Open Access

Investigation of aquaporins and apparent diffusion coefficient from ultra-high b-values in a rat model of diabetic nephropathy

European Radiology Experimental20171:13

https://doi.org/10.1186/s41747-017-0016-3

Received: 9 March 2017

Accepted: 12 July 2017

Published: 10 October 2017

Abstract

Background

To assess kidney damage in a rat model of type-2 diabetic nephropathy based on apparent diffusion coefficient (ADC) data obtained from ultra-high b-values and discuss its relationship to the expression of aquaporins (AQPs).

Methods

This study was approved by the institutional Animal Care and Use Committee. Thirty male Sprague-Dawley rats were randomised into two groups: (1) untreated controls and (2) diabetes mellitus (DM). All rats underwent diffusion-weighted imaging (DWI) with 18 b-values (0–4500 s/mm2). Maps of low ADC (ADClow), standard ADC (ADCst) and ultra-high ADC (ADCuh) were calculated from low b-values (0–200 s/mm2), standard b-values (300–1500 s/mm2) and ultra-high b-values (1700–4500 s/mm2), respectively. The expression of AQPs in the kidneys was studied using immunohistochemistry. Laboratory parameters of diabetic and kidney functions, ADClow, ADCst, ADCuh, and the optical density (OD) of AQP expression in the two groups were compared using an independent t test. Correlations between ADCs and the OD of AQP expression were evaluated by Pearson’s correlation analysis.

Results

ADCuh were significantly higher in the cortex (CO), outer stripe of the outer medulla (OS) and inner stripe of the outer medulla (IS), and the OD values of AQ-2 were significantly higher in the OS, IS and inner medulla (IM) in DM animals compared with control animals. ADCuh and OD values of AQP-2 expression were positively correlated in the OS, IS and IM of the kidney.

Conclusions

ADCuh may work as useful metrics for early detection of kidney damage in diabetic nephropathy and may be associated with AQP-2 expression.

Keywords

Diffusion-weighted imaging (DWI) Diabetic nephropathy Aquaporins Apparent diffusion coefficient (ADC) Ultra-high b-values

Keypoints

  • Diffusion-weighted imaging (DWI) with ultra-high b-values may be valuable for the detection of diabetic nephropathy

  • Apparent diffusion coefficient (ADC) calculated using ultra-high b-values is different between diabetes mellitus (DM) and control animals

  • ADCuh may be associated with water transportation by aquaporins

Background

As a global health problem that affects multiple organs of the body, diabetes mellitus (DM) is known to cause proteinuria, nephrotic syndrome in the kidney, and may ultimately result in renal failure [1]. A high urinary glucose level induces elevated urinary osmotic pressure, which in turn reduces renal water reabsorption, and finally leads to polyuria [2]. Although persistent polyuria is reported to cause severe dehydration, shock from serious dehydration rarely develops in diabetic patients. It is, therefore, believed that the kidney has a compensating mechanism that alleviates diabetic dehydration. Water channels or aquaporins (AQPs) are membrane-associated proteins that regulate transcellular water movement across the cell membrane. So far, at least 13 mammalian AQPs (AQP 0–12) have been identified, eight of which are constitutively expressed in the kidney [3] and play a vital role in the reabsorption of water from the renal tubular fluid [4].

Several recent studies have used diffusion-weighted imaging (DWI) methods to evaluate renal function in diabetic nephropathy [57]. However, their values of the apparent diffusion coefficient (ADC) obtained from standard b-values (0, 1000 s/mm2) do not differentiate early DM patients from controls in a consistent manner. Through linear regression, images taken at various b-values can be used to calculate the ADC in a particular region of interest. However, ADC estimates are susceptible to the impact of physiological changes in vivo, such as tubular flow and vascular flow, because true water diffusion contributes mainly to the signal acquired with b-values larger than approximately 200 s/mm2 whereas the signal from blood perfusion with a rapid flow in tubular and vascular tubes disappears together with b-values greater than 200 s/mm2 [8]. Recently, researchers who have used ultra-high b-value-DWI in some diseases suggested that ADC, when calculated using ultra-high b-values (ADCuh), may help to reveal AQP expression [9]. Thus, the question is: Can the slower water diffusion through the AQPs be quantified by DWI?

Therefore, this study, was aimed at assessing kidney damage in DM to verify the association between ADCuh and the permeability of AQPs in the cell membrane.

Methods

Animals and DM induction

All the experiments were conducted according to the guidelines of the Animal Care and Use Committee of Sichuan University, Chengdu, China and the Animal Ethics Committee Guidelines of the Animal Facility of the West China Hospital, Chengdu, China and had been given prior approval by the Experimental Animal Management Committee of Sichuan University, Chengdu, China under Contract 2016007A. Two groups of male Sprague-Dawley rats (Dashuo, Chengdu, China) were examined by magnetic resonance imaging (MRI): (1) untreated controls (n = 15) and (2) diabetic animals (n = 15). At the age of 6 to 7 weeks (weighing 180–200 g), the animals were fed a high-sucrose and high-fat diet (SCXK2014-028 Research Diet). Four weeks after the diet change, at the age of 10 to 11 weeks, DM was induced in group-2 animals by an intraperitoneal injection of a low dose of streptozotocin (STZ) (40 mg/kg Meilune). After the STZ treatment, 15 animals in the DM group that developed diabetes with a hyperglycemia of >16.7 mmol/lr underwent MRI.

Physiological and laboratory parameters

General parameters, such as body weight and food and water intake, were monitored regularly. Forty days after diabetes induction, urine was collected over a period of 24 h and urinary albumin and creatinine concentrations were quantified. Blood was collected from the tail vein. Serum glucose, urea, albuminuria and creatinine were determined using a clinical analyser (cobas c311, Roche, Basel, Switzerland). Creatinine clearance was calculated from serum and urinary creatinine concentrations and the urine volume, which was collected over a period of 24 h.

MRI protocol

Forty days after DM induction, all the animals underwent MRI examination using a 7.0-T small-animal scanner (Bruker Biospec 70/30, Ettlingen, Germany) and a rat heart array coil (Bruker Z114784). The animals were anaesthetised with 2% isoflurane/oxygen mixture throughout the MRI examination. The body temperature was kept constant at 37 °C using a heating blanket monitored with a rectal temperature probe. The respiration was monitored and kept constant at between 35 to 45 breaths per min throughout the examination. Respiratory motion was reduced by imaging the animals in the supine position and with respiratory trigger. Standard T2-weighted imaging was performed in axial and oblique coronal planes (repetition time/echo time 2032/27 ms; number of excitations 4; matrix 256 × 256; field of view 60 × 60 mm2; slice thickness 1.5 mm.) The coronal plane was orientated along the long axis of the kidney. To assess the diffusion, fat-saturated echo planar diffusion-map sequences were acquired (repetition time/echo time 800/30 ms; number of excitations 4; matrix 128 × 128; field of view 60 × 60 mm2; slice thickness 1.5 mm; three slices; b-values 0, 50,100, 150, 200, 300, 500, 800, 1000, 1300, 1500, 1700, 2000, 2500, 3000, 3500, 4000 and 4500 s/mm2). Scan times for the T2-weighted sequence and the diffusion-map sequence were about 4 min and 5 min, respectively. The total scan time of the whole examination was approximately 16 min.

MRI data analysis

Parameter maps of ADC were calculated using a monoexponential fit. The images were analysed by a radiologist blinded to the laboratory findings on an external workstation using Bruker software (ParaVision 5.0pl3 in/opt/PV5.0). The renal cortex (CO), the outer stripe of the outer medulla (OS), the inner stripe of the outer medulla (IS) and the inner medulla (IM) were identified as previously described [10]. On b = 0 images (which is essentially a T2-weighted image), regions of interest were placed onto the four anatomical layers of the kidney and copied to the ADC maps (Fig. 1). Maps of low ADC (ADClow), standard ADC (ADCst) and ADCuh were calculated from low b-values (0–200 s/mm2), standard b-values (300–1500 s/mm2) and ultra-high b-values (1700–4500 s/mm2), respectively. Mean ADC values were determined respectively for each of the anatomical layers in all the animals.
Fig. 1

Example of diffusion maps in an animal with streptozotocin-induced diabetes mellitus (DM) and a control animal. a-d, b = 0 image, ADClow, ADCst, and ADCuh maps in a DM animal. e-h, b = 0 image, ADClow, ADCst and ADCuh maps in a control animal. On b = 0 images, the four anatomical layers of the kidney can be distinctively observed. In these diffusion maps, regions of interest were placed onto the cortex (1), the outer stripe of the outer medulla (2), the inner stripe of the outer medulla (3) and the inner medulla (4) as exemplarily demonstrated in a. In DM animals, ADCuh increased apparently in CO, OS and IS compared with controls (panels d and h)

Renal immunohistochemistry

Kidney tissue samples (DM n = 15; controls n = 15) from the rats were obtained immediately after MRI. The rats were euthanised and underwent fixation via abdominal aortic perfusion with 4% paraformaldehyde in 1% phosphate-buffered saline. Coronal kidney sections (from 2 to 3 mm thick) were fixed overnight and embedded in paraffin. The paraffin sections were cut on a sliding microtome at 4 μm per section.

The sections were stained for AQP-1, AQP-2 and AQP-4 using immunohistochemical staining with anti-AQP-1 antibodies (ab15080, Abcam, Shanghai, China), anti-AQP-2 antibodies (ab15081, Abcam, Shanghai, China) and anti-AQP-4 antibodies (ab9512, Abcam, Shanghai, China), respectively. After accepting the antigen retrieved by using citrate antigen retrieval solution (P0081, Beyotime, Shanghai, China), the tissue sections were treated with 3% hydrogen peroxide to inactivate endogenous peroxidase. The sections were then incubated in a 1:20 goat serum for 30 min, rinsed, and incubated overnight at 4 °C with a 1:200 dilution of primary antibodies, including anti-AQP-1 antibodies, anti-AQP-2 antibodies and anti-AQP-4 antibodies. After three washes with phosphate-buffered saline, the sections were incubated for 90 min with biotinylated goat anti-rabbit immunoglobulin G antibody (SP-9001; Zhongshan Golden Bridge Biotechnology, Beijing, China). The sections underwent another three washes with phosphate-buffered saline and were then incubated with avidin-biotinylated horseradish peroxidase (SP-9001; Zhongshan Golden Bridge Biotechnology, Beijing, China) for 90 min. The sections were rewashed three times in phosphate-buffered saline and then incubated with diaminobenzidine and hydrogen peroxide (ZLI-9018; Zhongshan Golden Bridge Biotechnology, Beijing, China). The nucleus was stained with hematoxylin. The sections were then rinsed in water for 10 min, dehydrated and covered with a coverslip for microphotography.

High-powered images (×400 magnification) of the four anatomical layers of each kidney were taken separately (Leica ICC50 HD, Wetzlar, Germany). To analyse AQP-1, AQP-2 and AQP-4 staining, the regions covered by AQP-1, AQP-2 and AQP-4 staining were calculated by optical density (OD) [11]. We measured the OD values three times and averaged the results.

Statistical analysis

Statistical analysis was performed with SPSS 19.0 (SPSS Inc., Chicago, IL, USA). Quantitative data were tested for normal distribution by using the Kolmogorov-Smirnov test. Normally distributed data were expressed as mean ± standard deviation. Laboratory parameters of diabetic and kidney functions, ADClow, ADCst, ADCuh, and the OD of AQPs expression in the two groups were evaluated with an independent t test with equal variance.. Correlation analysis between ADCs and OD values of AQPs of the anatomical layers was performed by Pearson’s correlation analysis. To avoid the introduction of type-I errors, Bonferroni’s correction was applied to the preset level of significance (p < 0.050). The significance levels were, therefore, as follows: p < 0.0083 (0.05/6) in Table 1; p < 0.017 (0.05/3) in Tables 2, 3 and 4.
Table 1

Characteristics of the two groups

 

Controls

DM

p value

Body weight (g)

409 ± 23

286 ± 41

<0.001

Blood glucose (mmol/l)

5.8 ± 0.7

28 ± 4.1

<0.001

Creatinine clearance (ml/min)

1.5 ± 0.7

2.2 ± 1.1

0.048

Serum urea (mmol/l)

6.7 ± 0.6

9.7 ± 4.0

0.008

Urine output ml/24 h

9.5 ± 2.2

134 ± 21

<0.001

Urine albumin mg/l

2.5 ± 1.0

1.5 ± 0.7

0.007

Data are presented as mean ± standard deviation. Body weight and laboratory parameters were obtained after the day of the magnetic resonance imaging (MRI) scan. Comparisons between the diabetic group (DM) and controls were made by using an independent t test. Based on Bonferroni’s correction, p < 0.0083 (0.05/6) was considered to indicate a statistically significant difference

Table 2

Apparent diffusion coefficients (ADCs) in diabetes mellitus (DM) animals and controls

Position

Parameter (×10-3 s/mm2)

Controls

DM

p value

CO

ADClow

2.64 ± 0.525

2.52 ± 0.545

ns, 0.392

ADCst

1.22 ± 0.230

1.30 ± 0.228

ns, 0.220

ADCuh

0.196 ± 0.078

0.262 ± 0.050

<0.001

OS

ADClow

2.56 ± 0.543

2.36 ± 0.519

ns, 0.146

ADCst

1.27 ± 0.226

1.31 ± 0.243

ns, 0.501

ADCuh

0.190 ± 0.088

0.240 ± 0.067

0.016

IS

ADClow

2.33 ± 0.636

2.15 ± 0.536

ns, 0.250

ADCst

1.29 ± 0.275

1.22 ± 0.218

ns, 0.316

ADCuh

0.203 ± 0.081

0.268 ± 0.096

0.006

IM

ADClow

2.77 ± 0.561

2.66 ± 0.550

ns, 0.447

ADCst

1.67 ± 0.305

1.60 ± 0.317

ns, 0.405

ADCuh

0.140 ± 0.066

0.190 ± 0.101

ns, 0.027

Data are presented as mean ± standard deviation. Statistical analysis was performed using an independent t test between the two groups. DM diabetes mellitus, CO cortex, OS outer stripe of the outer medulla, IS inner stripe of the outer medulla, IM inner medulla, ADC low ADC calculated using the low b-values, ADC st ADC calculated using the standard b-values, ADC uh ADC calculated using the ultra-high b-values, ns not significant. Based on Bonferroni’s correction, p < 0.017 (0.05/3) was considered to indicate a statistically significant difference

Table 3

Optical density of aquaporins (APQs) positively stained on different anatomical layers of the kidney in diabetes mellitus (DM) animals and controls

Position

AQP

Controls

DM

p value

CO

AQP-1

0.050 ± 0.013

0.049 ± 0.017

ns, 0.818

AQP-2

0.026 ± 0.011

0.035 ± 0.010

ns, 0.025

AQP-4

0.014 ± 0.006

0.012 ± 0.006

ns, 0.504

OS

AQP-1

0.056 ± 0.011

0.048 ± 0.014

ns, 0.095

AQP-2

0.045 ± 0.011

0.060 ± 0.019

0.009

AQP-4

0.011 ± 0.003

0.009 ± 0.004

ns, 0.234

IS

AQP-1

0.042 ± 0.010

0.039 ± 0.009

ns, 0.341

AQP-2

0.036 ± 0.009

0.058 ± 0.016

0.000

AQP-4

0.020 ± 0.005

0.019 ± 0.008

ns, 0.644

IM

AQP-1

0.036 ± 0.010

0.031 ± 0.009

ns, 0.140

AQP-2

0.027 ± 0.010

0.040 ± 0.011

0.002

AQP-4

0.026 ± 0.008

0.025 ± 0.009

ns, 0.694

Data are presented as mean ± standard deviation. Statistical analysis was performed using an independent t test between the two groups. DM diabetes mellitus, DM diabetes mellitus, CO cortex, OS outer stripe of the outer medulla, IS inner stripe of the outer medulla, IM inner medulla ns, not significant. Based on Bonferroni’s correction, p < 0.017 (0.05/3) was considered to indicate a statistically significant difference

Table 4

Correlation between apparent diffusion coefficients (ADCs) and aquaporin-2 (AQP-2) in diabetes mellitus (DM) animals at four anatomical layers of the kidney

Position

AQP

ADClow

ADCst

ADCuh

r

p

r

p

r

p

CO

AQP-1

−0.123

ns 0.509

0.026

ns 0.890

−0.023

ns, 0.903

AQP-2

−0.178

ns 0.346

−0.044

ns 0.871

0.427

ns, 0.018

AQP-4

−0.109

ns 0.565

−0.098

ns 0.607

−0.174

ns, 0.358

OS

AQP-1

0.123

ns 0.516

0.141

ns 0.458

−0.157

ns, 0.407

AQP-2

−0.280

ns 0.134

−0.047

ns 0.806

0.598

0.000

AQP-4

−0.193

ns 0.306

−0.139

ns 0.464

−0.194

ns, 0.304

IS

AQP-1

−0.266

ns 0.155

−0.106

ns 0.576

−0.026

ns, 0.893

AQP-2

−0.277

ns 0.138

−0.139

ns 0.464

0.490

0.006

AQP-4

−0.073

ns 0.700

−0.036

ns 0.850

0.140

ns, 0.460

IM

AQP-1

0.175

ns 0.355

0.293

ns 0.115

−0.039

ns, 0.838

AQP-2

−0.109

ns 0.567

0.003

ns 0.986

0.451

0.012

AQP-4

0.141

ns 0.459

0.173

ns 0.362

−0.227

ns, 0.227

The r and p values were obtained using Pearson’s correlation analysis. DM diabetes mellitus, DM diabetes mellitus, CO cortex, OS outer stripe of the outer medulla, IS inner stripe of the outer medulla, IM inner medulla ns, not significant. Based on Bonferroni’s correction, p < 0.017 (0.05/3 tests) was considered to indicate a statistically significant difference

Results

Physiological and laboratory parameters

Streptozotocin-induced DM reduced bodyweight gain but increased serum glucose in the DM animals compared with controls (p < 0.001; Table 1). In DM animals, creatinine clearance was no difference compared with controls (p = 0.048) but the urinary output of the diabetic group was higher than that of controls by 14-fold. In addition, serum urea elevated significantly in the DM group compared with controls (p = 0.008). Urine albumin levels of the DM group, however, were reduced in comparison with that of controls (p = 0.007; Table 1).

DWI

All the MRI studies (n = 30) were included in the analysis. MRI could identify the four anatomical layers of the kidney (CO, OS, IS and IM) in all the animals (Fig. 1). An example of ADClow, ADCst and ADCuh maps in both a DM animal and a control animal is given in Fig. 1. ADCuh of the CO, OS and IS in the DM animals was significantly higher than that in controls (Table 2). Although ADCuh of the IM in the DM animals was significantly higher than that in controls at p < 0.050 level, there was no significant difference after Bonferroni’s correction. However, no significant difference was found in ADClow and ADCst in each region of interest between the two groups (Table 2).

Immunohistochemistry

In both DM and control kidney sections were found AQP-1, AQP-2 and AQP-4 immunoreactive cells (Fig. 2). AQP-1 staining and AQP-2 staining were prominent and widespread in different anatomical layers of the kidney in the diabetic rats. The OD value of AQP-2 expression in the OS, IS and IM in diabetic animals was significantly higher than that in controls (Table 3). Although the OD value of AQP-2 expression in CO in the DM animals was significantly higher than that in controls at p < 0.050 level, there was no significant difference after Bonferroni’s correction. However, there was no significant difference between the two groups in their OD values of AQP-1 and AQP-4 expression (Table 3).
Fig. 2

Immunocytochemical analysis of aquaporins (AQPs) on four different anatomical layers of the kidney. No difference was found between the group of streptozotocin-induced diabete mellitus (DM) animals (a-d) and the control group (e-h) in their AQP-1 expression on different anatomical layers of the kidney. Labeled AQP-1 was observed in the proximal tubules and Henle’s loops. The most intense labeling was found in the cortex and in the outer stripe of the outer medulla (a, b and e, f) in both groups. Labeled AQP-2 was observed on the apical plasma membrane and in intracellular cytoplasm domains. The labeling was the most intense in the outer stripe of the outer medulla (j and n). There was a noticeable increase in the AQP-2 labeling in OS, IS and IM in DM animals (j-l) compared with control animals (n-p). There was no significant difference between the tow group in there AQP-2 expression in CO (i and m). There was no significant difference between DM group (q-t) and control group (u-x) in their AQP-4 expression on different anatomical layers of the kidney. AQP-4 was detected in the collecting ducts and the proximal tubules. AQP-4 labeling was most intense in the inner medulla (t and x). Magnification × 400

Correlation between ADC values and AQP-2

ADCuh values were positively correlated with OD values of AQP-2 expression in the OS, IS and IM of the kidney (Table 4, Fig. 3). Although there were positive correlations between ADCuh and OD values of AQP-2 expression in CO at the p < 0.050 level, there was no significant difference after Bonferroni’s correction. The ADClow and ADCst values on the four anatomical layers of the kidney were not correlated with the OD values of AQP-2 expression. In addition, there were no correlations between ADCs and OD values of AQP-1 and AQP-4 expression (Table 4).
Fig. 3

Correlation between renal optical density (OD) values of aquaporin-2 (AQP-2) expression and values of apparent diffusion coefficient obtained with ultra-high b-value (ADCuh). Significantly positive correlations are depicted for the outer stripe of the outer medulla in (b) (r = 0.598, p < 0.001), the inner stripe of the outer medulla in (c) (r = 0.490, p = 0.006,) and the inner medulla in (d) (r = 0.451, p = 0.012). Although there was a positive correlation between ADCuh and OD values of AQP-2 expression in cortex in (a) (r = 0.427, p = 0.018), there was no significant difference after Bonferroni's correction

Discussion

We used an STZ-induced model of diabetes which used a high-fat diet combined with a low STZ dose. The animals developed features of type-1 and type-2 DM with reduced insulin production and symptoms of a metabolic syndrome as previously described [12]. All the high-fat-diet and STZ-treated rats developed typical hyperglycaemia, increased urine output, and loss of body weight as typical symptoms of diabetes. In our diabetic model of animals, serum urea increased significantly, which is consistent with previous studies [7].

During the progression of diabetic nephropathy, the primary pathological changes involved fibrosis, impaired renal function, as well as chronic kidney diseases, all of which are known to be associated with a decrease in ADC both in human and in animal studies [1315]. However, in our study, ADClow and ADCst of DM animals and control animals did not show any significant change. These results are in accordance with those of Cakmak et al. [5], who reported no significant difference in ADC values between the control group and earlier diabetic stages (stage 1 and stage 2). In an experimental rat diabetic nephropathy study by Ries et al. [6], no statistically significant difference was found between CO, IS and IM except for a subgroup of diabetic animals with oedematous cellular damage. In a study by Hueper et al. [7], the ADC values of the DM animals and controls were not different in their medulla and cortex. Interestingly, they found that medullary and cortical ADC was even elevated in diabetic animals with unilateral nephrectomy, which they explained by using low b-values of 0 and 300 s/mm2. However, if ADClow values were sensitive enough to the perfusion change within the kidney, higher ADClow values (due to hyperfiltration in the diabetic kidneys) should have been found in our study. The increased ADC in DM with unilateral nephrectomy in the study by Hueper et al. [7] was caused not only by hyperfiltration but also by a compensatively increased renal blood flow due to unilateral nephrectomy. Therefore, our results indicated that ADClow and ADCst measurements seem to reflect histopathological changes within the tissue not sensitive to the energy metabolism or the tubular flow or reabsorption.

We further suggest that ADCuh may serve as a promising biometric for the early detection of diabetic nephropathy. Moreover, ADCuh was lower than the ADCst and ADClow values, which corresponds with the finding that the higher b-value in the scan, the lower the estimated ADC values in the kidney [16]. ADC contained information not only about diffusion but also about perfusion and flow effects. Some studies [17] suggested that diffusion signal intensity with low b-values is mainly contributed by a fast diffusion component, as ADClow is mainly illustrated by the fast blood perfusion in the kidney. Diffusion signal intensity with high b-values is dominated primarily by a slow diffusion component, as ADCst is related to the slow water diffusion. Thus, according to the relationship between the b-value and diffusion signal intensity, the ADCuh measured at ultra-high b-value might have something to do with a slower diffusion component because we observed a weak ADCuh in the present study. Moreover, our findings indicate that ADCuh measurement is more sensitive than ADClow and ADCst measurement to the energy metabolism or the tubular flow or reabsorption.

The changes we found in ADCuh may correlate with the changes in AQP expression. In this research we used immunohistochemistry to measure the expression of AQP-1, AQP-2 and AQP-4 and we found that OD values of AQP-2 expression increased in the OS, IS and IM whereas expression of AQP-1and AQP-4 in different anatomical layers of the kidney in the diabetic animals did not change compared with controls. Aquaporins play an important role in the reabsorption of water from renal tubular fluid. In one study, polyuria and the inability to concentrate their urine has been reported in AQP-1-deficient mice [3]. In long-term type-1 diabetic rats, polyuria probably results from a reduced AQP-1 expression in renal tissues [18]. Earlier studies using STZ-induced animal models of DM also reported an increased AQP-2 expression in the kidneys [1923]. Our finding agrees with the abovementioned results and supports the proposition that an increased AQP-2 expression might work as a compensatory mechanism that alleviates dehydration.

In recent studies, attention has been paid to the relationship between the ADC and AQP expression in the membranes. A clinical study using ultra-high b-value-DWI in Parkinson’s disease suggests that ADCuh may help to illustrate the function of the AQP [9]. In addition, the speed of water transportation by AQPs in the membrane is about 0.45 × 10-3 mm/s slower than the water diffusion speed between cell gaps [24]. Moreover, our study showed a positive correlation between ADCuh and AQP-2 expression, even though only a limited correlation. Thus, based upon these findings, we propose that DWI-MRI with ultra-high b-values might be able to assess water transportation by AQPs.

There are several limitations in the present study. First, we only measured at one time point during the progression of the disease. Second, the investigated animal population was relatively small. A large number of subjects and multiple time point measurements during the progression of the disease can help to confirm our findings. Third, the higher b-values we used may lead to a lower signal-to-noise ratio. However, we used a 7.0-T small-animal scanner to compensate the drawback, which may help us obtain a more powerful signal-to-noise ratio. Another limitation is that the regions of interest were drawn manually. Finally, as optical OD values of AQP expression were semiquantitative they may not be highly accurate because OD values are susceptible to the condition of staining and exposure duration when the images are taken. However, this drawback may be compensated by the immunohistochemistry that we used to achieve OD values, which were stained under the same condition and in the same batch. Images were taken at the same exposure duration to make sure that our data are true and comparable.

In conclusion, we found significantly higher ADCuh values in CO, OS and IS and AQP-2 expression in the OS, IS and IM in DM animals compared with controls. Thus, we suggest that AQP-2 plays an important role in alleviating dehydration caused by poorly uncontrolled diabetes with polyuria and that DWI with ultra-high b-values may be valuable for noninvasive early detection and discriminative diagnosis of diabetic nephropathy. Lastly, the change in ADCuh may reflect function of the AQP.

Abbreviations

ADC: 

Apparent diffusion coefficient

ADClow

ADC calculated using low b-values

ADCst

ADC calculated using standard b-values

ADCuh

ADC calculated using ultra-high b-values

AQP: 

Aquaporin

CO: 

Cortex

DM: 

Diabetes mellitus

DWI: 

Diffusion-weighted imaging

IM: 

Inner medulla

IS: 

Inner stripe of the outer medulla

OD: 

Optical density

OS: 

Outer stripe of the outer medulla

STZ: 

Streptozotocin

Declarations

Acknowledgements

This trial was technically supported by GE Healthcare, China. This study was partly supported by National Key Research and Development Program of China (No. 2016YFA0201400); the International Cooperation and Exchange Projects of the National Natural Science Foundation of China (No. 81520108014).

Authors’ contribution

YW, HZ carried out the Imaging studies, participated in the sequence alignment and drafted the manuscript. YW, ZX and RZ carried out the immunoassays. LW participated in the sequence alignment. YW, ZS participated in the design of the study and performed the statistical analysis. FG and RL conceived of the study and participated in its design and coordination. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of Radiology, West China Hospital of Sichuan University
(2)
General Electronic Company Healthcare (China)

References

  1. Lane PH, Steffes MW, Fioretto P, Mauer SM (1993) Renal interstitial expansion in insulin-dependent diabetes mellitus. Kidney Int 43:661–667View ArticlePubMedGoogle Scholar
  2. Spira A, Gowrishankar M, Halperin ML (1997) Factors contributing to the degree of polyuria in a patient with poorly controlled diabetes mellitus. Am J Kidney Dis 30:829–835View ArticlePubMedGoogle Scholar
  3. Verkman AS (2000) Physiological importance of aquaporins: lessons from knockout mice. Curr Opin Nephrol Hypertens 9:517–522View ArticlePubMedGoogle Scholar
  4. Kortenoeven ML, Fenton RA (2014) Renal aquaporins and water balance disorders. Biochim Biophys Acta 1840:1533–1549View ArticlePubMedGoogle Scholar
  5. Cakmak P, Yagci AB, Dursun B et al (2014) Renal diffusion-weighted imaging in diabetic nephropathy: correlation with clinical stages of disease. Diagn Interv Radiol 20:374–378View ArticlePubMedPubMed CentralGoogle Scholar
  6. Ries M, Basseau F, Tyndal B et al (2003) Renal diffusion and BOLD MRI in experimental diabetic nephropathy. Blood oxygen level-dependent. J Magn Reson Imaging 17:104–113View ArticlePubMedGoogle Scholar
  7. Hueper K, Hartung D, Gutberlet M et al (2012) Magnetic resonance diffusion tensor imaging for evaluation of histopathological changes in a rat model of diabetic nephropathy. Invest Radiol 47:430–437View ArticlePubMedGoogle Scholar
  8. Sigmund EE, Vivier PH, Sui D et al (2012) Intravoxel incoherent motion and diffusion-tensor imaging in renal tissue under hydration and furosemide flow challenges. Radiology 263:758–769View ArticlePubMedGoogle Scholar
  9. Xueying L, Zhongping Z, Zhoushe Z et al (2015) Investigation of apparent diffusion coefficient from ultra-high b-values in Parkinson’s disease. Eur Radiol 25:2593–2600View ArticlePubMedGoogle Scholar
  10. Thomas SR (2009) Kidney modeling and systems physiology. Wiley Interdiscip Rev Syst Biol Med 1:172–190View ArticlePubMedGoogle Scholar
  11. Abdel-Hamid GA (2013) Effect of vitamin E and selenium against aluminum-induced nephrotoxicity in pregnant rats. Folia Histochem Cytobiol 51:312–319View ArticlePubMedGoogle Scholar
  12. Srinivasan K, Viswanad B, Asrat L et al (2005) Combination of high-fat diet-fed and low-dose streptozotocin-treated rat: a model for type 2 diabetes and pharmacological screening. Pharmacol Res 52:313–320View ArticlePubMedGoogle Scholar
  13. Lu L, Sedor JR, Gulani V et al (2011) Use of diffusion tensor MRI to identify early changes in diabetic nephropathy. Am J Nephrol 34:476–482View ArticlePubMedPubMed CentralGoogle Scholar
  14. Chen X, Xiao W, Li X et al (2014) In vivo evaluation of renal function using diffusion weighted imaging and diffusion tensor imaging in type 2 diabetics with normoalbuminuria versus microalbuminuria. Front Med 8:471–476View ArticlePubMedGoogle Scholar
  15. Togao O, Doi S, Kuro-o M et al (2010) Assessment of renal fibrosis with diffusion-weighted MR imaging: study with murine model of unilateral ureteral obstruction. Radiology 255:772–780View ArticlePubMedPubMed CentralGoogle Scholar
  16. Zhang JL, Sigmund EE, Chandarana H et al (2010) Variability of renal apparent diffusion coefficients: limitations of the monoexponential model for diffusion quantification. Radiology 254:783–792View ArticlePubMedPubMed CentralGoogle Scholar
  17. Notohamiprodjo M, Chandarana H, Mikheev A et al (2015) Combined intravoxel incoherent motion and diffusion tensor imaging of renal diffusion and flow anisotropy. Magn Reson Med 73:1526–1532View ArticlePubMedGoogle Scholar
  18. Vidotti DB, Arnoni CP, Maquigussa E, Boim MA (2008) Effect of long-term type 1 diabetes on renal sodium and water transporters in rats. Am J Nephrol 28:107–114View ArticlePubMedGoogle Scholar
  19. Bardoux P, Ahloulay M, Le Maout S et al (2001) Aquaporin-2 and urea transporter-A1 are up-regulated in rats with type I diabetes mellitus. Diabetologia 44:637–645View ArticlePubMedGoogle Scholar
  20. Song J, Knepper MA, Verbalis JG, Ecelbarger CA (2003) Increased renal ENaC subunit and sodium transporter abundances in streptozotocin-induced type 1 diabetes. Am J Physiol Renal Physiol 285:F1125–F1137View ArticlePubMedGoogle Scholar
  21. Mozaffari MS, Abdelsayed R, Liu JY et al (2012) Renal distal tubule proliferation and increased aquaporin 2 level but decreased urine osmolality in db/db mouse: treatment with chromium picolinate. Exp Mol Pathol 92:54–58View ArticlePubMedGoogle Scholar
  22. Satake M, Ikarashi N, Ichikawa Y et al (2010) The role of renal aquaporin 2 in the alleviation of dehydration associated with diabetic polyuria in KKAy mice. Life Sci 87:475–480View ArticlePubMedGoogle Scholar
  23. Li C, Wang W, Summer SN et al (2006) Hyperosmolality in vivo upregulates aquaporin 2 water channel and Na-K-2Cl co-transporter in Brattleboro rats. J Am Soc Nephrol 17:1657–1664View ArticlePubMedGoogle Scholar
  24. Zhu F, Tajkhorshid E, Schulten K (2004) Theory and simulation of water permeation in aquaporin-1. Biophys J 86:50–57View ArticlePubMedPubMed CentralGoogle Scholar

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