Diabetic nephropathy holistic approaches -
In many respects, the delivery of routine nephrology clinical care is uniquely positioned to be informed by systems-level analysis [ 15 ]. The availability of kidney tissue from biopsies and urine samples provides an opportunity to leverage insights from systems biology to improve patient care.
In the context of DKD, however, access to kidney tissue is more limited as kidney biopsies are usually only pursued when a CKD aetiology other than diabetes mellitus is suspected [ 14 ]. Thus, molecular studies of kidney tissue from biopsied cohorts of patients with DKD come with the caveat that such cohorts may be over-represented with patients with unusual mechanisms of disease progression or with alternative or additional CKD aetiologies and may thus fail to identify the most common disease mechanisms [ 14 ].
This point underscores the importance of initiatives such as the Kidney Precision Medicine Project KPMP , which is a multi-centre prospective cohort study of people with CKD and acute kidney injury who undergo a protocol kidney biopsy for research purposes at study entry [ 14 ].
The KPMP is focussing on the most prevalent kidney diseases, and thus in the context of CKD, is specifically recruiting patients with CKD attributed to diabetes mellitus, hypertension, or both [ 14 ]. Other multi-centre prospective cohort studies are also being conducted with the aim of improving access to human DKD tissue for research purposes.
For example, the Transformative Research in Diabetic Nephropathy TRIDENT consortium is coordinating the collection of kidney tissue from patients with DKD undergoing clinically indicated kidney biopsies across multiple centres in the USA [ 24 ].
The European Nephrectomy Biobank ENBiBA project is a multi-centre initiative of the Diabesity working group of the European Renal Association aiming to collect renal tissue from patients with diabetes mellitus, obesity, and metabolic syndrome at the time of nephrectomy for other indications [ 25 ].
A limitation of such cohort studies of human diabetic kidney tissue is that biopsies are obtained at a single point in time and thus offer a snapshot into molecular mechanisms underpinning disease progression to that point.
Longitudinal access to human kidney tissue for research purposes is limited by the invasive nature of the biopsy procedure, in effect making it very challenging to directly assess histological response to therapeutic interventions in human DKD.
This underscores the importance of preclinical studies of therapeutic interventions in DKD [ 23 ], as well as the development of alternative means of assessing treatment response in patients with DKD.
Imaging surrogates of treatment response, which could be obtained non-invasively by routinely available imaging modalities such as ultrasound or magnetic resonance imaging MRI , are also sought [ 26 ]. Early changes in routinely available clinical parameters may also predict longer term clinical outcomes after an intervention in patients with DKD [ 10 ].
For example, the parameter response efficacy PRE score, which incorporates data from multiple cardiovascular and renal risk markers, has been developed for this purpose and has been demonstrated to accurately predict treatment response to several drug classes, including angiotensin-II receptor blockers, GLP1RAs, endothelin receptor antagonists, and SGLT2is [ 10 ].
Aside from animal and human studies, in vitro studies in primary and immortalized renal cell lines offer a unique opportunity to explore mechanisms underpinning DKD progression.
The development of 3D kidney organoids from induced pluripotent stem cells has been a major advance in the field of discovery nephrology research [ 27, 28 ]. Kidney organoids recapitulate many aspects of the cellular complexity of the human kidney, and single-cell RNA sequencing scRNA-seq technologies can be applied to kidney organoids as a quality control measure to confirm the presence of specific cell types and to ensure reproducibility in the differentiation process [ 27, 28 ].
Kidney organoids may offer a powerful means of understanding molecular mechanisms underpinning DKD progression at single-cell resolution. For example, the utility of integrating scRNA-seq data from kidney organoids with bulk RNA-seq data from human glomerular tissue was highlighted by a recent study which demonstrated shared gene expression signatures between glomerular cells in kidney organoids and in the developing human kidney, elements of which were also found to be reactivated in progressive human glomerular disease [ 29 ].
Compared with standard 2D cell culture methods, 3D organoids may also offer an opportunity to study responses to genetic or pharmacological therapeutic approaches in multiple kidney cell types simultaneously, thus highlighting cell-specific mechanisms which could be targeted to attenuate CKD progression [ 17 ].
However, certain limitations of kidney organoids are recognised [ 27, 28 ]. For example, current kidney organoid models lack a dedicated circulation and fenestrated glomerular capillaries [ 27 ], primarily due to the paucity of endothelial cells, which are estimated to represent just 0.
Furthermore, the tissue culture media used in kidney organoid differentiation protocols are high in glucose, thereby potentially confounding disease versus control comparisons for studies with a DKD focus [ 27 ].
It is unclear whether organoids would mature normally in the presence of a normal glucose concentration [ 27 ]. Thus, refinements to kidney organoid differentiation protocols will be necessary before they can realize their full potential as a comprehensive in vitro model of DKD [ 27 ].
Renal slice culture from nephrectomy specimens could offer an additional platform, which although less amenable to genetic manipulation than organoids, have advantages regarding cellular composition and tissue integrity and maturity [ 31 ].
Integration of target discovery in organoids with subsequent assessment of pharmacological responses in renal slice culture could offer a pragmatic means of mitigating attrition rates between preclinical and early phase clinical studies.
Clinical phenotypic data derived from electronic health records are a rich resource which may be harnessed to individualise prognosis and treatment response [ 15, 16 ]. Clustering of patients with newly diagnosed adult-onset diabetes mellitus on the basis of 6 variables age, body-mass index, glycated haemoglobin, glutamic acid decarboxylase antibodies, and homoeostatic model assessment 2 HOMA2 estimates of β-cell function and insulin resistance across multiple independent Scandinavian cohorts reproducibly identified 5 subgroups of patients with substantially different risks of diabetes complications [ 32 ].
In particular, the severe insulin-resistant diabetes cluster, characterized by high body-mass index, hyperinsulinaemia, and mild hyperglycaemia, had the highest risks of incident DKD and end-stage kidney disease ESKD [ 32 ].
Assessment of individual proteins may also be used to enhance prognostication of adverse CKD outcomes in patients with diabetes mellitus [ 10 ]. More broadly, 17 proteins from the tumour necrosis factor-receptor superfamily, including sTNFR1 and sTNFR2, were strongly associated with year ESKD risk in cohorts of patients with type 1 and type 2 diabetes mellitus [ 35 ].
Circulating levels of kidney injury molecule-1 and N-terminal pro-brain natriuretic peptide also strongly predict DKD progression [ 37, 38 ]. sTNFR1, neutrophil gelatinase-associated lipocalin, C-reactive protein, and complement 3a with cleaved C-terminal arginine C3a-desArg were identified as the most strongly prognostic biomarkers [ 39 ].
The large amount of data generated by kidney imaging with clinically available modalities such as ultrasound and MRI is a potentially rich source of biomarkers to inform DKD prognostication and treatment response [ 16, 26 ]. Such biomarkers may be human-visible and quantifiable by manual, semi-automated, or automated means.
One such example in the field of autosomal dominant polycystic kidney disease is total kidney volume TKV , a surrogate marker of disease progression which correlates with cyst volume and decline in eGFR [ 40 ].
An automated segmentation method based on deep learning has been developed to calculate TKV in a fast and reproducible manner, and demonstrated good agreement with TKV values calculated from manual segmentations [ 41 ]. Alternatively, in the field of computer vision, high-dimensional numeric data may be extracted from radiologic images and analysed using machine or deep learning approaches to classify images and detect patterns which are not visible to the human eye.
As part of the Biomarker Enterprise to Attack Diabetic Kidney Disease BEAt-DKD consortium, the prospective, multi-centre iBEAt cohort study is the largest DKD imaging study to date and aims to determine whether ultrasound and MRI renal imaging biomarkers provide insight into the heterogeneity in DKD pathogenesis and can prognosticate adverse outcomes amongst patients with type 2 DKD [ 26 ].
A key advantage of imaging over other biomarker approaches to personalise DKD management is the fact that the left and right kidneys as well as the renal cortex and medulla can be assessed independently, potentially providing more granularity into functional and structural heterogeneity amongst patients with DKD [ 26 ].
As diabetic retinopathy and DKD are closely intertwined as microvascular complications of diabetes mellitus, retinal imaging is also a potentially rich source of imaging biomarkers to inform DKD management [ 42 ].
Endothelial and microvessel dysfunction contribute to the development of DKD and premature cardiovascular disease amongst patients with diabetes mellitus [ 42 ]. Homology between the vasculature of the eye and the kidney suggests that inferences regarding the microvasculature of the kidney can be made from retinal imaging, providing a rationale to image accessible microvessels in the eye to improve DKD prognostication [ 42 ].
For example, retinal images were used to train and validate a deep learning algorithm which accurately predicted CKD status in community-based Asian cohorts [ 43 ].
The area under the receiver operating characteristic curve of the deep learning algorithm improved when considered alongside conventional CKD risk factors such as age, gender, ethnicity, diabetes mellitus, and hypertension [ 43 ].
By capturing deeper vascular networks such as the choroidal circulation at near-histological resolution, the advent of optical coherence tomography OCT constitutes a major advance in retinal imaging which has transformed ophthalmology care [ 44 ].
OCT can now also be deployed in preclinical models of retinopathy [ 44 ]. Deep learning has been coupled with OCT imaging to triage and diagnose the commonest sight-threatening retinal diseases in an automated fashion and with similar accuracy to that of expert physicians [ 45 ].
Thus, combining the imaging power of cross-sectional chorioretinal OCT imaging with the analytical power of deep learning holds great promise as a means of developing prognostic imaging biomarkers related to adaptations of the renal microvasculature in people with diabetes mellitus [ 42 ].
Similar to radiologic images of the kidney, digitised whole slide images WSIs and transmission electron microscopy TEM images of kidney biopsies contain a wealth of data which may be optimally analysed using deep learning approaches [ 46, 47 ].
Deep learning approaches may be used to automate the extraction of descriptive and quantitative structural features from WSIs and TEM images with improved reproducibility [ 46, 47 ].
The concept of reproducibility is an important one as although an inter-pathologist intra-class correlation coefficient of 0. Furthermore, semi- or wholly automated means of classifying DKD histologically would reduce personnel requirements and improve efficiency of assigning DKD diagnoses in routine clinical care.
Thus, deep learning may support high-throughput and reproducible quantitative feature extraction in experimental models of renal injury.
Furthermore, the trained convolutional neural network performed well on human samples, thereby providing a link between automated histopathological assessment across the preclinical and clinical domains [ 49 ]. Indeed, a convolutional neural network was also used to segment PAS-stained kidney biopsy samples from 54 patients with DKD and classify them according to the Tervaert schema, achieving a high level of agreement with three independent pathologists [ 50 ].
In the assessment of kidney structural features, deep learning has mainly been applied to digital pathology images thus far, although researchers have started to evaluate this strategy on TEM images with reasonable success [ 52 ].
Some of the biomedical technologies which support omics analyses are outlined in Figure 1. In many cases, the application of multiple technologies to characterize a particular molecular domain often provides complementary rather than redundant information.
Moreover, integration of data from several molecular domains is key to characterizing the molecular heterogeneity of DKD, although integrative multi-omic analyses are not trivial owing to the complexity of the multiple high-dimensional datasets involved [ 17, 56 ].
It is also worth noting that changes in different molecular domains such as the transcriptome, the proteome, and the metabolome may not necessarily directly correlate [ 16, 57, 58 ]. For example, factors impacting translational efficiency will diminish mRNA-protein correlations for a given target, as will modalities of protein regulation other than gene transcription, such as post-translational modifications [ 57 ].
Furthermore, differences in the coverage of molecular domains by omics technologies may result in difficulties mapping insights from one to the other [ 16, 58 ]. Techniques that allow for integration of not only two data domains at a time such as the transcriptome and the proteome but also allow for simultaneous integration of clinical phenotypic data, imaging data, and histopathological data along with multiple molecular omics data domains are essential to gain more holistic insights into cellular function and interaction in a complex organ system such as the diabetic kidney [ 17, 56 ].
The current one-size-fits-all approach to DKD care ignores the clinically apparent heterogeneity in disease prognosis and treatment-responsiveness [ 10 ].
It is hoped that a systems biology approach to DKD research will pave the way for a precision medicine approach to routine DKD care by unravelling individual-level molecular mechanisms which underlie progressive DKD and which are amenable to targeting by existing or novel therapeutic strategies [ 15, 16 ].
Certain priorities for translational DKD research which may be advanced by a systems nephrology approach include: 1. The development of model systems in vitro or animal which reliably recapitulate progressive and advanced human DKD characterized by single-cell and spatially resolved transcriptomics, thereby enhancing the translational relevance of preclinical DKD studies;.
The identification of biomarkers which predict response to RAAS blockade, SGLT2is, and other emerging disease-modifying treatments for DKD in light of the inter-individual variability in treatment response; and. The delineation of mechanisms of DKD progression in the face of combined therapy with RAAS blockade and an SGLT2i, the current backbone of treatment, which may help define targets for novel therapies which minimise the significant residual risk of progressive renal functional decline.
However, the efficacy of appropriately targeted novel therapeutics may still be impacted by inter-individual pharmacokinetic differences. Thus, pharmacogenomic profiling will play an important role in optimising outcomes for individuals with DKD. While a comprehensive systems nephrology approach is now technically feasible in research studies, this must be balanced with plans for eventual implementation of elements of this paradigm in clinical practice.
The value of biological insights derived from the refined techniques currently available must be balanced against their clinical translatability; researchers and clinicians alike should grapple with this compromise from the outset in an effort to prioritize which elements of the systems nephrology paradigm offer benefit to the largest number of patients in clinical practice.
This will help to ensure that implementation of a systems nephrology approach in routine DKD care will not perpetuate, or indeed exacerbate, inequity in healthcare delivery. This manuscript was invited following a presentation at the European Renal Association Diabesity Working Group Annual CME in Maribor, Slovenia on September 16th—17th Figure 1 was created with BioRender.
This work was performed within the Irish Clinical Academic Training ICAT Programme, supported by the Wellcome Trust and the Health Research Board Grant No. This research was funded in whole, or in part, by the Wellcome Trust Grant No.
For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Martin wrote the manuscript and Neil G. Docherty provided proof-reading and critical review. Martin and Neil G. Docherty reviewed and approved the final manuscript.
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Review Articles September 11 Early Publication. A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice Subject Area: Nephrology. Martin Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
liampvmartin gmail. This Site. Google Scholar. Neil G. Docherty Neil G. Nephron 1— Article history Received:. Cite Icon Cite. Song Y, He K, Levitan EB, et al.
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For treating early-stage diabetic Nephropathy, Siddha practices primarily emphasize using herbal formulations; however, yoga therapy, diet, and other exercises are also included in Siddha treatment modalities. This work is licensed under a Creative Commons Attribution 4.
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Noi illa neri Siddha principles of social and preventive medicine: A Translation of Tamil Siddha Text Noi illa neri.
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William P. Diabetic nephropathy holistic approaches hephropathy, Neil G. Docherty; A Systems Nephrology Approach to Diabetic Kidney Disease Diabetic nephropathy holistic approaches and Practice. Background: Nepgropathy and staging approaxhes diabetic kidney Incorporating indulgences DKD via Wpproaches serial assessment approwches routine laboratory indices lacks the granularity holisitc to resolve the heterogeneous disease mechanisms driving progression in the individual patient. A systems nephrology approach may help resolve mechanisms underlying this clinically apparent heterogeneity, paving a way for targeted treatment of DKD. Summary: Given the limited access to kidney tissue in routine clinical care of patients with DKD, data derived from renal tissue in preclinical model systems, including animal and in vitro models, can play a central role in the development of a targeted systems-based approach to DKD. Diabetic nephropathy DN nephropatyy the leading cause of end-stage renal disease worldwide. With the rising prevalence of apprpaches, the Diaabetic of DN is likely apprkaches hit pandemic proportions. For many theranostic uses, Diabehic techniques have Diabetic nephropathy holistic approaches in biomedical Natural weight loss challenges. The Guarana for mental focus mechanisms involved Diabetic nephropathy holistic approaches DN are discussed wpproaches this review to assist in understanding its onset and progression pattern. Diabetic nephropathy DNa frequent consequence of diabetes, has become a hazard to human health as diabetes prevalence has increased globally during the previous few decades Kakitapalli et al. Diabetes affected more than million people worldwide inand the World Health Organization predicts that diabetes is expected to become the seventh greatest leading cause of mortality by WHO, In developed countries, the prevalence of diabetes has been observed to rise in tandem with the rise in living standards and subsequent changes in lifestyle Calvo-Hueros et al.
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