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Identifying Clinical and Biochemical Predictors of Progression of Diabetic Kidney Disease

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08/02/20 Medical Reference this

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ABSTRACT

Diabetic kidney disease is one of the biggest risk factors for premature cardiovascular disease mortality and is also the main cause of end stage renal disease in the UK. Due to the large number of patients affected by the condition worldwide, it is imperative that new assays be developed for the early prediction of diabetic kidney disease. In current clinical practice, urinary albumin:creatinine ratio and estimated glomerular filtration rate (eGFR) are used as markers of the disease. However, the precision of these markers is questionable, as eGFR calculations lack reliability in hyperfiltration status and tissue involvement varies greatly between individuals, meaning renal impairment can occur without the presence of albuminuria, especially in type II diabetic patients. Therefore, there is a need for novel biomarkers that can assist in the early prediction of the disease to improve prognosis and disease outcomes. By reviewing literature on the pathophysiology of the disease, limitations of the current clinical biomarkers and the scope for novel predictors, we will discuss the strengths of novel biomarkers and metabolomic signatures to assess if they have a place in clinical practice.

INTRODUCTION

Approximately 1 in 4 adults with diabetes are affected by diabetic kidney disease [1,2]. In many regions, specifically developed countries, diabetic kidney disease is one of the most common complications of diabetes mellitus, as well as the main cause of chronic kidney disease.

Diabetic kidney disease is characterised, initially, by a fall in eGFR or albuminuria. The characteristics are also indicators for poor health outcomes, including end-stage renal disease and ultimately, death [3]. Unfortunately, despite strict glycaemic and blood pressure control, many patients will still progress to end-stage renal disease. This is characterised by the amount of albuminuria as well as the severity of histopathology.    

Albuminuria can present as either microalbuminuria or macroalbuminuria. Microalbuminuria is defined as the abnormal increase of albumin excretion in the urine within the range 30–299 mg/g creatinine [4] or an albumin:creatinine ratio of 3.4-34.0 mg/mmol. Macroalbuminuria is defined as the abnormal increase of albumin excretion in the urine ≥300 mg/g creatinine or an albumin:creatinine ratio >34 mg/mmol [5].

According to the National Kidney Foundation and KDOQI clinical practice guidelines, diabetic kidney disease is defined as microalbuminuria or macroalbuminuria associated with the presence of diabetic retinopathy (for both type 1 and type 2 diabetics) and/or type 1 diabetes mellitus for a duration greater than 10 years [5]. It is also recommended by the National Kidney Foundation that the measurement of urinary albumin should be conducted using the albumin:creatinine ratio [6].

In clinical presentations, the most characteristic will include progressive albuminuria, hypertension and a decline in GFR for patients with long-standing diabetes (duration >10 years). Although rarely required, a kidney biopsy can commonly prove conclusive. The most common clinical signs are hypertension, signs of retinopathy or oedema. Patients may also present with certain risk factors such as sustained hyperglycaemia, obesity, dyslipidaemia and smoking.

Clinically, a number of assessments are required to make a diagnosis of diabetic kidney disease. The kidney function requires assessment as well as damage which is usually assessed using the albumin:creatinine ratio via urine dipstick testing. An estimated glomerular filtration rate is also calculated to aid diagnosis [7]. Although these tests are easy and quick to perform, they do carry their own limitations. Understanding these limitations and how they affect diagnostic methods will provide better direction for future improvements.

In the US, 10-15% of adult type I diabetic patients progress to end-stage renal disease which remains a major cause of morbidity and mortality worldwide [8]. Diabetic patients, the majority of which are type II, account for one third of all those that require chronic renal replacement therapy in the western world [9]. A large population is affected by diabetic kidney disease worldwide and with the future affected population expected to grow, attempts have been made to predict progression of the disease at earlier stages. This would rapidly improve the prognosis of the condition through potential for earlier intervention. There have been decades of research investigating the strategies for diabetic kidney disease prediction, but significantly efficacious predictors have not yet been developed or integrated into clinical practice.

Stages of Diabetic Kidney Disease

When discussing the need for more effective biomarkers in the diagnosis of diabetic kidney disease, it is important to understand the staging of the disease and its progression (Table 1) [20].

STAGE

SIGNS

DIAGNOSTIC CRITERIA

1

Early hypertrophy

Albumin:Creatinine ratio <30 mg/g creatinine

2

Structural lesions with no clinical signs

30 < Albumin:Creatinine ratio < 300 mg/g creatinine

3

Microalbuminuria

Albumin:Creatinine ratio >300 mg/g creatinine and/or persistent proteinuria

4

Nephropathy with clinical signs

Serum concentration of creatinine 2.0mg/dL with proteinuria

5

End stage renal disease (with uremia)

Dialysis

[Table 1]

The first stage of diabetic kidney disease onsets before any renal damage occurs. The characteristics of stage 1 diabetic kidney disease include renal vasodilation and hyperfiltration which occur early on in a recent onset diabetic patient. The hyperfiltration may be caused by a host of factors such as prostaglandins secretion, increased sodium/glucose absorption in the proximal convoluted tubule and hyperglycaemia, among others [20].

In the second stage of diabetic kidney disease, structural lesions begin to develop without any clinical signs of disease. One of the earliest abnormalities you would expect to see is glomerular basement membrane thickening where the kidney experiences significant hypertrophy in early diabetes. In type I diabetics, these is commonly seen in most patients between 1.5 and 2.5 years of onset [20]. These patients may experience a decline in function due to expansion of the mesangial cells, causing blockage of the glomerular capillaries. This reduces the surface area available for filtration.

Following this, microalbuminuria may be detected which leads to a third stage diagnosis. It is expected in this stage that the increase in albuminuria is slow and gradual, taking place over a number of years. The EDIC study suggests that persistent microalbuminuria develops most commonly in the second decade after diabetes onset [21]. The presence of microalbuminuria can be suggestive of damage to the endothelium in absence of specific renal lesions and can also suggest the loss of podocytes. There is a correlation between the number of podocytes in patients with type II diabetes mellitus and albuminuria change over time [22].

As mentioned previously, microalbuminuria has a certain predictive value with regards to advanced renal disease progression in those with type II diabetes mellitus. However, in type I diabetes mellitus, that predictive power is not as robust. Both normoalbuminuric and microalbuminuric patients will benefit from strict, optic glycaemic control as about one third of the patients with normal albumin levels develop diabetic kidney disease within a few years of diabetic onset [20].

Overt nephropathy is usually associated with a decline in GFR. With increased levels of albuminuria, the prevalence of hypertension, too, increases [23]. Risk factors which assist the development of overt nephropathy include smoking, increased lipid levels, poorly controlled diabetes and old age. End-stage renal disease is characterised by the requirement for renal replacement therapy, irrespective of GFR value (KDOQI). End-stage renal disease alone can be an important predictor for hospitalisation as well as adult deaths from heart failure [20]. According to the United Kingdom Prospective Diabetes Study 64, the rate of progression from one stage to the next is between 2% to 3% per year [1].

 

CURRENT BIOMARKER LIMITATIONS

eGFR

The glomerular filtration rate (GFR) is overall one of the best measurements to assess renal excretory function. To calculate the GFR, a 24 hour measurement of creatinine clearance is required which is not the most appropriate measurement for a clinical setting. For the convenience of fast results, an estimated glomerular filtration rate (eGFR) is calculated to assess renal function. The two main calculations for eGFR are the modification of diet in renal disease calculation [MDRD, eGFR = 175 × standardized Scr-1.154 × age-0.203 × 1.212 (if black) × 0.742 (if female), where Scr is serum creatinine] [10] and the chronic kidney disease – epidemiology creatinine calculation [CKD-EPI, eGFR = 141 × min (Scr/k, 1)α× max (Scr/k, 1)-1.209 × 0.993Age × 1.018 (if female) × 1.159 (if black), where k is 0.7 for females and 0.9 for males, α is -0.329 for females and -0.411 for males, min indicates the minimum of Scr/k or 1, and max indicates the maximum of Scr/k or 1] [11].

-work out if its okay to put equations like that with references

Despite the convenience of quick estimates, the eGFR does come with its limitations as a biomarker for the diagnosis of diabetic kidney disease. Serum creatinine levels can be influenced by muscle mass and diet, especially meat intake [12] which would therefore have an effect on the eGFR calculation.

Inaccuracies may also arise from the equation in certain circumstances. In patients with a GFR > 60 mL/min per 1.73 m2, the MDRD equation becomes less accurate [13]. As glomerular hyperfiltration is a sign of an early stage disease, this would cause a problem in the early diagnosis of diabetic kidney disease. Conversely, the CKD-EPI equation is more reliable for patients with a GFR > 90 mL/min per 1.73 m2 [14]. This would therefore be a more accurate equation to use with diabetic patients – why?

The P30 value of both equations is between 80% and 90% which means the eGFR calculations from the equations have a 80-90% chance of being within 30% of the GFR. (add some other stuff to transition to conclusion)

Therefore eGFR alone should not be used a sole marker for diagnosis of diabetic kidney disease.

Albuminuria

Albuminuria is assessed in clinical practice as a marker of glomerular dysfunction. As with GFR, a 24 hour urinary measurement of albumin is impractical due to its time consuming nature and lack of predictive power [15]. Therefore, the current standards of clinical practice dictate that albumin and creatinine levels should be assessed via urinary dipstick testing.

However, it is important to realise that there are many other factors that could explain an increase in urinary albumin excretion other than diabetic kidney disease. These factors include diet, physical activity, fever, menstruation, infection, marked hypertension and marked hyperglycaemia [15].

A crucial point to consider is the relationship between the presence of albuminuria and renal function decline. In the United Kingdom Prospective Diabetes Study 74, approximately only one half of all patients studied with renal impairment went on to have preceding albuminuria [16]. Another study in which this was made apparent was the Developing Education on Microalbuminuria for Awareness of Renal and Cardiovascular Risk in Diabetes study in which only 17% of patients with a normal urinary albumin:creatinine ratio were noted to have advanced chronic kidney disease [17]. The imbalance between renal impairment and urinary:creatinine ratio in these cases may be due to the complexity and heterogeneous nature of renal disease, especially with regards to type II diabetes. As previously mentioned, albuminuria is used as a marker of glomerular dysfunction which is characteristic of renal disease in type I diabetics [18]. However glomerular dysfunction is a much less common pathology of renal disease in type II diabetics. In type II diabetics, it is more commonly expected to see vascular or tubulointerstitial lesions as histological changes [19]. It is, therefore, important to understand that albuminuria as a marker alone is insufficient to outline diabetic kidney disease risk and care should be sought when using albuminuria as a prediction value.

 

Alternative Biomarkers

Limitations in biomarkers currently used in clinical practice such as eGFR and albuminuria for the prediction of progression of diabetic kidney disease created major investment into the search for more practical and effective biomarkers. There have been a large number of different biomarkers trialled and studied but larger scale studies will be required for validation of the benefits of these predictors.

Cystatin C

Cystatin C is a novel potential biomarker that has great potential to maintain use in clinical settings. Serum Cystatin C is a protein (13.3 kDa) encoded by the CST3 gene that is fully reabsorbed and catabolised in the proximal renal tubules after glomerular filtration [24]. It is not, however, returned to the blood. Unlike serum creatinine, Cystatin C levels are not affected by diet or muscle mass and they correlate well with GFR levels – there is a linear relationship between a decrease in GFR and an increase in Cystatin C levels [24]. Therefore, a Cystatin C based estimated GFR tends to be more accurate than a serum creatinine based estimated GFR when GFR > 60 mL/min per 1.73 m2 [25]. This suggests that Cystatin C could prove a better biomarker in the early diagnosis of diabetic kidney disease.

 

TNFR1/2

Tumor Necrosis Factor (TNF)-α is one of the key mediators of inflammation in the body.  (TNF)-α is able to carry out its function through two receptors – Tumour Necrosis Factor Receptor 1 (TNFR1) and Tumour Necrosis Factor Receptor 2 (TNFR2). These receptors can be found bound to albumin or in soluble form in serum [26]. Levels of TNFR1 and TNFR2 in serum have been shown to correlate with GFR levels in diabetic patients, independent of the presence of albuminuria [26]. Studies from both type I diabetic [27] and type II diabetic patients [28] have shown that plasma levels of TNFR1 and TNFR2 were capable of predicting advanced chronic kidney disease development over 12 years of follow up. This suggests that measuring serum levels of TNFR1 and TNFR2 can hold predictive power in the diagnosis of diabetic kidney disease.

Oxidative Stress

8-oxo-7,8-dihydro-2’-deoxyguanosine (8-oxodG) is a biomarker of DNA damage, assessing oxidative stress [29]. 8-oxdG is a product of oxidative damage in nuclear and mitochondrial DNA. When DNA damage occurs, 8-oxodG is directly excreted into urine as the repair mechanism is initiated. Therefore, urinary levels of 8-oxodG can act as a measure of oxidative stress [30]. In a cohort-study (5 years) of 532 Japanese type II diabetics, the baseline concentration of 8-oxodG levels in the urine was able to predict consequent diabetic kidney disease [31]. This showcases the potential for the use of 8-oxodG as a potential biomarker, provided more large-scale studies are carried out in different populations. (cohort study?)  

Metabolomics

Metabolomics is the large-scale study of the complete collection of metabolites within the organism where studies are carried out with the use of techniques such as nuclear magnetic resonance and mass spectrometry based profiling [32]. Understanding the human metabolome and changes associated with it will allow for the early identification of pathological changes in the disease course.

There have been a number of studies into the use of metabolomics in clinical practice. Sharma et al [33] obtained samples of 94 different urinary metabolites from patients with well-controlled diabetes, with and without diabetic kidney disease. From these 94 metabolites, a decreased urinary level in 13 showed an association with diabetic kidney disease – this could potentially be related to mitochondrial function. Another study which supports this is the Joslin Kidney Study where Niewczas et al [34] identified, in type II diabetic patients, a panel of 5 plasma metabolites which were able to predict the progression towards end-stage renal disease. This prediction was also independent of current biomarkers in clinical practice such as albumin:creatinine ratio and eGFR.

Many other studies were also able to showcase the prediction towards diabetic kidney disease and although the results from these studies are very promising for the future, it is important to note that the techniques used for analysis are very complex. Along with the current incompletion of human metabolome coverage, there are issues that require addressing before there can be a push for the use of metabolomics in clinical practice.

 

Conclusion

Diabetic kidney disease is a multi stage disease, with different levels of renal damage and dysfunction at each stage. The multi-stage aspect makes this disease very complex and therefore makes difficult, the early prediction of pathological changes. Therefore, one universal biomarker may never be sufficient and it may be that a multitude of efficacious biomarkers are required for conclusive diagnosis.

The current validated markers of practice are albuminuria and estimated glomerular filtration rate. Whilst they are able to assist in diagnosis, the ability of these markers to detect early pathological change in the disease process is limited. With the higher level of understanding of the disease process and pathology, in addition to the multitude of information from studies into novel biomarkers and metabolomics, there is a credible push for the next generation of biomarkers in clinical practice.

Currently, it seems that an integration between the next generation markers and traditional markers will be the most effective combination for diabetic kidney disease progression prediction in clinical practice. This is also taking into consideration the high cost of finances and resources required to study novel biomarkers. Large-scale cohort studies will still be required before the validation of novel biomarkers and their role in the prediction of diabetic kidney disease can occur.  

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