Retrospective Cohort Study Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Jun 15, 2024; 15(6): 1212-1225
Published online Jun 15, 2024. doi: 10.4239/wjd.v15.i6.1212
Evaluating new biomarkers for diabetic nephropathy: Role of α2-macroglobulin, podocalyxin, α-L-fucosidase, retinol-binding protein-4, and cystatin C
Jing-Jing Li, Department of Infectious Diseases, Inner Mongolia Medical University, Hohhot First Hospital, Hohhot 010000, Inner Mongolia Autonomous Region, China
Ru-La Sa, Zhao-Li Yan, Department of Endocrinology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010000, Inner Mongolia Autonomous Region, China
Yu Zhang, Department of Dermatology, Inner Mongolia Autonomous Region People’s Hospital, Hohhot 010000, Inner Mongolia Autonomous Region, China
ORCID number: Zhao-Li Yan (0009-0009-4202-3706).
Author contributions: Li JJ and Sa RL designed the research; Li JJ, Zhang Y, and Yan ZL performed the research; Zhang Y and Yan ZL contributed new reagents/analytic tools; Li JJ and Sa RL analyzed the data; Li JJ, Sa RL, and Zhang Y wrote the paper.
Supported by the Natural Science Foundation of Inner Mongolia Autonomous Region, No. 2022MS08057.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University (Approval No. WZ2024004).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The author declares no conflict of interest.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Zhao-Li Yan, FAASLD, Chief Doctor, Research Fellow, Department of Endocrinology, The Affiliated Hospital of Inner Mongolia Medical University, No. 1 Tongdao North Road, Hohhot 010000, Inner Mongolia Autonomous Region, China.
Received: January 7, 2024
Revised: February 27, 2024
Accepted: April 30, 2024
Published online: June 15, 2024
Processing time: 154 Days and 5.4 Hours


The intricate relationship between type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN) presents a challenge in understanding the significance of various biomarkers in diagnosis.


To elucidate the roles and diagnostic values of α2-macroglobulin (α2-MG), podocalyxin (PCX), α-L-fucosidase (AFU), retinol-binding protein-4 (RBP-4), and cystatin C (CysC) in DN.


From December 2018 to December 2020, 203 T2DM patients were enrolled in the study. Of these, 115 were diagnosed with DN (115 patients), while the remaining 88 patients were classified as non-DN. The urinary levels of α2-MG, PCX, and AFU and the serum concentrations RBP-4 and CysC were measured in conjunction with other relevant clinical indicators to evaluate their potential correlations and diagnostic utility.


After adjustments for age and gender, significant positive correlations were observed between the biomarkers CysC, RBP-4, α2-MG/urinary creatinine (UCr), PCX/UCr, and AFU/UCr, and clinical indicators such as urinary albumin-to-creatinine ratio (UACR), serum creatinine, urea, 24-h total urine protein, and neutrophil-to-lymphocyte ratio (NLR). Conversely, these biomarkers exhibited negative correlations with the estimated glomerular filtration rate (P < 0.05). Receiver operating characteristic (ROC) curve analysis further demonstrated the diagnostic performance of these biomarkers, with UACR showcasing the highest area under the ROC curve (AUCROC) at 0.97.


This study underscores the diagnostic significance of α2-MG, PCX, and AFU in the development of DN. The biomarkers RBP-4, CysC, PCX, AFU, and α2-MG provide promising diagnostic insights, while UACR is the most potent diagnostic biomarker in assessing DN.

Key Words: α2-macroglobulin, Podocalysin, α-L-fucosidase, Retinol binding protein-4, Cystatin C, Diabetic nephropathy

Core Tip: This study elucidates the diagnostic value of α2-macroglobulin (α2-MG), podocalyxin (PCX), α-L-fucosidase (AFU), retinol-binding protein-4 (RBP-4), and cystatin C (CysC) in type 2 diabetes mellitus and diabetic nephropathy (DN). It reveals that these biomarkers, especially urinary albumin to creatinine ratio (UACR), are strongly correlated with renal damage indicators. The research demonstrates the superior diagnostic capability of UACR for DN, while highlighting the importance of α2-MG, PCX, AFU, RBP-4, and CysC as complementary diagnostic tools. These findings provide valuable insights into the mechanisms of DN and enhance diagnostic accuracy in clinical practice.


Diabetes mellitus (DM) is a widespread metabolic disorder characterized by defects in insulin secretion, insulin action, or both, leading to hyperglycemia[1]. The rising incidence of type 2 diabetes mellitus (T2DM) positions it as a formidable challenge to global public health. Currently, T2DM affects approximately 6.4% of the population worldwide, with projections indicating that this number will increase to over 693 million by 2045[2]. This escalation diminishes the quality of life for affected individuals and increases their risk for several complications. Among these, diabetic nephropathy (DN) stands out as a major microvascular complication, significantly contributing to morbidity among patients with T2DM[3,4].

For the understanding and effective diagnosis of DN, biomarkers such as α-2-macroglobulin (α2-MG), podocalyxin (PCX), and α-L-fucosidase (AFU) have gained prominence. Notably, α2-MG, a noteworthy plasma proteinase inhibitor, remains undetectable in urine during the early stages of DN. This condition is primarily attributed to its large molecular weight, which restricts its passage through the glomerular filtration membrane[5,6]. Conversely, PCX, which is essential to the structural integrity of the podocyte glycocalyx, plays a vital role in maintaining podocyte morphology and the functionality of the slit diaphragm[7,8]. Elevated urinary levels of PCX are indicative of podocyte damage. Additionally, AFU, a lysosomal glycosidase predominantly located in renal tubular epithelial cells, exhibits increased urinary concentrations in DN, reflecting damage to these cells[9].

While microalbuminuria has traditionally been the cornerstone for DN diagnosis, its efficacy in detecting early DN and monitoring progression has been questioned. Evidence suggests that some diabetic individuals exhibit a marked estimated glomerular filtration rate (eGFR) decline and glomerular lesions despite normal urinary albumin excretion rates. This finding challenges the reliance solely on microalbuminuria for DN diagnosis[10,11]. Consequently, the search for more sensitive and specific biomarkers has intensified, aiming to provide a more comprehensive diagnostic framework.

This study, therefore, aims to evaluate a spectrum of clinical indicators, including α2-MG, PCX, AFU, retinol binding protein-4 (RBP-4), and Cystatin C (CysC), in the urine of T2DM patients with and without DN. By examining these biomarkers, we aspire to refine the diagnostic accuracy of DN, thereby offering a nuanced approach to early detection and management. This endeavor is not only of scientific merit but also holds substantial clinical value, potentially improving patient outcomes and mitigating the burden of T2DM complications.

Patient enrollment and study design

This research was conducted as a retrospective cohort study at the Inpatient Department of The Affiliated Hospital of Inner Mongolia Medical University between December 2018 and December 2020. Patients admitted during this period were screened, and those diagnosed with T2DM were selected for further analysis. Of these, 88 patients were identified with non-DN (NDN) and 115 with DN. Prior to their inclusion, participants were informed of the study objectives and protocols, and consent was obtained. The study design and protocols received approval from the Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University. Data were collected using a structured questionnaire that gathered essential clinical and demographic information from the participants.

Inclusion criteria

Participants were eligible for the study if they met the diagnostic criteria for T2DM, as outlined in Table 1. The diagnosis of DN was determined based on either a reduction in glomerular filtration rate (GFR) or an elevated urinary albumin to creatinine ratio (UACR). Specifically, DN was identified when the GFR decreased to an eGFR below 60 mL·min-1·1.73 m-2, or when the UACR exceeded 30 mg/g in two out of three repeated examinations conducted 3-6 months, excluding other factors such as infections[12].

Table 1 Diagnostic criteria for diabetes (World Health Organization, 1999).
Diagnostic criteria
Venous plasma glucose levels (mmol/L)
Diabetic symptoms and random blood glucose≥ 11.1 mmol/L
Fasting plasma glucose≥ 7.0 mmol/L
Plasma glucose 2 h after oral glucose tolerance test≥ 11.1 mmol/L
Exclusion criteria

Individuals were excluded from the study if they exhibited a rapid decrease in eGFR, a marked increase in UACR, or presented with nephrotic syndrome. Additionally, those who demonstrated a reduction in eGFR exceeding 30% within 2-3 months following treatment with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers were also omitted from the study. Exclusion criteria were further extended to severe liver function abnormalities, indicated by levels of glutamic-pyruvic transaminase or glutamic-oxalacetic transaminase rising above three times the upper normal limit, or gamma-glutamyltransferase levels exceeding five times the upper normal limit. Moreover, the presence of active urinary sediment, characterized by the detection of red blood cells, white blood cells, or cellular casts, the existence of renal diseases not related to DN, severe cardiac and cerebrovascular conditions, a medical history of malignancies and autoimmune disorders, and acute conditions such as diabetic ketoacidosis, diabetic hyperosmolar coma, severe infections, or other states of physiological stress, were also considered valid reasons for exclusion from the research.

Biochemical index assessment

In the morning, 3 mL of fresh fasting peripheral blood was drawn from each participant. The specimen was subjected to centrifugation at 3000 rpm for 15 min. Concurrently, a 5 mL specimen of the initial clean morning urine (midstream collection) was obtained and subjected to centrifugation at 1500 rpm for 10 min. Following centrifugation, the supernatants from both samples were collected and stored at -80 °C for subsequent analysis.

The investigation involved a detailed evaluation of several biochemical parameters, including hypersensitive C-reactive protein (hs-CRP), lymphocytes, neutrophils, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, total cholesterol, triglyceride, fasting insulin, fasting plasma glucose (FPG), urea nitrogen (urea), glycosylated hemoglobin, urinary microalbuminuria, uric acid, serum creatinine (Scr), 24-h total urine protein (UTP/24 h), and urinary creatinine (UCr). The concentrations of RBP-4 and CysC were measured using immunoturbidimetric methods. Furthermore, the quantification of α2-MG, PCX, and AFU concentrations was carried out using ELISA kits provided by CUSABIO BIOTECH CO, Ltd. (China).

U- denoted the protein content in urine samples. To mitigate the influence of urine concentration variability on the test outcomes, the levels of urinary α2-MG, PCX, and AFU were normalized by dividing the concentrations of these biomarkers by the corresponding UCr values.

Calculation indicators

Insulin resistance, indicated by homeostasis model assessment of insulin resistance (HOMA2-IR), was determined using the HOMA Insulin Resistance Index Calculator v2.2.3. Inputs for FPG and fasting insulin were processed through the software available at ( The NLR was established by dividing the total number of neutrophils by the number of lymphocytes. The eGFR calculation was performed using the CKD-EPI formula[13], which is widely recognized for its accuracy in estimating renal function. The body mass index (BMI) of participants was calculated using the standard formula: BMI (kg/m2) = Weight (kg)/height (m2).

Statistical analysis

Statistical analyses were conducted using SPSS software (version 20.0). The normality of continuous variables was assessed using the single-sample Kolmogorov-Smirnov (K-S) test. Variables adhering to a normal distribution were depicted as mean ± SD. The median and interquartile range were reported for data that did not follow a normal distribution, denoted as P50 (25th percentile, 75th percentile). Variance consistency across samples was verified using Levene’s test.

A two-independent samples T-test was employed for datasets exhibiting normal distribution and homogeneity of variances. Variables that did not meet these criteria were analyzed with the Mann-Whitney U rank-sum test. Categorical data were represented as frequencies and percentages n (%) and analyzed using the chi-square test.

A partial correlation analysis, adjusted for sex and age, was used to explore relationships between serum and urine biomarkers and other clinical measures. The diagnostic capabilities of each indicator for DN were assessed using receiver operating characteristic (ROC) curve analysis. Statistical significance was attributed to P < 0.05.

Biochemical and clinical differences between DN and NDN groups

We conducted an extensive biostatistical analysis to explore the differences between patients with DN and those without DN (NDN), as detailed in Table 2. The gender distribution was roughly similar across both groups; however, the median age of patients in the DN group was significantly higher, suggesting a potential age-related onset of DN (P = 0.021). Moreover, the incidence of hypertension was significantly elevated in the DN group, further emphasizing a potential link between hypertension and diabetic renal disease (P < 0.001).

Table 2 Comparison of clinical and laboratory parameters between diabetic nephropathy and non-diabetic nephropathy patients.
NDN (n = 88)
DN (n = 115)
F/Z value
P value
Age, yr57.03 ± 10.3460.57 ± 11.05a0.6800.021
Sex, male, n (%)58 (65.90)75 (65.20)0.0110.918
BMI, kg/m225.69 ± 2.6726.35 ± 3.727.6950.192
Hypertension, present, n (%)41 (46.60)97 (84.30)a32.650< 0.001
Course, yr8 (3.75, 15.00)12 (8.00, 20.00)a3.666< 0.001
FPG, mmol/L6.80 (5.60, 8.10)6.80 (5.68, 8.70)0.4690.639
HbA1c, %8.82 ± 1.778.70 ± 2.396.6760.681
NLR1.72 (1.34, 2.14)2.19 (1.70, 3.19)a4.547< 0.001
hs-CRP, mg/L1.55 (0.72, 3.37)2.60 (0.95, 7.20)a2.2930.022
TC, mmol/L4.35 ± 1.034.71 ± 1.53a8.0090.045
TG, mmol/L1.64 (1.08, 2.26)1.65 (1.12, 2.66)1.0640.287
LDL-C, mmol/L2.53 ± 0.772.61 ± 0.963.1810.544
HDL-C, mmol/L0.99 ± 0.200.96 ± 0.231.2280.265
Urea, mmol/L5.50 (4.63, 6.30)7.60 (5.80, 11.93)a6.726< 0.001
CysC, mg/L0.53 (0.45, 0.72)1.18 (0.65, 1.99)a7.419< 0.001
RBP-4, mg/L39.99 (30.69, 46.30)53.20 (41.00, 73.00)a6.284< 0.001
Scr, μmol/L68.00 (59.00, 79.75)105.00 (70.00, 199.00)a6.525< 0.001
eGFR, mL·min-1·1.73 m-295.99 (90.11, 104.82)57.01 (27.22, 93.84)a6.561< 0.001
UACR, mg/g6.30 (3.70, 15.45)446.20 (114.10, 1086.20)a11.542< 0.001
UTP/24 h, g/24 h0.04 (0.02, 0.08)1.31 (0.34, 4.07)a11.624< 0.001
U-α2-MG/UCr, mg/g1.20 (0.86, 2.02)5.78 (2.00, 17.31)a8.110< 0.001
U-PCX/UCr, μg/g2.91 (1.48, 5.04)12.83 (6.25, 42.27)a8.817< 0.001
U-AFU/UCr, nmol/g0.04 (0.03, 0.07)0.34 (0.05, 2.14)a7.105< 0.001

Biochemically, the levels of RBP-4, CysC, PCX/UCr, AFU/UCr, and α2-MG/UCr were markedly elevated in the DN group. These indicators are often associated with renal damage, a decrease in the GFR, and an enhanced inflammatory response (P < 0.05). Notably, CysC and RBP-4 have been recognized as sensitive biomarkers for early renal function impairment in recent years. Traditional renal function indicators, such as urea, Scr, eGFR, UACR, and UTP/24 h, also demonstrated significant disparities in the DN group compared to the NDN group, further affirming the compromised renal functions in patients with DN.

Therefore, these findings validate the pronounced differences in multiple biochemical and clinical indicators between DN and NDN and provide an experimental basis for further in-depth studies into the pathophysiological mechanisms of DN.

Analysis of the correlations among key biochemical biomarkers in DN

With adjustments for demographic factors (gender and age), several significant positive correlations were observed among key biochemical biomarkers in DN (Figure 1). The UACR, a pivotal biomarker measuring urinary protein excretion, exhibited significant positive correlations with RBP-4, CysC, U-PCX/UCr, U-α2-MG/UCr, and U-AFU/UCr (Table 3). These observations suggested that increases in these biochemical biomarkers correlate with rising urinary protein levels, indicative of potential renal function impairment or structural damage.

Figure 1
Figure 1 Correlation analysis of biomarkers and clinical indicators in diabetic nephropathy. A: Correlation heatmap of selected biomarkers and clinical indicators, where color intensity reflects correlation strength; deep blue for strong positive and lighter shades for weaker correlations; B: Significance heatmap for biomarker and clinical indicator correlations, with color depth indicating P value magnitude; darker blue for lower P values and higher significance, lighter for higher P values or nonsignificance. α2-MG: α2-macroglobulin; PCX: Podocalysin; AFU: α-L-fucosidase; RBP-4: Retinol binding protein-4; CysC: Cystatin C; UACR: Urinary albumin/creatinine ratio.
Table 3 Partial correlation analysis between cystatin C, retinol-binding protein-4, U-α2- macroglobulin, U-podocalyxin, U-α-L-fucosidase, and clinical indicators.





P value
P value
P value
P value
P value
UACR0.730< 0.0010.625< 0.0010.805< 0.0010.639< 0.0010.780< 0.001
eGFR-0.816< 0.001-0.702< 0.001-0.711< 0.001-0.604< 0.001-0.742< 0.001
CysC1.000-0.693< 0.0010.710< 0.0010.645< 0.0010.692< 0.001
RBP-40.693< 0.0011.000-0.530< 0.0010.432< 0.0010.588< 0.001
U-α2-MG/UCr0.710< 0.0010.530< 0.0011.000-0.820< 0.0010.874< 0.001
U-PCX/UCr0.645< 0.0010.432< 0.0010.820< 0.0011.000-0.746< 0.001
U-AFU/UCr0.692< 0.0010.588< 0.0010.874< 0.0010.746< 0.0011.000-
Scr0.953< 0.0010.680< 0.0010.755< 0.0010.645< 0.0010.715< 0.001
Urea0.799< 0.0010.651< 0.0010.657< 0.0010.568< 0.0010.703< 0.001
UTP/24 h0.665< 0.0010.506< 0.0010.740< 0.0010.523< 0.0010.743< 0.001
NLR0.417< 0.0010.1670.0180.311< 0.0010.299< 0.0010.313< 0.001

Furthermore, eGFR, a reflection of renal function, presented significant negative correlations with all the biomarkers mentioned earlier. This finding implies that as renal function diminishes, the concentrations of these biochemical biomarkers ascend, underlining their importance in the early detection of renal injury. Scr and urea, considered traditional renal function biomarkers, displayed strong positive correlations with RBP-4, CysC, U-PCX/UCr, U-α2-MG/UCr, and U-AFU/UCr. This finding reinforced the effect of these new biochemical biomarkers on assessing renal function. While NLR and hs-CRP were recognized as inflammation biomarkers, their correlations with the previously mentioned biochemical biomarkers remained undetermined. NLR demonstrated significant positive correlations with all biomarkers, whereas hs-CRP mostly lacked statistical significance in its correlations with most biomarkers.

Hence, the specified biochemical biomarkers, especially RBP-4, CysC, U-PCX/UCr, U-α2-MG/UCr, and U-AFU/UCr, not only exhibit strong correlations with traditional renal function biomarkers like UACR, eGFR, Scr, and urea, but also underscore their significance in the early identification and assessment of renal injury.

Evaluation of the value and precision of various biomarkers in the early diagnosis of DN

In the diagnosis of chronic kidney disease, commonly adopted criteria include eGFR values less than 60 mL·min-1·1.73 m-2 or UACR values exceeding or equal to 30 mg/g. To further delineate the diagnostic value of these biomarkers for DN, we conducted a ROC curve analysis, contrasting the sensitivity, specificity, and area under the ROC curve (AUCROC) values of various indicators (Table 4). Initially, the UACR as a singular biomarker demonstrated outstanding efficacy in DN diagnosis. The optimal diagnostic threshold for UACR was identified at 29.5 mg/g, whereupon exceeding this threshold, the sensitivity and specificity for diagnosing DN were recorded at 95% and 98%. This high accuracy in confirming and excluding DN with this biomarker is indicated by its AUCROC value at 0.97 (Figure 2), approaching perfection and further attesting to its pivotal role in DN diagnosis.

Figure 2
Figure 2 Diagnostic performance of biomarkers in early diagnosis of diabetic nephropathy. A: Bar graph comparing area under the receiver operating characteristic (ROC) curve values for biomarkers in the diagnosis of diabetic nephropathy; B: ROC curve analysis for individual biomarkers; C: ROC curve analysis for combined biomarkers. ROC: Receiver operating characteristic.
Table 4 Evaluation of area under the curve and its 95% confidence interval for various indicators.
Indicators waiting to be measured
Standard error
P value
95% confidence interval
UACR0.970.01< 0.001(0.95, 1.00)
eGFR0.770.03< 0.001(0.70, 0.84)
CysC0.800.03< 0.001(0.75, 0.86)
RBP-40.760.03< 0.001(0.69, 0.82)
U-α2-MG/UCr0.830.03< 0.001(0.78, 0.89)
U-PCX/UCr0.860.03< 0.001(0.81, 0.91)
U-AFU/UCr0.790.03< 0.001(0.73, 0.85)
U-α2-MG/UCr + U-PCX/UCr0.880.03< 0.001(0.83, 0.93)
U-PCX/UCr + U-AFU/UCr0.880.03< 0.001(0.84, 0.93)
U-α2-MG/UCr + U-AFU/UCr0.860.03< 0.001(0.80, 0.91)
U-α2-MG/UCr + U-PCX/UCr + U-AFU/UCr0.890.03< 0.001(0.85, 0.94)

Subsequently, the effectiveness of a combination of multiple biomarkers, such as U-α2-MG/UCr and U-PCX/UCr, for DN diagnosis was evaluated. This combination confirmed exceptionally efficacious, with the combined sensitivity and specificity in diagnosing DN recorded at 86% and 76% (Table 5). While these figures were marginally lower than the singular UACR biomarker, their AUCROC value of 0.88 confirmed the substantial effectiveness of this combination in DN diagnosis.

Table 5 Optimal diagnostic cut-off points and their sensitivity and specificity for various indicators.
Optimal diagnostic cut-off points
Youden index
U-α2-MG/UCr + U-PCX/UCr-0.860.760.62
U-PCX/UCr + U-AFU/UCr-0.750.860.61
U-α2-MG/UCr + U-AFU/UCr-0.680.900.58
U-α2-MG/UCr + U-PCX/UCr + U-AFU/UCr-0.890.750.64

In conclusion, whether used singularly or in combination, these biomarkers exhibit profound efficacy in DN diagnosis, offering promising insights for practical clinical application.


With the rising incidence of T2DM and DN, searching for effective diagnostic biomarkers has gained critical importance[14,15]. DN is the predominant chronic microvascular complication of diabetes and a leading contributor to end-stage renal disease. Traditionally, glomerular damage and increased permeability, manifested as proteinuria, have been recognized as primary indicators of DN. However, recent studies suggest that the proteinuria biomarkers currently in use may not accurately detect this condition in some diabetic patients[10,11]. Consequently, there is a need to explore more sensitive biomarkers to assess the severity and progression of DN. This study identified a positive correlation between the concentrations of biomarkers such as α2-MG, PCX, and AFU and the progression of DN, suggesting their potential as indicative molecules of DN. Notably, the AUCROC value for UACR reached 0.88, underscoring its potential as a highly promising indicator for DN progression.

This study revealed elevated levels of urinary α2-MG in patients with DN, indicating that urinary α2-MG levels rose with the aggravation of the condition. The major plasma proteinase inhibitor, α2-MG, is predominantly synthesized by hepatocytes and cells of the mononuclear phagocyte system. It plays a crucial role in regulating protease activity, nutritional support, signal transduction, and tissue reconstruction. Moreover, α2-MG is crucial in defending against infections and external toxins, as well as in regulating cytokines, hormones, and other bioactive lipid factors, thereby influencing a wide range of physiological and pathological processes[16]. In the context of DN, studies employing chromatographic analysis have demonstrated that urinary α2-MG retains its integrity, appearing in urine as a complete protein with a molecular weight identical to that in serum[17,18]. This finding underscores the stability of α2-MG and its potential utility in clinical diagnostics. Furthermore, the presence of α2-MG in urine is indicative of glomerular damage, which may result from enhanced filtration or the translocation of blood constituents into the urine. Thus, urinary α2-MG serves as a diagnostic marker capable of differentiating the causes of proteinuria.

Our investigation revealed elevated levels of PCX in the urine of patients with DN. Although urinary PCX does not surpass the urinary albumin to creatinine ratio (UACR) index in terms of diagnostic superiority, its value in identifying DN is more advantageous than many other biomarkers. PCX, a sialomucin associated with the CD34 family, is localized primarily in mesenchymal cells, the apical membrane area of podocytes, vascular endothelial cells, and platelets[19-21]. The surface charge of PCX is critical for maintaining the structure of the slit diaphragm by providing anti-adhesion properties. This function is essential for preventing the passage of negatively charged proteins through the slit diaphragm into the urine, thereby indicating specific barrier damage characteristic of DN[22]. Previous studies have shown that elevated urinary PCX levels can serve as an early marker for renal impairment in individuals with early-stage DN or lupus nephritis[23,24]. The detection of PCX in urine provides a non-invasive diagnostic approach, which is facilitated by straightforward sample collection methods. Additionally, the use of ELISA for measuring PCX offers several advantages, including affordability and high sensitivity, which make it suitable for widespread use in standard laboratory settings.

In this study, urinary levels of U-AFU/UCr were higher in the DN group than the NDN group. AFU is a lysosomal enzyme, initially identified in cell lysosomes and later recognized as a tumor biomarker for the diagnosis of hepatocellular carcinoma following the elevation reported in the serum of patients with hepatocellular carcinoma by Deugnier et al[25]. Subsequent research revealed its widespread distribution across various tissues, cells, and bodily fluids, with its primary function being the catalysis of the degradation metabolism of glycosylated oligosaccharides, glycoproteins, and other substances[26,27]. This substance is notably concentrated in the liver and kidneys, with a significant presence within the lysosomes of proximal renal tubular epithelial cells[27]. In our research, correlation analyses revealed positive associations between U-AFU/UCr and various markers such as CysC, UACR, RBP-4, U-PCX/UCr, U-α2-MG/UCr, Scr, UTP/24 h, urea, and NLR, while showing a negative relationship with eGFR, implying consistency with changes in renal function-related indicators. Thus, U-AFU/UCr can indicate the occurrence and extent of renal damage in DN. The AUCROC value for U-AFU/UCr was reported at 0.79, with a sensitivity of 63% and specificity of 92%. These findings demonstrated that while U-PCX/UCr and U-α2-MG/UCr presented lower diagnostic utility for DN, the sensitivity of U-AFU/UCr surpassed that of U-α2-MG/UCr and fell below U-PCX/UCr. Meanwhile, its specificity exceeded that of U-PCX/UCr but did not reach the specificity of U-α2-MG/UCr. The data highlight that each marker possesses unique strengths and weaknesses in the diagnostic process, and leveraging them collectively in clinical assessments can foster diagnostic accuracy.

Moreover, the levels of RBP-4 were found to be elevated in the DN group relative to the NDN group. RBP-4 exhibited positive correlations with UACR, CysC, Scr, urea, UTP/24 h, and the NLR, and a negative correlation with the eGFR, making it a reliable indicator of kidney functions[28]. With a molecular weight of 21 kDa, RBP-4 is predominantly found in insulin-responsive tissues, including skeletal muscle and adipose tissue. It forms a complex with retinol and transthyretin, facilitating the delivery of vitamin A to peripheral tissues. This complex is known to inhibit glomerular filtration. Approximately 4% of RBP-4 found in urine and 5% in serum manage to traverse the glomerular barrier and undergo reabsorption in the proximal convoluted tubules, positioning RBP-4 as an indicative marker of proximal tubular injury[29,30]. It is hypothesized that an increase in RBP-4 levels could be due to a diminished clearance rate, attributed to the decline in renal functionality. This results in a systemic accumulation of RBP-4 or a decreased efficiency in the renal clearance of the RBP-4 complex.

In our study, CysC levels were observed to be higher in the DN group than in the NDN group. CysC demonstrated positive correlations with UACR, RBP-4, SCr, urea, UTP/24 h, NLR, and hs-CRP, while showing an inverse relationship with eGFR, reinforcing its association with inflammation markers and renal health indicators. CysC, with a molecular weight of 13.3 kDa, is synthesized consistently in all nucleated cells. It undergoes unimpeded filtration by the glomeruli, followed by reabsorption and complete metabolism in the proximal tubules. Hence, CysC is acknowledged as a straightforward, reliable, and precise indicator of GFR. The AUCROC value for RBP-4 (0.70) and CysC (0.78) surpassed that of α2-MG, AFU, and PCX, indicating their superior clinical utility in diagnosing DN. The diagnostic accuracy of these biomarkers improves further when used in conjunction with other markers, highlighting their potential for enhancing DN diagnosis in clinical settings.

Lastly, the study identified elevated levels of PCX, AFU, and α2-MG in the DN group, which correlated with the progression of renal function. When used individually, the AUCROC value for these biomarkers outperformed RBP-4 and CysC, each displaying distinct advantages in sensitivity and specificity. The combination of these three indicators yields a higher diagnostic value, serving as a complement to the diagnosis of proteinuria and eGFR.

However, this study is subject to certain limitations. The method of sample storage may have led to the degradation or denaturation of some proteins, potentially affecting the results. Additionally, the limited sample size may restrict the generalizability of the findings, necessitating further validation with a larger cohort. Given these preliminary findings, future research should explore the trends of these biomarkers at various stages of DN and their interactions with other potential factors, such as genetics and lifestyle. Large-scale, multicentric studies could provide more stable and reliable data. Moreover, it was observed that indicators such as PCX, AFU, and α2-MG did not exhibit superiority over the UACR, which is currently used for diagnosis. This observation could be attributed to the high diagnostic value of UACR itself or to the limited sample size of this study, which may not fully reflect the comprehensive diagnostic capabilities of the newer biomarkers. Such insights underscore the need for continued investigation into the utility of these biomarkers in enhancing the accuracy of DN diagnosis.


DN, as a primary complication of T2DM, has emerged as a significant public health concern. The covert progression of DN and the lack of sensitive biomarkers currently impede early intervention and optimal treatment for patients. This research has highlighted the importance of α2-MG, PCX, AFU, RBP-4, and CysC in the onset and progression of DN, with UACR demonstrating the highest diagnostic value among all biomarkers (Figure 3). These findings provide potential viable biomarkers for the early diagnosis of DN and offer possible indications for early intervention in patients. Furthermore, our findings offer new insights into the mechanisms underlying the progression of DN and valuable references for future diagnostics and treatment strategies.

Figure 3
Figure 3 Molecular biomarkers and their role in diagnosing diabetic nephropathy. This figure provides a visual overview of the key molecular biomarkers implicated in diabetic nephropathy, highlighting their significance and potential utility in the early diagnosis and monitoring of the disease. α2-MG: α2-macroglobulin; PCX: Podocalysin; AFU: α-L-fucosidase; RBP-4: Retinol binding protein-4; CysC: Cystatin C; UACR: Urinary albumin/creatinine ratio; DN: Diabetic nephropathy.

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Endocrinology and metabolism

Country/Territory of origin: China

Peer-review report’s classification

Scientific Quality: Grade A, Grade B, Grade B

Novelty: Grade B

Creativity or Innovation: Grade B

Scientific Significance: Grade A

P-Reviewer: Ebraheim LLM, Egypt S-Editor: Chen YL L-Editor: A P-Editor: Xu ZH

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