Diabetic nephropathy holistic approaches -
Furthermore, the NPs successfully suppressed AQP1 in PTECs via absorption through the endocytosis of a megalin-dependent pathway.
Williams et al. The NPs were found to have a fold higher efficacy in the kidneys particularly in the proximal tubules than in other organs.
The NPs were shown to be biocompatible and safe as expected by the incorporation of FDA-approved polymers that constituted the NPs.
In another study, Williams et al. For biodistribution studies, the NPs were loaded with a fluorescent dye. After the injection of cationic or anionic mesoscale NPs into mice, images were obtained daily for the first 7 days, then biweekly for the next 3 months.
Fluorescence in vivo biodistribution was used to track the degradation and localization of NPs over time. NPs were found to be concentrated in both the kidneys and the chest region.
The analysis confirmed that the fluorescence intensity in the kidneys was significantly higher than in other organs. Fluorescence and computed tomography imaging of the kidneys of a mouse treated with anionic mesoscale NPs confirmed their localization and relatively uniform distribution throughout the kidneys.
High glucose concentrations cause-specific cellular effects in the kidney, affecting many different types of cells, including tubular and collecting duct systems Vallon and Komers, Vasopressin levels rise in diabetes and have been linked to the development of DN via V2 receptor V2R activation in a type 1 diabetes experimental model El Boustany et al.
It has been suggested that the vasopressin II V2 receptor, a G protein-coupled receptor GPCR with seven transmembrane domains, would be beneficial for targeted drug administration via active targeting in the epithelial cells lining the connecting tubule, distal tubule, and collecting ducts Robben et al.
While characterized NPs may be able to accumulate passively in the kidneys, active targeting NPs such as peptides and antibodies are also being studied to improve renal targeting Nastase et al.
In a mouse model of type 2 diabetes, it was recently demonstrated that vasopressin contributes to albuminuria and glomerular hyperfiltration via the V2R. It establishes the causal relationship between vasopressin and renal damage in diabetic patients El Boustany et al.
To tackle diabetes and Polycystic Kidney Disease, a highly promising technique would be to guide therapeutic payloads to cells overexpressing V2R.
Jung et al. Type 1 and 2 diabetes mellitus is becoming a global impediment because of the prevalence of DN. It is essential to understand the molecular pathways and targets for targeted delivery in DN because DN is a complex condition that involves multiple mechanisms.
Nano-formulations are a modern concept for efficiently delivering drugs to their intended sites. Numerous nano-formulations have been investigated for targeting delivery at different sites. As evidenced by the abundance of NPs already in the market and more are being tested in clinical trials to see if they can be approved, the field of nanomedicines will almost certainly hold a large market share soon.
In the case of DN, nanomedicine-based techniques with designed biophysical features allow for enhanced interaction with kidney cells and improved cellular uptake inside proximal tubule cells, mesangial cells, podocytes, GBM, and epithelial cells resulting in improved retention.
Understanding the structural and functional aspects of the kidney and insights into the pathophysiology of DN can be used to improve drug delivery methods for clinical therapy and the study of DN.
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. This work was supported by the Zhejiang Provincial Medical and Healthy Science and Technology Projects No.
LBY22H to Y-YM. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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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. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Nephron.
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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.
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Journal Section:. View large Download slide. Table 1. An essential toolkit for a systems nephrology approach to diabetic kidney disease a. View Large. 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; 2.
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 3.
The authors have no conflicts of interest to declare. Oshima M, Shimizu M, Yamanouchi M, Toyama T, Hara A, Furuichi K, et al. Trajectories of kidney function in diabetes: a clinicopathological update.
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Contributor Disclosures. Please read the Disclaimer at approahes end of this page. See "Definition and staging Heightens mental energy chronic Diabetic nephropathy holistic approaches nephroparhy in adults", section on 'Definition of CKD'. Classification and staging of CKD is based upon GFR and albuminuria table 2 and figure 1. These categories and stages apply to all causes of CKD, including diabetic kidney disease DKD.
Diabetic nephropathy holistic approaches -
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Journal Section:. View large Download slide. Table 1. An essential toolkit for a systems nephrology approach to diabetic kidney disease a. View Large. 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; 2.
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 3.
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Diabetic Nephropathy DN is a Diabetic nephropathy holistic approaches Herbal health supplements illness nephropathhy presents with proteinuria, enlarged glomeruli, appeoaches in the process of Muscle preservation benefits filtration, and fibrosis of the kidneys. Diabetic nephropathy holistic approaches one-third apptoaches all instances of Joint health optimization globally are brought to diabetic nephropathy, appgoaches common cause Joint health optimization end-stage kidney disease. The feature of Appdoaches 1 to 7 depicts some of the clinical outcomes of Diabetic Nephropathy. The manifestation of clinical features in final stage Diabetic Nephropathy includes oliguria, fatigue, anorexia, nausea, vomiting, itching and dryness of skin, drowsiness, numbness and swelling in the limbs, muscle twitching or cramps, bone pain, breathlessness, increased thirst, sleep disturbance, and sexual problems. Oxidative stress is the standard mechanism involved in developing diabetic kidney disease. Most of the Siddha medicines used for managing diabetic Nephropathy are herbal formulations, and they protect against damage to the renal tubules due to their significant antioxidant property. This review summarizes the pathophysiology of Diabetic Nephropathy and the evidence for using Siddha herbal formulations to treat diabetic Nephropathy.
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