Introduction

Diabetic nephropathy (DN) is one the most important of diabetes complications that can lead to renal dysfunction and end-stage renal disease (ESRD) in diabetic patients [1, 2]. In DN, proteins excrete from kidney [3], and high blood pressure (BP), decreasing kidney function, and excretion of more than 300 mg/day of protein are criteria for DN [2]. Many previous studies have indicated that family history, ethnicity, gestational diabetes, dyslipidemia, obesity, hypertension, and insulin resistance are major risk factors for DN [4]. About 50% of global ESRD is due to diabetes [5], and predictions indicate that 7.7% of the global population will have diabetes by 2030 [6,7,8]. Additional reports have demonstrated that the rate of diabetes will increase by about 70% between 2010 and 2030 [6,7,8], whilst according to a study in Iran, the prevalence of DN was reported to be about 31% [2]. DN, as one of the most serious microvascular complications of diabetes, accounts for 4 million mortalities per year in the world [8,9,10]. As the incidence of type 2 diabetes (T2D) increases, efforts to stop the progression of diabetes to diabetic kidney disease and ESRD are essential. Modification of risk factors, such as precise control of blood glucose, and management of hypertension and hyperlipidemia may contribute to delayed progression of diabetic kidney disease [6]. Diet can be used to control hypertension and blood glucose [11, 12], where it appears that greater adherence to plant-based diets and fewer processed foods can have positive affects diabetic nephropathy [13].

One of the diets that can help with DN is the dietary approaches to stop hypertension diet (DASH). In the DASH diet, individuals are encouraged to consume whole grains, fruits, vegetables, low-fat dairy products, legumes, seeds, fish, and poultry (lean meats), but, on the other hand, consumption of saturated fat, red meats, sweets, and sugar-sweetened beverages (SSBs) is limited [14, 15]. Higher amounts of protein, fiber, magnesium, calcium, potassium, antioxidant components, and unsaturated fatty acids in this diet, can help to reduce the risk of diabetes [16]. Studies have demonstrated that adherence to the DASH diet can prevent diabetes [11]. However, some clinical trials reported inconsistent results about the relationship between the DASH diet and diabetes [12, 17,18,19]. In one study, it was noted that adherence to the DASH diet, concomitant to an exercise program, can effect diabetes in persons with hypertension and overweight [20]. Further, blood pressure, as a risk factor for kidney diseases and diabetic nephropathy, is shown to be reduced by adherence to the DASH diet [21].

Given the high prevalence of diabetes and its progression to complications, such as DN and eventually ESRD, as well as the economic burden on health systems, it is important to discern preventative measures for DN progression. Since the DASH diet is effective in controlling both diabetes and BP, which are serious factors in the progression of DN, we sought to examine the relationship between DASH diet and odds of DN.

Methods

Study population

In the current case–control study, 210 women (105 cases and 105 controls) were recruited from the Kowsar Diabetes Clinic by convenience sampling in Semnan, Iran. 105 diabetic women without DN were recruited as the control group by a 1:1 matching to the DN cases, by age at 1-year intervals and by the duration of diabetes in 6-months intervals, from the same center. The inclusion criteria were women with T2D, aged between 30 and 65 years, and with a history of 3–10 years of T2D. The definition of diabetes used in this study is based on the American Diabetes Association (ADA) criteria: 2-h post-load blood glucose (2hrBG) ≥ 200 mg/dl or fasting blood glucose (FBG) ≥ 126 mg/dl; glycosylated hemoglobin (HbA1c) ≥ 6.5% [22]. The exclusion criteria were having autoimmune disorders or previous history of cancer, coronary angiography, hepatic disease, myocardial infarction, or stroke. Total energy intake of < 500 or > 3500 kcal/day and/or poor response to the food-frequency questionnaire (FFQ) were also considered as exclusion criteria. DN was defined as urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g in a random spot urine sample in the present study [23]. This work was approved by the Ethics Committee of Tehran University of Medical Sciences (Ethics Number: IR.TUMS.REC.1395.2644) and the Ethics Committee of Semnan University of Medical Sciences (Ethics Number: IR. SEMUMS. REC.1395.66) and was conducted in line with the guidelines of the Declaration of Helsinki.

Dietary intake assessment and DASH diet score calculation

A validated FFQ was used to assess dietary intake of participants [24]. Then, participants reported their intake of food items daily, weekly, monthly, or yearly. Portion sizes were finally converted into g/day using household measurements. After that, these amounts were adjusted for energy intake using the residual method [25]. For estimating the energy and nutrient intakes, dietary intakes were analyzed using NUTRITIONIST 4 (First Data Bank, San Bruno, CA) software. For computing the DASH diet score, the components were classified into groups, based on their intake ranking. The component score for nuts and legumes, fruits, vegetables, low-fat dairy products, and whole grains were ranked by decile. For example, decile 1 was assigned 1 point and decile 10, 10 points. For sodium, sweetened beverages, red and processed meats, lower intakes were desired. Hence, the lowest decile was given a score of 10 points and the highest 0 points. The component scores were finally summed to obtain an overall DASH score, ranging from 0 to 80 [26].

Assessment of other variables

Age, diabetes duration, medical history, and current drug usage were recorded by trained interviewers. Weight (kg) was measured while subjects were wearing light clothing, without shoes. Body mass index (BMI, kg/m2) was computed as weight (kg) divided by the square of height (m). After a resting period ≥ 5 min, BP was measured on the left arm using a manual sphygmomanometer. A validated physical activity questionnaire (IPAQ) [27] was utilized to evaluate individuals’ physical activity (PA).

Blood biomarkers assessment

Participant’s past 3 months medical records were used to obtain their fasting blood sugar (FBS), 2hrBG, HbA1c, total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), total serum creatinine (Cr), and blood urea nitrogen (BUN) levels.

Statistical analysis

The distribution of the quantitative variables was assessed using the Kolmogorov–Smirnov test. Independent samples T tests and chi-square tests were used to compare quantitative and qualitative variables between cases and controls, and they were presented as mean ± SD and frequency (%), respectively. Energy-adjusted dietary intakes, across DASH diet scores, were compared using independent samples T tests. To investigate the relationship between DASH diet and DN, logistic regression was used to determine the odds ratio (OR) of DN and its 95% confidence interval (CI).

Linear regression analysis was used to determine the relationship between DASH score and its subcategories with biochemical markers, in crude and adjusted models. In adjusted models, energy intake, age, PA, BMI, cardiovascular disease history, and type of drug used (angiotensin receptor blockers, angiotensin-converting enzyme inhibitors, beta-blockers, metformin, sulphonylurea, and insulin) were controlled. Data analysis was performed using SPSS software (Version 25, SPSS Inc., Chicago, IL, USA) and P < 0.05 was, a priori, considered statistically significant.

Results

Participants and study characteristics across case and control group

Overall, 210 subjects were enrolled in the study, including 105 cases and 105 control with DM. The basic characteristics of the participants, according to case and control status, are presented in Table 1. There was a significant difference between serum albumin, ACR, Hb1Ac, LDL, creatinine, Angiotensin receptor blockers (ARBs), and Angiotensin-converting enzyme (ACEIs) usage (P < 0.001). The frequency of cases and controls across following DASH diet are shown in Fig. 1. Counts of patients above vs below the median of DASH diet scores were significantly different (P < 0.001). 64.5% of subjects who were in the lower adherence group had DN, whilst 35.5% of patients with higher adherence had DN. On other hand, mean ± SD of age, BMI, and diabetes duration of cases was 55.33 ± 7.04 (years), 28.686 ± 4.74 (kg/m2), and 7.60 ± 2.21 (years), respectively (P > 0.05). Finally, mean ± SD of FBS (167.10 ± 50.62) and cholesterol level (185.15 ± 38.12) (mg/dl) were marginally higher among the cases group (P < 0.05).

Table 1 General characteristics of population based on case and control groups
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Fig. 1
figure 1

The frequency of cases and controls across lower and higher adherence of DASH diet

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Dietary intakes of cases and controls across lower and higher adherence of DASH diet

The DASH diet subgroups, macronutrients, fat and water soluble vitamins, minerals, and other parts of dietary intakes were reported in Table 2. Overall, the adherence to DASH diet was divided to two groups of above and below the median. Among DASH diet subcategories, consumption of fruits, vegetables, nuts and legumes, and dairy products were increased in both case and control groups (P < 0.05). Meat intake was decreased among case and control groups, whilst sweetened beverages and whole grains intake were significantly different between lower and higher adherence of DASH diet in the case group with DN (P < 0.05).

Table 2 Dietary intakes of cases and controls across low and high adherence of DASH diet
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The DN cases consumed significantly more magnesium and zinc (P < 0.05). In addition, energy, protein, carbohydrate, and fat intake were decreased with increasing DASH diet adherence in the control group (P < 0.05). Among simple sugars, consumption of sucrose was significantly reduced in higher DASH diet adherence in both cases and controls (P < 0.05).

Baseline characteristics of cases and controls groups among lower and higher adherence of median of DASH diet

The general characteristics and dietary intakes of the participants in increasing adherence of the DASH diet were shown in Table 3. The participants in the highest column of the DASH diet had lower albumin serum level (6.11 ± 5.097), height (159.93 ± 6.177), BUN (0.87 ± 0.18), and higher creatinine level (0.87 ± 0.18), after adjusting for confounders including age, energy intake, and physical activity, across the control group (P < 0.05). Compared to the control group, diastolic blood pressure (DBP), FBS, and HbA1c levels were decreased in case groups after adjusting for mentioned confounders, aligned with higher adherence to the DASH diet(P < 0.05).

Table 3 Baseline characteristics of cases and controls groups among low and high adherence of DASH diet
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Association between adherence to a DASH-style diet and the odds of DN

The ORs for DN across the lowest and highest median of the DASH diet subcategories were detailed in Table 4. In the crude model of total DASH diet, participants in the high adherence group of the DASH diet were 74% less likely to have DN (ORs: 0.26; 95% CI 0.14–0.47; P < 0.001). After controlling for energy intake, age, physical activity, and BMI, adherence to the DASH diet remained negatively associated with the odds of DN (ORs: 0.20; 95% CI 0.10–0.38; P < 0.001) (Model 1). In model 2, which was adjusted for model 1 + diabetes duration, cardiovascular diseases history, and drug usage (ARBs, ACEIs, beta-blockers, metformin, sulphonyl urea, and insulin), the inverse association remained (OR: 0.16; 95% CI 0.07–0.34; P < 0.001). Among DASH diet subcategories, intakes of vegetables (80%), fruits (88%), nuts and legumes (87%), and low-fat dairy (73%) decreased the risk of DN in model 2 (P < 0.001). In addition, intake of whole grains was inversely associated with high risk of DN among women (OR: 0.51; 95% CI 0.27–0.95; P = 0.03) in model 2. Moreover, logistic regression showed that red meat consumption increased the risk of DN in the model 2 (OR: 1.20; 95% CI 0.10–0.41; P < 0.001).

Table 4 Odds ratio and 95% confidence intervals of diabetic nephropathy according to following DASH diet among subjects
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Association between DASH-style diet and biochemical markers of participants

The association between DASH score and subcategories with biochemical markers, are presented in Table 5. There was no significant association between FBS, TG, HDL, total cholesterol and BUN with DASH score and subcategories (P > 0.05). However, there was an inverse relationship between DASH score and both creatinine (β = − 0.08, 95% CI  = − 0.14, − 0.01, P value = 0.02) and DBP (β = − 6.50, 95% CI  = − 11.91, − 1.08, P value = 0.01), after adjusting for potential confounders the results remained significant. Among DASH diet subcategories, low fat dairy was inversely related to LDL (β = − 16.28, 95% CI  = − 29.7, − 2.77, P value = 0.01) (in model 2), DBP (β = − 6.41, 95% CI  = − 11.63, − 1.20, P value = 0.01), and Alb (β = − 6.85, 95% CI  = − 2.11, − 11.59, P value = 0.005) (in crude and adjusted model). Additionally, vegetables maintained negative association with ACR (β = − 45.05, 95% CI  = − 90.95, 0.84, P value = 0.04), and SBP (β = − 9.09, 95% CI  = − 15.94, − 2.25, P value = 0.01) after adjusting in model 1 and 2. Fruits also displayed a negative association with creatinine in model 2 (β = − 0.06, 95% CI  = − 0.13, − 0.001, P value = 0.04). Moreover, nuts and legumes were inversely associated with ACR (β = − 76.73, 95% CI  = − 130.05, − 23.42, P value = 0.005) and Alb (β = − 7.04, 95% CI  = − 12.82, − 1.27, P value = 0.01), after adjusting for confounders, the results remained significant. Whole grains had a negative relationship with both creatinine after adjustment (β = − 0.07, 95% CI  = − 0.14, 0.01, P value = 0.02), and DBP (β = − 6.47, 95% CI  = − 11.72, − 1.22, P value = 0.01). Besides, sodium demonstrated a positive association with DBP (β = − 6.58, 95% CI  = − 13.19, − 0.51, P value = 0.03), which remained significant after adjusting. Red meat was also positively related to creatinine (β = − 0.096, 95% CI  = − 0.16, 0.02, P value = 0.009) after adjusting in model 2.

Table 5 Association between DASH-style diet and biochemical markers of participants
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Discussion

The present study demonstrated that higher adherence to the DASH diet yielded a 74% reduction in the odds of DN. These findings support our hypothesis that people who adhere to the DASH diet decrease the risk of DN among the adult population. After adjusting for confounders, such as energy intake, age, physical activity, and BMI, the DASH diet remained negatively associated with the odds of DN. Thus, the DASH diet appears to be effective in controlling the risk of DN among adults. To our knowledge, the present study is the first to examine the effects of the DASH eating pattern and subcategories on DN and other biochemical markers among type 2 diabetes.

As the incidence of T2D increases, efforts to stop the progression of diabetes to diabetic kidney disease and ESRD are essential [28]. There are several main risk factors for DN, including, family history, ethnicity, gestational diabetes, dyslipidemia, hyperglycemia, obesity, being a smoker, high blood cholesterol, hypertension, and insulin resistance. The DASH diet, which promotes the consumption of fruits, vegetables, low-fat dairy products, and sodium restriction, was created for people with hypertension [29]. This diet is rich in antioxidants, unsaturated fatty acids, fiber, and low-fat dairy, all of which may be crucial for reducing insulin resistance, inflammatory levels, and metabolic disruption [30].

In the present study we found that, there was an inverse relationship between DASH score and serum Cr. In line with our study, a cross-sectional study of participants with T2D in Taiwan demonstrated that greater adherence to a healthy diet and higher intake of fish and vegetables was negatively correlated with serum Cr, and positively associated with eGFR [31]. Given our results, nuts and legumes were inversely associated with ACR and Alb. The DASH diet plan’s positive benefits on metabolic parameters could possibly be attributed to a larger consumption of legumes. Additionally, the DASH diet includes more soy products, which may be linked to better cardio-metabolic health and lower plasma levels of CRP [32]. The DASH study shown that despite an increase in calories from protein, the DASH diet did not raise urinary albumin excretion rate [33].

In our study, we indicated an inverse association between DASH score and DBP. Previous investigations indicated that this diet can be used to control hypertension and blood glucose, according to recent studies [11, 12]. It appears that greater adherence to plant-based diets and less consumption of processed foods can have a positive impact on DN [13]. In the DASH diet, individuals are encouraged to consume whole grains, fruits, vegetables, low-fat dairy products, legumes, seeds, fish, and poultry (lean meats), whilst consumption of saturated fat, red meats, sweets, and SSBs is restricted [14, 15]. Higher amounts of protein, fiber, magnesium, calcium, potassium, antioxidant components, and unsaturated fatty acids in this diet, can help to reduce the risk of diabetes [34]. Several studies have demonstrated the association between the DASH diet and the risk of diseases; for instance, the insulin resistance atherosclerosis study demonstrated that adherence to the DASH dietary pattern may have the potential to prevent T2D [11].

In the present study, vegetables maintained negative association with ACR and SBP after adjusting for confounding variables. Additionally, in an adjusted model, Fruits also displayed a negative association with Cr.

A prospective cohort study among south Korean found that intake of a diet rich in fruits, and vegetables were associated with a decreased risk of CKD [35]. The DASH trial demonstrated that dietary patterns rich in vegetables, fruit, and low-fat dairy products can reduce systolic and diastolic blood pressure [36]. Hypertension is a risk factor for kidney diseases and also DN, which is reduced by adherence to the DASH diet, according to recent studies [21]. Concomitantly intervention studies have revealed that the DASH diet has beneficial effects on total and LDL cholesterol, insulin sensitivity, and weight management [37, 38]. Moreover, in postmenopausal women without diabetes, better adherence to the DASH diet was associated with a lower prevalence of metabolic syndrome, according to a cross-sectional study using Korean National Health and Nutrition Examination Survey (KNHANES) [39]. Additionally, the DASH diet can significantly protect against cardiovascular disease (CVD) 20%, Coronary heart disease (CHD) 21%, stroke 19%, and Heart failure (HF) 29%, according to a systematic review and meta-analysis of observational prospective studies [38]. In addition, elderly adults with a high intake of the DASH-style diet were reported to have lower odds of having Chronic kidney disease (CKD), according to a cross-sectional study using the KNHANES data [40]. Furthermore, the DASH diet has been associated with a lower risk of kidney damage, particularly the decline in glomerular filtration rate [35, 41]. Additionally, the relationship between the DASH diet and CKD progression or CKD-related complications has not been reported in East Asia [42]. Some clinical trial studies have demonstrated inconsistent results regarding the relationship between the DASH diet and diabetes [43, 44]. In addition, adherence to the DASH diet with a routine exercise program can affect diabetes in persons with hypertension and overweight [38]. The National Health and Nutrition Survey, Japan, found that better adherence to the DASH diet was inversely associated with metabolic risk factors, including waist circumference, TC, LDL cholesterol, and BMI [45]. Generally, the potential direct relationship between greater adherence to the DASH and kidney function has been largely explained by positive impacts of this diets on levels of cardiometabolic risk factors including blood pressure [46, 47], glycemic control [11], and lipid profile [48].

Furthermore, concordant with our hypothesis, there is an association between the DASH diet and the risk of DN. Evidence regarding the association between diets and DN patients is scarce; however, a case–control study among 105 women demonstrated that adherence to the DASH was inversely and dose-dependently associated with risk of DN [49]. Moreover, a prospective cohort study revealed that a DASH-style diet was associated with a lower risk for CKD and DN [50]. In addition, a prospective study using data from the Singapore Chinese Health Study found that greater adherence to a DASH-style diet was associated with a 29% lower risk of developing T2D [51]. The DASH dietary pattern may be beneficial both in the prevention and management of diabetes mellitus nephropathy [41].

The strengths of our study include that, to best our knowledge, this is the first study to have examined the association of DASH diet and risk of DN among Iranian adults, as well as considering a wide range of confounders. On the other hand, our study has several limitations that should be noted. The case–control design of the study precludes casual inferences. The second limitation is the use of FFQ for dietary assessment, which might result in misclassification of participants. Although we controlled for several confounding variables, the existence of residual confounding cannot be excluded. Furthermore, as in all epidemiologic studies, random errors might affect our results because diet and lifestyle information might be collected with some degree of error.

Conclusion

We found that DASH diet adherence may be associated with lower odds of DN. Further studies, with large sample sizes, are needed to confirm the veracity of this association.