Diabetic nephropathy (DN) is the most common cause of chronic kidney disease worldwide, occurring in 30%–40% of individuals with diabetes in the United States [1,2,3], and the number of affected patients continues to rise. DN is classically defined by progressive proteinuria with worsening renal function and reduced glomerular filtration rate (GFR) [2,4,5,6,7], and many patients are clinically diagnosed with DN without histologic confirmation by renal biopsy. These individuals tend to have a long-standing history of diabetes and frequently have evidence of concomitant diabetic retinopathy and/or neuropathy. However, some patients with diabetes and renal dysfunction may present earlier in the disease course or with atypical clinical features such as hematuria, an abrupt increase in proteinuria, or acute kidney injury. Additionally, an increasing number of patients with non-proteinuric diabetic kidney disease (DKD), characterized by declining renal f unction in the absence of associated albuminuria, have been recognized over the last several years, and the true prevalence of DKD is likely underestimated. In this setting, it can be challenging to determine which patients may have underlying non-diabetic kidney disease (NDKD) for which renal biopsy may be of substantial benefit. In the present editorial, we briefly review the role of renal biopsy in the management of patients with diabetes, as well as some of the more recent investigations into potential digital and molecular methods of evaluating and diagnosing DKD.
1 Historical view of kidney pathology and current renal biopsy practice in diabetes
Since Kimmelstiel and Wilson first described the association of specific glomerular lesions with diabetes in 1936 [8], numerous studies have sought to characterize the types of kidney disease occurring in patients with diabetes. DN has a varied histologic appearance, but characteristically presents with diffuse thickening of glomerular basement membranes (GBMs) and mesangial expansion. Importantly, GBM thickening is an early finding in DN, and may precede the development of clinical microalbuminuria [9, 10]. Basement membranes continue to thicken as the disease progresses, with a subsequent increase in mesangial matrix material. Mesangial expansion may at first be mild before developing the more characteristic nodular appearance (Kimmelstiel-Wilson nodules), which often shows areas of mesangiolysis with associated microaneurysm formation of glomerular capillary loops [11]. Global and segmental glomerulosclerosis is frequently found later in the disease course, with associated variable and sometimes extensive podocyte foot process effacement by electron microscopy [9, 11]. Other common morphologic findings include hyalinosis of glomeruli and Bowman capsule (capsular drops), afferent and efferent arteriolar hyalinosis, interstitial scarring with associated inflammation and eosinophils, and diffuse linear accentuation of glomerular and tubular basement membranes with polyclonal IgG and albumin by immunofluorescence microscopy [3, 9, 11,12,13]. Additionally, patients with non-proteinuric DKD have been shown to have more extensive arteriosclerosis and tubulointerstitial scarring in relation to the severity of glomerular lesions [5, 7]. In an attempt to more reliably stratify the histologic lesions of DN, a system was developed in 2010 which categorizes glomerular lesions into four classes based on their severity [14]. While this scheme has produced good interobserver agreement with regard to the glomerular lesions of DN, a wide variety of lesions may still be encountered within each class, and tubulointerstitial involvement is evaluated separately and excluded from the overall grade. Therefore, although useful for characterizing the glomerular lesions of DN, the clinical and predictive utility of this classification scheme has yet to be established [2, 3, 14].
There has been extensive investigation into the types of kidney disease occurring in patients with diabetes over the last several decades. Although limited by selection bias toward patients with atypical clinical presentations, studies have identified NDKD in anywhere from 12% to 81% of biopsies [15], with more recent retrospective studies from large centers reporting NDKD as the primary diagnosis in up to 50% of the biopsies from patients with diabetes [9, 12]. Similar to the general population, the most common NDKD diagnoses include focal segmental glomerulosclerosis (FSGS), IgA nephropathy, hypertensive nephrosclerosis, and acute tubular injury, with hypertensive nephrosclerosis, acute tubular injury, and secondary FSGS frequently accompanying a diagnosis of DN. Indications for biopsy in diabetic patients often include hematuria, active urine sediment, and acute kidney injury, or a rapid decline in renal function, positive serologic studies, monoclonal gammopathy with renal dysfunction, and nephrotic syndrome [1, 12, 15]. Although considered atypical in DN, hematuria has not been shown to reliably identify NDKD, and may be present in around a third of cases of DN [15]. One study found the presence of monoclonal gammopathy to be more often associated with NDKD; however, only about 25% of those cases showed evidence of paraprotein-related disease on renal biopsy [12], and it was not found to be predictive of NDKD in a later study [9]. While a long-standing history of diabetes (>10 years) was predictive of underlying DN [12], the presence of diabetic retinopathy as a predictor of DN is debatable [9, 16]. Interestingly, nephrotic-range proteinuria has been shown to be more common in patients with DN compared to NDKD in multiple studies [9, 12, 15]. While these studies do not separate patients who experience an abrupt onset from those with progressive proteinuria, this finding argues against using isolated severe proteinuria as the sole indication for renal biopsy in patients with diabetes.
Although progressive proteinuria is classically associated with DN, studies over the past several years found that between 25% and 50% of patients with diabetes and CKD did not have associated microalbuminuria [2,3,4,5,6], somewhat limiting the use of albuminuria as a marker of DN. These patients were shown to have a lower risk of CKD progression [7] and differing associations with cardiovascular events and mortality than patients with typical DN [4]. Clinically, patients with non-proteinuric DKD may have lower rates of retinopathy [4], with other possible risk factors including older age and female sex [5, 7]. Renal biopsy in non-proteinuric DKD tends to demonstrate heterogeneous histopathologic changes with more extensive interstitial and vascular involvement compared to classic DN, as previously mentioned. These features suggest that non-proteinuric DKD may have a different pathogenesis, with greater contribution from arteriosclerosis and tubulointerstitial scarring rather than microvascular injury [4, 5, 7]. Another important and often underrecognized form of DKD occurs in the post-transplantation setting in patients without a history of diabetes. Although the rate of post-transplant diabetes has improved with modern immunosuppression, between 10% and 30% of solid organ transplant patients may go on to develop diabetes [17]. DKD in this setting is thought to be related to a combination of insulin resistance and β-cell dysfunction; however, more research is needed to better understand the pathophysiology and prognostic significance of post-transplantation DKD. Ultimately, due to the widely variable clinical presentation of DKD, renal biopsy is likely underutilized in patients with diabetes based on the current biopsy indications [1, 12, 15].
2 Kidney biopsy in diabetes in the era of precision medicine
Besides traditional histopathologic evaluation of renal biopsies, investigators have recently begun to explore the use of digital pathology and machine learning as means of assisting pathologists in the classification of histopathologic lesions in DN [18]. While significant work remains, computational methods have been shown to be able to reliably detect and classify glomerular structures and lesions in DN through machine learning. In the future, utilization of artificial intelligence by pathologists may allow for more extensive and detailed histopathologic evaluation of renal biopsies, leading to a better understanding of the prognostic significance of the morphologic and structural changes seen with DN and improved diagnostic precision in affected patients.
Another area of interest is the underlying molecular alterations associated with the development and progression of DKD. Genetic susceptibility for DKD was first observed in families several decades ago and has been subsequently well-documented [19]. Over the past decade, a number of studies have been performed on renal biopsy tissue in an attempt to better understand the molecular pathways involved with DN in the hopes of identifying potential biomarkers which could ultimately augment or partially replace renal biopsy in patients with diabetes. For example, one study using single cell RNA sequencing identified altered expression of genes related to processes such as ion transport and angiogenesis in patients with DN [20]. Additionally, several genome-wide association studies (GWAS) have been conducted on renal biopsy tissue from diabetic patients of different backgrounds and ethnicities, and have identified several genetic variants and single nucleotide polymorphisms (SNPs) associated with DN. These SNPs are associated with numerous kidney functions, including GBMs and inflammatory pathways [19, 21, 22]; however, the precise mechanisms through which these genetic alterations are involved with DN has yet to be elucidated. Another area of study has dealt with microRNAs (miRNAs), which are key regulators of gene expression and govern the expression of >60% of protein-coding genes [23, 24]. A number of miRNAs have been identified in association with various pathways and processes related to DN. For instance, MiR-192 and miR-216a are involved in the transforming growth factor-β (TGF-β) signaling pathway, which plays a role in DN by contributing to interstitial fibrosis and extracellular matrix accumulation [24]. The expression of miRNA-21, which is involved in processes such as collagen gene expression and podocyte injury, was altered in DN [23, 24]. In addition to possible usefulness as biomarkers, targeting miRNAs may eventually provide a new therapeutic option in DN. Several small molecules have already been shown to inhibit or induce miRNAs in some cancer cells and other disease models and could potentially have future utility in the treatment of DN [24].
Likewise, proteomic analysis of renal tissue also has the potential to discover disease-specific proteins and biomarkers. In a recent study, proteomic analysis of laser microdissected glomeruli from formalin-fixed, paraffin-embedded renal tissue in patients with DN showed overexpression of the extracellular matrix protein, nephronectin [25]. Associated with a number of renal and urologic diseases, the role of nephronectin in the development of diabetic glomerulosclerosis is intriguing and requires further studies. Lastly, in addition to tissue-based biomarkers, some circulating and urinary proteins have also been suggested as prognostic indicators in DKD. These include circulating tumor necrosis factor receptors 1/2 and IL-15RA, which are inflammatory markers associated with progression to end-stage renal disease [26], and urinary kidney injury molecule-1 (KIM-1) an epithelial transmembrane glycoprotein expressed from the renal proximal tubular cells after injury [27]. Although the use of molecular testing and digital pathology shows promise for the evaluation and prognostication of patients with DKD, further studies are necessary before these modalities can be ready for use in daily practice.
There are currently nearly half-a-billion people with diabetes worldwide, and the number of affected individuals is expected to continue to increase [28]. A significant percentage of these patients will develop some degree of renal dysfunction related to diabetes; however, DKD has a highly variable clinical presentation, and differentiating these patients from those with underlying NDKD based solely on clinical and laboratory findings can be difficult if not impossible. Although molecular testing and artificial intelligence may someday supplement or reduce the utilization of renal biopsy in patients with diabetes, there is currently no established role for these studies in daily clinical practice. Thus, with between 33%–50% of biopsied patients receiving a primary diagnosis of NDKD and up to 20% of patients having a diagnosis favoring therapy beyond standard treatment for DN [9], histopathologic diagnosis continues to be the most reliable method for differentiating DN from NDKD and providing valuable prognostic information. Suffice it to say, renal biopsy will remain an important tool in the management of patients with diabetes and renal dysfunction for the foreseeable future.
Source of Funding
Nil.
Conflict of Interest
Xinjin Zhou is an Editorial Board Member of the journal. The article was subject to the journal's standard procedures, with peer review handled independently of this editor and his research groups.
REFERENCES
[1] Afkarian M, Zelnick LR, Hall YN, Heagerty PJ, Tuttle K, Weiss NS, et al. Clinical manifestations of kidney disease among US adults with diabetes, 1988–2014. JAMA 2016; 316: 602–10.10.1001/jama.2016.10924Search in Google Scholar PubMed PubMed Central
[2] Klessens CQF, Woutman TD, Veraar KAM, Zandbergen M, Valk EJ, Rotmans JI, et al. An autopsy study suggests that diabetic nephropathy is underdiagnosed. Kidney Int 2016; 90: 149–56.10.1016/j.kint.2016.01.023Search in Google Scholar PubMed
[3] Thomas MC, Brownlee M, Susztak K, Sharma K, Jandeleit-Dahm KA, Zoungas S, et al. Diabetic kidney disease. Nat Rev Dis Primers 2015; 1: 15018.10.1038/nrdp.2015.18Search in Google Scholar PubMed PubMed Central
[4] Di Paolo S, Fiorentino M, De Nicola L, Reboldi G, Gesualdo L, Barutta F, et al. Indications for renal biopsy in patients with diabetes. Joint position statement of the Italian Society of Nephrology and the Italian Diabetes Society. Nutr Metab Cardiovasc Dis 2020; 30: 2123–32.10.1016/j.numecd.2020.09.013Search in Google Scholar PubMed
[5] Ekinci EI, Jerums G, Skene A, Crammer P, Power D, Cheong KY, et al. Renal structure in Normoalbuminuric and albuminuric patients with type 2 diabetes and impaired renal function. Diabetes Care 2013; 36: 3620–6.10.2337/dc12-2572Search in Google Scholar PubMed PubMed Central
[6] Kopel J, Pena-Hernandez C, Nugent K. Evolving spectrum of diabetic nephropathy. World J Diabetes 2019; 10: 269–79.10.4239/wjd.v10.i5.269Search in Google Scholar PubMed PubMed Central
[7] Yamanouchi M, Furuichi K, Hoshino J, Toyama T, Hara A, Shimizu M, et al. Nonproteinuric versus proteinuric phenotypes in diabetic kidney disease: A propensity score-matched analysis of a nationwide, biopsy-based cohort study. Diabetes Care 2019; 42: 891–902.10.2337/dc18-1320Search in Google Scholar PubMed
[8] Kimmelstiel P, Wilson C. Intercapillary lesions in the glomeruli of the kidney. Am J Pathol 1936; 12: 83–98.Search in Google Scholar
[9] Sanghavi SF, Roark T, Zelnick LR, Najafian B, Andeen NK, Alpers CE, et al. Histopathologic and clinical features in patients with diabetes and kidney disease. Kidney360 2020; 1: 1217–25.10.34067/KID.0003962020Search in Google Scholar PubMed PubMed Central
[10] Anders H-J, Huber TB, Isermann B, Schiffer M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol 2018; 14: 361–77.10.1038/s41581-018-0001-ySearch in Google Scholar PubMed
[11] Jen K, Laszik Z. Metabolic diseases of the kidney. Silva's diagnostic renal pathology. Cambridge: Cambridge University Press. 2017; 314–6.Search in Google Scholar
[12] Sharma SG, Bomback AS, Radhakrishnan J, Herlitz LC, Stokes MB, Markowitz GS, et al. The modern spectrum of renal biopsy findings in patients with diabetes. Clin J Am Soc Nephrol 2013; 8: 1718–24.10.2215/CJN.02510213Search in Google Scholar PubMed PubMed Central
[13] Dai D-F, Sasaki K, Lin MY, Smith KD, Nicosia RF, Alpers CE, et al. Interstitial eosinophilic aggregates in diabetic nephropathy: allergy or not? Nephrol Dial Transplant 2015; 30: 1370–6.10.1093/ndt/gfv067Search in Google Scholar
[14] Tervaert TWC, Mooyaart AL, Amann K, Cohen AH, Cook HT, Drachenberg CB, et al. Pathologic classification of diabetic nephropathy. J Am Soc Nephrol 2010; 21: 556–63.10.1681/ASN.2010010010Search in Google Scholar
[15] Mak SK, Gwi E, Chan KW, Wong PN, Lo KY, Lee KF, et al. Clinical predictors of non-diabetic renal disease in patients with non-insulin dependent diabetes mellitus. Nephrol Dial Transplant 1997; 12: 2588–91.10.1093/ndt/12.12.2588Search in Google Scholar
[16] Fiorentino M, Bolignano D, Tesar V, Pisano A, Biesen WV, Tripepi G, et al. Renal biopsy in patients with diabetes: a pooled meta-analysis of 48 studies. Nephrol Dial Transplant 2017; 32: 97–110.10.1093/ndt/gfw070Search in Google Scholar
[17] Sharif A, Cohney S. Post-transplantation diabetes - state of the art. Lancet Diabetes Endocrinol 2016; 4: 337–49.10.1016/S2213-8587(15)00387-3Search in Google Scholar
[18] Ginley B, Lutnick B, Jen KY, Fogo AB, Jain S, Rosenberg A, et al. Computational segmentation and classification of diabetic glomerulo-sclerosis. J Am Soc Nephrol 2019; 30: 1953–67.10.1681/ASN.2018121259Search in Google Scholar PubMed PubMed Central
[19] Rich SS. Genetic contribution to risk for diabetic kidney disease. Clin J Am Soc Nephrol 2018; 13: 1135–7.10.2215/CJN.07240618Search in Google Scholar PubMed PubMed Central
[20] Wilson PC, Wu H, Kirita Y, Uchimura K, Ledru N, Rennke HG, et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc Natl Acad Sci USA 2019; 116: 19619–25.10.1073/pnas.1908706116Search in Google Scholar PubMed PubMed Central
[21] Salem RM, Todd JN, Sandholm N, Cole JB, Chen WM, Andrews D, et al. Genome-wide association study of diabetic kidney disease highlights biology involved in glomerular basement membrane collagen. J Am Soc Nephrol 2019; 30: 2000–16.10.1681/ASN.2019030218Search in Google Scholar PubMed PubMed Central
[22] Heinzel A, Muhlberger I, Stelzer G, Lancet D, Oberbauer R, Martin M, et al. Molecular disease presentation in diabetic nephropathy. Nephrol Dial Transplant 2015; 30: 17–25.10.1093/ndt/gfv267Search in Google Scholar PubMed
[23] Ishii H, Kaneko S, Yanai K, Aomatsu A, Hirai K, Ookawara S, et al. MicroRNAs in Podocyte injury in diabetic nephropathy. Front Genet 2020; 11: 993.10.3389/fgene.2020.00993Search in Google Scholar PubMed PubMed Central
[24] Yarahmadi A, Shahrokhi SZ, Mostafavi-Pour Z, Azarpira N. MicroRNAs in diabetic nephropathy: From molecular mechanisms to new therapeutic targets of treatment. Biochem Pharmacol 2021; 189: 114301.10.1016/j.bcp.2020.114301Search in Google Scholar PubMed
[25] Nakatani S, Wei M, Ishimura E, Kakehashi A, Mori K, Nishizawa Y, et al. Proteome analysis of laser microdissected glomeruli from formalin-fixed paraffin-embedded kidneys of autopsies of diabetic patients: nephronectin is associated with the development of diabetic glomerulosclerosis. Nephrol Dial Transplant 2012; 27: 1889–97.10.1093/ndt/gfr682Search in Google Scholar PubMed
[26] Niewczas MA, Pavkov ME, Skupien J, Smiles A, Md Dom ZI, Wilson JM, et al. A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes. Nat Med 2019; 25: 805–13.10.1038/s41591-019-0415-5Search in Google Scholar PubMed PubMed Central
[27] Kapoula GV, Kontou PI, Bagos PG. Diagnostic performance of biomarkers urinary KIM-1 and YKL-40 for early diabetic nephropathy in patients with type 2 diabetes: a systematic review and meta-analysis. Diagnostics 2020; 10: 909.10.3390/diagnostics10110909Search in Google Scholar PubMed PubMed Central
[28] Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2015; 157: 107843.10.1016/j.diabres.2019.107843Search in Google Scholar PubMed
© 2021 Wesley Hiser et al., published by Sciendo
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Articles in the same Issue
- Editorial
- Renal biopsy in patients with diabetes: Yesterday, today, and tomorrow
- Perspective
- Diabetic kidney disease, a potentially serious issue resulting from collision of the coronavirus disease 2019 and diabetes global pandemics
- Review
- Insulin therapy in diabetic kidney disease
- Original Article
- Analysis of dapagliflozin-induced expression profile of long noncoding RNAs in proximal tubular epithelial cells of diabetic kidney disease
- Case Report
- Diabetic nephropathy patient with heavy proteinuria: A case report
Articles in the same Issue
- Editorial
- Renal biopsy in patients with diabetes: Yesterday, today, and tomorrow
- Perspective
- Diabetic kidney disease, a potentially serious issue resulting from collision of the coronavirus disease 2019 and diabetes global pandemics
- Review
- Insulin therapy in diabetic kidney disease
- Original Article
- Analysis of dapagliflozin-induced expression profile of long noncoding RNAs in proximal tubular epithelial cells of diabetic kidney disease
- Case Report
- Diabetic nephropathy patient with heavy proteinuria: A case report