Introduction: The Clinical Need for Serum Fibrosis Markers in Diabetic Kidney Disease

Diabetic kidney disease (DKD) remains one of the most common and severe complications of diabetes mellitus, affecting approximately 20 to 40 percent of patients with diabetes and serving as the leading cause of end-stage renal disease (ESRD) worldwide. The global burden of DKD continues to rise, driven by the increasing prevalence of type 2 diabetes and improved survival of patients with diabetes. The hallmark pathological feature of DKD is progressive kidney fibrosis, which correlates directly with declining renal function and adverse outcomes. Early detection and accurate staging of fibrosis are therefore essential for timely intervention, yet current clinical tools—albuminuria and estimated glomerular filtration rate (eGFR)—are relatively insensitive to early fibrotic changes and cannot distinguish active fibrogenesis from established scar. Serum fibrosis markers have emerged as promising noninvasive tools for assessing the extent of kidney scarring, monitoring disease progression, and guiding therapeutic decisions. This article reviews the current state of serum fibrosis biomarkers in DKD, focusing on key markers, their clinical relevance, limitations, and future directions, while emphasizing their potential to transform clinical practice when integrated with traditional risk assessment.

Pathophysiology of Kidney Fibrosis in Diabetic Kidney Disease

Kidney fibrosis is the final common pathway of virtually all chronic kidney diseases, including DKD. In diabetes, persistent hyperglycemia, along with hemodynamic changes such as intraglomerular hypertension and activation of the renin-angiotensin-aldosterone system (RAAS), drives multiple profibrotic pathways. Transforming growth factor-beta (TGF-β) signaling is the central mediator, but other pathways—including advanced glycation end product (AGE) accumulation, oxidative stress, and inflammation—also contribute. Hyperglycemia and mechanical stretch stimulate the production of cytokines and growth factors that promote myofibroblast activation. Inflammatory cells, particularly macrophages, infiltrate the kidney parenchyma and release profibrotic mediators such as interleukin-6, tumor necrosis factor-alpha, and monocyte chemoattractant protein-1, further amplifying the fibrotic cascade.

Activated myofibroblasts—derived from resident fibroblasts, pericytes, and via epithelial-to-mesenchymal transition (EMT) and endothelial-to-mesenchymal transition (EndoMT)—produce excessive extracellular matrix (ECM) components such as collagen types I, III, and IV, fibronectin, and laminin. Simultaneously, degradation of ECM is impaired due to reduced activity of matrix metalloproteinases (MMPs) and increased levels of tissue inhibitors of metalloproteinases (TIMPs). The net result is progressive accumulation of fibrotic tissue, leading to glomerulosclerosis, tubulointerstitial fibrosis, and ultimately loss of renal function. The severity of tubulointerstitial fibrosis is one of the strongest histological predictors of DKD progression. Given that kidney biopsy remains invasive, costly, and not suitable for routine monitoring or repeated assessments, there is a pressing need for blood-based biomarkers that reflect the fibrotic process. Serum fibrosis markers offer a window into this ongoing pathological remodeling and could serve as surrogates for histological changes.

Key Serum Fibrosis Markers in DKD

Transforming Growth Factor-Beta (TGF-β)

TGF-β is considered the master regulator of fibrosis. In the kidney, the TGF-β1 isoform is primarily profibrotic. It stimulates ECM synthesis, inhibits ECM degradation, and induces myofibroblast differentiation through SMAD-dependent and SMAD-independent pathways. Elevated serum TGF-β1 levels have been consistently reported in patients with DKD compared to healthy controls, and these levels correlate with the degree of proteinuria and decline in eGFR. For example, a study of 120 patients with type 2 diabetes found that serum TGF-β1 was significantly higher in those with macroalbuminuria than in those with microalbuminuria or normoalbuminuria, and it independently predicted eGFR decline over three years. However, TGF-β is not specific to kidney fibrosis; it is elevated in many systemic conditions—including liver fibrosis, pulmonary fibrosis, and cancer—and its measurement can be confounded by platelet activation during blood collection, as platelets store large amounts of TGF-β. Despite these drawbacks, TGF-β remains a valuable marker when interpreted alongside other clinical parameters and when measured using standardized protocols that minimize platelet contamination.

Connective Tissue Growth Factor (CTGF)

CTGF (also known as CCN2) acts downstream of TGF-β and mediates many of its profibrotic effects. It promotes fibroblast proliferation, ECM production, and cell adhesion, and it also modulates angiogenesis and inflammation. In patients with DKD, serum CTGF levels are significantly higher than in diabetic patients without nephropathy or healthy controls, and they correlate with histologic fibrosis severity. Longitudinal studies show that rising CTGF levels predict progression from microalbuminuria to macroalbuminuria and decline in eGFR, independent of baseline kidney function and albuminuria. CTGF is more specific to fibrosis than TGF-β and is less affected by blood collection artifacts, making it a promising candidate for clinical monitoring. Furthermore, CTGF levels decrease in response to RAAS blockade and SGLT2 inhibitor therapy, suggesting utility as a pharmacodynamic biomarker.

Matrix Metalloproteinases and Tissue Inhibitors

MMPs and TIMPs are key regulators of ECM turnover. In DKD, an imbalance between MMPs and TIMPs contributes to ECM accumulation. Serum levels of MMP-2 and MMP-9 have been studied extensively, but results are conflicting. Some studies show elevated MMP-2 in early DKD, while others report decreased MMP-9 activity, possibly due to posttranslational modifications or complex formation with TIMPs. More consistent is the elevation of TIMP-1 and TIMP-2. Elevated TIMP-1 levels correlate with tubulointerstitial fibrosis severity on biopsy and predict progression to ESRD. The MMP-9/TIMP-1 ratio has been proposed as a dynamic index of ECM remodeling, with a lower ratio indicating a profibrotic state. However, standardization of assays and careful interpretation in the setting of systemic inflammation are required, as inflammatory conditions can also alter MMP/TIMP balance. Novel assays measuring specific MMP-TIMP complexes may improve specificity.

Galectin-3

Galectin-3 is a beta-galactoside-binding lectin involved in fibrosis, inflammation, and cell adhesion. It is expressed by macrophages and renal epithelial cells and promotes myofibroblast activation and ECM deposition via interaction with TGF-β receptors. Serum galectin-3 levels are elevated in patients with DKD and have been associated with lower eGFR, higher proteinuria, and increased risk of ESRD and cardiovascular mortality. In the CRIC (Chronic Renal Insufficiency Cohort) study, galectin-3 improved risk prediction for ESRD beyond traditional risk factors. However, galectin-3 is also a marker of heart failure and systemic inflammation, so its kidney specificity is limited. Nevertheless, it may provide added prognostic value when combined with other biomarkers and clinical data, especially given its strong association with cardiovascular outcomes in DKD.

Fibroblast Growth Factor-23 (FGF-23)

FGF-23 is primarily known as a phosphate-regulating hormone, but it has emerged as a biomarker of kidney fibrosis independent of its effects on mineral metabolism. In chronic kidney disease, FGF-23 levels rise early and strongly predict progression and mortality. In DKD, elevated FGF-23 is associated with tubulointerstitial fibrosis severity on biopsy, independent of serum phosphate. The mechanism may involve direct profibrotic effects on renal tubular cells via FGF receptor activation and downstream signaling through mitogen-activated protein kinase pathways. FGF-23 measurement is routinely available in many reference laboratories, and its clinical utility is supported by a large body of evidence from observational studies and clinical trials. For instance, in the FIDELIO-DKD trial of finerenone, baseline FGF-23 levels independently predicted kidney outcomes, and finerenone reduced FGF-23 levels, suggesting a renoprotective mechanism.

Collagen Turnover Products

Fragments of collagen generated during ECM synthesis or degradation can be measured in serum as markers of fibrogenesis and fibrolysis. Examples include the N-terminal propeptide of type III procollagen (PIIINP), the C-terminal telopeptide of type I collagen (CTX-I), and the neo-epitope C3M from type III collagen cleavage. PIIINP has been linked to the severity of tubulointerstitial fibrosis in DKD and predicts CKD progression. A multimarker panel including several collagen-derived peptides (such as PRO-C3, PRO-C6, and C3M) may offer a composite picture of fibrosis dynamics. These markers are still under investigation, but they hold promise for noninvasive assessment of active fibrogenesis. Their advantage is that they directly reflect ECM turnover, which is the core pathological process. However, confounders such as liver fibrosis, bone remodeling, and wound healing must be considered.

Other Emerging Markers

Additional candidates include angiopoietin-2, vascular endothelial growth factor (VEGF), osteopontin, and soluble urokinase plasminogen activator receptor (suPAR). These molecules participate in the complex interplay between inflammation, angiogenesis, and fibrosis. For example, suPAR is elevated in DKD and promotes podocyte injury and proteinuria. Multi-omics approaches have identified circulating microRNAs (e.g., miR-21, miR-192, and miR-29) that regulate fibrotic pathways and may serve as biomarkers. miR-21, in particular, is upregulated in fibrotic kidneys and can be measured in serum exosomes; its levels correlate with fibrosis severity in DKD. While not yet ready for routine clinical use, these emerging markers highlight the evolving landscape of DKD biomarker research. The most promising approach may be to combine multiple markers into a composite score that captures different aspects of the fibrotic process.

Clinical Utility of Serum Fibrosis Markers

Early Detection and Risk Stratification

Current screening for DKD relies on albuminuria and eGFR, but these tests are insensitive for detecting early fibrotic changes. Serum fibrosis markers may identify patients at risk before significant albuminuria appears. For instance, elevated CTGF or galectin-3 in normoalbuminuric patients predicts future progression to microalbuminuria and eGFR decline. In a cohort of patients with type 1 diabetes, high serum levels of TIMP-1 and galectin-3 at baseline were associated with a threefold increased risk of developing macroalbuminuria over 10 years, independent of HbA1c and blood pressure. Incorporating such markers into routine screening could enable earlier intervention with RAAS blockade or newer therapies like SGLT2 inhibitors and GLP-1 receptor agonists, which have demonstrated antifibrotic properties in clinical trials. The challenge is to determine the optimal timing and frequency of biomarker measurement to maximize cost-effectiveness.

Monitoring Disease Progression

Serial measurements of fibrosis markers can track the evolution of kidney damage. In clinical trials, reductions in TGF-β, CTGF, or TIMP-1 levels correlate with slower eGFR decline. Conversely, rising levels signal progression and may prompt intensification of therapy. Using biomarkers as surrogate endpoints could accelerate drug development by providing early evidence of antifibrotic efficacy. For example, in a phase II trial of the antifibrotic agent pirfenidone in DKD, changes in serum procollagen type III N-terminal peptide (PIIINP) at 6 months predicted eGFR changes at 12 months. Such biomarker-based endpoints could reduce trial duration and sample size, enabling more efficient development of novel therapies.

Predicting End-Stage Renal Disease

The ultimate goal of biomarker research is to identify patients at high risk of ESRD. Several studies have shown that elevated serum fibrosis markers, alone or in combination with traditional risk factors, improve prediction of ESRD. For example, a panel including FGF-23, galectin-3, and CTGF added to clinical models outperformed eGFR and albuminuria alone in predicting kidney failure over five years in a cohort of patients with DKD. More recently, the CKD273 proteomic classifier, which includes multiple fibrosis-related peptides, has demonstrated ability to predict progression to macroalbuminuria and eGFR decline years in advance. The integration of such multimarker panels into risk scores could allow for more precise identification of patients who would benefit from early aggressive therapy or enrollment in clinical trials.

Evaluating Response to Therapy

As antifibrotic drugs enter clinical development, biomarkers are needed to monitor their pharmacodynamic effects. Serum markers that decrease in response to treatment could help personalize dosing and duration. In studies of finerenone, a nonsteroidal mineralocorticoid receptor antagonist with antifibrotic properties, reductions in serum levels of inflammation markers like hsCRP and fibrosis markers like FGF-23 were observed. Similarly, SGLT2 inhibitors (e.g., dapagliflozin, empagliflozin) have been shown to reduce levels of CTGF and TIMP-1. The ability to measure early changes in fibrosis markers could guide clinical decision-making—for instance, switching to combination therapy if a patient’s biomarker levels remain elevated despite initial treatment. This biomarker-guided approach is still investigational but holds promise for personalized DKD management.

Limitations and Challenges

Despite their potential, serum fibrosis markers face several hurdles before widespread clinical adoption. Lack of assay standardization is a major issue; results vary across laboratories and platforms, complicating the establishment of universal cutoffs and reference ranges. Many markers are not specific to kidney fibrosis—they are influenced by systemic inflammation, liver disease, heart failure, and cancer. Single markers often have modest sensitivity and specificity, so panels or integrated scores may be necessary to achieve clinically useful diagnostic accuracy.

Other challenges include the need for validation in diverse populations (different ethnicities, stages of CKD, and diabetes types), understanding the impact of concurrent medications (especially RAAS blockers, which can lower some marker levels), and determining optimal sampling timing relative to disease progression and time of day (some markers have diurnal variation). Additionally, the dynamic nature of fibrosis—with phases of active deposition and quiescent scar turnover—means that marker levels may vary over time in ways not yet fully characterized. Serial measurements may be more informative than single time points, but this increases cost and complexity. Finally, regulatory qualification of biomarkers for specific contexts of use (e.g., surrogate endpoints in clinical trials) requires rigorous evidence, which is still being gathered.

Future Directions and Emerging Technologies

Proteomics and Metabolomics

Advances in proteomics and metabolomics are expanding the biomarker toolkit. Proteomic classifiers based on urine and serum peptides (e.g., CKD273) have shown ability to predict DKD progression years in advance. These classifiers incorporate fibrosis-related fragments such as collagen alpha-1 chain fragments and have been validated in multiple cohorts. The next step is integration into clinical decision support systems. Metabolomic profiling has identified altered levels of metabolites like tryptophan, kynurenine, and certain acylcarnitines that correlate with fibrosis severity. Combining proteomic and metabolomic signatures may yield even more robust panels.

MicroRNAs and Noncoding RNAs

MicroRNAs involved in TGF-β signaling, such as miR-21, miR-192, and miR-29, are being explored as circulating biomarkers. miR-21 is upregulated in fibrotic kidneys and can be measured in serum exosomes. Its levels correlate with fibrosis severity and may predict response to therapy. Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are another emerging class. Their stability in blood and tissue-specific expression patterns make them attractive biomarker candidates, though research in DKD is still preclinical. Exosome-based isolation of these RNAs may enhance specificity.

Artificial Intelligence and Multimarker Models

Artificial intelligence (AI) and machine learning models that integrate multiple biomarker inputs with clinical data offer the potential for highly accurate risk stratification. Such models could incorporate traditional markers (eGFR, albuminuria), serum fibrosis markers (TGF-β, CTGF, FGF-23, galectin-3), demographic variables, and even imaging data to generate individualized predictions. A recent study used random forest models to identify the most important fibrosis markers for predicting eGFR decline and found that a combination of CTGF, TIMP-1, and galectin-3 performed best. AI could also help identify novel biomarker combinations and interactions that traditional regression models might miss.

Emerging Therapies and Biomarker Validation

Ongoing clinical trials testing novel antifibrotic agents—such as finerenone, bardoxolone methyl, and baricitinib (a JAK inhibitor)—are measuring serum fibrosis markers as secondary endpoints, which will generate further validation data. The clinicaltrials.gov registry lists dozens of studies evaluating fibrosis biomarkers in DKD. The FDA Biomarker Qualification Program has also recognized the potential of certain fibrosis markers, and qualified biomarkers could accelerate regulatory approval of new therapies.

Integrating Serum Fibrosis Markers into Clinical Practice

For serum fibrosis markers to become routine, several steps are needed. First, large-scale prospective studies must validate the clinical utility of specific marker panels in diverse populations. The National Kidney Foundation and the International Society of Nephrology have called for biomarker validation using standardized protocols. Second, commercial assays must be automated, cost-effective, and widely available to allow routine use. Third, clinical decision support tools should be developed to help interpret biomarker results alongside eGFR and albuminuria, providing actionable recommendations. Some markers, such as FGF-23, are already available through major reference laboratories and are used in nephrology practice for assessing mineral bone disorder in CKD. Expanding their use for fibrosis assessment requires evidence that they modify clinical decision-making and improve patient outcomes.

A PubMed search for "diabetic kidney disease fibrosis biomarkers" returns over 8,000 publications, highlighting intense research interest. The KDIGO 2020 guidelines for diabetes management in CKD recommend considering novel biomarkers for risk stratification where available, though they stop short of endorsing specific markers due to insufficient evidence. As data accumulate, it is likely that serum fibrosis markers will be incorporated into future guideline recommendations, particularly for identifying patients at high risk who may benefit from newer antifibrotic therapies.

Conclusion

Serum fibrosis markers represent a valuable addition to the clinical armamentarium for diabetic kidney disease. By providing a noninvasive window into renal fibrogenesis, markers such as TGF-β, CTGF, TIMP-1, galectin-3, and FGF-23 can enhance early detection, improve risk stratification, and monitor disease progression and treatment response. While limitations in specificity and standardization remain, the field is moving toward multimarker panels and integrated risk scores that combine multiple biomarkers with clinical data. With continued research, technological refinement, and regulatory qualification, serum fibrosis biomarkers are poised to transform the management of DKD, enabling more personalized and timely interventions that slow or halt the progression to kidney failure. The integration of these markers into routine clinical care will ultimately depend on demonstrated improvements in patient outcomes and cost-effectiveness, but the foundation is being laid through ongoing clinical trials and collaborative research efforts.