Introduction
End-stage kidney disease (ESKD) is becoming more common around the world. The rising prevalence corresponds to an increase in the number of persons with diabetes, which is the major cause of ESKD. Early detection of chronic kidney disease (CKD) in diabetic patients, as well as adequate care, is critical for delaying the progression of renal function loss and preventing ESKD. The rate of CKD progression and response to treatment differs among diabetic individuals, emphasizing the importance of tailoring individual treatment. In this review, we discuss current developments in precision medicine in diabetic kidney disease (DKD) as well as opportunities for future research.
DKD pathophysiology is multifactorial, with multiple mechanisms implicated in disease origin and development. Genetic variables have been found in studies to contribute to the development of DKD, however these frequently interact with other external factors, resulting in a weak heritability pattern. So far, genetic testing has had limited utility in terms of facilitating early diagnosis, classifying progression, or evaluating response to therapy. Several biomarker-based techniques are now being investigated to identify individuals at high risk of ESKD and to aid in the decision-making process for targeted therapy. These investigations have resulted in the identification and validation of a few inflammatory proteins, such as circulating tumour necrosis factor receptors, that are excellent predictors of kidney disease development.
The aim of this review is to provide the current status of precision medicine for patients with DKD. The review will summarize recent precision medicine approaches with respect to the diagnosis, prognosis and response to treatment in these patients.
Precision medicine approaches in monogenic diabetes
The last decade has seen progress in our understanding of the genetic basis of monogenic causes of diabetes. Neonatal diabetes mellitus (NDM) and maturity-onset diabetes mellitus (MODY) are the most frequently diagnosed forms of monogenic diabetes. The transient NDM is primarily due to overexpression of chromosome 6q24, including the gene PLAGL1 (6q24-q25) and HYMAI (6q24.2). For example, it is now known that patients with transient NDM and 6q24 methylation abnormalities respond well to low-dose sulphonyureas derivatives, whereas other NDM require insulin treatment.
MODY is an autosomal dominant non-insulin-dependent form of diabetes, where 14 genetic variants have been identified so far, and which is primarily caused by mutations in the HNF1A, HNF4a and GCK genes. Dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonist can be further added to optimize glycaemic control. The renal and cardiovascular prognosis of patients with MODY-HNF polymorphism is similar when comparing patients with type 1 and type 2 diabetes. Thus, precision medicine in monogenic causes of diabetes has made significant progress with respect to both risk stratification as well as individualization of therapy.
Biomarker-based approaches for diagnosis of DKD
Genetic biomarkers to improve diagnosis of DKD
Unlike monogenic disease, where genetic testing can accurately confirm a diagnosis, the utility of genetics for establishing a molecular cause of DKD remains elusive. Recent results from a large study in patients with DKD revealed that a novel signal near GABRR1 was associated with the presence of microalbuminuria among European subjects. However, no replication of the signal was found among Asian individuals. Another study conducted in patients with type 1 diabetes with or without kidney disease involving approximately 400 participants identified 16 genes associated with DKD. Of these 16 genes, four were spcifically related to glomerular basement membrane collagen and kidney function (COL4A3, BMP7, COLEC11 and DDR1). However, it should be mentioned that identification of a genome-wide significant locus with kidney related traits does not necessarily reflect the presence of a causal relationship between a gene and disease susceptibility.
To overcome some of the shortcomings of using polymorphisms in a single gene, efforts to combine multiple loci with small genetic effects have been initiated to develop a polygenic risk score for DKD. The score is used to estimate an individual’s risk for disease over time based upon their genetic liability. Genome-wide association studies using simultaneous screening of multiple single nucleotide polymorphisms have been conducted to detect susceptibility regions that predispose an individual to DKD, and more than 30 genetic variants for DKD have been identified so far.
Biomarker clusters for improved diagnosis of DKD
We must also look into the risks associated with Central Venous Catheter (CVC) insertion. Catheter-related bloodstream infection is one of the leading causes of morbidity and mortality in people with renal insufficiency. This risk may increase following transplantation due to immunosuppression. Furthermore, putting a CVC during the post-transplantation period for PD to HD conversion may increase the risk of central venous stenosis, which would not only cause symptoms but also limit future dialysis access options if the transplant failed.
Biomarker-based approaches to improve prediction of prognosis in high risk patients
Single proteins as prognostic biomarkers of DKD
Over the last decade, numerous proteins have been identified from plasma or urine samples that could be potentially added to albuminuria and eGFR. For example, 17 proteins from the pro-inflammatory tumour necrosis factor (TNF) superfamily, TNF receptor 1 and TNF receptor 2 (TNFR1 and TNFR2) in particular, were found to be strongly associated with 10-year risk of ESKD. The protein kidney injury molecule-1 (KIM-1) was also identified as one of the highly specific biomarkers to predict DKD progression (Figure 1). Although TNFR1, TNFR2 and KIM-1 have been shown to be strong predictors of DKD progression, it is unclear whether the use of these biomarkers can guide pharmacotherapy to delay kidney function loss.
Multiple proteins as prognostic biomarkers of DKD
Since DKD is a multi-factorial disease with different pathophysiological processes involved, it is likely that a multiple biomarker panel will predict disease progression better than a single biomarker. Development of high-throughput omics profiling enables simultaneous and highly sensitive analysis of various peptides and metabolites in urine and plasma samples. In an early study among Pima Indians, urinary proteomic profiling has been successfully applied to predict the 10-year risk for development of DKD. Others have developed a urinary proteome-based classifier consisting of 273 peptides (CKD273 classifier) using capillary electrophoresis coupled mass spectrometry.
In a study involving 88 patients with type 2 diabetes, the CKD273 classifier predicted the incidence of micro- and macroalbuminuria independently from other clinical parameters. Therefore, CKD273 classifier may be used to predict early kidney changes when there is still an option for therapeutic intervention.
Biomarker-based approaches to improve drug response prediction
Baseline biomarkers
A pharmacogenetic study from the RENAAL (Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan) trial has demonstrated that an insertion (I)/deletion (D) polymorphism influences the response to the ARB losartan among DKD patients. Specifically, patients with DD genotype showed marked reductions in kidney failure when receiving losartan as compared with those having the ID or II genotype. Also, in a study among CKD patients without type 2 diabetes, those having the DD genotype had greater treatment response to the angiotensin-converting-enzyme inhibitors (ACEi) ramipril than those with the ID or II genotype.
Dynamic biomarkers
A post hoc analysis of the Aliskiren Trial in Type 2 Diabetes Using Cardiorenal Endpoints (ALTITUDE) trial showed that patients with the highest NT-proBNP levels at baseline were at highest risk of cardiovascular complications, but did not respond to the investigational drug aliskiren.
An optimal dynamic biomarker that predicts the efficacy or safety of a drug is the one that reflects the mechanisms of action of the drug and is ideally involved in DKD progression. For example, sodium–glucose co-transporter 2 inhibitors (SGLT2i) decrease not only HbA1c, but also body weight, blood pressure and albuminuria. Changes in each of these risk markers may contribute to the long-term benefit of these drugs.
Given this complexity, it seems appropriate to develop a score that integrates the early changes in multiple cardiovascular and renal risk markers of a drug to predict its long-term clinical effect. Such a score, a multiple parameter response efficacy (PRE) score, has been developed. Studies with ARB, glucagon-like peptide receptor agonists (GLP1-RA), ERA and SGLT2i have shown that integrating short-term changes in multiple biomarkers into a score perform better in predicting the long-term effect of a drug than changes in any single biomarker (Figure 2).
Conclusion
For patients with DKD, advances in genetics and molecular biology have opened up new possibilities for biomarker identification and individualised therapy. Although significant progress has been achieved in diagnosing and predicting disease development, finding the right treatment for the right patient at the right time remains a challenge. Many other external factors are involved in DKD progression, therefore a genetic-based approach to unravelling the pathophysiology and finding genetic markers for diagnosis and prognosis is typically insufficient. Novel protein-based biomarkers have been discovered as a result of advances in omics technology, which may help in risk classification and treatment selection.
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