To investigate whether fluctuations between the fasting and 2-h postload glucose ([2-hPBG]-fasting blood glucose [FBG]) states are associated with glomerular hyperfiltration (GHF) in middle-aged and elderly Chinese patients with newly diagnosed diabetes.
Design and Methods
In this study, we included 679 newly diagnosed diabetes patients who were ≥40 years old. All the subjects were divided into two groups; those with HbA1c<7% and ≥7%. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to estimate the glomerular filtration rate (GFR). GHF was defined as an eGFR ≥ the 90th percentile. First, a multiple linear regression analysis was used to estimate the association of 2-hPBG-FBG with eGFR. Then, a generalized additive model was used to explore the possible nonlinear relationship between 2-hPBG-FBG and eGFR. Next, the 2-hPBG-FBG values were divided into four groups as follows: 0–36, 36–72, 72–108 and ≥108 mg/dl. Finally, a multiple logistic regression analysis was used to investigate the association of 2-hPBG-FBG with the risk of GHF.
For the group with HbA1c<7%, the eGFR and the percentage of GHF were significantly higher compared with the group with HbA1c≥7%. After adjusting for age, gender, body mass index (BMI), systolic blood pressure (BP), diastolic BP, fasting insulin, cholesterol, triglycerides, smoking, drinking and glycated hemoglobin (HbA1c), 2-hPBG-FBG was significantly associated with increased eGFR and an increased risk of GHF (the GHF risk increased by 64.9% for every 36.0 mg/dl [2.0 mmol/L] 2-hPBG-FBG increase) only in those patients with HbA1c<7%. Additionally, 2-hPBG-FBG and eGFR showed a nonlinear association (P<0.001).
Citation: Hou X, Wang C, Wang S, Yang W, Ma Z, Wang Y, et al. (2014) Fluctuation between Fasting and 2-H Postload Glucose State Is Associated with Glomerular Hyperfiltration in Newly Diagnosed Diabetes Patients with HbA1c < 7%. PLoS ONE 9(10): e111173. https://doi.org/10.1371/journal.pone.0111173
Editor: Mohammad E. Khamseh, Institute of Endocrinology and Metabolism, Islamic Republic of Iran
Received: April 26, 2014; Accepted: September 20, 2014; Published: October 31, 2014
Copyright: © 2014 Hou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.
Funding: This study was supported by grants from the Chinese Society of Endocrinology and National Clinical Research Center for Metabolic Diseases, the National Natural Science Foundation of China (No. 81100617), the Medical and Health Science and Technology Development Projects of Shandong Province (2011HD005), the National Science and Technology Support Plan (2009BAI80B04), the Natural Science Foundation of Shandong Province (ZR2012HM014), the International Science and Technology Projects of Shandong Province (2012GGE27126), the Business Plan of Jinan Students Studying Abroad (20110407), and the Special Scientific Research Fund of Clinical Medicine of the Chinese Medical Association (12030420342). The funders had no roles in the study design, data collection or analysis, in the decision to publish, or in the preparation of the manuscript.
Competing interests: ZM is an employee of China National Heavy Duty Truck Group Corporation Hospital. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
Diabetic nephropathy (DN), which is characterized by an initial period of glomerular hyperfiltration (GHF) followed by progressively increasing proteinuria and a gradual decline in the glomerular filtration rate (GFR), is the leading cause of chronic kidney disease (CKD) and is associated with increased cardiovascular mortality in diabetic patients worldwide , . As a functional change occurring during the early stage of diabetes, GHF is likely to correlate with the progression of DN and may even contribute to the initiation of this disease , . The prevalence of GHF reported in type 2 diabetic patients has varied widely from 7 to 73% , , , . Therefore, the screening for risk factors of GHF during the early stage of type 2 diabetes is critical for DN prevention.
Hyperglycemia is closely related to the development of the vascular complications of diabetes and can be prevented by the strict control of blood glucose , . As a relatively objective indicator of average blood glucose within the past two or three months, glycated hemoglobin (HbA1c) has been used as the standard for glucose control. However, the incidence of diabetic complications increases more rapidly in proportion to postprandial glucose or to peak glucose levels compared with average blood glucose levels, indicating the important role of blood glucose fluctuations in the development of diabetic complications , , , . A growing body of research has suggested that glucose fluctuations may accelerate the renal complications associated with diabetes independent of the presence of hyperglycemia; however, the results are inconsistent. One study of type 1 diabetes patients has revealed that glucose fluctuations do not predict the development of nephropathy . In contrast, two other studies have indicated that glucose fluctuations may affect the severity of CKD in patients with type 2 diabetes , . Therefore, it is necessary to further clarify the influence of glucose fluctuations on the changes in renal function that occur in association with diabetes, particularly during the early stage of the disease (as indicated by GHF).
However, the definition of glucose fluctuation is currently uncertain, and the monitoring of glucose fluctuation is complicated in clinical practice and large epidemiological studies. Because the most common glucose fluctuations occur following meals, differences between the fasting and postprandial glucose levels may reflect the state of glucose fluctuation (to some extent). Therefore, it should be determined whether these differences are related to GHF. However, the total calorie intake often differs between subjects because of dietary variations. To standardize calorie intake to evaluate glycemic control, we performed the 75-g oral glucose tolerance test (OGTT) in a representative sample of the Chinese population and used 2-h postload blood glucose (2-hPBG) to represent the postprandial glucose levels. We investigated whether the fluctuations between fasting blood glucose (FBG) and 2-hPBG-FBG were associated with GHF in middle-aged and elderly Chinese patients newly diagnosed with diabetes.
Materials and Methods
This work was performed as part of the baseline survey of the REACTION study assessing the association of diabetes and cancer, which included 259,657 adults (40 years of age and older) from 25 communities across mainland China from 2011 to 2012 . This study was approved by the Ruijin Hospital Ethics Committee of the Shanghai Jiao Tong University School of Medicine. Written informed consent was obtained from all participants.
For this screening study, 10,028 subjects were randomly recruited who were ≥40 years old and resided in the Shandong province from January 2012 to April 2012. A 75-g OGTT was performed for all participants. We first selected the 2044 subjects whose 75-g OGTT results were indicative of diabetes (see below). Then, 1181 previously diagnosed diabetic patients were excluded based on their medical histories. Further exclusion criteria were as follows: (1) lower 2-hPBG than FBG levels; (2) missing data for the calculation of the eGFR; (3) previously diagnosed kidney disease, including autoimmune or drug-induced kidney disease, nephritis, renal fibrosis or renal failure, or prior kidney transplant and current dialysis treatment; (4) previously diagnosed hepatic disease, including fatty liver, liver cirrhosis and autoimmune hepatitis; and (5) any malignant disease. A total of 679 subjects (423 women) were eligible for the analysis.
Demographic characteristics, lifestyle information and previous medical histories were obtained by trained investigators through a standard questionnaire. BMI was calculated as weight (kg) divided by height squared (m2). Blood pressure (BP) was measured 3 times consecutively (OMRON Model HEM-752 FUZZY, Omron Company, Dalian, China), and the average reading was used for the analysis. After an overnight fast, venous blood samples were collected for measurements of FBG, fasting insulin, cholesterol, triglycerides and creatinine. 2-hPBG was measured after subjects had completed the 75-g OGTT. HbA1c was measured by high-performance liquid chromatography (VARIANT II and D-10 Systems, BIO-RAD, USA). The homeostasis model assessment of insulin resistance (HOMA-IR) index was calculated as follows: fasting insulin concentration (mIU/L)×FPG concentration (mmol/L)/22.5 . The eGFR was calculated from the creatinine level using the following formulas according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) .
There is no standard definition of hyperfiltration. Previous studies have arbitrarily used eGFR thresholds of 125 to 140 mL/min/1.73 m2 as the defining criteria for hyperfiltration. However, GFRs decrease with age . The present study contained only middle-aged and elderly subjects, who were expected to present with relatively low eGFRs. Therefore, GHF was defined as an eGFR ≥ the 90th percentile (101.81 mL/min/1.73 m2) in this study.
Newly diagnosed diabetes has been defined by the World Health Organization (WHO) in 1999  as FBG≥126 mg/dl (7.0 mmol/L) and/or 2-hPBG≥200 mg/dl (11.1 mmol/L) without a history of diabetes. For newly diagnosed diabetes patients who are not receiving antidiabetic drug therapy, HbA1c levels may be reflective of the clinical course of diabetes. Therefore, we divided all subjects into two groups according to the target value of HbA1c  as follows: patients with HbA1c<7% were considered to be at the very early stage, and those with HbA1c ≥7% were considered to be at a comparably later stage.
Normally distributed continuous variables are expressed as the mean±SD, and variables with non-normal distributions are presented as the median (interquartile range). Categorical variables are presented as numbers (%). The differences between the HbA1c groups were detected by Student’s t test (normally distributed continuous variables), Mann-Whitney U test (skewed continuous variables), or chi-square test (categorical variables). After verifying the assumption of a linear relationship between the dependent and independent variables that were introduced into the linear regression model (assessed using a histogram of the residuals, together with a scatterplot of the standardized residuals versus the standardized predicted values in the different models), a multiple linear regression analysis was used to estimate the association of 2-hPBG-FBG with eGFR. Three models were constructed as follows: the first was not adjusted; the second was adjusted for age, gender, BMI, systolic BP and diastolic BP; and the third was adjusted for age, gender, BMI, systolic BP, diastolic BP, Log (fasting insulin), cholesterol, Log (triglyceride), smoking, drinking and HbA1c. We used a generalized additive model to explore the possible nonlinear association of 2-hPBG-FBG with eGFR after adjusting for the aforementioned factors. The 25th, 50th, and 75th percentiles of 2-hPBG-FBG were 37.8 mg/dl (2.1 mmol/L), 75.6 mg/dl (4.2 mmol/L) and 108 mg/dl (6.0 mmol/L), respectively. To facilitate the use of the 2-hPBG-FBG values in clinical practice, they were divided into four groups according to 36 mg/dl (2.0 mmol/L) intervals as follows: 0–36, 36–72, 72–108 and ≥108 mg/dl. Then, the association of 2-hPBG-FBG (the four groups of 2-hPBG-FBG were introduced as ordinal dummy variables) with the risk of GHF was estimated using a multiple logistic regression analysis for the same three models. P<0.05 was considered statistically significant. The analysis of the generalized additive models was performed using Empower Stats 2.13.0. The data were analyzed using SPSS 16.0 (SPSS Inc., Chicago. IL).
Characteristics of study participants
We included 679 subjects (423 women) with newly diagnosed diabetes who were divided into two groups based on HbA1c, using 7% as the cut-off value. As shown in Table 1, BMI, FBG, 2-hPBG, 2-hPBG-FBG, HbA1c, HOMA-IR, triglycerides, creatinine and the percentages of smoking and drinking subjects were significantly lower in the group with HbA1c<7%. In contrast, the eGFR and the percentage of GHF were higher compared with the subjects with HbA1c≥7%.
2-hPBG-FBG is closely associated with eGFR in newly diagnosed diabetes patients with HbA1c<7%
As shown in Table 2, we constructed three models to analyze the association of 2-hPBG-FBG with eGFR (the calculation of HOMA-IR and 2-hPBG-FBG both involved FBG, so we adjusted for fasting insulin rather than for HOMA-IR in model 3). The linearity of the relationship was assessed using a histogram of the residuals for each model together with a scatterplot of the standardized residuals versus the standardized predicted values, which indicated an approximately linear relationship. Interestingly, a significantly positive association of 2-hPBG-FBG with eGFR was observed in all three models for the newly diagnosed diabetes patients with HbA1c<7% only, which was independent of age, gender, BMI, systolic BP, diastolic BP, fasting insulin, cholesterol, triglycerides, smoking, drinking and HbA1c. Moreover, we found a nonlinear association of 2-hPBG-FBG and eGFR using a generalized additive model after multivariable adjustment (P<0.001; Fig. 1). In contrast, a negative relationship between HbA1c and eGFR was observed in the patients with HbA1c (%) ≥7.
The association was analyzed in a generalized additive model (df = 3, P<0.001) adjusted for age, gender, BMI, systolic BP, diastolic BP, fasting insulin, cholesterol, triglycerides, smoking, drinking and HbA1c.
2-hPBG-FBG is closely associated with increased risk of GHF in newly diagnosed diabetes patients with HbA1c<7%
As shown in Table 3, we analyzed the association of increased 2-hPBG-FBG with the risk of GHF for the three models. As expected, 2-hPBG-FBG significantly increased the risk of GHF in the newly diagnosed diabetes patients with HbA1c<7% but not in the subjects with HbA1c≥7%. In model 1, a 38.2% increase in GHF risk for every 36 mg/dl (2.0 mmol/L) 2-hPBG-FBG increase was observed. After adjusting for age, gender, BMI, systolic BP and diastolic BP, the GHF risk increased by 61.4% for every 36 mg/dl (2.0 mmol/L) 2-hPBG-FBG increase. Further adjustments for fasting insulin, cholesterol, triglycerides, smoking, drinking and HbA1c revealed a 64.9% increase in the GHF risk. In contrast, HbA1c (an indicator of average blood glucose) was not associated with an increased risk of GHF.
Because GHF is a phenomenon occurring early in the clinical course of diabetes that may lead to the development of DN due to its associated glomerular damage, it is crucial to screen for risk factors of GHF during the early stage of diabetes. Therefore, we selected newly diagnosed diabetes patients as our study population. In addition, to further subdivide these patients, they were placed into two groups based on their HbA1c values. Interestingly, we found that the fluctuations between the fasting and 2-hPBG-FBG states were closely associated with eGFR and the increased risk of GHF in the patients with very early-stage diabetes only (HbA1c<7%), which indicated the importance of glucostasis between the fasting and 2-h postload states. This association was not observed for those patients with poorly controlled diabetes, which may have been because for newly diagnosed diabetics, HbA1c may be essentially reflective of the clinical course of diabetes. Those patients with higher HbA1c levels may have had diabetes for a longer period of time and thus may not have been at the very early stage of the disease. Because GHF is always present at the very early stage of diabetes, those patients with higher HbA1c levels may not have possessed GHF any longer. In contrast, they may have presented with decreased eGFR.
The two main risk factors for DN are hyperglycemia and arterial hypertension . Therefore, we adjusted for HbA1c, which is reflective of the average blood glucose level within the past two to three months, in addition to BP to analyze the association of blood glucose fluctuations with GHF. The traditional risk factors for DN also include sex, age, smoking, drinking, dyslipidemia and insulin resistance . Thus, we further adjusted for the above risk factors in model 3 to verify the relationship between blood glucose fluctuations and GHF. Notably, because the calculations of HOMA-IR and 2-hPBG-FBG both involve FBG, we added fasting insulin rather than HOMA-IR to model 3. Interestingly, the GHF risk increased by 64.9% for every 36 mg/dl (2.0 mmol/L) 2-hPBG-FBG increase after adjusting for age, gender, BMI, systolic BP, diastolic BP, fasting insulin, cholesterol, triglycerides, smoking, drinking and HbA1c. In contrast, average blood glucose (HbA1c) was not associated with GHF.
Two recent studies have revealed that HbA1c fluctuation independently affects the development of CKD in type 2 diabetes patients , , reflecting the effects of long-term glycemic fluctuations on renal complications. The Renal Insufficiency And Cardiovascular Events (RIACE) Italian multicenter study  has shown that in patients with type 2 diabetes, HbA1c fluctuations affect (albuminuric) CKD more than average HbA1c levels. In addition, a study  conducted in Hong Kong has found that long-term glycemic fluctuations as expressed by the SD of HbA1c predict the development of renal and cardiovascular complications. Although the effects of long-term glycemic fluctuations on renal complications have been investigated in some studies, population studies of the influences of short-term glycemic fluctuations, and particularly those of glucose fluctuations, between the fasting and 2-h postload states on renal function are scarce, and few studies have been conducted using animal models, which have shown that blood glucose fluctuations may induce renal pathological changes in diabetic rodents , . Blood glucose fluctuation may accelerate the development of kidney fibrosis in diabetic mice by increasing collagen production and inhibiting collagen degradation, and both the ERK/MAPK and TGF-β/Smad signaling pathways seem to play roles in its development, leading to changes in the GFR . Moreover, a more serious degree of glomerular sclerosis has been observed in association with blood glucose fluctuations in mice compared with those with sustained high blood glucose levels . The influences of glucose fluctuations on endothelial function have also been explored , . Glucose fluctuations may induce the production of reactive oxygen species (ROSs) in endothelial cells, facilitating the development of vascular endothelial dysfunction in rats with type 2 diabetes , . In the present study, we also observed that glucose fluctuations led to the increased risk of GHF during the early stage of diabetes.
Creatinine-based equations for estimating the GFR include the Cockcroft-Gault equation, the Modification of Diet in Renal Disease (MDRD) equation and the CKD-EPI equation. Currently, the Cockcroft-Gault equation has been supplanted by the latter two equations . In addition, the CKD-EPI equation is more accurate and precise in estimating the GFR than the MDRD equation in middle-aged and older South Asians . Therefore, we selected the CKD-EPI equation to calculate the eGFR.
Our study contains some limitations. First, a cross-sectional study cannot infer causality between 2-hPBG-FBG and GHF. Second, fluctuations between the fasting and 2-h postload glucose (2-hPBG-FBG) states do not precisely reflect glucose fluctuations. However, as a simple indicator of glucose fluctuations, 2-hPBG-FBG may be easily applicable in clinical practice. Third, our study contained only middle-aged and older Chinese people; age and ethnic differences should be taken into account when assessing these results. Fourth, we diagnosed diabetes based on one 75-g OGTT, which should be repeated and confirmed. Finally, the GFR based on creatinine levels and estimated by the CKD-EPI equation may not accurately reflect kidney function, which may have affected the accuracy of the GHF estimation. However, the gold standard method for measuring the GFR (the isotope clearance measurement) is very expensive and time-consuming, so the use of creatinine-based equations to estimate the GFR is logical for large epidemiological studies.
In conclusion, we found that fluctuations between the fasting and 2-hPBG-FBG states are associated with an increased risk of GHF in newly diagnosed diabetes patients with HbA1c<7% but not in subjects with HbA1c≥7%. For newly diagnosed diabetics, HbA1c as well as 2-hPBG-FBG should be strictly controlled to prevent diabetes complications. In addition, longitudinal studies are needed to explore the extent to which the 2-hPBG-FBG value should be controlled in clinical practice to effectively aid in the prevention of DN.
Conceived and designed the experiments: XH. Performed the experiments: XH CW SW WY ZM YW CL ML X. Zhang X. Zhao YS JS PL KL LG MW FL WL FY JY LW MT JL RZ SC LC. Analyzed the data: XH. Contributed reagents/materials/analysis tools: XH. Contributed to the writing of the manuscript: XH SC LC.
- 1. Reutens AT, Atkins RC (2011) Epidemiology of diabetic nephropathy. Contrib Nephrol 170: 1–7.
- 2. Valmadrid CT, Klein R, Moss SE, Klein BE (2000) The risk of cardiovascular disease mortality associated with microalbuminuria and gross proteinuria in persons with older-onset diabetes mellitus. Arch Intern Med 160: 1093–1100.
- 3. Magee GM, Bilous RW, Cardwell CR, Hunter SJ, Kee F, et al. (2009) Is hyperfiltration associated with the future risk of developing diabetic nephropathy? A meta-analysis. Diabetologia 52: 691–697.
- 4. Brenner BM, Hostetter TH, Olson JL, Rennke HG, Venkatachalam MA (1981) The role of glomerular hyperfiltration in the initiation and progression of diabetic nephropathy. Acta Endocrinol Suppl (Copenh) 242: 7–10.
- 5. Bruce R, Rutland M, Cundy T (1994) Glomerular hyperfiltration in young Polynesians with type 2 diabetes. Diabetes Res Clin Pract 25: 155–160.
- 6. Keller CK, Bergis KH, Fliser D, Ritz E (1996) Renal findings in patients with short-term type 2 diabetes. J Am Soc Nephrol 7: 2627–2635.
- 7. Lebovitz HE, Palmisano J (1990) Cross-sectional analysis of renal function in black Americans with NIDDM. Diabetes Care 13: 1186–1190.
- 8. Lee KU, Park JY, Hwang IR, Hong SK, Kim GS, et al. (1995) Glomerular hyperfiltration in Koreans with non-insulin-dependent diabetes mellitus. Am J Kidney Dis 26: 722–726.
- 9. Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, et al. (2000) Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321: 405–412.
- 10. The Diabetes Control and Complications Trial Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J Med 329: 977–986.
- 11. Gerich JE (2003) Clinical significance, pathogenesis, and management of postprandial hyperglycemia. Arch Intern Med 163: 1306–1316.
- 12. Ceriello A, Hanefeld M, Leiter L, Monnier L, Moses A, et al. (2004) Postprandial glucose regulation and diabetic complications. Arch Intern Med 164: 2090–2095.
- 13. Heine RJ, Balkau B, Ceriello A, Del Prato S, Horton ES, et al. (2004) What does postprandial hyperglycaemia mean? Diabet Med 21: 208–213.
- 14. Meigs JB, Nathan DM, D'Agostino RB Sr, Wilson PW (2002) Fasting and postchallenge glycemia and cardiovascular disease risk: the Framingham Offspring Study. Diabetes Care 25: 1845–1850.
- 15. Kilpatrick ES, Rigby AS, Atkin SL (2006) The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care 29: 1486–1490.
- 16. Penno G, Solini A, Bonora E, Fondelli C, Orsi E, et al. (2013) HbA1c variability as an independent correlate of nephropathy, but not retinopathy, in patients with type 2 diabetes: the Renal Insufficiency And Cardiovascular Events (RIACE) Italian multicenter study. Diabetes Care 36: 2301–2310.
- 17. Luk AO, Ma RC, Lau ES, Yang X, Lau WW, et al. (2013) Risk association of HbA1c variability with chronic kidney disease and cardiovascular disease in type 2 diabetes: prospective analysis of the Hong Kong Diabetes Registry. Diabetes Metab Res Rev 29: 384–390.
- 18. Ning G (2012) Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. J Diabetes 4: 172–173.
- 19. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, et al. (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28: 412–419.
- 20. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, et al. (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150: 604–612.
- 21. Hoy WE, Douglas-Denton RN, Hughson MD, Cass A, Johnson K, et al.. (2003) A stereological study of glomerular number and volume: preliminary findings in a multiracial study of kidneys at autopsy. Kidney Int Suppl: S31–37.
- 22. Alberti KG, Zimmet PZ (1998) Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 15: 539–553.
- 23. American Diabetes Association (2009) Standards of medical care in diabetes–2009. Diabetes Care 32 Suppl 1S13–61.
- 24. Zelmanovitz T, Gerchman F, Balthazar AP, Thomazelli FC, Matos JD, et al. (2009) Diabetic nephropathy. Diabetol Metab Syndr 1: 10.
- 25. Cheng X, Gao W, Dang Y, Liu X, Li Y, et al. (2013) Both ERK/MAPK and TGF-Beta/Smad signaling pathways play a role in the kidney fibrosis of diabetic mice accelerated by blood glucose fluctuation. J Diabetes Res 2013: 463740.
- 26. Wang H, Wang A, Lei M, Liao J, Hu W (2013) [Effect of blood glucose fluctuation and the sustained high blood glucose on renal pathological change and collagen IV expression in diabetic rats]. Zhong Nan Da Xue Xue Bao Yi Xue Ban 38: 818–823.
- 27. Wang JS, Yin HJ, Guo CY, Huang Y, Xia CD, et al. (2013) Influence of high blood glucose fluctuation on endothelial function of type 2 diabetes mellitus rats and effects of Panax Quinquefolius Saponin of stem and leaf. Chin J Integr Med 19: 217–222.
- 28. Ge QM, Dong Y, Zhang HM, Su Q (2010) Effects of intermittent high glucose on oxidative stress in endothelial cells. Acta Diabetol 47 Suppl 197–103.
- 29. Delanaye P, Mariat C (2013) The applicability of eGFR equations to different populations. Nat Rev Nephrol 9: 513–522.
- 30. Jessani S, Levey AS, Bux R, Inker LA, Islam M, et al. (2014) Estimation of GFR in South Asians: a study from the general population in Pakistan. Am J Kidney Dis 63: 49–58.