmHg)

113 ± 15

120 ± 19

<0.05

Diastolic blood pressure (mmHg)

76.5 ± 10.6

78.3 ± 10.9

<0.05

Hypertension (%)

28.6

48.4

<0.01

Serum triglycerides (mg/dl)

123 (85–176)

140.0 (100–191)

<0.05

Serum cholesterol (mg/dl)

187 ± 37

201 ± 39

<0.05

eGFR (ml/min/1.73 m2)

74.5 ± 9.2

54.3 ± 5.5

<0.05

Data are mean ± SD or median (interquartile range 25–75) unless otherwise stated

CKD was defined as eGFR <60 ml/min/1.73 m2

t Test and Mann–Whitney U test were used for normally and non-normally distributed continuous variables, respectively, and Chi square test for categorical variables

BMI body mass index, eGFR estimated glomerular filtration rate

Multivariate-adjusted ORs for CKD across quartiles of macronutrients intake are shown in Table 2. Plant protein in model 2 was significantly associated with the risk of CKD (OR 0.70, 95 % CI 0.51–0.97; P for trend <0.05). In the age-, sex-, and energy-adjusted model, an inverse association was observed for PUFA 0.78 (95 % CI 0.56–0.99; P for trend <0.05) and n-6 fatty acids intake 0.74 (95 % CI 0.57–0.98; P for trend <0.05). Further adjustment for serum triglycerides, serum cholesterol, BMI, and hypertension did not change these inverse associations. In contrast, a significant positive association of animal protein (OR 1.37, 95 % CI 1.05–1.79; P for trend <0.05) and of animal to plant protein ratio (OR 1.28, 95 % CI 1.04–1.58; P for trend <0.05) with CKD was consistently evident. Also fructose intake higher than 39.9 g/day, had a higher risk of CKD compared to intake lower than 9.7 g/day (OR 1.32, 95 % CI 1.02–1.72); however, the association did not have a significant trend in the intake quartiles (P for trend = 0.082). A non-significant association was observed between consumption of other macronutrients and CKD.
Table 2

Odds ratio (95 % confidence interval) for chronic kidney disease (CKD) according to the quartile (Q) of dietary intake of different nutrients

 

Intake of nutrient

P for trend

Q1 (n = 1,329)

Q2 (n = 1,329)

Q3 (n = 1,329)

Q4 (n = 1,329)

Total protein (g/day)

54.2 ± 9.1

75.3 ± 5.1

94.3 ± 6.3

131.1 ± 5.6

 

 Model 1

1.00

1.00 (0.80–1.25)

1.03 (0.79–1.33)

0.78 (0.55–1.10)

0.185

 P value

0.163

0.959

0.869

0.162

 

 Model 2

1.00

1.04 (0.80–1.25)

1.02 (0.78–1.32)

0.78 (0.56–1.10)

0.152

 P value

0.166

0.983

0.952

0.144

 

Plant protein (g/day)

22.0 ± 4.1

32.2 ± 2.4

41.9 ± 3.2

61.2 ± 2.7

 

 Model 1

1.00

0.85 (0.68–1.05)

0.79 (0.50–1.23)

0.72 (0.52–0.99)

0.067

 P value

0.177

0.127

0.302

0.039

 

 Model 2

1.00

0.83 (0.67–1.03)

0.78 (0.50–1.20)

0.70 (0.51–0.97)

0.052

 P value

0.159

0.111

0.264

0.034

 

Animal protein (g/day)

21.8 ± 5.2

34.9 ± 3.2

48.2 ± 4.8

82.9 ± 2.2

 

 Model 1

1.00

1.18 (0.95–1.45)

1.22 (0.96–1.54)

1.32 (1.06–1.63)

0.045

 P value

0.213

0.341

0.541

0.023

 

 Model 2

1.00

1.17 (0.90–1.51)

1.29 (0.97–1.72)

1.37 (1.05–1.79)

0.038

 P value

0.241

0.368

0.560

0.017

 

Animal protein to plant protein ratio

0.58 ± 0.14

0.94 ± 0.09

1.32 ± 0.14

2.09 ± 0.99

 

 Model 1

1.00

1.02 (0.83–1.26)

1.32 (1.07–1.62)

1.29 (1.05–1.59)

0.002

 P value

0.010

0.797

0.009

0.015

 

 Model 2

1.00

1.03 (0.83–1.27)

1.30 (1.05–1.60)

1.28 (1.04–1.58)

0.001

 P value

0.014

0.805

0.013

0.018

 

Total carbohydrate (g/day)

215.2 ± 37.0

302.1 ± 20.6

376.9 ± 24.6

518.5 ± 92.5

 

 Model 1

1.00

1.12 (0.85–1.48)

1.00 (0.67–1.45)

1.01 (0.65–1.84)

0.690

 P value

0.692

0.602

0.754

0.636

 

 Model 2

1.00

1.12 (0.85–1.48)

0.98 (0.68–1.42)

1.08 (0.64–1.82)

0.680

 P value

0.625

0.592

0.662

0.601

 

Simple sugar (g/day)

70.2 ± 14.5

105.9 ± 8.4

137.3 ± 10.5

203.4 ± 7.2

 

 Model 1

1.00

1.07 (0.86–1.32)

1.14 (0.90–1.45)

1.11 (0.81–1.35)

0.608

 P value

0.745

0.543

0.270

0.519

 

 Model 2

1.00

1.08 (0.87–1.34)

1.15 (0.90–1.46)

1.11 (0.82–1.52)

0.511

 P value

0.740

0.484

0.267

0.498

 

Fructose (g/day)

9.7 ± 2.6

15.8 ± 1.6

22.3 ± 2.2

39.9 ± 65.4

 

 Model 1

1.00

1.26 (1.02–1.57)

1.38 (1.11–1.75)

1.34 (1.04–1.74)

0.054

 P value

0.032

0.030

0.004

0.024

 

 Model 2

1.00

1.27 (1.02–1.57)

1.39 (1.10–1.74)

1.32 (1.02–1.72)

0.082

 P value

0.038

0.034

0.005

0.032

 

Fiber (g/day)

19.4 ± 2.0

30.4 ± 1.3

40.8 ± 1.8

64.9 ± 2.8

 

 Model 1

1.00

1.05 (0.85–1.31)

1.09 (0.86–1.38)

0.94 (0.71–1.26)

0.605

 P value

0.676

0.412

0.864

0.613

 

 Model 2

1.00

1.07 (0.86–1.32)

1.10 (0.57–1.40)

0.96 (0.72–1.27)

0.592

 P value

0.716

0.402

0.929

0.620

 

Total fat (g/day)

45.2 ± 8.6

65.8 ± 5.0

85.8 ± 6.3

123.1 ± 6.1

 

 Model 1

1.00

1.04 (0.84–1.29)

0.93 (0.72–1.21)

1.06 (0.79–1.47)

0.971

 P value

0.664

0.717

0.608

0.715

 

 Model 2

1.00

1.05 (0.84–1.30)

0.94 (0.73–1.22)

1.08 (0.77–1.50)

0.962

 P value

0.640

0.670

0.653

0.647

 

Saturated fatty acid (g/day)

14.1 ± 2.8

20.9 ± 1.7

27.4 ± 2.2

42.3 ± 6,7

 

 Model 1

1.00

1.00 (0.81–1.23)

1.06 (0.84–1.34)

1.08 (0.81–1.42)

0.725

 P value

0.917

0.975

0.585

0.606

 

 Model 2

1.00

1.02 (0.82–1.26)

1.10 (0.87–1.39)

1.11 (0.83–1.46)

0.548

 P value

0.845

0.851

0.434

0.493

 

Monounsaturated fatty acid (g/day)

14.7 ± 2.8

21.5 ± 1.7

28.1 ± 2.1

42.6 ± 6.6

 

 Model 1

1.00

1.08 (0.88–1.34)

0.87 (0.68–1.11)

1.08 (0.80–1.45)

0.687

 P value

0.140

0.443

0.262

0.621

 

 Model 2

1.00

1.09 (0.88–1.35)

0.88 (0.68–1.12)

1.10 (0.81–1.48)

0.534

 P value

0.139

0.429

0.296

0.541

 

Polyunsaturated fatty acid (g/day)

8.1 ± 1.6

12.5 ± 1.4

17.0 ± 1.4

27.5 ± 6.7

 

 Model 1

1.00

1.00 (0.81–1.24)

0.93 (0.74–1.17)

0.78 (0.56–0.99)

0.023

 P value

0.202

0.961

0.546

0.079

 

 Model 2

1.00

0.99 (0.80–1.23)

0.92 (0.73–1.16)

0.73 (0.55–0.99)

0.023

 P value

0.231

0.963

0.505

0.080

 

n-3 fatty acids (g/day)

0.5 ± 0.1

0.9 ± 0.1

1.3 ± 0.1

3.4 ± 5.5

 

 Model 1

1.00

1.20 (0.93–1.54)

1.07 (0.81–1.39)

1.02 (0.75–1.37)

0.609

 P value

0.751

0.357

0.546

0.939

 

 Model 2

1.00

1.22 (0.95–1.58)

1.08 (0.82–1.42)

1.07 (0.79–1.45)

0.780

 P value

0.769

0.351

0.506

0.842

 

n-6 fatty acids (g/day)

6.6 ± 1.4

10.5 ± 1.0

14.6 ± 1.3

24.7 ± 6.8

 

 Model 1

1.00

1.02 (0.81–1.27)

0.94 (0.75–1.19)

0.74 (0.57–0.98)

0.011

 P value

0.057

0.810

0.631

0.034

 

 Model 2

1.00

1.02 (0.83–1.22)

0.93 (0.74–1.18)

0.75 (0.57–0.97)

0.012

 P value

0.063

0.820

0.589

0.035

 

Model 1 adjusted for age, sex, and energy intake

Model 2 additionally adjusted for serum triglycerides, serum cholesterol, body mass index (BMI), hypertension

Each 20 g increase of plant protein was inversely associated with the risk of CKD (OR 0.84, 95 % CI 0.74–0.96; P = 0.012) and each 20 g increase of animal protein showed a non-significant positive association with the risk of CKD (OR 1.05, 95 % CI 0.99–1.11; P > 0.05; data not shown).

Multivariate-adjusted mean values of eGFR across quartiles of macronutrients intake are shown in Table 3. No significant association was observed between total dietary protein, animal protein, carbohydrate, simple sugar, fructose, SFA, MUFA, or n-3 fatty acids and eGFR. An inverse relationship was observed between the intake of plant protein, dietary fiber, PUFA and n-3 fatty acids and eGFR in the fully adjusted model (P < 0.05).
Table 3

Multivariate-adjusted estimated glomerular filtration rate (eGFR) according to the quartile (Q) of dietary intake of different nutrients

 

Intake of nutrient

P

Q1 (n = 1,329)

Q4 (n = 1,329)

Total dietary protein (g/day)

54.2 ± 9.1

131.1 ± 5.6

 

 Mean (SE)

67.4 (0.32)

68.0 (0.33)

0.451

Plant protein (g/day)

22.0 ± 4.1

61.2 ± 2.7

 

 Mean (SE)

66.7 (0.30)

68.2 (0.32)

0.015

Animal protein (g/day)

21.8 ± 5.2

82.9 ± 2.2

 

 Mean (SE)

68.1 (0.25)

67.3 (0.25)

0.079

Total dietary carbohydrate (g/day)

215.2 ± 37.0

518.5 ± 92.5

 

 Mean (SE)

67.1 (0.37)

68.0 (0.40)

0.629

Simple sugar (g/day)

70.2 ± 14.5

203.4 ± 7.2

 

 Mean (SE)

67.7 (0.25)

67.8 (0.31)

0.652

Fructose (g/day)

9.7 ± 2.6

39.9 ± 65.4

 

 Mean (SE)

67.8 (0.27)

67.7 (0.27)

0.562

Dietary fiber (g/day)

19.4 ± 2.0

64.9 ± 2.8

 

 Mean (SE)

67.0 (0.28)

68.2 (0.29)

0.020

Total dietary fat (g/day)

45.2 ± 8.6

123.1 ± 6.1

 

 Mean (SE)

67.5 (0.31)

67.7 (0.33)

0.967

Saturated fatty acids (g/day)

14.1 ± 2.8

42.3 ± 6,7

 

 Mean (SE)

67.8 (0.28)

67.4 (0.28)

0.582

Monounsaturated fatty acids (g/day)

14.7 ± 2.8

42.6 ± 6.6

 

 Mean (SE)

67.9 (0.29)

67.4 (0.30)

0.681

Polyunsaturated fatty acids (g/day)

8.1 ± 1.6

27.5 ± 6.7

 

 Mean (SE)

67.3 (0.28)

68.2 (0.29)

0.061

n-3 fatty acids (g/day)

0.5 ± 0.1

3.4 ± 5.5

 

 Mean (SE)

67.9 (0.26)

68.4 (0.26)

0.050

n-6 fatty acids (g/day)

6.6 ± 1.4

24.7 ± 6.8

 

 Mean (SE)

67.3 (0.28)

68.4 (0.29)

0.011

Mean (standard error, SE) adjusted for age, sex, and energy intake, serum triglycerides, serum cholesterol, body mass index (BMI), hypertension

Discussion

In this study, we found that higher intake of plant protein was directly associated with eGFR and a decreased risk of CKD, while a higher animal to plant protein ratio and animal protein intake were associated with increased risk of CKD. Also high PUFA and n-6 fatty acids and dietary fiber intakes were directly associated with eGFR and inversely associated with CKD risk.

The present study shows that animal and plant protein can affect kidney function.

Increasing by 20 g the plant protein intake was accompanied by a 16 % decrease in risk of CKD. Lin et al. investigated 3,348 women from the Nurses’ Health Study and found that high intakes of animal protein with a median of 61.2 g/day, after adjusting for age, hypertension, BMI, diabetes, cigarette smoking, physical activity, and cardiovascular disease, damaged renal function; however after additional adjustment for animal fat the result was not significant. Furthermore, plant protein was not associated with CKD or eGFR [24]. Also a cross-sectional study showed that the ORs for the presence of CKD were significantly decreased in the highest quartile of animal protein intake in women [7]. One study reported that in women with mild renal function (eGFR >55 and <80 ml/min/1.73 m2), high intakes of nondairy animal protein were associated with a significantly greater change in estimated eGFR [−1.21 ml/min/1.73 m2 (CI −2.34 to −0.33) per 10 g increase in nondairy animal protein intake] [8]. Higashiyama et al. [7] in a study conducted in 7,404 participants from the National Survey on Circulatory Disorders and the National Nutrition Survey showed that plant protein intake was inversely associated with CKD and increased eGFR. A favorable association of plant protein consumption with CKD may be attributed to the healthy lifestyle associated with higher intake of vegetables. In contrast, animal protein consumption is mostly associated with high intake of saturated fatty acids and sodium from red meat and processed meats, and in fact with a non-healthy life style. Increased plant protein in the diet along with consumption of isoflavones subsequently increased antioxidant capacity [25, 26]. Animal protein consumption has a direct correlation with the risk of stone formation [27, 28] through higher uric acid excretion and decreased excretion of urinary citrate [28, 29]. Decreased kidney function was significantly associated with kidney stones [30].

In the current study, a higher consumption of dietary fiber, independently of hypertension and dyslipidemia, was associated with increased eGFR. Our results are consistent with those of the PREDIMED study, which reported that in 2,123 non-diabetic individuals higher dietary intakes of fiber (36 g/day) had a 32 % decreased risk of CKD [9]. Similarly, in the Blue Mountains Eye Study (1997–1999), participants in the highest quartile of cereal fiber intake had a 50 % reduced risk of incident moderate CKD compared to those in the lowest quartile [14]. Several mechanisms underlie the association between fiber and CKD. First, data show the role of hyperuricemia as a risk factor for CKD and as one of the factors for the progression of existing kidney disease [31]; fiber intake was found to be correlated with a 50 % lower risk of hyperuricemia and with a decrement of uric acid concentration of up to 7.5 % [32]. Second, evidence reveals an inverse association of dietary fiber with the homeostasis model assessment-estimated insulin resistance (HOMA-IR) [33]; in addition, studies suggest that the highest quartile of HOMA-IR had a 70 % increased risk for CKD [34]. Finally, other mechanisms may play a role regarding fiber, such as regulation of post prandial blood sugar [35] and reduction of oxidative stress [36]; these may be explained by the probiotic effects of fiber in the small intestine and regulation of intestinal bacteria, increased insulin sensitivity and glycemic index of diet, and the higher bioaccessibility of antioxidants [37, 38].

We observed a direct association between fructose intake and CKD. In general, the Iranian diet has a high carbohydrate content (~58 % of energy), and carbohydrate-containing foods have fructose. Subjects with higher fructose intake had greater consumption of sweetened beverages than those with lower intake (16 vs. 51 g/day). The major sources of fructose in the current study were fruits and sweetened beverages. The mean intake of fructose, as a percentage of total fructose, from apple, sweetened beverages, grapes, tangerine, and banana was reported as 12, 10, 8, 8, and 7 % respectively. Previous investigations show that high intake of fructose can increase the risk of kidney stones [39] by increasing the excretion of calcium [40], and oxalate [41], and thereby raising the incidence of calcium oxalate stone formation [42]. Furthermore, high fructose intake has been reported to increase the risk of high serum triglycerides, impaired fasting plasma glucose, abdominal obesity, and hypertension leading to metabolic syndrome [4345]; insulin resistance, hypertension, dyslipidemia and inflammation may cause kidney dysfunction [46].

Previous investigations suggested that intake of PUFA and n-6 fatty acids is positively associated with CKD and inversely with eGFR, whereas our results show that the top quartiles of PUFA and n-3 fatty acids intake had a decreased risk of CKD and a higher eGFR. In a study that examined the association between n-6 PUFA intake and renal dysfunction, 2,600 Blue Mountains Eye Study participants aged >50 years were investigated [13], and no significant associations between n-6 PUFA consumption (mean intake 9 g/day) and CKD risk were found. In another study that examined dietary fatty acids and kidney dysfunction, saturated fatty acids were directly associated with albuminuria and CKD (OR 1.33, 95 % CI 1.07–1.66) [47].

Inflammation might be one possible pathophysiologic link between dietary intake and kidney function. C-reactive protein (CRP) concentration has been reported to be positively associated with CKD [48]. Consumption of 16 g/day fiber reduced the probability of elevated CRP concentrations by up to 63 % [49] or substituting SFA intake with n-6 PUFA may decrease inflammation [50]. It is worth mentioning that western dietary patterns characterized by high intakes of saturated fat, animal protein, simple sugar, fructose and low intakes of fiber, PUFA and MUFA are associated with high plasma concentrations of markers of inflammation [51, 52].

Our study has several noteworthy limitations. First, the present study is a cross-sectional study, and hence the association between each nutrient intake and renal function does not prove a causal relationship. Second, as no complete Iranian FCT exists, we had to use the USDA FCT.

In conclusion, the results of our survey suggest that higher intakes of plant protein decrease the risk of CKD, while animal protein has the opposite effect, i.e. increases the risk of CKD. Also higher intakes of PUFA, n-6 fatty acids, and fiber appear to decrease the risk of CKD and are directly associated with eGFR.

Acknowledgments

This work was funded by a grant from the Research Institute for Endocrine Sciences, Shadid Beheshti University of Medical Sciences, Tehran, Iran. All authors read and approved the final manuscript. The authors express appreciation to the participants in the Tehran Lipid and Glucose Study for their enthusiastic support, and the staff of the Tehran Lipid and Glucose Study Unit of the Research Institute for Endocrine Sciences, for their valuable help. We would like to acknowledge Ms. N. Shiva for critical edition of English grammar and syntax of the manuscript.

Conflict of interest

None of the authors had any personal or financial conflicts of interest.

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