Diabetic kidney disease, biomarkers, and finerenone

Abstract

Diabetic kidney disease (DKD), the leading cause of kidney failure worldwide, is associated with an increased risk of cardiovascular disease (CVD). A complex pathobiology involving hemodynamic, metabolic, and immune dysregulation promotes inflammatory and fibrotic pathways that contribute to kidney disease progression and CVD in individuals with DKD. While the standard treatment approach incorporating renin‐angiotensin‐aldosterone system (RAAS) blockers and sodium‐glucose cotransporter‐2 inhibitors (SGLT2i) reduces the risk for kidney disease progression and CVD, the high residual risk that persists despite these treatments underscores the need for novel therapies for DKD. Finerenone, a nonsteroidal mineralocorticoid receptor antagonist (MRA), is a proven therapeutic option for DKD that targets inflammatory and fibrotic pathways involved in its progression. Furthermore, finerenone has been evaluated for DKD outcomes in phase 3 clinical trials, has a favourable side effect profile compared to steroidal MRAs, reduces the risks of major kidney and CVD outcomes in clinical trials when used with RAAS blockers, and is the only MRA specifically approved for DKD and recommended for DKD in treatment guidelines. The integration of novel biomarkers of CKD and CVD into the clinical management of DKD may improve early identification of at‐risk individuals and allow for patient‐specific therapeutic strategies. This review provides a brief overview of the pathogenesis of DKD and the role of mineralocorticoid receptor activation in DKD pathobiology, summarises the role of finerenone in the treatment paradigm of DKD, evaluates current and emerging biomarkers of DKD and finerenone's impact on these biomarkers, and provides forward‐looking guidance on future research for biomarker‐driven precision medicine in DKD.

1. INTRODUCTION

Diabetes is a global public health epidemic impacting more than 500 million individuals. ¹ Approximately 30%–40% of individuals with diabetes develop diabetic kidney disease (DKD). ² DKD, the leading cause of kidney failure worldwide, is associated with an increased risk of cardiovascular disease (CVD). ³ Although progressively worsening albuminuria followed by a decline in kidney function is regarded as the classic DKD phenotype, substantial variability exists in the progression of DKD in clinical practice. ⁴ Three alternative DKD trajectory phenotypes have been described: normoalbuminuric glomerular filtration rate (GFR) decline, where there is a progressive loss of kidney function (often at >3–5 mL/min/1.73 m² per year) without significant albuminuria; rapid progressor phenotype, where there is steep GFR decline (often >5–10 mL/min/1.73 m² per year) with variable levels of albuminuria; and fluctuating albuminuria phenotype, where albuminuria fluctuates over time with periods of regression (decrease to the mildly increased range) or relapse (increase to moderately or severely increased range). ⁴ The heterogeneity in DKD trajectories underscores the need for a better understanding of DKD pathobiology and a biomarker‐guided approach to improve disease identification, risk stratification, and patient‐specific therapeutic planning. ⁵

Metabolic derangements, activation of pro‐inflammatory and pro‐fibrotic pathways, and hemodynamic alterations are key pathologic drivers of DKD pathogenesis. ⁶ These mechanisms lead to progressive kidney disease, which can persist even when there is an improvement in glycaemic control. ⁷ , ⁸ For many years, the management of DKD has centred on controlling glucose levels and using RAS inhibitors, primarily aimed at addressing the hemodynamic dysregulation driving DKD progression. ⁹ Newer treatment options like glucagon‐like peptide 1 receptor agonists (GLP‐1RAs) and SGLT2is have more pleiotropic effects and slow CKD progression and reduce cardiovascular events in clinical trials. ¹⁰ , ¹¹ , ¹² However, despite these therapeutic advancements, the progression to kidney failure and cardiovascular morbidity persists, underscoring a residual risk and the need for novel interventions for patients with DKD. ¹⁰ , ¹¹

Aldosterone, the primary mineralocorticoid in humans, promotes hemodynamic dysregulation, inflammation, and fibrosis, making the mineralocorticoid receptor (MR) an important target to address the residual risk in DKD. ¹³ While mineralocorticoid receptor antagonists (MRAs) can counteract these effects, finerenone—a nonsteroidal MRA—is currently the only MRA specifically approved for CKD. It exhibits unique pharmacological features with a more favourable safety profile compared to steroidal MRAs. ¹⁴ This review provides a brief overview of the pathogenesis of DKD and the role of MR activation in DKD pathobiology, summarises the role of finerenone in the treatment paradigm of DKD, evaluates current and emerging biomarkers of DKD and finerenone's impact on these biomarkers, and provides forward‐looking guidance on future research for biomarker‐driven precision medicine in DKD, with the goal of providing a current understanding of the potential for biomarkers to address the residual risk in DKD and refine the clinical application of finerenone.

2. PATHOGENESIS OF DKD AND THE ROLE OF MINERALOCORTICOID RECEPTOR ACTIVATION

While the mechanisms underlying the onset and progression of DKD have not been fully elucidated, the DKD pathogenesis is thought to be driven by three interconnected processes: metabolic derangements, activation of pro‐inflammatory and pro‐fibrotic pathways, and hemodynamic alterations. ⁶ , ¹⁵ The relative contribution of these processes may vary among individuals, and, as DKD tends to have familial clustering, genetic and epigenetic factors likely also influence DKD susceptibility. ¹⁵ , ¹⁶ Ultimately, these pathogenic processes lead to glomerular hypertrophy, extracellular matrix expansion, vascular hyalinosis, glomerulosclerosis, interstitial inflammation, tubular atrophy, and interstitial fibrosis in the kidney. ¹⁵ Clinically, these pathological changes are characterised by progressive albuminuria and decline in kidney function that culminates in kidney failure. The three interconnected processes leading to DKD are briefly described in the following sections.

2.1. Metabolic derangements

Hyperglycaemia leads to tissue glycation and the formation of advanced glycation end products (AGEs) which reduce nitric oxide bioavailability and promote transcription factors, reactive oxygen species (ROS), and other signalling molecules that contribute to vascular dysfunction, cellular hypertrophy, inflammation, and oxidative stress. ¹⁵ , ¹⁷ Independent of glycation, hyperglycaemia contributes to kidney damage by stimulating protein kinase C, hexosamine, and polyol pathways, which result in the expansion of extracellular matrix, release of pro‐inflammatory and pro‐fibrotic cytokines, and redox imbalance. ¹⁷ Ferroptosis, a form of cell death characterised by iron‐dependent accumulation of lipid peroxides, has been linked to DKD in animal models. ¹⁸ Hyperglycaemia, hyperlipidaemia, and chronic inflammation contribute to iron accumulation in patients with DKD, which may drive ferroptosis and oxidative damage in DKD. ¹⁹

2.2. Activation of pro‐inflammatory and pro‐fibrotic pathways

Several inter‐connected pathways contribute to inflammation and fibrosis in DKD. Tissue hyperglycaemia is associated with the recruitment of activated leukocytes and macrophages ²⁰ which leads to the release of various cytokines, chemokines, activated complement products, and ROS. These mediators then amplify the inflammatory reactions and directly damage tubular cells, mesangial cells, endothelial cells, and podocytes. ¹⁵ , ¹⁷ Hyperglycaemia is also associated with the infiltration of activated myofibroblasts in the kidney, ²¹ which facilitates the deposition of extracellular matrix and plays a role in kidney fibrosis. ²² Glomerulosclerosis and tubulointerstitial fibrosis are pathologic end results of pro‐fibrotic pathways. Fibrosis, resulting from the accumulation of activated myofibroblasts from various sources, is the final common pathway for kidney function loss in DKD. ²³

2.3. Hemodynamic alterations

An increase in intra‐glomerular pressure and a resultant rise in single‐nephron GFR, also known as hyperfiltration, plays a key role in the onset and progression of DKD. ²⁴ Hyperglycaemia increases the expression of proximal tubular sodium‐glucose cotransporters and enhances proximal sodium and glucose reabsorption, inhibiting tubulo‐glomerular feedback and increasing intra‐glomerular pressure. ¹² Hyperglycaemia may also activate RAS, increasing systemic blood pressure and intra‐glomerular pressure. ²⁵ Decreased bioavailability of nitric oxide and an increase in cyclooxygenase‐2 prostanoids are thought to contribute to decreased afferent arteriolar tone. ¹⁷ Mediators such as angiotensin II, endothelin 1, thromboxane A2, and ROS are thought to contribute to increased efferent arteriolar tone. ¹⁷ Hyperfiltration, over time, damages the glomerular filtration barrier and leads to worsening albuminuria and DKD progression.

2.4. The role of mineralocorticoid receptor activation in DKD

Mineralocorticoid receptor (MR) activation plays a central role in the progression of DKD and CVD. Angiotensin II stimulates the secretion of aldosterone from the outermost layer of the adrenal cortex. ²⁶ Aldosterone then acts on intracellular MRs, which stimulate gene transcription of effector proteins and exert non‐genomic effects on a number of cellular signalling pathways. ²⁷ MR activation plays a vital physiologic role in regulating blood pressure and electrolyte balance primarily through its action in the distal nephron. ¹⁷ MRs are also expressed in other cells, including cardiomyocytes, podocytes, endothelial cells, smooth muscle cells, fibroblasts, inflammatory cells, and adipocytes. ²⁸

Hyperglycaemia is associated with RAS activation and the consequent increase in MR activity. MR overactivation promotes salt retention and induces signalling pathways that lead to extracellular matrix accumulation, inflammation, oxidative stress, and vascular dysfunction. ²⁹ MR overactivation stimulates monocyte differentiation to pro‐inflammatory M1 macrophages; production of pro‐inflammatory cytokines like interleukin‐1 beta (IL‐1β) and interleukin‐6 (IL‐6); release of pro‐fibrotic proteins including transforming growth factor beta (TGF‐β), a key mediator of kidney fibrosis, ³⁰ fibronectin, and osteopontin; and upregulation of signalling proteins like Rac1. ¹³ , ³¹ Aldosterone, via an MR and Rac1 within kidney cells, activates the NLRP3 inflammasome, leading to podocyte damage, glomerular sclerosis, and tubulointerstitial inflammation. ⁷ In addition, MR overactivation increases ROS, which can cause direct cellular injury. ²⁹ It can also promote vascular dysfunction by increasing endothelin‐1 expression and decreasing nitric oxide bioavailability. ²⁹ Moreover, high serum aldosterone levels correlate with an increased risk of kidney failure in individuals with diabetes and CKD. ³² Taken together, these mechanisms illustrate how MR overactivation drives DKD progression and CVD, a process that can be mitigated by steroidal or nonsteroidal MR antagonists (MRAs). Spironolactone and eplerenone are steroidal MRAs that have long been used to treat severe heart failure and hyperaldosteronism. Steroidal MRAs have a high side effect burden, particularly for hyperkalaemia, gynecomastia, and sexual dysfunction. ³³ Finerenone is a nonsteroidal MRA with unique pharmacodynamic and pharmacokinetic properties that provide a more balanced tissue distribution and better side effect profile than steroidal MRAs, and is the only MRA approved to treat DKD.

3. FINERENONE MECHANISM OF ACTION

Finerenone is a third‐generation, nonsteroidal compound that uniquely binds to the MR, providing high potency, selectivity, and a distinct recruitment of nuclear cofactors. ³⁴ Unlike first‐generation spironolactone and second‐generation eplerenone, finerenone is potent and selective, acting as a large, passive antagonist to the MR. ³⁵ This mechanism modifies the transcriptional cascade in a cell‐specific manner and acts as an inverse agonist, suppressing proinflammatory and profibrotic gene expression, which are key drivers of kidney damage induced by MR activation, regardless of aldosterone presence. ³⁶ A recent biomarker study suggests that finerenone not only acts on RAS and downstream targets of the MR/aldosterone transcription factor complex, but also upstream by affecting proteins involved in energy metabolism, haemostasis, immune‐related, neurohormonal, and remodelling‐associated pathways, which may also contribute to the clinical benefits observed with fineronone. ³⁷ Finerenone's pharmacokinetics differ significantly from steroidal MRAs (Table 1), with minimal urinary excretion, a shorter half‐life, an absence of active metabolites, greater polarity, and less lipophilicity, leading to high tissue penetration and balanced kidney‐heart distribution. ³⁸ These properties allow finerenone to mitigate aldosterone ‘escape’ seen with long‐term RAS inhibition, which has been shown to slow DKD progression and reduce CVD morbidity. ³⁸ , ³⁹

TABLE 1.

Pharmacologic characteristics of steroidal and nonsteroidal MRAs. ⁹⁵

	Spironolactone	Eplerenone	Finerenone
Type of MRA	Steroidal		Nonsteroidal
Receptor selectivity	Potent and non‐selective; passive	Less potent and more selective	Potent and selective; bulky and passive
Affinity to MR	Finerenone > spironolactone >> eplerenone 96
Inhibitory effect on aldosterone‐dependent gene activation	Finerenone > spironolactone 97
Half‐life	<2 h	4 h	2–3 h
Tissue distribution a	Kidney > heart		Balanced kidney–heart 98
Metabolites	Multiple, active metabolites	None	None
Effect on inflammatory response and fibrosis in mouse model of cardiac fibrosis 99	n/a	(At equinatriuretic dose to finerenone) less significant effects on inflammation and fibrosis	(At equinatriuretic dose to eplerenone) strong inhibition of inflammation and fibrosis
Sexual side effects	Observed	Low frequency	Not observed or rare
Risk of hyperkalaemia	Eplerenone ~ finerenone 100 Spironolactone > finerenone 93
Effect on SBP	Spironolactone > finerenone 93 Spironolactone > eplerenone 101 Eplerenone ~ finerenone 100

Abbreviations: MR, mineralocorticoid receptor; MRA, mineralocorticoid receptor antagonist; n/a, not applicable; SBP, systolic blood pressure.

^a Based on rodent studies.

Finerenone has a lower risk for hyperkalaemia compared with steroidal MRAs. ³⁶ , ³⁸ This is likely explained by the unique attributes of finerenone, including the short half‐life, lack of active metabolites, unique MR specificity, and balanced heart‐kidney distribution. ³⁵ A recent target trial emulation study utilising real‐world data confirmed the lower incidence of hyperkalaemia with finerenone versus spironolactone. ⁴⁰ Other reports using real‐world data have also confirmed the lower risk of hyperkalaemia with finerenone treatment in patients with DKD. ⁴¹ , ⁴² The FIDELIO‐DKD trial demonstrated that finerenone‐associated hyperkalaemia can be managed by treatment interruption, given finerenone's short half‐life and absence of active metabolites. ⁴³ Further, an integer risk score model for new‐onset hyperkalaemia in patients with CKD and type 2 diabetes was recently developed and validated using data from FIDELITY. ⁴⁴ Implementation of this model could potentially further reduce the risk of hyperkalaemia with finerenone treatment by identifying high‐risk patients.

4. FINERENONE IN THE CURRENT TREATMENT PARADIGM OF DKD

First‐line treatment for most people with CKD includes SGLT2is and RAS inhibitors to reduce the risk for CKD progression and CVD events. ⁴⁵ Statin or statin plus ezetimibe treatment is recommended for patients with non‐dialysis dependent CKD who are aged ≥50 years to reduce the risk for CVD. ⁴⁶ In addition, GLP‐1 RAs and non‐steroidal MRAs (e.g., finerenone) are recommended for cardiorenal protection in individuals with CKD and diabetes, with evidence for their use being strongest among those with a high level of albuminuria. ⁴⁷

While there is strong direct evidence of the cardiorenal benefit of finerenone added to RAS‐inhibitors in DKD, ⁴⁸ , ⁴⁹ there is a relative dearth of data on its impact as an add‐on therapy in individuals who are on both a RAS‐inhibitor and a SGLT2i. A recent randomised controlled trial testing the impact of the simultaneous initiation of finerenone and empagliflozin in patients with DKD with albuminuria who were already on a RAS‐inhibitor (CONFIDENCE study) found that initial therapy with finerenone plus empagliflozin led to a reduction in albuminuria that was 32% greater than that with empagliflozin alone and 29% greater than that with finerenone alone. ⁵⁰ Interestingly, while the increase in mean serum potassium level was similar with combination therapy and finerenone alone, the frequency of hyperkalaemia (serum potassium level of >5.5 mmol/L) was lower by approximately 15%–20% with combination therapy than finerenone alone, suggesting that the SGLT2i‐finerenone combination may mitigate finerenone's hyperkalaemia risk. ⁵⁰ Even though this trial was not designed to evaluate hard cardiorenal endpoints, the change in albuminuria over a 6‐month period is considered a valid surrogate for CKD progression. The reduction in albuminuria suggests the potential for combination therapy to predict a reduction in the incidence of adverse kidney and CVD events. ⁵¹ Moreover, a secondary analysis of FIDELITY, which pooled patient‐level data from the FIDELIO‐DKD and FIGARO‐DKD studies, found a trend towards a lower risk of cardiorenal events in patients receiving a SGLT2i. ⁵² As with the CONFIDENCE study, this analysis also suggested that the risk of hyperkalaemia was reduced among patients who received a SGLT2i at baseline. A similar secondary analysis of FIDELITY noted that the cardiorenal benefits of finerenone on composite CVD and kidney outcomes as well as hyperkalaemia were similar among patients using and not using a GLP‐1 RA at baseline. ⁵³ Interpretation of this analysis is limited by the small sample size and number of events. A well‐powered clinical trial is needed to assess whether the combination of finerenone with GLP‐1 RA in patients on a RAS‐inhibitor or a RAS‐inhibitor‐SGLT2i combination provides incremental cardiorenal benefits.

An actuarial analysis using data from SGLT2i, finerenone, and GLP‐1 RA trials reported that compared with conventional care, the combination of the three drugs was associated with an HR (95% confidence interval) of 0.65 (0.55–0.76) for major adverse cardiovascular events in patients with type 2 diabetes and at least moderately increased albuminuria. ⁵⁴ Projected gains in survival free from hospitalised heart failure, CKD progression, cardiovascular death, and all‐cause death were also reported. The findings of this analysis provide additional support for the potential of combination therapy.

5. BIOMARKERS IN DKD

5.1. Current validated biomarkers in DKD

Biomarkers play a crucial role in early diagnosis of DKD and can serve as prognostic markers for adverse kidney and CVD outcomes (Table 2 and Figure 1). Currently, the diagnosis of DKD and the prediction of DKD complications rely heavily on eGFR and albuminuria. Albuminuria, assessed as urine albumin‐to‐creatinine ratio (UACR), is classified as normal or mildly increased (UACR <30 mg/g creatinine, previously termed ‘normoalbuminuria’), moderately increased (UACR 30 to 300 mg/g creatinine, previously termed ‘microalbuminuria’), and severely increased (UACR >300 mg/g creatinine, previously termed ‘macroalbuminuria’). ⁵⁵ Higher albuminuria levels linearly correlate with an elevated risk of kidney failure and CVD. ⁵⁶ Albuminuria, even within the normal ranges (UACR <30 mg/g), is a strong predictor of kidney disease progression and CVD in patients with diabetes. ⁵⁶ , ⁵⁷ Moreover, albuminuria in combination with eGFR helps identify at‐risk groups for future adverse kidney and CVD events. ⁵⁸ , ⁵⁹ , ⁶⁰ , ⁶¹

TABLE 2.

Biomarkers (validated and under development) in DKD.

	Biomarker	Proposed function	Notes
Biomarkers in standard of care	eGFR (validated)	Overall estimate of kidney function; used to evaluate onset and progression of CKD 102	‐
	UACR (validated)	Indicator of kidney damage to assess CKD stage and monitor kidney health 103	‐
	Cystatin C (under development)	Surrogate indicator for GFR estimation 104	Glomerular marker; potentially promising biomarker for early DKD diagnosis and prediction of disease progression
Novel DKD biomarkers under development	TNFR1 and TNFR2	Elevated concentrations are associated with increased risk of kidney failure 105	Adjust for age, sex, MAP, ACR, and GFR
	NGAL	Urine‐NGAL‐to‐creatinine ratio may differentiate DKD from non‐DKD (Sp, 90.5%) 106	Distal tubules and collecting duct release during kidney injury; a definite marker of acute kidney damage 104
	KIM‐1	Predictor of CKD development and kidney fibrosis 107	In early DKD, glomeruli KIM‐1 expression is significantly elevated
	CKD273 (urine)	Detects progression from normo‐ to microalbuminuria and micro‐ to macroalbuminuria 69	Needs to be adjusted for baseline albuminuria status, eGFR, and RASi use
Biomarkers of inflammation and fibrosis in DKD	GDF‐15	Prognostic marker for kidney decline in DKD similar to the UACR 108	Diagnostic marker for mitochondrial diseases
	FN	Predictive value for microalbuminuria and overt proteinuria; micro‐macrovascular complications in T1D and T2D 104	Fibrillar cell‐surface protein with a soluble plasma form associated with glomerular extracellular matrix constriction
	Osteopontin	Plasma levels independently correlated with presence and severity of DKD 104	Implicated in the pathogenesis of several forms of CKD where it promotes inflammation and fibrosis and regulates calcium and phosphate metabolism
	Col1A1	Negatively correlated with serum levels of ALT, AST, LDL, TG, TC, FBG, OGTT, HbA1c 109	Col1A1 was the most significantly DEP in the ECM‐receptor interaction pathway
	IL17C/D	Significant correlation with DKD inflammation 110	‐
	WNT9a	Reflection of anti‐fibrotic activity 111	Induced in CKD kidney tubular epithelium to promote fibroblast activation
	NPY	Implicated in aldosterone secretion; may play causal role in albuminuria 112	Reduced expression in diabetic, insulin‐resistant podocytes and glomeruli
Biomarkers of CVD complications in CKD	hs‐cTnI	Predicts cardiovascular events and all‐cause mortality in patients with CKD and kidney failure 113	Serum levels of hs‐cTnI reflect subclinical myocardial injury in ambulatory patients
	cTnT	Predictor of unfavourable CAD events in CKD populations 114	cTnI, found only in cardiomyocytes, enters the bloodstream following damage to myocardial cells
	NT‐proBNP, BNP	Correlate with the severity of HF and LV dysfunction; useful in guiding HF management in CKD 114	Due to its longer half‐life, NT‐proBNP levels may remain more stable compared to BNP

Abbreviations: ACR, albumin‐to‐creatinine ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BNP, B‐type natriuretic peptide; CAD, coronary artery disease; CKD, chronic kidney disease; CKD273, panel biomarker for CKD progression that combines data on 273 urinary peptides; Col1A1, collagen type I alpha 1; cTnI, cardiac troponin I; cTnT, cardiac troponin T; CVD, cardiovascular disease; DEP, differentially expressed protein; DKD, diabetic kidney disease; ECM, extracellular matrix; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; FN, fibronectin; GDF‐15, growth differentiation factor‐15; GFR, glomerular filtration rate; HbA1c, haemoglobin A1c; HF, heart failure; hs‐cTnI, high‐sensitivity cardiac troponin I; IL17C/D, interleukin 17C/D; KIM‐1, kidney injury molecule‐1; LDL, low‐density lipoprotein; LV, left ventricle; MAP, mean arterial pressure; NGAL, neutrophil gelatinase‐associated lipocalin; NPY, neuropeptide Y; NT‐proBNP, N‐terminal pro b‐type natriuretic peptide; OGTT, oral glucose tolerance test; RASi, renin‐angiotensin system inhibitor; Sp, specificity; T1D, type 1 diabetes; T2D, type 2 diabetes; TC, total cholesterol; TG, triglycerides; TNFR1/2, tumour necrosis factor receptor 1 and 2; UACR, urinary albumin‐to‐creatinine ratio; WNT9a, wingless‐related integration site 9a.

FIGURE 1.

Select biomarkers in diabetic kidney disease. Biomarkers linked to inflammation and fibrosis, including TNFR1/2, GDF‐15, fibronectin, IL17C/D, WNT9a, NPY, and osteopontin, are depicted as systemic contributors to disease progression. At the glomerulus, markers such as cystatin C, eGFR, UACr, CKD273, and NPY reflect glomerular function and injury. Proximal tubule‐specific biomarkers, including NGAL and KIM‐1, and distal tubule‐associated NGAL levels indicate tubular damage. Additionally, biomarkers for CVD complications in CKD—hs‐cTnI, cTnT, NT‐proBNP, and BNP—are noted, underscoring the intersection of DKD and systemic cardiovascular risk. CKD273 is identified in the urine as a composite proteomic biomarker, representing the integrated output of kidney injury processes. BNP, brain natriuretic peptide; CKD, chronic kidney disease; CKD273, panel biomarker for CKD progression that combines data on 273 urinary peptides; cTnT, cardiac troponin T; CVD, cardiovascular disease; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; GDF‐15, growth differentiation factor 15; hs‐cTnI, high‐sensitivity cardiac troponin I; IL17C/D, interleukin‐17C/D; KIM‐1, kidney injury molecule‐1; NGAL, neutrophil gelatinase‐associated lipocalin; NPY, neuropeptide Y; NT‐proBNP, N‐terminal pro b‐type natriuretic peptide; TNFR1/2, tumour necrosis factor receptor 1 and 2; UACr, urine albumin‐to‐creatinine ratio; WNT9a, wingless‐related integration site 9a.

While albuminuria and eGFR are undoubtedly valuable markers, they have important limitations in identifying early DKD. The PIMA Indian study demonstrated that structural abnormalities in the kidney often appear well before the disease is detected by current clinical markers. ⁶² A substantial subset of patients with DKD may also have progressive DKD without accompanying increase in urinary albumin excretion, limiting the utility of albuminuria as a risk discriminator for this subgroup. ⁶³ , ⁶⁴ In the same way, eGFR, derived from serum creatinine, cystatin C, or a combination of both, often lacks accuracy and precision when compared to measured GFR in the early stages of DKD. ⁶⁵ , ⁶⁶ , ⁶⁷ These limitations of current biomarkers underscore the critical need to identify novel biomarkers that can discern early structural kidney damage and delineate high‐risk DKD subgroups. ⁶⁸

5.2. Novel biomarkers under development in DKD

Numerous single and panel biomarkers have been tested in multiple studies showing prediction of kidney failure in patients with type 2 diabetes. This includes biomarkers like NGAL‐1 and soluble TNF receptor 1 and 2, fibroblast factors 21 and 23, GDF‐15, KIM‐1, and CKD273. ⁶⁹ A machine learning model called KidneyIntelX, which incorporates biomarkers with high prognostic value (TNFR1, TNF2, and KIM‐1) along with laboratory data from electronic health records (EHRs), was recently used to risk stratify for kidney, heart failure, and death outcomes in 1278 participants in the Canagliflozin Cardiovascular Assessment Study. ⁷⁰ Participants scored as high risk using this bio‐prognostication test seemed to derive more benefit from treatment with canagliflozin versus placebo, suggesting that KidneyIntelX may have therapeutic implications for patients with DKD. Similarly, there are panel biomarkers like CKD273, developed using mass spectrometry–based methods, that combine data on 273 urinary peptides. CKD273 has been shown to predict rapid decline in eGFR better than albuminuria. ⁷¹ Additionally, a strong correlation was observed between the CKD273 score and the occurrence of microalbuminuria in the post hoc analysis of the diabetic retinopathy candesartan trials (DIRECT‐Protect 2 study). ⁷² Furthermore, a higher baseline CKD273 score was associated with a greater reduction in albuminuria in the spironolactone group compared to placebo based on a secondary analysis of a clinical trial involving patients with type 2 diabetes and resistant hypertension. ⁷³ While emerging evidence on the use of urinary and plasma proteomics to identify biomarker signatures useful in predicting progression of DKD is promising, most panel biomarkers lack rigorous well‐powered external validation studies with hard clinical kidney and CVD endpoints.

In summary, the existing cardiorenal biomarkers offer insights into disease progression and may aid clinicians in risk stratification and treatment optimisation. ⁶⁸ , ⁷⁴ Recent research efforts have focused on identifying and validating new biomarkers that capture the hemodynamic, metabolic, and inflammatory/fibrotic mechanisms of DKD. While these efforts represent promising movement towards the development of new biomarkers in the evaluation of DKD progression and treatment effects, it must be noted that to date, none of these novel biomarkers and bio‐prognostication tests have been fully validated. Additional research is needed to clarify their clinical utility, position in current diagnostic and clinical decision‐making algorithms, and cost effectiveness. These novel biomarkers have the potential to herald an era of pathobiology‐targeted personalised treatment options for patients with DKD. ⁷⁴ , ⁷⁵

5.3. Clinical predictors in DKD

Hyperglycaemia, hypertension, hyperlipidaemia, obesity, insulin resistance, and periodontal disease are well‐accepted modifiable risk factors for DKD. ⁶² , ⁷⁶ Efforts have been made in leveraging a broad array of data from EHR for predicting eGFR decline in patients with type 2 diabetes mellitus. ⁷⁷ While using predictive models that included albuminuria and eGFR along with established risk factors such as older age, diabetes duration, glycaemic control, and systolic blood pressure, the addition of clinical predictors beyond albuminuria and eGFR showed only marginal improvement in prediction accuracy. ⁷⁸ , ⁷⁹ , ⁸⁰ Machine learning models have also been used to develop prediction algorithms using EHR data and biomarker data. The Klinrisk model, one such machine learning model, was recently validated in a pooled dataset of two phase 3 clinical trials of finerenone in patients with CKD and type 2 diabetes. ⁸¹ In this validation study, the Klinrisk model predicted >40% eGFR decline or kidney failure at 2 and 4 years accurately with an area under the curve (AUC) of 0.81 for 2 years and 0.86 for 4 years. ⁸¹ The Klinrisk model was also tested in a validation study using data from two clinical trials of the SGLT2i canagliflozin in high‐risk patients with type 2 diabetes. ⁸² The model demonstrated high accuracy in predicting CKD progression (>40% eGFR decline or kidney failure) at 1 and 3 years (AUC, 0.81 and 0.88, respectively).

6. IMPACT OF FINERENONE ON BIOMARKERS OF CKD AND CVD

6.1. Preclinical evidence

Preclinical studies have shown that finerenone favourably impacts biomarkers associated with arterial stiffness, oxidative stress and endothelial dysfunction. Studies using the Munich Wistar Frömter rat model of chronic kidney disease showed that finerenone significantly reduced arterial stiffness compared with the control and resulted in an enlargement of the fenestrae area. ⁸³ , ⁸⁴ The reduced arterial stiffness was related to changes in elastin organisation, normalisation of matrix metalloproteinase (MMP)‐2 and MMP‐9 activity and oxidative stress reduction (higher nitric oxide bioavailability and reduced superoxide anion levels vs. control). ⁸⁴ Another study using this model found that finerenone improved endothelial function by increasing the availability of nitric oxide. ⁸⁵ This was modulated by a decrease in levels of superoxide anion and an upregulation in phospho‐endothelial nitric oxide synthase and superoxide dismutase activity. In rats with acute kidney injury, finerenone treatment resulted in lower levels of KIM‐1, NGAL‐2, TGF‐β, and collagen‐I mRNA in the kidney cortex. ⁸⁶ These findings emphasise the ability of finerenone to affect levels of measurable indicators of MR overactivation, such as nitric oxide and TGF‐β.

6.2. Clinical evidence

Finerenone has been shown in clinical trials to favourably impact biomarkers associated with CKD and CVD endpoints in patients with DKD (Table 3). In particular, a post hoc mediation analysis of two large phase 3 clinical trials of finerenone showed that the reduction in albuminuria alone mediated 84% and 37% of the treatment effect of finerenone on kidney and CVD outcomes, respectively. ⁸⁷ Further, a prespecified analysis of the Finerenone Trial to Investigate Efficacy and Safety Superior to Placebo in Patients with Heart Failure (FINEARTS‐HF) trial demonstrated that finerenone resulted in early and sustained reductions in UACR, irrespective of baseline Kidney Disease Improving Global Outcomes (KDIGO) CKD risk category; participants with higher kidney risk had greater relative reductions. ⁸⁸ Considering that albuminuria is strongly correlated with endothelial dysfunction, ⁸⁹ the observed reduction suggests the possibility of finerenone's ability to improve endothelial function, as observed previously in a rat model of CKD. ⁸⁵ In addition to lowering albuminuria and stabilising eGFR, finerenone reduces fibronectin and osteopontin, which are measurable indicators of MR overactivation in the kidney, suggesting its possible role in mitigating kidney fibrosis and inflammation. ⁹⁰ , ⁹¹ Finerenone also lowers NT‐proBNP and high‐sensitivity cardiac troponin T (cTnT) levels, indicating its effect in reducing cardiac stress and injury. ⁹² These improvements in biomarkers align with clinical findings showing reduced risks of cardiovascular death, kidney failure, and hospitalisations for heart failure with finerenone. ⁹² The data from the Mineralocorticoid Receptor Antagonist Tolerability Study (ARTS) further supports these findings on cardiorenal biomarkers of hemodynamic stress. ⁹³ Thus, finerenone's ability to target multiple pathophysiological pathways offers a promising advance in managing DKD.

TABLE 3.

Finerenone biomarker studies and outcomes in DKD.

Study (year)	Population; median follow‐up	Biomarker findings	Select endpoints (finerenone versus comparator)
FIGARO‐DKD 49 (2021)	CKD G2‐G4, A2‐A3 with T2D (N = 7352); 3.4 years	UACR reduction from BL 32% > PBO a (ratio of the LSM change from BL, 0.68; 95% CI, 0.65–0.70) ≥40% decrease in eGFR: 9.5% versus 10.8 (HR, 0.87; 95% CI, 0.76–1.01)	Time to death from cardiovascular causes b : 12.4% versus 14.2% (PBO; HR, 0.87; 95% CI, 0.76–0.98; P = 0.03)
FIGARO‐BM 37 (2025)	CKD G2‐G4, A2‐A3 with T2D in FIGARO‐DKD for ≥24 months (N = 929)	Finerenone was associated with a reduction in fibronectin, osteopontin, NFT3, SPINT2, CKMT1A/B, ANGPTL2, and NPY, and an increase in RAB6A, APOL1, and CD69
FIDELIO‐DKD 48 (2020)	CKD G3b‐G4, A2‐A3 with T2D (N = 5674); 2.6 years	UACR reduction from BL: 31% > PBO a (ratio of the LSM change from BL, 0.69; 95% CI, 0.66–0.71) ≥40% decrease in eGFR: 16.9% versus 20.3% (HR, 0.83; 95% CI, 0.72–0.92)	Time to kidney failure b : 17.8% versus 21.2% (PBO; HR, 0.82; 95% CI, 0.73–0.93; p = 0.001)
FIDELITY 92 (2022)	CKD G2‐G4, A2‐A3 with T2D (N = 13 026) w/o HFrEF pooled from FIGARO‐DKD and FIDELIO‐DKD; 3.0 years	≥40% decrease in eGFR: 12.5% versus 14.8% (HR, 0.84; 95% CI, 0.76–0.92)	Composite cardiovascular outcome b : 12.7% versus 14.4% (PBO; HR, 0.86; 95% CI, 0.78–0.95; p = 0.0018)
FIDELITY‐BM (2023)		In the FIDELITY subcohort, levels of NT‐proBNP were reduced by ~18% early and persistently in the finerenone arm (vs. PBO). In FIGARO‐BM, these findings were confirmed In FIGARO‐BM, cTnT levels improve upon finerenone treatment with significant reduction in both cardiac markers (p ≤ 0.05) at 24 months
ARTS Trial 93 (2013)	HFrEF and CKD G1‐G3 (N = 392); day 29 ± 2	Median decrease in serum levels (pg/mL) at visit 7 for 10 mg QD (IQR): Serum BNP: −31.0 (−122; 5) NT‐proBNP: −193.65 (630; 102)	Pooled data from finerenone 5 and 10 mg daily: Significantly lower incidence of hyperkalaemia, renal failure, and renal impairment versus spironolactone (3.7% vs. 12.7%, p = 0.0284; 1.5% vs. 7.9%, p = 0.0352; 6.0% vs. 28.6%, p < 0.0001)
ARTS‐DN 115 (2015)	T2D and CKD G1‐G3, A2‐A3 (N = 823); 3 months	UACR at day 90 versus BL b : reduced in finerenone 7.5‐, 10‐, 15‐, and 20‐mg/d groups (7.5 mg/d: 0.79 [90% CI, 0.68–0.91; p = 0.004]; 10 mg/d: 0.76 [90% CI, 0.65–0.88; p = 0.001]; 15 mg/d: 0.67 [90% CI, 0.58–0.77; p < 0.001]; 20 mg/d: 0.62 [90% CI, 0.54–0.72; p < 0.001])	No difference in incidence of ≥30% eGFR decrease, AEs, or SAEs between PBO and finerenone
ARTS‐HF 100 (2016)	HFrEF with T2D and CKD G1‐G3, or without T2D and CKD G3 (N = 1066); 3 months	Proportion of patients with >30% decrease in plasma NT‐proBNP at day 90 from BL b : 37.2% in eplerenone group versus 30.9%, 32.5%, 37.3%, 38.8%, and 34.2% in finerenone 2.5 → 5, 5 → 10, 7.5 → 15, 10 → 20, and 15 → 20‐mg groups, respectively (p = 0.42–0.88)	Composite endpoint of all‐cause death: Lower incidence at day 90 in all finerenone groups versus eplerenone, except for 2.5 → 5 mg finerenone

Abbreviations: A1–A3, CKD classification based on albuminuria; AE, adverse event; ANGPTL2, angiopoietin‐related protein 2; APOL1, apolipoprotein‐L1; ARTS, MinerAlocorticoid Receptor Antagonist Tolerability Study; ARTS‐DN, MinerAlocorticoid Receptor Antagonist Tolerability Study‐Diabetic Nephropathy; ARTS‐HF, MinerAlocorticoid Receptor Antagonist Tolerability Study‐Heart Failure; BL, baseline; BNP, B‐type natriuretic peptide; CD69, cluster of differentiation 69; CI, confidence interval; CKD, chronic kidney disease; CKMT1A/B, B‐type natriuretic peptides, and U(biquitous)‐type mitochondrial creatine kinase; cTnT, cardiac troponin T; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; FIDELIO‐DKD, Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease; FIDELITY, Combined FIDELIO‐DKD and FIGARO‐DKD Trial programme analYsis; FIDELITY‐BM, biomarker substudy to FIDELITY; FIGARO‐BM, biomarker substudy to FIGARO‐DKD; FIGARO‐DKD, Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease; G1–G5, CKD classification based on eGFR; HFrEF, heart failure with reduced ejection fraction; HR, hazard ratio; IQR, interquartile range; LSM, least squares mean; NFT3, neurotrophin‐3; NPY, neuropeptide Y; NT‐proBNP, N‐terminal pro b‐type natriuretic peptide; PBO, placebo; QD, daily; RAB6A, Ras‐related protein Rab‐6A; SAE, serious adverse event; SPINT2, serine peptidase inhibitor, Kunitz type 2; T2D, type 2 diabetes; UACR, urine albumin‐to‐creatinine ratio.

^a At 4 months.

^b Study primary endpoint.

7. BIOMARKERS IN CLINICAL PRACTICE AND GUIDANCE FOR FUTURE RESEARCH

The evaluation of eGFR and albuminuria to assess disease trajectory and treatment impact is a standard practice in DKD management. At present, these are major biomarkers used for aiding in diagnosis and individualised treatment decisions in patients with DKD. While this approach is consistent with current guidelines, eGFR and albuminuria alone are insufficient to inform our understanding of pathobiological mechanisms underpinning DKD and CVD in an individual patient.

Emerging and novel DKD biomarkers identified through Omics technologies hold promise for defining disease mechanisms and potential therapeutic targets. However, the current data are not sufficiently mature to translate to clinical practice. Clinical applicability of novel mechanistic biomarkers remains uncertain, as they have not been rigorously validated in large, diverse populations. Lack of standardisation in biomarker testing methods, additional resources needed for biomarker testing, and complexity in interpreting biomarker results in individuals with multiple co‐morbidities have further impeded their adoption in routine clinical practice.

Future research should focus on the validation and replication of existing biomarker panels in diverse cohorts. The incorporation of biomarkers into studies during drug development processes and clinical trials is needed to evaluate how specific agents affect biomarker levels and whether the changes in biomarkers correlate with long‐term kidney and CVD outcomes. Refinement of statistical methods is also essential to develop robust biomarker models and to improve their discrimination and calibration. In addition, cost–benefit analyses are needed to understand how new biomarkers add value above the current standard of care.

In the future, these improvements in biomarker research may herald the way for precision medicine in DKD. Novel biomarkers reflecting tubular injury (e.g., KIM‐1, NGAL), extra‐cellular matrix damage (e.g., CKD273), inflammation or fibrosis (e.g., TNFR1/2, GDF‐15), mineralocorticoid pathway activation (e.g., urinary aldosterone metabolites, osteopontin, fibronectin), metabolic stress (e.g., advanced glycation end‐products), or intraglomerular hypertension (e.g., podocyte stress biomarkers) may help identify patients who are more likely to benefit from targeting specific mechanisms of DKD pathogenesis. For example, patients with elevated markers of intraglomerular hypertension may derive disproportionate benefit from RAS‐blockade or SGLT2i; individuals with elevated biomarkers of inflammation or fibrosis may respond more favourably to finerenone; and those with severe metabolic stress signatures may benefit most from GLP‐1 RA. By aligning each drug's mechanism with an individual's biologic disease drivers, a biomarker‐guided strategy may improve therapeutic selection (i.e., which drug to start first), reduce unnecessary exposure, and maximise kidney and cardiometabolic protection at the level of the individual patient.

8. CONCLUSION

DKD has a complex pathobiology involving the dysregulation of hemodynamic, metabolic, and immune systems and the promotion of inflammatory and fibrotic signalling pathways that contribute to progressive kidney damage and CVD. While eGFR and albuminuria are well‐accepted biomarkers for risk stratification and therapeutic monitoring in DKD, they are not helpful in delineating underlying pathological mechanisms and lack sensitivity in identifying early histologic changes. Novel and emerging biomarkers hold promise for early disease identification, risk stratification, and individualised therapeutic targets.

Finerenone, a nonsteroidal MRA, targets multiple DKD pathobiological pathways and has been shown in clinical trials to reduce the risk for major kidney and CVD endpoints in patients with diabetes. Finerenone favourably affects standard kidney biomarkers like albuminuria and eGFR slope and improves markers of inflammation, fibrosis, and cardiac stress. Recent data suggest that CKD‐associated CVD risk is modifiable with finerenone, and changes in albuminuria mediate a considerable portion of finerenone's effect on CKD progression and CVD. ⁸⁷ , ⁹⁴ In the future, advancements in biomarker discovery and research are expected to provide an opportunity for early diagnosis, better risk stratification, real‐time monitoring of treatment effectiveness, and personalised treatment plans based on biomarker signatures for patients with DKD.

AUTHOR CONTRIBUTIONS

Ashish Verma: Conceptualisation, writing—original draft, writing—review and editing. Ashish Upadhyay: Conceptualisation, writing—original draft, writing—review and editing.

FUNDING INFORMATION

This review was supported by Bayer Corporation. The authors wrote the paper independently with the assistance of a medical writer, who was funded by the sponsor. The sponsor is also the manufacturer of finerenone.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

The authors have nothing to report.