Correlation of diabetic renal hypoperfusion with microvascular responses of the skeletal muscle: a rat model study using diffuse correlation spectroscopy

Using diffuse correlation spectroscopy, we assessed the renal blood flow and thigh muscle microvascular responses in a rat model of type 2 diabetes. The blood flow index at the renal surface decreased significantly with arterial clamping, cardiac extirpation, and the progression of diabetic endothelial dysfunction. Renal blood flow measured in diabetic and nondiabetic rats also showed a significant correlation with the reactive hyperemic response of the thigh muscle. These results suggest shared microcirculatory dysfunction in the kidney and skeletal muscle and support endothelial responses in the skeletal muscle as a potential noninvasive biomarker of renal hypoperfusion.


Introduction
Diabetes mellitus is a complex metabolic disorder resulting from an irregularity in insulin secretion, insulin action, or both and leads to persistent abnormally high blood sugar levels and glucose intolerance [1].Diabetes mellitus increases the risk of capillary disorders that cause serious complications such as neuropathy and nephropathy [2][3][4].However, it is difficult for patients with diabetes to recognize their microcirculatory deficits because capillary disorders mostly progress without any noticeable symptoms, unless they result in a decline in the quality of life [5,6].Therefore, an easy-to-use and noninvasive device that can diagnose diabetic capillary disorders in early stages and regularly monitor the microcirculatory status is required.
Neuropathy, a common complication of diabetes, is associated with impaired microcirculation and increases the risk of muscle weakness and foot ulcers [7].We have previously demonstrated the feasibility of the noninvasive measurement of microvascular malfunctions in muscle tissues caused by hyperglycemic stress [8].We measured the mechanical pain threshold of the hind paw and blood flow in the thigh muscle of a rat model of streptozotocin-induced type 1 diabetes for 10 weeks and showed that a decrease in microvascular reactivity during the early stages of hyperglycemia can predict the severity of peripheral neuropathy in later stages.
Nephropathy, another complication of diabetes, increases the risk of end-stage renal failure owing to difficulties in restoring renal function [9,10].Although traditional biomarkers such as serum creatinine, urine creatinine, creatinine clearance, and their combinations are available to evaluate renal function and diagnose nephropathy, limitations have been reported with their use owing to variations within the normal range, influenced by factors such as age, sex, muscle mass, and type and stage of renal failure [11,12].Recent studies have revealed the influence of renal hypoxia on the progression of tubulointerstitial damage and nephropathy [13][14][15][16][17].A novel monitoring technology for renal blood flow could serve as a sensitive tool for assessing both the renal oxygenation status and progression of nephropathy.
Therefore, in this study, we used diffuse correlation spectroscopy (DCS) to measure the renal blood flow and microvascular responses in the thigh muscles of a rat model of type 2 diabetes.Muscle tissues and renal corticomedullary junctions were targeted because of their dense distribution of capillary vessels [18,19].The primary advantages of the DCS measurement performed in this study are as follows: (1) DCS can noninvasively detect the blood flow in tissues at a depth of one-third to one-half of the source-detector distance [20], practically up to approximately 1.5 cm from the probe installation surface in the case of biological tissues [21,22] and (2) the target of DCS measurement is the blood flow of microvessels such as capillaries [23].
Endothelial dysfunction-induced capillary disorder may affect the overall microcirculation, affecting not only the skeletal muscle but also the renal tissue [24].Considering the consequences of endothelial dysfunction, we hypothesized the presence of an association between the progression of disorders in both skeletal muscles and kidneys linked through the circulatory system.We hypothesized that the renal function could be evaluated through the noninvasive measurement of microvascular responses in the skeletal muscle.
We aimed to identify blood flow biomarkers in the skeletal muscle that could be used to noninvasively assess the renal blood flow.To achieve this, this study involved two main investigations: the first study (Study 1) investigated the feasibility of measuring renal blood flow using DCS.We demonstrated a reduction in the blood flow index (BFI) on the renal surface after clamping the renal artery and subsequent blood loss owing to cardiac extirpation.The second study (Study 2) was performed to determine the relationship between reduced renal blood flow and microvascular responses of the skeletal muscle induced by diabetes-induced endothelial dysfunction.We measured the renal blood flow and blood flow responses of the lower limb skeletal muscle during the reactive hyperemia test in rat models of type 2 diabetes and nondiabetic control rats.This study offers valuable insights into the vascular pathology in diabetes and demonstrates the potential of the proposed noninvasive biomarker evaluation method in estimating renal perfusion from the skin surface.

DCS
The DCS system consists of a long coherence length, continuous-wave laser with a wavelength of 785 nm (DL785-199-S, 100 mW, CrystaLaser, Reno, Nevada, USA) as the source and a photoncounting avalanche photodiode (COUNT-T-100-FC, Laser Components, Olching, Germany) as the detector [8,25,26].A multimode optical fiber (FT400EMT, Thorlabs Japan Inc., Tokyo, Japan) serving as the source probe carries photons from the source to the measuring target, and a single-mode optical fiber (S630-HP, Thorlabs Japan Inc., Tokyo, Japan) acting as the detector probe carries photons to the detector.To prevent extraneous light from contaminating the detector probe, the experiments were performed in a dark room.Although the intensity of the laser beam (maximum output at the fiber end: 60 mW) was higher than the limit of the American National Standards Institute for human skin exposure, care was taken to confirm that the surface of the tissues did not experience overheating or color changes throughout and after each measurement.
An in-house program, developed using the Laboratory Virtual Instrument Engineering Workbench (LabVIEW), collected diffuse light intensity data from the avalanche photodiodes using a 32-bit counter board (USB-6341, National Instruments, Austin, TX, USA) at a constant rate.The autocorrelation function g 2 (r, τ) of the normalized light intensity (Eq.( 1)) was calculated using a fast Fourier transform-based software autocorrelator from the time course of the detected light intensity reflecting the speed of red blood cells [27].
where g 2 (r, τ) is the autocorrelation function of the normalized light intensity, I(r, τ) is the detected light intensity at position r and time t, τ is the delay from t, and the brackets ⟨⟩ represent time averages.
To quantify the speed of blood flow from the measured g 2 (r, τ), we approximated the measured autocorrelation data using the theoretical autocorrelation function g 2 (r, τ) derived from Green's function solution of the diffusion correlation equation for a point light source on a semi-infinite medium.The theoretical autocorrelation function (Eq.( 2)) is expressed as follows: where and where β is a constant, µ′ S is the reduced scattering coefficient, µ a is the absorption coefficient, α is the fraction of photon scattering events from moving scatterers out of the total scatterers, k 0 is the wavenumber of light in a medium, D B is the effective diffusion coefficient of scatterers, R eff is the effective reflection coefficient, and ρ is the distance between the source and the detector.We defined αD B in k D as the BFI for every 1 s, a relative value of the mean speed of blood flow within a volume of the tissue through which the emitted light has traveled.

Animals
This study was conducted in accordance with the Institutional Guide for Animal Experiments and the Guide for the Care and Use of Laboratory Animals at St. Marianna University School of Medicine and School of Science and Technology, Meiji University.The rat model of type 2 diabetes used in this study was a spontaneously diabetic torii (SDT) fatty rat, which mimics the human pathophysiology of type 2 diabetes, accompanied by obesity, hyperlipidemia, and hypertension [28].In this rat model, renal function parameters such as blood urea, urine volume, and urine protein increase from 4 weeks of age; the increase of diastolic blood pressure and the appearance of the pathological findings in the renal tubes develop independently from 8 weeks of age; diffuse glomerulosclerosis appears at 16 weeks of age; caudal motor nerve conduction velocity decreases at 24 weeks of age; nodular-like lesions appear and the number of sural nerve fibers decreases at 40 weeks of age [28][29][30][31].
The outline of the two investigations is summarized in Fig. 1.In Study 1, we used two male SDT fatty rats of 16 weeks of age to confirm whether DCS could be used to measure the renal blood flow.In Study 2, we used eight male Sprague-Dawley (SD) rats as control rats and seven age-matched male SDT fatty rats.Renal blood flow and microvascular responses of the skeletal muscles were investigated at 24 and 22 weeks of age.As one SD rat had a technical problem during the DCS measurement and one SDT fatty rat died at 23 weeks of age, data analysis was conducted on data obtained from seven SD rats and six SDT fatty rats.
Body weight, systolic blood pressure, blood glucose levels, and muscle strength were measured prior to all experiments at 24 weeks of age in Study 2. The hind limb suspension test was performed to evaluate the decline in muscle strength in rats with diabetes-induced muscle atrophy [32,33].Rats were placed on a metal wire mesh plate equipped with a digital force gauge (ZTS-100N; IMADA Co., Ltd., Japan).The tail of each rat was gently pulled and the maximum clinging force before detachment from the mesh plate was recorded.Three measurements were obtained from each rat, and the mean value was used as the muscle strength.In Study 2, we performed a detailed analysis of urinary biomarkers of renal hypoxia in the same group of rats, the methods and results of which are described in our companion study by Tanabe et al. [34].

Measurement of the renal BFI using DCS
The rats underwent left abdominal laparotomy in the supine position under inhalation anesthesia with 2% isoflurane, and the emitter and detector probes were placed directly on the left renal surface.The source-detector distance was set to 4 mm to detect the microvascular blood flow in the corticomedullary junction area, which is located approximately 2 mm below the renal surface.These probes were secured to a cylindrical aluminum holder with an inner diameter of 7 mm.The probes were placed in the areas excluding the renal arteries and veins at an angle (θ) of 45°< θ < 75°relative to the horizontal plane, which is perpendicular to the renal surface, as shown in Fig. 2. The raw BFI was calculated every 1 s using the diffuse light intensity, which was collected at a sampling rate of 400 kilo-sampling per second (kS/s).After obtaining stable BFI values for at least 5 min, raw BFI data were acquired for 2 min.
We used the optical parameters µ′ S = 10.2 cm −1 and µ a = 0.64 cm −1 to estimate the BFI values based on the previously reported optical properties of Wister rat kidneys [35,36].BFI values exceeding three times the median absolute deviation from the median or 1.5 times the interquartile range of the first or third quantile were considered outliers and excluded from the analysis.The mean value of raw BFI data for 2 min was defined as the renal BFI.
In Study 1, we measured the raw BFI under three different conditions for 2 min each -resting condition, occlusion condition induced by renal artery clamping, and death condition after cardiac extirpation -in two SDT fatty diabetic model rats.In Study 2, we measured the raw BFI for 2 min only under the resting condition in both diabetic and nondiabetic control rats.Measurement of the resting condition were performed for 2 min after the probes were positioned on the surface of the left kidney and BFI values were confirmed stable for at least 5 min.

Measurement of the muscle BFI using DCS
The rats were positioned on a metal wire mesh grid in the lateral position under 2% isoflurane inhalation anesthesia.The DCS optical probes were independently mounted in an aluminum cylinder and secured to the surface of the left hind limb using a rubber holder and Velcro tape.The limb was secured in a limb mold [8] to avoid motion artifacts during arterial occlusion while performing the reactive hyperemia test.To minimize noise arising from hair interference between the optical probes and the skin surface, the hairs on the left hind limbs were shaved the day before measurement.The muscle BFI was calculated every 1 s using the diffuse light intensity, which was collected at a sampling rate of 100 kS/s.The muscle BFI of the left thigh was measured during the reactive hyperemia test for 13 min.The source-detector distance was set to 15 mm to detect microvascular responses in the thigh muscle [18].
The reactive hyperemia test is a dynamic assessment of the vascular function of a given tissue by examining how the blood vessels respond to changes in blood flow demand.Because of the close relationship between vascular reactivity and endothelial function, we investigated the relationship between the reduction in renal blood flow and the parameters obtained from this test in diabetic and nondiabetic rats.The test includes 2 min of baseline rest, 3 min of blood flow occlusion by pulling down an elastic band wrapped around the root of the left hind limb of the rat [8], and 8 min of post-occlusive rest.The occlusion was applied with maximum force by the same experimenter for all measurements to obtain a constant and stable occlusion for all rats.
We used the parameters µ′ S = 6.5 cm −1 and µ a = 0.15 cm −1 to estimate the BFI values based on the previously reported optical properties of the hind limb muscle of SD rats [37,38].BFI values exceeding three times the median absolute deviation from the median of neighboring 20 points were considered outliers and replaced using linear interpolation, followed by smoothening with a moving average of five points.A representative time course of the BFI in the reactive hyperemia test is shown in Fig. 3.

Statistical analysis
In Study 1, the changes in renal BFI owing to arterial clamping and death conditions are represented as the ratio of renal BFI to those observed under the rest condition.Owing to the limited number of samples, a qualitative analysis was performed to illustrate the mean BFI values under each condition in both rats.
In Study 2, the distribution of the raw BFI values at the left kidney were visually examined over the 2 min recording period to determine the stability of the measurement.The renal BFI, the mean value of the raw BFI data obtained during the 2 min resting period, was further calculated for each rat for subsequent statistical analyses.For the muscle BFI measurement, we evaluated microvascular responses with the following four parameters depicted in Fig. 3: (1) the mean value of the baseline BFI before occlusion (baseline muscle BFI); (2) the peak BFI value after occlusion (maximum post-occlusive reactive hyperemia [PORHmax]); (3) the time to PORHmax after the release of occlusion (Tp); (4) the sum of BFI values measured for 3 min after the release of occlusion (area under the curve [AUC]).Parameter (1) was calculated from the muscle BFI data, whereas the others were calculated from the baseline-normalized time course of BFI values, obtained by dividing the muscle BFI data by the mean value of the baseline BFI.These parameters are commonly used to study microvascular function in both animal models and humans [39,40].
Due to the small sample size of the data, non-parametric statistical methods were used in this study.Statistical differences in body weight, systolic blood pressure, blood glucose levels, muscle strength, muscle strength per body weight, renal BFI values, and the four parameters of the muscle BFI were analyzed between SD and SDT fatty rats using the Wilcoxon rank sum test.Spearman's correlation analysis was conducted between the renal BFI and each of the four parameters obtained from the muscle BFI, as well as the systemic parameters of body weight, systolic blood pressure, and muscle strength.Blood glucose level was not subjected to a correlation analysis because of the almost binary distribution of values.This comprehensive analysis aimed to explore the relationship between renal hypoperfusion and the deterioration of microvascular responses in skeletal muscles, considering both the local and systemic factors.
All values are presented as median (first quartile, third quartile).We considered p < 0.05 to be statistically significant and p < 0.10 to be a statistically significant trend.

Study 1: feasibility of DCS for renal blood flow measurement
Figure 4 shows the changes in renal BFI values under the occlusion and death conditions relative to the rest condition in the two rats.We confirmed that the BFI during occlusion was less than one-tenth of the BFI at rest and that the BFI under the death condition decreased to approximately zero, that is, less than 1% of the BFI at rest.

Rest
Occlusion Death

Study 2: comparison of systemic and blood flow parameters between SD and SDT fatty rats
Table 1 presents the results of the statistical comparisons of systemic and renal blood flow parameters obtained at 24 weeks of age as well as muscle blood flow parameters obtained at 22 weeks of age in both SD and SDT fatty rats.The blood glucose levels and the body weights of SDT fatty rats were significantly higher and lower than those of SD rats, respectively.The systolic blood pressure in SDT fatty rats tended to be significantly higher than those in SD rats.Those symptoms are the expected characteristics of SDT fatty rats.Muscle strength and muscle strength per body weight were significantly lower in SDT fatty rats than in SD rats.The renal BFI was considerably higher than the baseline muscle BFI in both rat groups.a Data are presented as medians (first and third quartiles).BFI: blood flow index, Tp: the time to PORHmax after the release of the occlusion, PORHmax: the maximum BFI value after the occlusion, AUC: the sum of BFI values measured for 3 min after the release of occlusion.
Figure 5 shows the distribution of raw BFI values at the left kidney within 2 min of recording.Despite variations in the BFI values owing to the manual support of the probe holder and the physiological movements of anesthetized rats, the results demonstrated a robust suppression of the raw BFI at the left kidney in SDT fatty rats compared with that in SD rats.
Figure 6 shows the results of the time course of the mean BFI during the reactive hyperemia test.The solid lines represent the mean BFI and the shaded areas indicate the SEs.No significant difference was observed between SD and SDT fatty rats in baseline muscle BFI (Table 1 and Fig. 6).The BFI during occlusion was maintained at less than 20% of the baseline muscle BFI by adjusting the strength of the occlusion.However, motion artifacts, which were particularly notable approximately 70 s after the start of occlusion, resulted in a higher BFI in SD rats during occlusion.Following the release of occlusion, PORHmax and AUC in SDT fatty rats tended to be lower than those in SD rats, whereas Tp was comparable between the two rat groups (Table 1 and Fig. 6).

Study 2: relationship between renal BFI and systemic or muscle BFI parameters
Table 2 presents the results of the correlation analyses of the correlation coefficient, confidence interval, and significant probability between renal BFI and seven systemic or muscle BFI parameters.Body weight, muscle strength, muscle strength per body weight, and AUC were significantly and positively correlated with the renal BFI (Fig. 7).a BFI: blood flow index, Tp: the time to PORHmax after the release of occlusion, PORHmax: the maximum BFI value after the occlusion, AUC: the sum of BFI values measured for 3 min after the release of occlusion.

Discussion
In this study, we aimed to identify the blood flow biomarkers in the skeletal muscle that could be used to noninvasively assess the renal blood flow.The main outcomes of this study can be summarized as follows: (1) we successfully measured the renal blood flow using DCS and (2) found a significant correlation between the renal blood flow and the cumulative volume of reactive hyperemia.These findings, validated by experiments conducted on a rat model of type 2 diabetes, not only provide valuable insights into the vascular pathology in diabetes but also offer a promising noninvasive biomarker evaluation method for estimating renal perfusion from the skin surface.
In Study 1, we observed a decrease in renal BFI, which was associated with a decrease in renal blood flow.During arterial occlusion, the renal BFI showed a significant reduction, indicating almost no inflow of blood to the kidney; however, it did not reach zero.In addition to the artifacts introduced by the mechanical deflection of the probe position owing to the manual holding of the probes and the physiological movements derived from breathing and heartbeats, it is possible that the brief occlusion did not cause the kidney to stop functioning, allowing the blood and fluids to move within the kidney [41][42][43].Even under the death condition, in which there was no blood supply owing to the removal of the heart, the renal BFI did not reach zero.The BFI values remaining during the occlusion and death conditions may indicate the biological zero state resulting from the Brownian motion of macromolecules within the interstitium [44], which is also supported by the previous flow phantom study [22] and the in vivo porcine DCS study [45].
A comparison of renal and skeletal muscle blood flows showed larger BFI values in the renal data of both SD and SDT fatty rats.A simple numerical comparison is inappropriate in this case because of the different histological structures and the use of assumed optical properties.However, this is consistent with the results of previous studies, which revealed that renal blood flow is faster than muscle blood flow [46][47][48].
In Study 2, we measured the renal blood flow and microvascular responses in the thigh muscles of SD and SDT fatty rats of the same age.Although SDT fatty rats are obese, the body weight of SDT fatty rats at 24 weeks of age was significantly lower than that of SD rats.This suggests that SDT fatty rats have severely advanced diabetes and are insulin-inhibited, resulting in a loss of energy uptake [31].The renal blood flow was significantly lower in SDT fatty rats than in SD rats at 24 weeks of age.As SDT fatty rats develop renal disorders and renal hypoxia at 24 weeks of age [34], this suggests that DCS can detect a decline in renal function.
Based on the evidence of hyperglycemia and hypertension trends confirmed in SDT fatty rats compared with SD rats, we further investigated the effect on the parameters of microvascular responses in the reactive hyperemia test.Although not statistically significant, the baseline muscle BFI of SDT fatty rats was highly variable and often higher than that of SD rats.This suggests the effects of an increased circulating blood volume and increased vascular stiffness.The pathophysiological features inherent in SDT fatty rats as well as the persistent hyperglycemia causing persistent hypertension might increase the circulating blood volume [29].In addition, sustained hyperglycemia and hypertension lead to the development of endothelial dysfunction [8], which, in turn, might increase the microvascular stiffness.The reduction in microvascular responses owing to the development of endothelial dysfunction was also evident from the decreasing tendencies of PORHmax and AUC.However, although we assumed that the Tp would be also prolonged for the same reason, there was no significant difference in Tp between SD and SDT fatty rats.Tp may not be an optimal parameter of microvascular responses in the current study because the small and gradual changes after occlusion in reactive hyperemia resulted in highly variable Tp values.These results suggest that various factors, such as hyperglycemia, hypertension, and endothelial dysfunction, cause a variety of symptoms and that DCS could detect the reduction in microvascular responses caused by these factors using the reactive hyperemia test in SDT fatty rats.
We analyzed the correlation between renal blood flow and systemic or skeletal muscle response parameters to identify the potential noninvasive biomarkers of renal dysfunction.Renal blood flow was significantly correlated with body weight, muscle strength, and AUC and showed a correlation trend with PORHmax.Previous studies have revealed that endothelial dysfunction causes renal failure [24], and the SDT fatty rats used in this study have already developed renal disorders at 24 weeks of age [28,30].This result suggests that the reduction in renal function corresponds to a reduction in microvascular responses in the skeletal muscle owing to endothelial dysfunction.In other words, endothelial dysfunction in the skeletal muscle is also associated with renal dysfunction, and the kidneys as well as skeletal muscles are impaired in this condition.The significant correlation between renal blood flow and muscle strength also supports the pathophysiology of systemic endothelial dysfunction in which a reduced capacity to deliver oxygen impairs muscle strength and renal function [7].These results suggest the potential of DCS in monitoring renal blood flow both invasively and noninvasively by evaluating the microvascular responses of skeletal muscles.
This study had some limitations.First, DCS measurement has the potential to detect changes in the renal blood flow; however, it remains unclear whether it is more sensitive than other conventional methods of measuring the renal blood flow.In addition, as SDT fatty rats have several factors that can change the microvascular responses, such as hyperglycemia, hypertension, and endothelial dysfunction, they have the potential to clarify the effects of each symptom such as nephropathy and their associations.Therefore, the performance of the DCS measurement system against renal damage needs to be evaluated by measuring the renal blood flow more frequently, together with various conventional measurement methods such as clearance.Second, the sample size was small.Further validation studies are needed to confirm the current observation of reduced renal blood flow in SDT fatty rats compared to control rats beyond individual differences.Also, other potential muscle blood flow parameters that correlate with renal blood flow responses may have been overlooked.Third, in this study, we adopted optical coefficients measured under similar measurement conditions [35][36][37][38].However, because optical coefficients vary depending on various factors, such as the thickness and composition of each tissue [49,23,50] as well as the pathology, it is necessary to measure the optical coefficient for each measurement to measure the BFI more accurately.Fourth, since the occlusion in this study was performed manually and the pulling force was not measured, there might be differences in the state of occlusion.Even if there was some instability of occlusion due to the different body compositions of the two groups of rats, our results suggest inadequate occlusion in the SD rat group compared with the SDT fatty rat group, based on the larger BFI values during occlusion.In this case, this may not affect our conclusion of an impaired reactive hyperemia response of the SDT fatty rat group compared to the SD rat group.Fifth, the contribution of renal blood flow and other fluids to the renal BFI warrants further investigation.This may be revealed by comparing the results of DCS measurements with those of multiple existing tests for measuring the renal plasma flow; glomerular filtration rate; and renal function, such as para-aminohippuric acid clearance, creatinine clearance, and renal dynamic scintigraphy [51,12,52].Furthermore, verification using a single nephron through micropuncture or microperfusion may be useful [19].
In conclusion, we proposed a novel noninvasive method that can measure the renal blood flow and detect renal disorders.Capillary blood flow in the kidneys can be directly measured using DCS, and we found that renal blood flow is correlated with microvascular responses in the thigh muscle.This suggests a relationship between endothelial dysfunction in skeletal muscles and reduced renal function.Future research combining microvascular responses and the evaluation of renal function using DCS, which is a cost-effective and noninvasive measurement method of the microcirculation status, would further contribute to the understanding of the pathophysiology of nephropathy and capillary disorders in skeletal muscles, which may lead to the early detection of complications in patients with diabetes at high risk for renal disorders.

Fig. 2 .
Fig. 2. DCS measurement on the left kidney.(A) The rat is anesthetized and placed in the supine position.Optical probes are placed on the exposed surface of the left kidney.(B) Schematic representation of probe placement on the left kidney.Optical probes are positioned perpendicular to the surface of the left kidney, avoiding interference with the renal arteries and veins.

Fig. 3 .
Fig. 3. Representative time course of the BFI and the derived parameters of microvascular responses in the reactive hyperemia test of the thigh muscle.The test includes 2 min of baseline rest, 3 min of blood flow occlusion, and 8 min of post-occlusive rest.We evaluated the mean value of the baseline BFI before occlusion (baseline muscle BFI), the peak BFI value after the occlusion (maximum post-occlusive reactive hyperemia [PORHmax]), the time to PORHmax after the release of occlusion (Tp), and the sum of BFI values measured for 3 min after the release of occlusion (area under the curve [AUC]).The parameters, except for the baseline muscle BFI, used in the time course of BFI are obtained by normalizing muscle BFI data with the baseline data of the muscle BFI.

Fig. 4 .
Fig. 4. Ratio of the renal BFI to the rest condition under three conditions -at rest, occlusion by renal artery clamp, and death by cardiac extirpation -for the two rats at 16 weeks of age.The dashed and dotted lines indicate the mean values measured for 2 min under each condition in each rat.

Fig. 5 .
Fig. 5. Distribution of the raw BFI values at the left kidney every 1 s of the 2 min rest period for each rat.The red and blue plots indicate the renal BFI of SD and SDT fatty rats, respectively.The asterisk indicates a significant difference between SD and SDT fatty rat groups.BFI: blood flow index

Fig. 6 .
Fig. 6.Mean BFI time course in the thigh muscle during the reactive hyperemia test in SD rats and SDT fatty rats.Solid lines represent the mean values and shaded areas represent the SEs.Red indicates SD rats and blue indicates SDT fatty rats.BFI: blood flow index.

Fig. 7 .
Fig. 7. Correlations between renal BFI and (a) body weight, (b) muscle strength, and (c) AUC.Red and blue circles indicate SD and SDT fatty rats, respectively.BFI: blood flow index, AUC: the sum of BFI values measured for 3 min after the release of occlusion.