Contemporary Perspectives on Chronic Renal Disorders

ABSTRACT

The prevalence of renal diseases and its associated burden on healthcare have tremendously risen in the past few years. From simple markers assessing kidney function, current renal research delves into understanding the diseases at the cellular and molecular levels and not just at treating, but at improving quality of life, arresting progression and providing personalized diagnostics and therapy. This narrative review highlights the improvements in diagnostic applications of kidney disease and briefly discusses a few notable biomarkers emphasizing the high throughput omics technologies, as well as contemporary perspectives on renal research. A thorough literature search was performed on PubMed, Scopus, Web of Science, and Medline. Suitable Mesh terms were included for the search strategy, and relevant evidence was documented. Language models and pharmacognosy, along with other omics strategies, impose a better understanding of the renal disease, and the remarkable discoveries of noninvasive biomarkers, urine 273‐peptide classifier, and urine peptides‐based fibrosis classifier have unraveled the associations between mechanistic studies and novel therapeutic drugs. Strides in biomarker research have been able to delineate stages and types with superior specificity and sensitivity, thereby providing a better diagnosis. Renal research reflects a powerful, dynamic, and multifaceted field that drives better advancements and discoveries in personalized medicine, drug interventions, and patient‐centered outcomes. Understanding the tangled relationship of the etiology of kidney disease, these developments and future research hold promise for individuals affected by kidney diseases

Summary

• Giant strides have been made in the screening, diagnosis, management, and prognosis of noncommunicable diseases, especially diabetes mellitus and related complications. Comprehensive research to understand the pathophysiology of diabetic kidney disease has laid the stress on early identification and intervention to bring about more favorable outcomes.

• In the era of personalized medicine, it is worthwhile to employ suitable diagnostic and therapeutic strategies based on genetic and biomarker profiling.

• With the advent of artificial intelligence, simulation studies have drastically reduced the research period, allowing for faster implementation of the results. This review intends to bring forth a few of such advancements that can be adopted to enhance patient care.

1. Introduction

Recent years have made remarkable advances in the field of renal diseases, with scientists and clinicians working together to understand the complex mechanisms of a variety of renal diseases [1]. Emerging evidence suggests that renal diseases, including immunoglobulin‐A nephropathy (IgAN), membranous Nephropathy (MN), and chronic kidney disease (CKD), have the potential to progress to End Stage Renal Disease (ESRD). Consequently, it is imperative to gain a comprehensive understanding of their pathophysiology and to investigate innovative pharmaceutical interventions and therapeutic strategies [1, 2]. Type‐2 diabetes mellitus (T2DM) patients are likely to develop diabetic kidney disease (DKD), a microvascular consequence that affects 20%–40% of people [3]. An intricate series of processes like the release of free radicals, the development of hyperglycemia, and tubular injury culminate in renal damage and fibrosis during the sequel of DKD [4]. Metabolomics, proteomics, genomics, and lipidomics are examples of high‐throughput omics techniques that are widely being used. These techniques have proven to be successful in developing novel biomarkers that can accurately reflect the complex disease process [5]. Clinical trials and several therapeutic advancements that specifically target the underlying mechanisms of various kidney‐related diseases have been a field of interest ever since the compromised quality of life in patients has been merited [5]. Furthermore, renal disease research should aim to provide better improvements in both clinical and community practices.

The Kidney Disease Outcomes Quality Initiative (KDOQI) classifies CKD into five categories based on Glomerular Filtration Rate (GFR) [6], and the Kidney Disease Improving Global Outcomes (KDIGO) has refined this classification, with the inclusion of abbreviations like T‐kidney transplant, D‐Dialysis, P‐Proteinuria. Classification of stages of CKD, along with these classifications, an estimative way to determine kidney function is provided by Modification of Diet in Renal Disease (MDRD) and, later, Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) [7]. These calculators were significant since they provide values in near proximity to the standard reference. CKD is divided into five stages based on estimated glomerular filtration rate (eGFR): Grade 1 represents normal or high kidney function with eGFR of ≥ 90 mL/min, while Grade 2 represents mildly decreased kidney function with eGFR of 60–89 mL/min. Grade 3a corresponds to mild to moderate increased decline in kidney function with eGFR of 45–59 mL/min, while Grade 3b indicates moderate to severely increased decline in kidney function with eGFR of 30–44 mL/min. Grade 4 represents a severely increased decline in kidney function with an eGFR of 15–29 mL/min, and Grade 5 indicates kidney failure with an eGFR of < 15 mL/min.

When considered alongside comorbidities, the KDIGO comprehensive classification system has proven to be effective in stratifying risk and predicting the prognosis of CKD [8].

2. Epidemiological Patterns and Risk Factors

Evidence suggests that the commonness of CKD has been growing globally, making it feasible to provide data in regard to the renal disease prevalence around the globe that includes stratifying patients based on geographical locations [9]. A study conducted in 2010 assessed the commonness of CKD by combining the data from 33 population‐based representative studies worldwide, and data presented that for people of 20 years and above, CKD prevalence was 10.4% in men and 11.8% in women [10]. Another study reported the prevalence of CKD based on geographical locations, stages of CKD, age, as well as gender and reported a worldwide frequency of 13.4% of the population with CKD Stages 1–5 and 10.6% had Stages 3–5; this meta‐analysis report examined 6,908,440 patients [11]. One of the nine breakthroughs released by the American Kidney Fund [12] defines cardiovascular‐kidney‐metabolic syndrome (CKM) as a condition that connects heart disease, T2DM, and obesity with renal diseases. The later stages of CKD are considered an irreversible change affecting the functions of nephrons, thereby producing free radicals, inflammation, and ultimately progressing to fibrosis [13, 14] Figure 1 represents the disease risk factor and prevalence. The article by Foreman et al. [13] predicts a substantial increase in the global prevalence of CKD by the year 2040, primarily attributed to aging populations as well as rising incidences of diabetes and hypertension. These projections underscore the pressing necessity for developing and implementing effective healthcare strategies [13].

Figure 1.

Chronic kidney disease (CKD)—risk factors and projected prevalence. This figure outlines the primary risk factors associated with CKD. The most significant among these are (a) diabetes and (b) hypertension, followed by (c) acute kidney injury, (d) genetic predisposition, (e) smoking, and (f) obesity. In the lower right section, a bar chart (g) illustrates the projected prevalence of CKD over time, featuring data points from 2013, 2016, and 2020, along with a forecast for 2040. The 2040 projection is based on forecasting models from Foreman et al. [13].

The past years have made a notable discovery, exploring the field of novel drugs like Sodium dependent glucose transporter‐2 inhibitor (SGLT2i), sulfonylureas, bio‐guanides, Angiotensin‐converting enzymes inhibitors (ACEi), and addressing specific genes as diagnostic markers for various renal diseases. These significant contributions in nephrology have evolved and changed the perspectives on patient care. This review briefly discusses the latest advancements and notable discoveries in renal research and patient well‐being.

3. Pluripathology

CKD is often interconnected with other conditions like Hypertension, Cardiovascular diseases, and Diabetes [15] that complicate the patient's quality of life. Research in comorbidity is growing, especially in advanced CKD stages beyond these conditions. A disconcerting finding was observed by Hawthorne et al. [16], who reported that along with 55% of them with hypertension, 40% of the 978 participants showed musculoskeletal disorders at all stages when stratified as per KDIGO guidelines. CKD Stages 1 and 2 were related to lung conditions, and CKD Stages 3–5 to cardiac issues [16]. This high prevalence of musculoskeletal disorders among individuals with renal disease poses significant challenges, like increased physical burden, affecting mobility and overall health. Cardiometabolic heart problems are found to increase threefold in CKD Stages 3–5. Knowing the impact of multimorbidity in the framework of renal disease is crucial for developing more comprehensive and personalized approaches to patient care.

4. Historical Overview of Renal Disease Research

The kidney disease studies take back to the Hippocratic era and the medical term nephrology arising in the twentieth century. Research in nephrology has advanced remarkably in recent years. According to the National Kidney Foundation (NKF), albumin, a protein found in the serum, is estimated by measuring urine albumin and creatinine ratio in patients with kidney failure [17] albuminuria (> 300 mg/day) and proteinuria (> 150 mg/day). Additionally, eGFR is said to be the gold standard and is recognized by clinicians in the analysis of kidney disease patients. A few of the well‐studied markers are presented in Table 1. Besides, indicators of kidney disorders such as tubular concentrating abnormalities, chronic pyuria, cellular casts, hematuria, and inadequate renal acidification can be detected by urine microscopy examination [30].

Table 1.

Most studied markers of renal disease research.

Markers	Strengths	Limitations
Creatinine a. Serum creatinine b. Creatinine clearance	Evaluate kidney function Gold standard marker for glomerular damage [18]	Influenced by various physiological factors like age, sex, and muscle mass [19]
Albumin a. Uine microalbumin b. Urine microalbumin	Marker for renal function [20] and comorbidities related to CKD, such as diabetes and hypertension [21]	Assays can vary by 40% across albumin concentrations, making urine albumin determinations less accurate [22] Underlying inflammatory conditions are not known
Cystatin‐c, also known as diagnostic marker superior to creatinine for CKD	National Institute for Health and Clinical Excellence has recommended the measuring of cystatin‐c for kidney function in subjects with reduced albuminuria and glomerular filtration rate [23] Neither secreted nor reabsorbed by the proximal tubular cells thus considered to be an effective marker for kidney function	Cystatin‐c is also affected by the thyroid and various steroid medications Elderly people or those with less muscular mass may overestimate GFR with Cystatin C [24]
BTP	Unlike serum creatinine, BTP is not dependent on muscle mass and shows higher sensitivity in early glomerular filtration rate detection particularly in elders or people with low muscle mass [25]	Elevated BTP levels at the tissue level due to the biological process involved in renal elimination have not been explored. Unlike serum creatinine and cystatin C, the presence of multiple isoforms and lack of standardized reference materials pose as hindrances for the utility of BTP [25].
NGAL, often referred to as LCN2	Protein encoded by the LCN2 gene in humans [26] Increases with sepsis/inflammation, sensitivity is high in AKI [27, 28] Urinary NGAL levels can diagnose CKD since they have a negative correlation with GFR and a positive correlation with serum creatinine [28]	NGAL is also produced by kidney tubule cells at low levels [27]
B2M	Present in proximal tubular cells and is a promising marker for tubular injury and for kidney transplantation recipients for detecting tubulointerstitial disease [29]	It is also reported elevated levels of B2M after treatment with nephrotoxic drugs [29]

Abbreviations: AKI, acute kidney injury; B2M, B2‐macroglobulin; BTP, beta‐trace protein; CKD, chronic kidney disease; GFR, glomerular filtration rate; LCN2, lipocalin‐2; NGAL, neutrophil gelatinase‐associated lipocalin.

5. A New Era of Kidney Care

Renal research still faces many challenges despite its goal of advancing existing innovations for the betterment of patients. In the middle of the 20th century, several national and international organizations were established, making nephrology a recognized medical specialty. Improvements in diagnostic methods and a focus on treating renal disease comorbidities are the results of medical progress. For this, novel approaches are being developed, and the below Table 2 shows a few of the recently studied markers as diagnosis and prognosis of renal diseases, and in the fields of genetics, lipidomics, proteomics, glycomics, metabolomics, radiomics and imaging techniques, organoid research, artificial kidneys, cell therapies, and pharmacogenetics. Artificial intelligence has a lot to offer these sectors, including the potential to speed up the development and use of innovative tools for kidney disease detection, treatment, and prevention.

Table 2.

Recent markers used in renal research.

Markers	Strengths	Limitations
suPAR	Regulates the glomerular filtration barrier	Connected to a variety of pathologic illnesses, including rheumatologic diseases and ARDS
	Marker for primary FSGS and CRD, moreover elevated levels of suPAR have been significantly linked to cardiovascular diseases and mortality in CKD patients [31, 32]	SuPAR's pathophysiologic or prognostic significance in kidney transplantation is still unknown [32]
KIM‐1	AKI marker A biomarker that shows explicit damage to the kidney's proximal tubule, which is where the most nephrotoxic medications and ischemia cause damage [33]	Early stages of CKD, KIM‐1 acts as protective factor, but in later stages it promotes inflammation and fibrosis [33]
uRBP4	Tubulointerstitial lesions in FSGS patients and marker for renal transplantation [34]	URBP4 is also increased in renal diseases due to inflammation, sepsis and injury [34]
C4d staining	Determine how IgAN develops into ESRD [35]	No uniform glomerular deposition of C4d is observed, and most studies were retrospective, which does not conclude this as a diagnostic marker [36]
PLA2R	Diagnostic marker for Idiopathic Membranous Nephropathy mechanistically activates the JAK‐STAT pathway and alleviates apoptosis and cell death in kidney cells [37, 38]	Limited prognostic significance as it does not provide information about overall kidney function (glomerulus) [38]
THSD7A	Idiopathic membranous nephropathy marker [39]	Most of the studies have shown varied sensitivity ranges (0–30%) of THSD7A tests [40]
Tumor necrosis factor receptor type 1 and 2	Inflammatory cytokines that are identified in podocytes of glomerulus and involved in producing a response for inflammation [41]	There is a longitudinal change in TNRF1,2 in People with ESKD [42]

Abbreviations: ARDS, acute respiratory distress syndrome; C4d, complement component 4; CRD, chronic renal disease; ESKD, end‐stage kidney disease; ESRD, end‐stage renal diseases; FSG, focal segmental glomerulosclerosis; IgAN, immunoglobulin A nephropathy; JAK/STAT, Janus kinase/signal transduction and transcription activation; KIM‐1, kidney injury molecule‐1; PLA2R, M‐type phospholipase A2 receptor; suPAR, soluble urokinase plasminogen activator receptor; THSD7A, thrombospondin type‐1 domain‐containing 7A; uRBP4, urinary retinol‐binding protein 4.

5.1. Recent Markers in Renal Research

In the growing field of renal research, discovering novel biomarkers has become pivotal for improving the diagnosis and management of kidney diseases. Table 2 provides an overview of such markers that have shown promise in renal research. These biomarkers, including THSD7A, sUPAR, KIM1, and uRBP4, represent imperative advantages in renal research. The table showcases a diverse range of biomarkers, covering from indicators of tubular injury to markers of endothelial dysfunction and inflammation.

5.2. Genetic Profiling in Kidney Disease

By focusing on genes that control kidney homeostasis, the genetics approach, which encompasses molecular, developmental, medical, genomic, and epigenetics, helps to understand the differences and distinctiveness in the viewpoint of gene regulation and gene expression that are involved in kidney disease and thus plays a critical role in treatment. To date, kidney health and illness have been linked to more than 600 genes. Thirty percent of adult nondiabetic CKD cases are caused by monogenic diseases [43]. Twenty‐four new loci connected to eGFR [44] were discovered in a meta‐analysis of gene loci related to renal function that includes information from 133,413 subjects and subsequently confirmed in 42,166 subjects [44]. The trans‐ethnic meta‐analysis found 12 loci, which had a perfectly consistent effect on eGFR in European, Asian, and African individuals. They are displayed in Figure 2 [44, 45].

Figure 2.

Consistent gene loci observed in European, Asian, and African populations.

Certain mutations have been linked to hereditary kidney illnesses by molecular genetics and genomics, such as autosomal‐dominant [46], recessive [47] polycystic kidney disease, and autosomal‐dominant tubulointerstitial kidney disease [48]. Genetic factors that predispose to the development of DKD [49] and lupus nephritis [50] have also been identified. Understanding common glomerular illnesses like IgA nephropathy, MN, and apolipoprotein L1 (APOL1)‐mediated kidney disease has been made easier by a number of genetic investigations [51]. In 15 separate cohorts globally, research in CKD that combined APOL1 risk genotypes with genome‐wide association studies (GWAS) for renal function found a genome‐wide polygenic score (GPS) with repeatable performance and revealed that the degree of risk equivalent to a positive family history was almost double for those in the top 2% of the risk score distribution across all examined cohorts [52]. Certain conditions, such as autosomal dominant polycystic kidney disease (ADPKD), are associated with a high risk of end‐stage renal disease (ESRD) that is initiated by the mutation in specific genes, PKD‐1 and PKD‐2 [53].

5.3. Mitochondrial DNA (Mt DNA) in Kidney Disease

With roughly 16,500 base pairs, mitochondrial Mt DNA is a tiny, circular, double‐stranded molecule. The double mitochondrial membrane encloses it. Just 37 genes make up Mt DNA, and they encode two ribosomal RNAs, 22 transfer RNAs, and 13 proteins [54]. Because Mt DNA has unique characteristics and is exposed to oxidative stress and other sorts of cellular damage, it has attracted much attention as a potential biomarker in the context of renal disease [55]. Research has shown that elevated urinary Mt DNA copy numbers and increased eGFR decline were observed in IgAN patients with minor glomerular abnormalities [56]. Figure 3 shows mitochondria damage and thus release of Mt DNA [56]. Researchers have thus looked into whether Mt DNA could act as a biomarker linked to the development of diabetic nephropathy since persistent sterile inflammation and mitochondrial malfunction are frequent characteristics of T2DM [55]. Additionally, there is attention to the co‐occurrence of renal illness in primary mitochondrial cytopathies, a diverse group of disorders where mutations in nuclear or Mt DNA affect the functionality of mitochondrial respiratory chain components [57]. There is growing experimental evidence that several types of CKD are associated with substantial mitochondrial structural and functional modifications in several renal cell types [58, 59, 60, 61]. Compounds that directly target mitochondria have become more and more viable treatment choices for people suffering from renal illness in recent years. While preclinical studies provide the most substantial evidence, several drugs are undergoing clinical trial testing [62]. The increasing research on mitochondrial targeting in kidney disease highlights the significance of comprehending the intricate relationship between renal pathology and mitochondrial malfunction [63]. Future studies in this field could create innovative treatment and diagnostic approaches for various renal disorders, which could enhance patient outcomes and lessen the heavy medical load connected to these illnesses.

Figure 3.

Mitochondrial dysfunction is a notable consequence of chronic kidney disease (CKD). A decline in glomerular filtration rate (GFR), coupled with elevated levels of inflammation, contributes to this dysfunction by reducing the production of ATP and NADH, diminishing mitochondrial biogenesis, and increasing the generation of reactive oxygen species (ROS). The excessive release of ROS, including superoxide (•O²⁻) and hydroxyl ions (•OH), leads to oxidative stress, which further damages the kidneys. This detrimental process results in the release of mitochondrial DNA fragments into the bloodstream, which may serve as potential biomarkers.

5.4. Lipids Profiling in Kidney Disease

Lipidomics has garnered interest in CKD because of the established lipid alterations. The common lipid abnormalities associated with CKD are high triglycerides, VLDL, and low HDL [64]. Studies also have found that patients with CKD have significantly low levels of PUFA, which is another contributor to high cholesterol [64, 65]. Technologies like mass spectroscopy are employed to study further alterations in the structure of the lipoprotein molecules. The spectral fingerprint, which is obtained due to the mass‐to‐charge ratio, is depicted before and after their fragmentation. Databases such as METLIN‐XCMS [66] can be searched for lipid spectrals. As CKD advances, the severity of dyslipidemia also increases. According to an evaluation conducted from 2001 to 2010 by the National Health and Nutrition Examination Survey (NHANES), the prevalence of dyslipidemia is markedly elevated in patients with CKD Stage 4, reported at 67.8%, in comparison to earlier Stages 1–3. Furthermore, there is a notable increase in the utilization of lipid‐lowering medications among individuals in Stage 4 of CKD [67]. A study conducted by Herman et al. has proven lipid deposition in kidney biopsies of DN patients is associated with downregulation of genes associated with clearance of cholesterol, such as ATP‐binding cassette A1 (ABCA1), ATP‐binding cassette G1 (ABCG1), and apolipoprotein E (apoE) [68]. Interesting finding linked with signaling pathways and the genes responsible for activation suggests that abnormal regulation of sterol regulatory element binding proteins (SREBPs) and carbohydrate responsive element binding protein (ChREBP) have an effect on the pathogenesis of DN [67, 69]. These genes are also associated with lipotoxicity and thereby get involved in inflammation and the generation of inflammatory cytokine TNF‐α (tumor necrosis factor‐α) [69], which in turn increases damage to the renal podocytes [70], hence understanding the mechanistic propagation of inflammatory markers and severity of kidney disease.

5.5. Protein Profiling in Kidney Disease

Clinical proteomics was founded on the notion that proteins are essential substances in the onset and course of disease [71]. Proteomics analysis has improved the diagnosis and prognosis of AKI by protein profiling of kidney biopsy samples and blood samples. Urine proteomics analysis is a noninvasive liquid biopsy that could replace renal tissue biopsy, especially when not available [72]. The proteomic approach helps to better the prognosis of kidney disease. To date, there are over 2000 proteins assessed in healthy human urine, 1823 of which were recognized by Marimuthu et al. [73] with more manuscripts published within the last two decades. The most common approach applied in proteomics and peptidomics is coupled capillary electrophoresis with mass spectrometry (CE‐MS), a separation technique, and then analysis based on the mass of individual proteins/peptides [74]. Specific urine biomarkers for acute kidney injury (AKI) have been found by proteomic analysis. These include overexpression of α‐1‐antitrypsin, β‐2‐microglobulin, angiotensinogen, and plasminogen activator of the urokinase‐type [75], with the use of CE‐MS. Good et al. [76] were capable of differentiating between patients with CKD of different origins and controls with a healthy kidney in terms of urine peptides. The idea was to find biomarkers that are linked to CKD and then combine these to make it possible to detect molecular changes early on to foretell the onset or course of CKD. According to the study, 273 urine peptides were found to differ between CKD and healthy controls. These are all listed as additional information and may be accessed at www.mcponline.org/content/9/11/2424/suppl/DC1. Clinical significance of protein profiling is presented in Figure 4.

Figure 4.

Protein profiling in chronic kidney disease (CKD). The cycle represents a pathological cascade initiated by altered regulation of protein. Dysregulation of proteins/peptides leads to metabolic disturbances and pertinent inflammation. Thereby, the key extracellular matrix proteins are overexpressed, causing fibrosis. The flowchart on the right represents the development of the CKD 273 peptide classifier, a urinary proteomics tool that identifies urinary peptides of CKD, including collagen, inflammatory, and stress response markers. These peptides serve as biomarkers, allowing early stratification.

CKD273, a commercially accessible diagnostic test for early CKD identification, has been widely used and validated by many researchers in several cross‐sectional and cohort studies [77, 78]. Using the CKD273 classifier for yearly kidney damage screening in individuals with T2DM was expensive. However, the yield in terms of quality‐added life years was better than yearly urine albumin excretion screening. Patients who are at an elevated risk of renal or cardiovascular illnesses connected with diabetes would benefit the most from CKD273 classifier‐based screening [79]. Niewczas et al. [80] determined the Kidney Risk Inflammatory Signature (KRIS), which consists of 17 proteins that are enhanced for members of the tumor necrosis factor receptor superfamily, by looking at 194 circulating inflammatory proteins in 525 individuals with type 1 and type 2 diabetes [80]. A study conducted by Pérez et al. investigated the proteomics and peptidomics of Minimal Change Disease (MCD) and Focal Segmental Glomerulosclerosis (FSGS). The proteomic analysis of FSGS revealed elevated levels of calretinin (CALB2) in the urine of patients, while levels of 39S ribosomal protein L17, transferrin, histatin C, and alpha‐1‐antitrypsin were found to be reduced in comparison to MCD [81]. SuPAR and NGAL levels in serum are generally estimated in FSGS and MCD; however, urinary peptides would help differentiate between various types of renal disease and may further assist targeted drug development [82].

The development of renal fibrosis in patients with CKD is also linked to a few key urine proteases, such as cathepsin D, matrix metalloproteinase 2, collagenase 3, matrix metalloproteinase‐14, α‐2‐HS‐glycoprotein, and 19 different collagen peptide fragments discovered by Catanese L et al. These findings led to the development of the urinary peptide‐based fibrosis classifier FPP_BH29 to assess interstitial fibrosis and tubular atrophy (IFTA) in CKD patients [83]. Urinary fibrinogen was recently studied as a risk factor for fibrosis progression [84]. It was also found that high serum fibrinogen levels are shown as predictors of mortality in Stages 3 and 4 of CKD patients. Considering the difficulties with invasive tissue biopsy, identifying urinary peptides from the urine sample may serve as better markers of diagnosis and prognosis of the disease.

5.6. Glycomics Profiling in Kidney Disease

Glycans regulate several biological processes, such as protein folding, cell‐cell communications, and immune function [85]. One of the most important functions of glycans is regulating the glomerular basement membrane (GBM), which connects the endothelial cells to podocytes [85]. Knowing the presence of enzymatic AGEs, a study in DN patients was aimed to understand the glycopatterns by using lectin microarray, identified glycopatterns: Siaα2‐6Gal/GalNAc, that is increased in DN particularly at the stage of macroalbuminuria compared with NDRD patients [86]. Lectin microarrays and HPLC were the commonly used techniques for N‐glycosylation to date, but identified glycoproteins cannot be characterized. Mass spectrometry (MS) based analysis is now widely used for its ability of characterization to study the N‐linked glycosylation in various biological samples [87]. In individuals with advanced CKD, postprandial hyperglycemia is attributed to alterations in osmotic diuresis and an increase in muscle insulin resistance. Furthermore, factors such as vitamin D deficiency, obesity, and the accumulation of uremic toxins may also contribute to developing insulin resistance in these patients, exacerbating postprandial hyperglycemia [88]. In Immune‐mediated kidney disease (IgAN), abnormal glycosylation of the IGA1 molecule leads to the deposition of immune complexes. A better understanding of glycome profiles can be achieved only by developing extremely sensitive and specific methods. This integrated approach is pivotal for clinicians to deliver personalized treatments and better patient care.

5.7. Metabolites Profiling in Kidney Disease

Metabolomics is intricately linked to CKD. The advanced stages of CKD reveal most serum metabolic parameters that mirror the severity of loss in GFR, tubular function, and endocrinal function [89, 90]. The uremic toxins include acylcarnitines, glycerolipids, dimethylarginines, metabolites of tryptophan, the citric acid cycle, and the urea cycle [90]. Significant associations of these have been found with cardiovascular health, infections, compromised handling of metabolites by the body, and neuronal signal conductance disturbances, and the most recent update is the gut‐microbiome interaction with uremic toxins [91]. Few toxins like Asymmetric dimethylarginine (ADMA). Trimethylamine N‐oxide (TMAO) has been widely studied in CKD‐associated CVD [92]. Rats injected with uric acid developed systemic hypertension and glomerular sclerosis, emphasizing the significance of purine catabolic products in the development of kidney disease and associated mortality [93]. Urinary oxalates and phenylacetyl glutamate are other metabolites implicated in CKD [94]. The recent interest in the relationship between gut microbiota, metabolite transport, and toxin accumulation in CKD is considered as a promising field of research [95]. Gut bacteria's breakdown of specific dietary components like tryptophan, phenylalanine, and tyrosine yields precursors of nephrotoxins such as p‐cresol, trimethylamine‐N‐oxide, and indole. Some toxins are expelled through feces, while transporters in the colon cells absorb the remainder into the body. This build‐up of toxins can have significant adverse effects on the kidneys and heart. Figure 5 represents the formation, transport, and excretion of the human metabolites modified by the gut microbiome, which are observed to increase when renal function declines. It remains unclear whether this is directly caused by gut dysbiosis or due to the pathology of CKD [95].

Figure 5.

Crosstalk between gut microbiome and metabolites in chronic kidney disease (CKD). CKD is driven by dysregulated systemic metabolism. Changes in gut microbiota and hepatic metabolism produce uremic toxins and impair amino acid metabolism. These systemic changes impact renal injury and endothelial dysfunction, leading to exacerbated stress and inflammation within the kidney. This leads to epithelial‐mesenchymal transition (EMT) and CKD pathology. BCRP, breast cancer resistance protein; MATE, multidrug and toxin extrusion protein; MRP, multidrug resistance‐associated protein; OAT(P), organic anion transporter (protein); OCT, organic cation transporter.

5.8. Organoids in Kidney Disease

The limited availability of renal tissue for detailed study of structural and functional alterations in disease states makes 3D organoids essential tools for replicating the in vivo microenvironment. Kidney organoids have been used in studying Polycystic Kidney disease and congenital nephrotic syndrome [96]. 3D models give an assessable approach for the in vitro studies of various podocytopathies. 3D human glomeruli developed from induced pluripotent stem cells (iPSCs) were found to retain the gene expressions even after 96 h, thereby providing a suitable window period for toxicity screening [97]. In a study by Takasato et al. [98], human iPSC‐derived kidney organoids were generated, containing podocytes, Bowman's capsules, renal tubules, stromal cells, and endothelial cells [98]. Apart from this paradigm, kidney organoids produced by patients have a lot of potential as a screening tool for possible medicinal drugs. However, large‐scale production is required for the efficient use of organoids for this purpose to facilitate high throughput screening [99]. Recent developments of coculture systems with organoids brought a better understanding of cellular interactions. Embryonic stem cells (ESCs) mimic the 3D structures of the kidney. A recent study utilized co‐cultures of hiPSC‐derived podocytes and human kidney glomerular ESCs in a microfluidic device, demonstrating a striking similarity to adriamycin‐induced glomerular injury and proteinuria [100]. The applications of this technology bring forth several critical issues that demand immediate attention. These issues encompass the reproducibility of results and the variations in the clones generated [101]. It is imperative to address these concerns by implementing strategies to enhance the scalability of organoid cultures, facilitate the generation of specific cell types, and refine the vascularization of kidney organoids.

5.9. Advances in Microscopy and Imaging Techniques in Kidney Disease

Over the past two decades, imaging technologies have significantly improved resolution and sensitivity [102, 103]. The 2D imaging techniques like confocal microscopy use a variable confocal aperture (pinhole) in front of the detector to gather light from a narrow region around the specimen and point illumination that is focussed and swept over it, and this helps in evaluating the tubular function of kidneys in physiological and pathological conditions [104]. Light sheet microscopy and optical projection tomography use 3D imaging techniques. Light sheet microscopy is used to study cleared tissues as it provides multi‐angle illumination, whereas optical projection tomography also includes multi‐angle illumination and reformation of data [105]. The limitations of 2D imaging techniques [106] were overcome by the 3D techniques, which increased depth perception and refractive properties of the tissues, giving access to subcellular structures too [107]. However, these microscopes did not provide efficient details of < 200 nm size [105]. An electron microscope, which employs an electron beam rather than light, helps in understanding the details of up to 0.2 nm, where researchers could view the glomerulus, and study the layer, endothelial cells, GBM, and podocytes [108]. Single biomolecules can be seen at the atomic level in tissues using cryo‐electron microscopy, and high‐resolution measurements of many molecules can be made simultaneously using matrix‐assisted laser desorption/ionization imaging mass spectrometry, allowing for the examination of their interactions at the tissue level [108, 109]. These technologies have advanced the knowledge of physicians on disease progression and changes in kidney structure and function.

Radiomics, the science of analysis of medical images for the assessment of size, shape, and textural characteristics, provides valuable spatial information about the distribution and patterns of pixels or voxels [110], adding value to diagnostics. Mathematical models and algorithms underpin radiomics and imaging methods, including computed tomography (CT), magnetic resonance imaging (MRI), X‐ray, and ultrasonography studies [111, 112]. Radiometric data offers additional corroborating evidence for histopathologic conclusions and can be integrated for linkage with genomic data, particularly in tumors of the lung and kidney. Over the past decade, Kidney Blood‐oxygen‐level‐dependent imaging or BOLD‐contrast imaging (BOLD MRI) has acquired validation in preclinical and clinical studies [113], especially in renal stenosis and kidney transplant [114, 115, 116]. Recently, a study on CKD patients revealed the application of BOLD MRI in terms of functional alterations in the cortex and medulla [117, 118].

5.10. Bioengineering in CKD

Implantation of Bio‐artificial kidneys (iBAK) could address the problems and support their standard of life in patients with renal failure [119]. This approach uses cell‐based strategies to create a fully operational model: wearable organs (artificial kidneys) that replicate the functions of the healthy kidney [120, 121]. A recent study by Kim et al. has used iBAK, which utilizes silicon nanopore membrane to protect against acute rejection and support the human renal epithelial cells (HRECs) in pigs (in‐vitro). These iBAK are composed of bioreactors and hemofilters where the hemofilter filters the blood and performs other essential functions of the kidneys, and the bioreactor performs critical functions of various kidney cells [122]. According to NKF (https://www.kidney.org/news-stories/future-artificial-kidneys), artificial kidneys will be a boon for Stages 4 and 5 kidney disease patients. However, hematopoiesis must be supported by erythropoietin injections. Risk of thromboembolism may cause failure of the device. If supplemented with therapeutic anticoagulants, the patients are likely to develop a hematoma, eventually causing failure of the device with fatal outcomes [122]. Hence improvisations in the devices are the need of the hour.

5.11. AI in Kidney Disease

Artificial intelligence in healthcare involves using AI to mimic human thinking in analyzing, presenting, and understanding complex medical and healthcare data. It also aims to surpass human abilities by offering new methods to diagnose, treat, and prevent diseases [123]. Electronic Syndromic Surveillance (ESS), an AI tool, is found to detect AKI long before any biochemical changes take place [124]. A similar electronic model, AKI sniffer was developed and used in the diagnosis of AKI and said to be 85% sensitive and specific [125]. The random forest (RF) model identified the discrimination of Stage 2 from Stage 3 of AKI and recorded 91% sensitivity, 71% specificity, and 53% detection within 6 months of onset [126]. In Australia, a pilot program called Electronic Diagnosis and Management Assistance to Primary Care in Chronic Kidney Disease (EMAP‐CKD) was implemented to utilize e‐technologies for the early detection of CKD. The program involved the development of software equipped with advanced algorithms trained to find patients at risk of CKD [127]. Among patients with CKD‐associated Mineral and Bone Metabolism Disorders (CKD‐MBD), traditional statistical models prove a nonlinear relationship. Therefore, predictive language learning models offer a more effective approach [127]. For example, the models developed by Mariano et al. [128] and Kleiman et al. [129] using RF algorithms were markedly significant in the detection of CKD‐MBD when compared to the classical statistical methods, with an AUC value of 0.872. AUC of 0.794 was obtained by utilizing Roche/IBM models built from real‐world data (RWD) to predict CKD as a sustained consequence of diabetes with equal or improved accuracy when compared to those using clinical trial data [130, 131]. Over the recent years, ML models have been developed to predict kidney failure in IgAN patients [132, 133, 134] since 40%‐50% of patients of IgAN develop kidney failure. An ML model: absolute renal risk (ARR) was designed as a prognostic marker for the diagnosis of the same in IgAN patients using hypertension and proteinuria (> 1 g/24 h) [135]. Artificial intelligence can be used to enhance therapeutic interventions, including optimizing drug dosing, predicting individual treatment responses, and finding promising novel therapeutic targets. Researchers have used machine learning models to personalize the dosing of immunosuppressant medications for kidney transplant recipients [136].

5.12. Pharmacogenetic Profiling in Kidney Disease

Harnessing the power of pharmacogenomics enables tailored selection of the best medication and dosage for each patient, leading to maximal efficacy and minimal adverse effects [137]. Knowledge of biomolecules involved in the pathophysiology of kidney disorders helps develop targeted drugs. Bardoxolone methyl and palmitoylethanolamide, which target inflammation, a pathognomonic feature of DKD, were successfully tried in Phase‐2 clinical trials [138]. The personalized approach to drug treatment in pharmacogenomics, offers significant advantages, particularly in tailoring antihypertensive and cardiovascular medications to individual needs [139]. To date, there are about 44 pharmacogenes related to kidney function and disease, a few of which are described in Table 3. Incorporating pharmacogenomics into renal research has eased the development of a more personalized approach to kidney healthcare, where treatment strategies are customized based on the unique genetic characteristics of individual patients. However, the practice of genetic testing is expensive and quietly challenging.

Table 3.

Pharmacogenetics in renal research.

Pharmacogenetics	Functions	Management of disease
CYP2C19	Angiotensin‐converting enzyme	CKD [140]
CYP3A4	ACE inhibitors and ARBs	CKD [141]
SLCO1B1	Protein that influences the pharmacokinetics of statins	CKD associated with cardiovascular comorbidities [142]
ABCB1	Affects the disposition of various drugs used for renal disease, such as calcineurin inhibitors and antihypertensive medications	CKD associated hypertension [143]
HLA‐B*57:01	Associated with an increased risk of abacavir‐induced hypersensitivity reactions	HIV‐related kidney disease [144]

Abbreviations: ABCB1, AT‐binding cassette subfamily B member 1; ACE, angiotensin‐converting enzymes; ARBs, angiotensin II receptor blockers; CKD, chronic kidney disease; CYP2C19, cytochrome P450 2C19; CYP3A4, cytochrome P450 3A4; HIV, human immunodeficiency virus; HLA‐B*57:01, histocompatibility complex, class I, B 57:01; SLCO1B1, solute carrier organic anion transporter family member 1B1.

5.13. Stem Cell Therapy in Kidney Disease

ESCs and iPSCs are said to be building blocks of regenerative medicine that differentiate into nephron progenitor cells (NPCs). Ureteric bud (UB) is extensively studied in kidney health [145]. Mesenchymal stem cells (MSCs) are another type that are primitive and with self‐restoring capacity that can be a potential therapeutic target for CKD [145]. A study using MSCs in the treatment of CKD has proven MSCs as an alluring cell therapy as they encourage recovery and halt kidney failure [145]. However, the long‐term culturing of MSCs results in decreased proliferation and alteration in gene expression [146]. Chimeric antigen receptor T‐cells (CAR‐T cells) are another variant that is developed by genetic engineering where the receptor specific to a tumor antigen is inserted into the T‐cells [147]. CAR‐T cell therapy is extensively studied in malignancies like myeloid leukemia, multiple myeloma, and several autoimmune diseases [148]. Its application to renal disorders is limited to the treatment of MN [149]. A small yet significant risk of AKI and cytokine release syndrome (CRS) has been reported following CAR‐T cell therapy, which warrants further systematic evaluation of its role in kidney disorders [150].

6. Nutrition in Kidney Disease

Nutrition is essential in managing and slowing the development of kidney disease. Individuals with renal disease frequently need tailored dietary adjustments to maintain optimal health and prevent additional complications [151]. Undernutrition is prevalent among individuals with advanced kidney disease and has been linked to decreased patient survival [152]. According to the Academy of Nutrition and Dietetics and the American Society of Enteral and Parenteral Nutrition, insufficient intake of protein, loss of appetite, and loss of muscle mass compromise CKD outcomes [153]. Maintaining adequate nutrition to support life quality and at the same time keeping in check the electrolyte imbalance, which is the most challenging exercise and worsens with a progressive disease state. Nutrition management in kidney disease is evolving with a focus on supplemented iron, calcium Vit D, and appropriate protein or amino acids. The stages of CKD drugs, contraindications, side effects, age, economic status, and renal replacement therapy play a significant role in the nutritional advice to the patients [154]. Keeping in mind the emotional morals of the patients, future studies are warranted in the development of suitable nutraceuticals and optimizing dietary approaches to halt the disease progression and preserve kidney function, as this is one area that needs serious consideration.

Author Contributions

Literature search, drafting, and visualization: Deenadhayalan Ashok. Conceptualization and supervision: Poornima Ajay Manjrekar. Domain expert and revision: Bhushan C. Shetty. Literature search and drafting: Sujina S.S. Critical revision and editing: Rukmini Mysore Srikantiah. Drafting and editing: Sowndarya Kollampare.

Use of Large Language Models, AI, and Machine Learning Tools

AI‐based tools (e.g., Grammarly and scholarcy.com) were used for language editing and summarizing existing literature. The authors take full responsibility for the content and accuracy of the manuscript.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

No new data were generated for this study. All data supporting this review are available in the cited references.