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World J Diabetes. May 15, 2010; 1(2): 48-56
Published online May 15, 2010. doi: 10.4239/wjd.v1.i2.48
Renal function in diabetic nephropathy
Pradeep Kumar Dabla
Pradeep Kumar Dabla, Department of Biochemistry, Lady Hardinge Medical College, New Delhi 110001, India
Author contributions: Dabla PK contributed solely to this paper.
Correspondence to: Pradeep Kumar Dabla, MD, Postal Paradise Apartment, Flat-428, Block-E, Pocket-3, Sector-18, Rohini, New Delhi 110085, India. pradeep_dabla@yahoo.com
Telephone: +91-98-68524455
Received: December 25, 2009
Revised: April 25, 2010
Accepted: May 2, 2010
Published online: May 15, 2010

Abstract

Diabetic nephropathy is the kidney disease that occurs as a result of diabetes. Cardiovascular and renal complications share common risk factors such as blood pressure, blood lipids, and glycemic control. Thus, chronic kidney disease may predict cardiovascular disease in the general population. The impact of diabetes on renal impairment changes with increasing age. Serum markers of glomerular filtration rate and microalbuminuria identify renal impairment in different segments of the diabetic population, indicating that serum markers as well as microalbuminuria tests should be used in screening for nephropathy in diabetic older people. The American Diabetes Association and the National Institutes of Health recommend Estimated glomerular filtration rate (eGFR) calculated from serum creatinine at least once a year in all people with diabetes for detection of kidney dysfunction. eGFR remains an independent and significant predictor after adjustment for conventional risk factors including age, sex, duration of diabetes, smoking, obesity, blood pressure, and glycemic and lipid control, as well as presence of diabetic retinopathy. Cystatin-C (Cys C) may in future be the preferred marker of diabetic nephropathy due differences in measurements of serum creatinine by various methods. The appropriate reference limit for Cys C in geriatric clinical practice must be defined by further research. Various studies have shown the importance of measurement of albuminuria, eGFR, serum creatinine and hemoglobin level to further enhance the prediction of end stage renal disease.

Key Words: Chronic kidney disease, End stage renal disease, Glomerular filtration rate, Estimated glomerular filtration rate, Microalbumin, Cockcroft-Gault formula, Modification of diet, Renal disease, Cystatin-C



INTRODUCTION

Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction and failure of various organs, especially the eyes, kidneys, nerves, heart and blood vessels. Type 2 diabetes mellitus has quickly become a global health problem due to rapidly increasing population growth, aging, urbanization and increasing prevalence of obesity and physical inactivity. There is, therefore, an urgent need to prevent diabetes and its complications. Diabetes is the major cause of end-stage renal disease (ESRD) both in the U.S. and around the world and has enormous medical, social and economic consequences. Diabetes affects the kidney in stages. At the onset of diabetes, the kidney grows large and the glomerular filtration rate (GFR) becomes disturbed. Most recent basic and clinical research has pointed toward sclerosis and kidney failure. The morbidity and mortality caused by diabetes mellitus can be reduced by regular screening, early detection, and appropriate treatment of chronic complications. Thus, this discussion will focus on development of kidney damage, the various markers available and approaches to development of future markers to enhance the detection of ESRD at the earliest possible stage.

EPIDEMIOLOGY OF RENAL FAILURE

The global prevalence of diabetes is expected to increase from 4% in 1995 to 5.4% by the year 2025[1]. Currently, the countries with the largest number of diabetic patients are India, China and United States. The acute and chronic complications of diabetes mellitus are major causes of hospital admissions. Asian patients have shown evidence of macro and micro vascular disease at the time of diagnosis of diabetes when compared to Europeans[2].

Diabetes is the most common cause of kidney failure, accounting for nearly 44 percent of new cases[3]. Even when diabetes is controlled, the disease can lead to chronic kidney disease (CKD) and kidney failure. Kidney failure is the final stage of chronic kidney disease. Nearly 24 million people in the United States have diabetes and nearly 180 000 people are living with kidney failure as a result of diabetes[4]. The prevalence of nephropathy in India was less (8.9% in Vellore, 5.5% in Chennai) when compared with the prevalence of 22.3% in Asian Indians in the UK[5]. In chronic renal failure patients the prevalence of diabetic nephropathy was 30.3% followed by chronic interstitial nephritis (23%) and chronic glomerulonephritis (17.7%)[6].

African Americans, American Indians, and Hispanics or Latinos develop diabetes, CKD, and kidney failure at rates higher than Caucasians. Scientists have not been able to explain these higher rates and the interplay of various risk factors.

RISK FACTORS FOR DEVELOPMENT OF DIABETIC COMPLICATIONS

Diabetic nephropathy is the kidney disease that occurs as a result of diabetes. Nephropathy is the leading cause of chronic renal failure worldwide and is responsible for renal failure in about one third of patients who undergo dialysis. It is suggested that patients with common risk factors including greater duration of diabetes, hypertension, poor metabolic control, smoking, obesity and hyperlipidemia are more prone to develop diabetic complications[7]. In a retrospective study done by Klag et al[8] it was found that elevations of blood pressure are a strong independent risk factor for end-stage renal disease and that interventions to prevent the disease need to emphasize the prevention and control of both high-normal and high blood pressure.

The “Asian Indian Phenotype” refers to certain unique clinical and biochemical abnormalities in Indians which include increased insulin resistance, higher waist circumference despite lower body mass index, lower adiponectin and higher C-reactive protein levels. This phenotype makes Asians more prone to diabetes and premature complications. Asians show a trend towards higher systolic and diastolic blood pressures, possibly due to more patients with nephropathy, although this is not significantly different. Total cholesterol and LDL cholesterol levels were very similar between Europeans, Americans and Asians, but HDL cholesterol was significantly lower and triglycerides level was significantly higher in Asian patients[9].

James Sowers et al reviewed aspects of the association of diabetes with renal disease, emphasizing that CKD and albuminuria are associated with increased rates of cardiovascular disease (CVD) and mortality[10], and should be considered part of the cardiovascular risk factors in persons with diabetes. Lorenzo et al[11], found that the development of glomerular filtration rate < 60 mL/min per 1.73 m2 was associated with increased fasting insulin, triglycerides, free fatty acids, and uric acid and also with antihypertensive treatment, although not with waist circumference, controlling for age, sex, ethnicity, blood pressure, glucose, and C-reactive protein in nondiabetic persons. This points towards the association of CKD with the risk of development of diabetes.

PATHOGENESIS OF KIDNEY DAMAGE

There are various mechanisms of albuminuria which involve abnormalities of the glomerular endothelial barrier[12], causing excessive filtration as well as reduction of renal tubular cell albumin degradation and reabsorption. Glomerular hypertension, inflammation, and oxidative stress worsen albuminuria, with angiotensin-II[13] and mechanical stress factors contributing to these processes.

Renal disease in diabetes is found to be associated with abnormalities of vasodilatation and generates reactive oxygen species mediated by endothelial derived nitric oxide (NO), suggesting linkage between vascular and metabolic abnormalities. Angiotensin II and aldosterone, interacting with pulse pressure and increased systolic blood pressure, activate NADP oxidase, which acts as mediator of oxidative stress. Angiotensin II increases metabolism of NO to peroxynitrite[13], which further impairs endothelial-derived vasodilation.

In another mechanism, decrease in the ability to produce endothelial progenitor cells (EPCs), which can be quantitated with the cellular marker CD 34, leads to increased CVD risk. These cells derived from bone marrow, play a role in replacing damaged endothelium and are reduced in people with decreased endothelium-dependent vasodilation[14]. Both angiotensin II and aldosterone inhibit production of EPCs, while angiotensin converting enzyme inhibitors (ACEIs) increase their levels.

Sowers et al[15] discussed the relationship between dyslipidemia and CKD, hypothesizing that the mechanism of action of statins is an increase in endothelial NO synthase transcription via the phosphatidylinositol 3-kinase (PI3K) pathway and statin-induced inhibition of the mobilization of small molecular weight G-proteins. Also, Sowers suggested that the direct renin inhibitor aliskiren has similar benefits in renal disease to those of angiotensin receptor binders.

Charles Heilig et al[16] discussed the relationship of renal glucose transporter expression in relation to the development of diabetic nephropathy. Expression of GLUT1, the major mesangial glucose transporter, regulates extracellular matrix production. Mesangial cells overexpressing GLUT1 show increased production of both types I and IV collagen, as well as increased fibronectin and laminin production, leading to a phenotype similar to that of diabetes. In animal models, GLUT1 overexpression in glomeruli creates a nephropathy phenotype resembling that of diabetic renal disease with increased mean glomerular volume, mesangial expansion, and sclerosis.

DEVELOPEMENT OF KIDNEY DISEASE

Diabetic nephropathy takes many years to develop. In some people, the filtering function of the kidneys is actually higher than normal in the first few years of their diabetes.

In people who are developing kidney disease small amounts of the blood protein albumin begin to leak into the urine, a condition called microalbuminuria. The kidney’s filtration function usually remains normal during this period. As the disease progresses further, more albumin leaks into the urine, a stage known as macroalbuminuria or proteinuria. As the amount of albumin in the urine increases, the kidney’s filtering function usually begins to drop, resulting in the body’s retention of various wastes. As kidney damage develops, blood pressure also often rises or hypertension may attenuate the process of renal injury. Early detection of renal damage may help to delay the process.

As these processes are slow, kidney damage rarely occurs in the first 10 years of diabetes, and 15 to 25 years will usually pass before kidney failure occurs. For people who live with diabetes for more than 25 years without any signs of kidney failure, the risk of ever developing it decreases.

BIOMARKERS OF CHRONIC TUBULAR DYSFUNCTION

The early detection of diabetic nephropathy, resulting in timely intervention with particular attention to blood pressure control (thus limiting proteinuria), glycemic control, smoking cessation, and accentuation of cardiovascular risk, can improve long-term outcomes and retard progression to ESRD.

Tubular proteinuria results when glomerular function is normal but the proximal tubules have diminished absorbing capacity. The biomarkers of this process are as Table 1.

Table 1 The various tests for chronic tubular dysfunction in diabetic nephropathy.
Test nameUse
Blood urea nitrogen (serum or plasma)Initial diagnosis of acute or chronic kidney disease
Method: Spectrophotometry
Creatinine (serum or plasma)Initial diagnosis of acute or chronic kidney disease
Method: Spectrophotometry
Microalbumin (urine)May be used as a screening test
Method: ImmunoturbidimetricUseful in diabetic patients to assess baseline renal function
Useful in monitoring diabetic nephropathy in insulin-dependent diabetes mellitus
Creatinine based glomerular filtration rate (estimated)Estimate renal function and use as monitoring tool
Method: Spectrophotometry(Test reports serum creatinine reference intervals)
Cystatin-C based glomerular filtration rate (estimated)May be useful sensitive marker of renal disease; however, test lacks specificity due to reference range inavailability
Method: Nephelometry
Retinol-binding protein 4 (RBP4)May be used as a marker for early diabetic nephropathy. Limited studies are available
Method: Non-commercial enzyme-linked immunosorbant assay (ELISA)
AdiponectinShown inverse correlation with renal dysfunction in type 2 diabetes
Method: Competitive radioimmunoassay
Connective tissue growth factor (CTGF)CTGF excretion is correlated inversely with GFRs
Method: ELISA
Alpha-1-microglobulin (urine)May indicate renal involvement in diabetic patients
Method: Nephelometry
Liver type fatty acid binding protein (L-FABP)Expressed in proximal tubular cells and may associated with severity of diabetic nephropathy. Larger conclusive studies are required
Method: ELISA
Blood urea nitrogen

Blood tests for Blood urea nitrogen (BUN) and creatinine are the simplest way to monitor kidney function. These substances are normal metabolic waste products that are excreted by the kidneys. Urea is a byproduct of protein breakdown. A test can be done to measure the amount of urea nitrogen in the blood. In kidney disease, these substances (as well as numerous others) are not excreted normally, and so they accumulate in the body thus causing an increase in blood levels of urea. The normal level of BUN is 7-20 mg/dL[17].

Creatinine

Serum creatinine is primarily a metabolite of creatine, almost all of which is located in skeletal muscle. The normal level of creatinine is 0.8 to 1.4 mg/dL. Females usually have a lower creatinine (0.6 to 1.2 mg/dL) than males, because they usually have less muscle mass[17].

The amount of creatine per unit of skeletal muscle mass is consistent and the breakdown rate of creatine is also consistent. Thus, plasma creatinine concentration is very stable and a direct reflection of skeletal muscle mass[18]. Interestingly, Nobuko Harita et al[19], hypothesized that, lower serum creatinine is associated with an increased risk of type 2 diabetes, which might reflect a lower volume of skeletal muscle. Skeletal muscle is a major target tissue of insulin and a lower volume of skeletal muscle would mean fewer target sites for insulin which causes increase in insulin resistance. This leads to the development of type 2 diabetes[20]. This may explain in part the pathogenesis of type 2 diabetes associated with lower serum creatinine.

Creatinine based GFR

GFR is the best measure of kidney function since it accounts for age, BMI and sex. GFR measures the rate at which the kidneys' two million glomeruli filter plasma in order to process it and remove waste products from it. If the kidneys are injured by chronic kidney disease, the GFR gradually declines, and the amount of remaining kidney function can be estimated by measuring or calculating the GFR. The normal value for GFR in a normal-sized person is 100-150 mL/min.

Currently the two most common methods for determining GFR are creatinine clearance and estimated GFR (eGFR).

Creatinine clearance: Creatinine clearance requires a 24 h urine collection. A blood sample is drawn at some point during the 24 h period, and creatinine clearance, can then be calculated. However, because a small amount of creatinine is released by the filtering tubes in the kidneys, creatinine clearance is not exactly the same as the GFR. In fact, creatinine clearance usually overestimates the GFR, particularly in patients with advanced kidney failure. Normal clearance values are: Male: 97 to 137 mL/min; Female: 88 to 128 mL/min[21].

There are several factors that may interfere with the accuracy of the test. These include: (1) Incomplete urine collection; (2) Pregnancy; and (3) Vigorous exercise. Creatinine clearance measurements can also be affected by drugs, such as: cimetidine, trimethoprim, and drugs that can damage the kidneys (cephalosporins).

The creatinine clearance test should only be done for patients who are medically stable. Other patients may have a rapidly changing creatinine clearance, and therefore any result may be inaccurate.

eGFR: Formula-derived eGFR results have become widely used in clinical practice. The National Service Framework for Renal Services in the U.K. recommends the adoption of formula-derived eGFR in the annual evaluation of all patients with diabetes[22]. It is anticipated that this process will aid early identification and therefore improve longterm outcomes for those with diabetic nephropathy.

The American Diabetes Association recommends estimation of glomerular filtration rate by eGFR (in millilitres per min per 1.73 m2), which is calculated by the Cockcroft-Gault (CG) formula[23], corrected for Body Surface Area (BSA), and the Modification of Diet in Renal Disease (MDRD)[24] equation in all patients with diabetes. (1) CG derived eGFR: A simpler method for estimating creatinine clearance is based upon a formula (the Cockcroft-Gault formula) that includes a person's age, gender, weight, and serum creatinine level, but does not require the collection of a 24 h urine sample. The CG formula is[23] as follows: eGFR = [140 - age (years)] × weight (kg) × k × c/serum creatinine (μmol/L), (k is 1.23 for men and 1.04 for women and c adjusts for BSA[25]. c = 1.73/BSA, with BSA calculated using the following DuBois[26] formula), {BSA (m2) = [weight (kg)] 0.425 × [height (cm)] 0.725 × 0.007184}; (2) MDRD derived eGFR: Renal function can be assessed by serum creatinine and eGFR and calculated using the abbreviated MDRD formula as follows[24]: eGFR = 186 × (SCR × 0.011)-1.154× (age)-0.203× (0.742, if female) × (1.210 if African American) (SCR was serum creatinine expressed as μmol/L). Renal function has been graded according to the Kidney Disease Outcomes Quality Initiative guidelines: stage 1, ≥ 90 mL/min per 1.73 m2; stage 2, 60-89 mL/min per 1.73 m2; stage 3, 30-59 mL/min per 1.73 m2; stage 4, 15-29 mL/min per 1.73 m2; and stage 5, < 15 mL/min per 1.73 m2 (gf4-13); (3) Reexpressed MDRD equation: As significant error is introduced in the MDRD equation by use of different creatinine assays or calibration methods, the simplified MDRD was recently recalculated with serum creatinine measurements calibrated to an enzymatic assay[27]: eGFR = 175 × [serum creatinine (mg/dL)]-1.154× (years)-0.203× 0.742 (if female) × 1.212 (if African American); (4) MCQ equation: A new equation was developed by Rule et al[28] for GFR estimation in chronic kidney disease patients and in healthy persons for the diagnosis of chronic kidney disease. This is expressed as: (1.911 + 5.249/SCr - 2.114/SCr2) - [0.00686 × age (years)] - 0.205 if female, where SCr is serum creatinine [in milligrams per deciliter]; and (5) Considerations: eGFR is used for assessment of kidney function in patients with diabetes. However, despite validation in chronic kidney disease, eGFR has limitations in patients with preserved kidney function. These equations do have recognized limitations, including a tendency to significantly underestimate higher levels of GFR[29]. Additionally, Parving and colleagues[30] demonstrated that in type 2 diabetic subjects with macroalbuminuria, eGFR had a poor sensitivity for GFR values < 60 mL/min per 1.73 m2. In obese patients with established kidney disease, the Cockcroft-Gault equation overestimates GFR while underestimating GFR in lean subjects, possibly due to increasing weight; while performance of the MDRD equation in such patients is consistent regardless of weight. Bias of the Cockcroft-Gault formula was most pronounced in lean subjects, diminishing with increasing body weight. Conversely, bias of the MDRD equation increased with increasing body weight[31]. In obese patients, excess body weight is mainly adipose tissue, whereas creatinine is primarily generated by muscle. In the Cockcroft-Gault equation, body weight is proportional to GFR; therefore, increasing body weight without a proportional increase in creatinine generation will tend to increase the estimation of GFR. However, weight is not included in the MDRD equation and therefore cannot influence performance.

There are reports[32,33] that variation in calibration of the creatinine assay has an adverse impact on the performance of eGFR to estimate GFR, particularly at low levels of serum creatinine.

Cystatin-C based eGFR

A large percentage of individuals with type 2 diabetes pass through a period of pre-diabetes and may experience early renal dysfunction, e.g. a GFR > 60 mL/min per 1.73 m2. Serum creatinine has been found to be defecient to detect mild renal impairment, even when used with prediction equations[34,35]. So, interest has developed in Cystatin-C, a non-glycosylated basic protein, as a potential endogenous filtration marker of GFR. Cystatin-C is a cysteine protease inhibitor that is produced by virtually all nucleated cells and released into the bloodstream. It is entirely filtered by the kidney glomerulus and metabolized by the proximal tubule[36].

Various formulae have been used to measure serum cystatin levels by different methodologies. Recent estimations were done using a particle-enhanced immunonephelometric[37] assay or immunoturbidimetric assays. In all formulae, Cys C is serum cystatin-C (in milligrams per liter).

Arnal et al[38] estimated eGFR in 208 patients aged 1-80 years with various etiologies as follows: Cys-eGFR (Arnal-Dade) = 74.835/(Cys C1.333). Rule et al[39] studied native kidney disease (gf7-15) patients (n = 204) having hypertension as suspected etiology: Cys-eGFR (Rule) = 66.8 - (Cys C) - 1.30. Isotopic GFR (iGFR) was measured by iothalamate clearance. MacIsaac et al[40] studied 126 diabetic patients (mainly type 2 diabetes). The iGFR was measured by clearance of 99mTc-diethylene-triamine-penta-acetic acid. The equation is as follows: Cys-eGFR (MacIsaac) = (84.6/Cys C) - 3.2. Tan et al[41] used an unbiased conversion algorithm between plasma cystatin-C and iGFR measured by iohexol clearance in type 1 diabetes, including a subgroup of healthy subjects as follows: Cys-eGFR (Tan) = (87.1/plasma Cys C) - 6.87. Erosha et al[37] measured GFR using the equation Cys-GFR (Erosha) = (86.7/Cys C) - 4.2. The intra- and interassay coefficient of variation for Cystatin-C were 2.58 and 3.95%, respectively, at a concentration of 1.54 mg/L.

Considerations: It has been shown that Cystatin-C is a more sensitive indicator of mild renal impairment and may better estimate the GFR than serum creatinine[42]. Moreover, concentrations of Cystatin-C are not affected by sex, age, or muscle mass[43]. There is supportive evidence that the reciprocal of Cystatin-C correlates more closely with isotopic GFR than the CG or MDRD equations in subjects with mild renal impairment[44]. As the identification of those with pre-diabetes is assuming greater importance, Cystatin-C may play a role in detection of the association between renal and heart disease in etiology of pre-diabetics. Recent clinical trials[45,46] among people with pre-diabetes have provided convincing evidence that early intervention can significantly delay or prevent the progression to type 2 diabetes. However, concerns remain regarding intrapatient variation and the effect of certain drugs and hormonal levels on Cystatin-C concentration[47].

Microalbumin (urine)

Albuminuria is a well-known predictor of poor renal outcomes in patients with type 2 diabetes and in essential hypertension[48,49]. Albuminuria has also been shown to be a predictor of cardiovascular outcomes in these populations. There is emerging data that reduction of albuminuria leads to reduced risk of adverse renal and cardiovascular events[50,51]. It has become increasingly clear that albuminuria should not only be measured in all patients with type 2 diabetes and hypertension, but also that steps should be taken to suppress albuminuria in order to prevent future renal and cardiovascular adverse events. Albuminuria may reflect underlying renal expression of vascular damage, hypertension, endothelial dysfunction[12], and low-grade inflammation[52].

Expected results: Microalbuminuria is defined as levels of albumin ranging from 30 to 300 mg in a 24 h urine collection[53]. Overt albuminuria, macroalbuminuria, or proteinuria is defined as a urinary albumin excretion of > 300 mg/24 h. Urinary albuminuria comprises 20%-70% or urinary total protein excretion. Albuminuria can be measured in several ways (Table 2): (1) measurement of albumin-to creatinine ratio in a random or first morning spot collection; (2) 24 h urine collection with measurement of creatinine to verify adequacy of the collection; and (3) timed (4 h or overnight) urine collections.

Table 2 Expected results for microalbuminuria.
TestsNormalMicroalbuminuriaMacroalbuminuria
24 hr protein (mg)< 150< 500≥ 500
24 hr albumin (mg)< 3030-300> 300
Timed collection for albumin (μg/min)< 2020-200> 200
Spot sample collection for albumin (μg albumin/mg creatinine)< 3030-300> 300

Considerations: South Asians are very prone to obesity and type 2 diabetes. This explains their susceptibility for central obesity and insulin resistance. It also indicates the higher rates of end-stage diabetic nephropathy in migrant South Asians[54]. They have a three times higher risk of developing diabetic nephropathy and an almost 40-fold increased risk for end-stage diabetic nephropathy when compared with Caucasians[55]. Prataap K et al[56] shows that central obesity is an early and independent risk factor for increased albuminuria in normoglycemic South Asian subjects.

Recently, Michiaki Fukui et al[57] showed the association of serum bilirubin level with microalbuminuria and subclinical atherosclerosis in patients with type 2 diabetes. Serum bilirubin concentrations were significantly lower in patients with cardiovascular disease (CVD). They were an independent determinant of CVD and had a significant inverse correlation to the log urinary albumin excretion.

PROSPECTIVE FUTURE MARKERS

Several biochemical markers have the potential to be markers of CKD progression. These parameters might reflect diminished glomerular filtration, disturbances in tubular function or unknown contributors to kidney function that are unrelated to glomerular or tubular function. The evidence is still too sparse for most of these markers to be recommended for broad clinical use in the diagnosis of CKD progression. The emerging parameters associated with CKD progression must be studied further to determine whether they are causally related to progression of CKD or whether they simply predict the probability of progression. Few of these are as follows.

Retinol-binding protein 4

Retinol-binding protein 4 (RBP4) is a small visceral protein (approximately 21 kDa), mainly synthesized in the liver and catabolized in the kidneys after glomerular filtration[58]. RBP4 is complexed by transthyretin before delivering its ligand retinol to the target tissues[59]. RBP 4 was initially reported as an adipokine that impairs insulin sensitivity. The concentrations of this adipokine were found to increased in human subjects with impaired glucose tolerance (IGT) and type 2 diabetes compared with normal glucose tolerance subjects[60].

In prior studies, urinary RBP 4 excretion is found to be increased in early diabetic nephropathy and might even be a marker of early renal damage preceding microalbuminuria[61]. Andrea Henze et al[62] evaluated the influence of eGFR on RBP4 level and found that gradual elevation of RBP4 serum levels was accompanied by decline in eGFR in both, type 2 diabetic and non-diabetic subjects. No influence of type 2 diabetes mellitus or other parameters of diabetes (HbA1c, fasting serum glucose, BMI) on RBP4 serum concentration was seen. The association of RBP4 with several other metabolic parameters has been studied but limited studies are available on the relationship between this adipokine and mild to moderate decrease in GFR.

Adiponectin

Adiponectin is a recently discovered 30 kDa protein exclu sively secreted by adipocytes and is present at concen trations of 5-30 μg/mL in healthy humans. It is consid ered to be an important modulator of insulin sensitivity, dyslipidemia with anti-inflammatory properties[63,64].

Julie Lin et al[65] found the inverse correlation of serum adiponectin with the presence of renal dysfunction in men with type 2 diabetes, the majority of whom had well-preserved eGFR (87% had eGFR > 60 mL/min per 1.73 m2).Adiponectin was 2.5 times higher in hemodialysis patients (15.0 vs 6.3 μg/mL, P < 0.0001)[66] and three times higher in pediatric peritoneal dialysis patients when compared with healthy control subjects.

Connective tissue growth factor

Connective tissue growth factor (CTGF) is a 36 to 38 kDa polypeptide with functions in extracellular matrix production and other profibrotic activity mediated by transforming growth factor-β1. Other biological functions of CTGF include angiogenesis, chondrogenesis, osteogenesis, and cell adhesion, migration, proliferation, and differentiation[67]. The upregulation of CTGF has been observed in human and experimental diabetic nephropathy[68].

Nguyen et al[69] revealed that urinary CTGF excretion is associated with urinary albumin excretion and associated inversely with glomerular filtration rate, both important clinical markers for severity of renal disease. Further, they have shown that[70] the plasma CTGF level correlates with rate of decline in GFR and that it is an independent predictor of both ESRD and mortality in patients with type 1 diabetic nephropathy. Baseline plasma CTGF was higher in patients with diabetic nephropathy than in patients with normoalbuminuria.

α1-microglobulin

α1-microglobulin is a 26 000-31 000 Da glycoprotein which exists in blood as a free form and or complexed with IgA and albumin. Because of its low molecular weight, the free form is filtered freely through the renal glomerular basement membrane and reabsorbed by the proximal tubular cells[71]. Hence, any proximal tubular cell dysfunction results in increased quantities of α1-microglobulin in the urine.

Urinary α1- microglobulin levels were found to be elevated in both type 1 and type 2 diabetic subjects. In type 2 diabetic subjects, α1-microglobulin excretion was directly correlated with albuminuria and HbA1c levels, and was decreased with improved glycemic control in causacians[72,73]. Similar findings have been shown in Asian population[74].

Transforming growth factor-β

Transforming growth factor (TGF)-β1 has a central role in fibrotic kidney disease and interstitial fibrosis (5). In type 2 diabetic patients, urinary TGF-β1 is elevated[75] and associated with histologically-proven severe mesangial expansion[76]. Although urinary TGF-β1 measurement has been suggested as a marker for diabetic nephropathy, not all studies have shown the association of urinary TGF-β1 with diabetic nephropathy. Eija et al[77] did not find a difference in urinary TGF-β1 excretion between microalbuminuric and normoalbuminuric patients and only weak correlation was found with urinary glucose and β1-microglobulin. The association between urinary TGF-β1 and diabetic nephropathy may not be a direct one.

Liver-type fatty acid binding protein (L-FABP)

This protein is expressed in proximal tubular cells[78]. It has been shown that urinary L-FABP (U-LFABP) excretion is strongly associated with structural and functional tubular kidney damage in diabetic nephropathy[79]. Suzuki et al[80] performed a cross-sectional study in 356 adult type 2 diabetic patients and found a significant association between the stage of diabetic nephropathy and U-LFABP, although no significant difference between the normoalbuminuric and microalbuminuric groups was seen. Stine et al[81] have shown that U-LFABP, is elevated in type 1 diabetic patients compared with nondiabetic healthy control subjects and that the level further increases with micro and macroalbuminuria, reflecting increased tubular damage. There were no significant correlations between U-LFABP and sex, age, or A1C. Large studies with long-term follow-up are required to confirm these findings.

CONCLUSION

Diabetic nephropathy, especially related to type 2 diabetes, has become the single most important cause of ESRD worldwide. Management of traditional risk factors such as hypertension, hyperlipidemia, and smoking to improve cardiovascular and renal outcomes continues to be important in patients with chronic kidney disease. There is, however, growing recognition that nontraditional risk factors such as increased urinary albumin excretion, hypoalbuminemia, elevated serum creatinine levels, and/or decreased haemoglobin levels may also be important in individuals with chronic kidney disease. The RENAAL risk score for ESRD emphasizes the importance of the identification of levels of albuminuria and hypoalbuminemia as well as increased serum creatinine, and decreased haemoglobin levels to predict the development of ESRD in patients with type 2 diabetes and nephropathy. Albuminuria is a known strong predictor for ESRD, but the contribution of serum albumin, serum creatinine, and hemoglobin level further enhances the prediction of ESRD[82].

Footnotes

Peer reviewer: Richard E Katholi, MD, Prairie Cardiovascular Consultants, Clinical Professor of Pharmacology and Medicine, Southern Illinois University School of Medicine, Springfield, IL 62794-9420, United States

S- Editor Zhang HN L- Editor Hughes D E- Editor Liu N

References
1.  King H, Aubert RE, Herman WH. Global burden of diabetes, 1995-2025: prevalence, numerical estimates, and projections. Diabetes Care. 1998;21:1414-1431.  [PubMed]  [DOI]
2.  Chowdhury TA, Lasker SS. Complications and cardiovascular risk factors in South Asians and Europeans with early-onset type 2 diabetes. QJM. 2002;95:241-246.  [PubMed]  [DOI]
3.  United States Renal Data System USRDS 2007 Annual Data Report. Bethesda, MD: National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, U.S. Department of Health and Human Services. 2007;.  [PubMed]  [DOI]
4.  National Institute of Diabetes and Digestive and Kidney Diseases National Diabetes Statistics, 2007. Bethesda, MD: National Institutes of Health, U.S. Department of Health and Human Services. 2008;.  [PubMed]  [DOI]
5.  Ramachandran A. Epidemiology of diabetes in India--three decades of research. J Assoc Physicians India. 2005;53:34-38.  [PubMed]  [DOI]
6.  Ramachandran A. Socio-economic burden of diabetes in India. J Assoc Physicians India. 2007;55 Suppl:9-12.  [PubMed]  [DOI]
7.  Daneman D. Early Diabetes-Related Complications in Adolescents. Horm Res. 2005;63:75-85.  [PubMed]  [DOI]
8.  Klag MJ, Whelton PK, Randall BL, Neaton JD, Brancati FL, Ford CE, Shulman NB, Stamler J. Blood pressure and end-stage renal disease in men. N Engl J Med. 1996;334:13-18.  [PubMed]  [DOI]
9.  Mohan V, Sandeep S, Deepa R, Shah B, Varghese C. Epidemiology of type 2 diabetes: Indian scenario. Indian J Med Res. 2007;125:217-230.  [PubMed]  [DOI]
10.  Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296-1305.  [PubMed]  [DOI]
11.  Bloomgarden ZT. Diabetic nephropathy. Diabetes Care. 2008;31:823-827.  [PubMed]  [DOI]
12.  Stehouwer CD, Henry RM, Dekker JM, Nijpels G, Heine RJ, Bouter LM. Microalbuminuria is associated with impaired brachial artery, flow-mediated vasodilation in elderly individuals without and with diabetes: further evidence for a link between microalbuminuria and endothelial dysfunction--the Hoorn Study. Kidney Int Suppl. 2004;S42-S44.  [PubMed]  [DOI]
13.  Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis. 2003;41:1-12.  [PubMed]  [DOI]
14.  Hill JM, Zalos G, Halcox JP, Schenke WH, Waclawiw MA, Quyyumi AA, Finkel T. Circulating endothelial progenitor cells, vascular function, and cardiovascular risk. N Engl J Med. 2003;348:593-600.  [PubMed]  [DOI]
15.  Sowers JR. Hypertension, angiotensin II, and oxidative stress. N Engl J Med. 2002;346:1999-2001.  [PubMed]  [DOI]
16.  Heilig CW, Brosius FC 3rd, Cunningham C. Role for GLUT1 in diabetic glomerulosclerosis. Expert Rev Mol Med. 2006;8:1-18.  [PubMed]  [DOI]
17.  Molitoris BA Acute kidney injury. In Goldman L, Ausiello D, editors. Cecil Medicine. 23rd ed. Philadelphia, Pa: Saunders Elsevier 2007; chap 121.  [PubMed]  [DOI]
18.  Marti5n RF Renal Function. In Clin Chem Theory, Analysis, Correlation. 4th ed. Kaplan LA,Pesce AJ, Kazmierczak SC, editors. St. Louis, Missouri, Mosby. Philadelphia, Pa: Saunders Elsevier 2003; 483-484.  [PubMed]  [DOI]
19.  Harita N, Hayashi T, Sato KK, Nakamura Y, Yoneda T, Endo G, Kambe H. Lower serum creatinine is a new risk factor of type 2 diabetes: the Kansai healthcare study. Diabetes Care. 2009;32:424-426.  [PubMed]  [DOI]
20.  DeFronzo RA, Gunnarsson R, Björkman O, Olsson M, Wahren J. Effects of insulin on peripheral and splanchnic glucose metabolism in noninsulin-dependent (type II) diabetes mellitus. J Clin Invest. 1985;76:149-155.  [PubMed]  [DOI]
21.  Bazari H Approach to the patient with renal disease. In Goldman L, Ausiello D, editors. Cecil Medicine. 23rd ed. Philadelphia, Pa: Saunders Elsevier 2007; chap 115.  [PubMed]  [DOI]
22.  Department of Health Renal Team The National Service Framework for Renal Services. Part 2: chronic kidney disease, acute renal failure and end of life care 2005;  Available from: http://www.kidney. org.uk/campaigns/Renal-nsf/nsf-pt2.pdf.  [PubMed]  [DOI]
23.  Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16:31-41.  [PubMed]  [DOI]
24.  Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130:461-470.  [PubMed]  [DOI]
25.  Chudleigh RA, Dunseath G, Peter R, Harvey JN, Ollerton RL, Luzio S, Owens DR. Influence of body weight on the performance of glomerular filtration rate estimators in subjects with type 2 diabetes. Diabetes Care. 2008;31:47-49.  [PubMed]  [DOI]
26.  Du Bois D, Du Bois EF. A formula to estimate the approximate surface area if height and weight be known. 1916. Nutrition. 1989;5:303-311; discussion 312-313.  [PubMed]  [DOI]
27.  Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247-254.  [PubMed]  [DOI]
28.  Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med. 2004;141:929-937.  [PubMed]  [DOI]
29.  Vervoort G, Willems HL, Wetzels JF. Assessment of glomerular filtration rate in healthy subjects and normoalbuminuric diabetic patients: validity of a new (MDRD) prediction equation. Nephrol Dial Transplant. 2002;17:1909-1913.  [PubMed]  [DOI]
30.  Rossing P, Rossing K, Gaede P, Pedersen O, Parving HH. Monitoring kidney function in type 2 diabetic patients with incipient and overt diabetic nephropathy. Diabetes Care. 2006;29:1024-1030.  [PubMed]  [DOI]
31.  Rigalleau V, Lasseur C, Perlemoine C, Barthe N, Raffaitin C, Chauveau P, Combe C, Gin H. Cockcroft-Gault formula is biased by body weight in diabetic patients with renal impairment. Metabolism. 2006;55:108-112.  [PubMed]  [DOI]
32.  Coresh J, Astor BC, McQuillan G, Kusek J, Greene T, Van Lente F, Levey AS. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis. 2002;39:920-929.  [PubMed]  [DOI]
33.  Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function--measured and estimated glomerular filtration rate. N Engl J Med. 2006;354:2473-2483.  [PubMed]  [DOI]
34.  Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the modification of diet in renal disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol. 2005;16:459-466.  [PubMed]  [DOI]
35.  Nielsen S, Rehling M, Schmitz A, Mogensen CE. Validity of rapid estimation of glomerular filtration rate in type 2 diabetic patients with normal renal function. Nephrol Dial Transplant. 1999;14:615-619.  [PubMed]  [DOI]
36.  Randers E, Kristensen JH, Erlandsen EJ, Danielsen H. Serum Cystatin-C as a marker of the renal function. Scand J Clin Lab Invest. 1998;58:585-592.  [PubMed]  [DOI]
37.  Premaratne E, MacIsaac RJ, Finch S, Panagiotopoulos S, Ekinci E, Jerums G. Serial measurements of Cystatin-C are more accurate than creatinine-based methods in detecting declining renal function in type 1 diabetes. Diabetes Care. 2008;31:971-973.  [PubMed]  [DOI]
38.  Thomas L, Huber AR. Renal function--estimation of glomerular filtration rate. Clin Chem Lab Med. 2006;44:1295-1302.  [PubMed]  [DOI]
39.  Rule AD, Bergstralh EJ, Slezak JM, Bergert J, Larson TS. Glomerular filtration rate estimated by Cystatin-C among different clinical presentations. Kidney Int. 2006;69:399-405.  [PubMed]  [DOI]
40.  Macisaac RJ, Tsalamandris C, Thomas MC, Premaratne E, Panagiotopoulos S, Smith TJ, Poon A, Jenkins MA, Ratnaike SI, Power DA. Estimating glomerular filtration rate in diabetes: a comparison of cystatin-C- and creatinine-based methods. Diabetologia. 2006;49:1686-1689.  [PubMed]  [DOI]
41.  Tan GD, Lewis AV, James TJ, Altmann P, Taylor RP, Levy JC. Clinical usefulness of Cystatin-C for the estimation of glomerular filtration rate in type 1 diabetes: reproducibility and accuracy compared with standard measures and iohexol clearance. Diabetes Care. 2002;25:2004-2009.  [PubMed]  [DOI]
42.  Sarnak MJ, Katz R, Stehman-Breen CO, Fried LF, Jenny NS, Psaty BM, Newman AB, Siscovick D, Shlipak MG. Cystatin-C concentration as a risk factor for heart failure in older adults. Ann Intern Med. 2005;142:497-505.  [PubMed]  [DOI]
43.  Coll E, Botey A, Alvarez L, Poch E, Quintó L, Saurina A, Vera M, Piera C, Darnell A. Serum Cystatin-C as a new marker for noninvasive estimation of glomerular filtration rate and as a marker for early renal impairment. Am J Kidney Dis. 2000;36:29-34.  [PubMed]  [DOI]
44.  Dharnidharka VR, Kwon C, Stevens G. Serum Cystatin-C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40:221-226.  [PubMed]  [DOI]
45.  Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393-403.  [PubMed]  [DOI]
46.  Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344:1343-1350.  [PubMed]  [DOI]
47.  Filler G, Bökenkamp A, Hofmann W, Le Bricon T, Martínez-Brú C, Grubb A. Cystatin-C as a marker of GFR--history, indications, and future research. Clin Biochem. 2005;38:1-8.  [PubMed]  [DOI]
48.  Keane WF, Brenner BM, de Zeeuw D, Grunfeld JP, McGill J, Mitch WE, Ribeiro AB, Shahinfar S, Simpson RL, Snapinn SM. The risk of developing end-stage renal disease in patients with type 2 diabetes and nephropathy: the RENAAL study. Kidney Int. 2003;63:1499-1507.  [PubMed]  [DOI]
49.  Pinto-Sietsma SJ, Janssen WM, Hillege HL, Navis G, De Zeeuw D, De Jong PE. Urinary albumin excretion is associated with renal functional abnormalities in a nondiabetic population. J Am Soc Nephrol. 2000;11:1882-1888.  [PubMed]  [DOI]
50.  Brenner BM, Cooper ME, de Zeeuw D, Keane WF, Mitch WE, Parving HH, Remuzzi G, Snapinn SM, Zhang Z, Shahinfar S. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med. 2001;345:861-869.  [PubMed]  [DOI]
51.  Ibsen H, Olsen MH, Wachtell K, Borch-Johnsen K, Lindholm LH, Mogensen CE, Dahlöf B, Devereux RB, de Faire U, Fyhrquist F. Reduction in albuminuria translates to reduction in cardiovascular events in hypertensive patients: losartan intervention for endpoint reduction in hypertension study. Hypertension. 2005;45:198-202.  [PubMed]  [DOI]
52.  Ritz E. Minor renal dysfunction: an emerging independent cardiovascular risk factor. Heart. 2003;89:963-964.  [PubMed]  [DOI]
53.  Standards of medical care in diabetes Diabetes Care. 2005;28 Suppl 1:S4-S36.  [PubMed]  [DOI]
54.  Mather HM, Keen H. The Southall Diabetes Survey: prevalence of known diabetes in Asians and Europeans. Br Med J (Clin Res Ed). 1985;291:1081-1084.  [PubMed]  [DOI]
55.  Chandie Shaw PK, Baboe F, van Es LA, van der Vijver JC, van de Ree MA, de Jonge N, Rabelink TJ. South-Asian type 2 diabetic patients have higher incidence and faster progression of renal disease compared with Dutch-European diabetic patients. Diabetes Care. 2006;29:1383-1385.  [PubMed]  [DOI]
56.  Chandie Shaw PK, Berger SP, Mallat M, Frölich M, Dekker FW, Rabelink TJ. Central obesity is an independent risk factor for albuminuria in nondiabetic South Asian subjects. Diabetes Care. 2007;30:1840-1844.  [PubMed]  [DOI]
57.  Fukui M, Tanaka M, Shiraishi E, Harusato I, Hosoda H, Asano M, Hasegawa G, Nakamura N. Relationship between serum bilirubin and albuminuria in patients with type 2 diabetes. Kidney Int. 2008;74:1197-1201.  [PubMed]  [DOI]
58.  Goodman DS Plasma Retinol-Binding Protein. Orlando, Florida, New York Academic Press, Inc. Part 2: chronic kidney disease, acute renal failure and end of life care 1984; .  [PubMed]  [DOI]
59.  Blaner WS. Retinol-binding protein: the serum transport protein for vitamin A. Endocr Rev. 1989;10:308-316.  [PubMed]  [DOI]
60.  Cho YM, Youn BS, Lee H, Lee N, Min SS, Kwak SH, Lee HK, Park KS. Plasma retinol-binding protein-4 concentrations are elevated in human subjects with impaired glucose tolerance and type 2 diabetes. Diabetes Care. 2006;29:2457-2461.  [PubMed]  [DOI]
61.  Abahusain MA, Wright J, Dickerson JW, de Vol EB. Retinol, alpha-tocopherol and carotenoids in diabetes. Eur J Clin Nutr. 1999;53:630-635.  [PubMed]  [DOI]
62.  Henze A, Frey SK, Raila J, Tepel M, Scholze A, Pfeiffer AF, Weickert MO, Spranger J, Schweigert FJ. Evidence that kidney function but not type 2 diabetes determines retinol-binding protein 4 serum levels. Diabetes. 2008;57:3323-3326.  [PubMed]  [DOI]
63.  Nedvídková J, Smitka K, Kopský V, Hainer V. Adiponectin, an adipocyte-derived protein. Physiol Res. 2005;54:133-140.  [PubMed]  [DOI]
64.  Becker B, Kronenberg F, Kielstein JT, Haller H, Morath C, Ritz E, Fliser D. Renal insulin resistance syndrome, adiponectin and cardiovascular events in patients with kidney disease: the mild and moderate kidney disease study. J Am Soc Nephrol. 2005;16:1091-1098.  [PubMed]  [DOI]
65.  Lin J, Hu FB, Curhan G. Serum adiponectin and renal dysfunction in men with type 2 diabetes. Diabetes Care. 2007;30:239-244.  [PubMed]  [DOI]
66.  Zoccali C, Mallamaci F, Tripepi G, Benedetto FA, Cutrupi S, Parlongo S, Malatino LS, Bonanno G, Seminara G, Rapisarda F. Adiponectin, metabolic risk factors, and cardiovascular events among patients with end-stage renal disease. J Am Soc Nephrol. 2002;13:134-141.  [PubMed]  [DOI]
67.  Perbal B. CCN proteins: multifunctional signalling regulators. Lancet. 2004;363:62-64.  [PubMed]  [DOI]
68.  Riser BL, Denichilo M, Cortes P, Baker C, Grondin JM, Yee J, Narins RG. Regulation of connective tissue growth factor activity in cultured rat mesangial cells and its expression in experimental diabetic glomerulosclerosis. J Am Soc Nephrol. 2000;11:25-38.  [PubMed]  [DOI]
69.  Nguyen TQ, Tarnow L, Andersen S, Hovind P, Parving HH, Goldschmeding R, van Nieuwenhoven FA. Urinary connective tissue growth factor excretion correlates with clinical markers of renal disease in a large population of type 1 diabetic patients with diabetic nephropathy. Diabetes Care. 2006;29:83-88.  [PubMed]  [DOI]
70.  Nguyen TQ, Tarnow L, Jorsal A, Oliver N, Roestenberg P, Ito Y, Parving HH, Rossing P, van Nieuwenhoven FA, Goldschmeding R. Plasma connective tissue growth factor is an independent predictor of end-stage renal disease and mortality in type 1 diabetic nephropathy. Diabetes Care. 2008;31:1177-1182.  [PubMed]  [DOI]
71.  Ekstrom B, Peterson PA, Berggard I. A urinary and plasma alpha1-glycoprotein of low molecular weight: isolation and some properties. Biochem Biophys Res Commun. 1975;65:1427-1433.  [PubMed]  [DOI]
72.  Hofmann W, Guder WG. [Urinary proteins in patients with diabetes mellitus]. Klin Wochenschr. 1989;67 Suppl 17:37-39.  [PubMed]  [DOI]
73.  Martin P, Hampton KK, Walton C, Tindall H, Davies JA. Microproteinuria in type 2 diabetes mellitus from diagnosis. Diabet Med. 1990;7:315-318.  [PubMed]  [DOI]
74.  Hong CY, Hughes K, Chia KS, Ng V, Ling SL. Urinary alpha1-microglobulin as a marker of nephropathy in type 2 diabetic Asian subjects in Singapore. Diabetes Care. 2003;26:338-342.  [PubMed]  [DOI]
75.  Sharma K, Ziyadeh FN, Alzahabi B, McGowan TA, Kapoor S, Kurnik BR, Kurnik PB, Weisberg LS. Increased renal production of transforming growth factor-beta1 in patients with type II diabetes. Diabetes. 1997;46:854-859.  [PubMed]  [DOI]
76.  Sato H, Iwano M, Akai Y, Kurioka H, Kubo A, Yamaguchi T, Hirata E, Kanauchi M, Dohi K. Increased excretion of urinary transforming growth factor beta 1 in patients with diabetic nephropathy. Am J Nephrol. 1998;18:490-494.  [PubMed]  [DOI]
77.  Korpinen E, Teppo AM, Hukkanen L, Akerblom HK, Grönhagen-Riska C, Vaarala O. Urinary transforming growth factor-beta1 and alpha1-microglobulin in children and adolescents with type 1 diabetes. Diabetes Care. 2000;23:664-668.  [PubMed]  [DOI]
78.  Maatman RG, van de Westerlo EM, van Kuppevelt TH, Veerkamp JH. Molecular identification of the liver- and the heart-type fatty acid-binding proteins in human and rat kidney. Use of the reverse transcriptase polymerase chain reaction. Biochem J. 1992;288:285-290.  [PubMed]  [DOI]
79.  Kamijo A, Sugaya T, Hikawa A, Okada M, Okumura F, Yamanouchi M, Honda A, Okabe M, Fujino T, Hirata Y. Urinary excretion of fatty acid-binding protein reflects stress overload on the proximal tubules. Am J Pathol. 2004;165:1243-1255.  [PubMed]  [DOI]
80.  Suzuki K, Babazono T, Murata H, Iwamoto Y. Clinical significance of urinary liver-type fatty acid-binding protein in patients with diabetic nephropathy. Diabetes Care. 2005;28:2038-2039.  [PubMed]  [DOI]
81.  Nielsen SE, Sugaya T, Tarnow L, Lajer M, Schjoedt KJ, Astrup AS, Baba T, Parving HH, Rossing P. Tubular and glomerular injury in diabetes and the impact of ACE inhibition. Diabetes Care. 2009;32:1684-1688.  [PubMed]  [DOI]
82.  Keane WF, Zhang Z, Lyle PA, Cooper ME, Zeeuw DD, Grunfeld JP. Risk Scores for Predicting Outcomes in Patients with Type 2 Diabetes and Nephropathy: The RENAAL Study. Clin J Am Soc Nephrol. 2006;10:1-7.  [PubMed]  [DOI]