Expression and Significance of Protein Arginine Methyltransferase 1 in the Serum of Patients with Diabetes Kidney Disease

Abstract

Purpose

This study aimed to investigate the expression levels and significance of Protein Arginine Methyltransferase 1 (PRMT1) in patients with diabetic kidney disease (DKD).

Patients and Methods

236 patients with Type 2 Diabetes Mellitus (T2DM) who were admitted to Hebei Provincial People’s Hospital from October 2023 to May 2025 were enrolled in this study. According to the urinary albumin–creatinine ratio (UACR), they were divided into normal albuminuria group (D1), microalbuminuria group (D2) and macroalbuminuria group (D3). 93 healthy people undergoing routine examination during the same period served as normal control (NC). Collect clinical data, use Enzyme-Linked Immunosorbent Assay (ELISA) to detect serum PRMT1. Detection of glucose and lipid metabolism and renal function indicators were used to evaluate intergroup differences and explore the relationship between PRMT1 and DKD.

Results

Serum PRMT1 level rose gradually from NC group to D1, D2 and D3 groups (P<0.05), in T2DM patients, there was a significant positive association between PRMT1 expression and UACR (P < 0.05). Multivariate logistic regression analysis demonstrated that elevated PRMT1 expression was independently associated with both early and clinical DKD, and receiver operating characteristic (ROC) analysis indicated PRMT1 had discriminative ability for DKD.

Conclusion

Serum PRMT1 concentrations were significantly elevated in patients with DKD compared with those with type 2 diabetes without nephropathy and healthy individuals, and levels increased with advancing disease severity, which implies that PRMT1 could be a valuable diagnostic marker for DKD.

Video Abstract

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Introduction

As a leading global public health concern, diabetes mellitus (DM) is no longer a health issue exclusive to developed countries; instead, its incidence is rising more swiftly in developing regions.^1,2 Epidemiological investigations have demonstrated that against the backdrop of growing population aging and altered lifestyle patterns, the global prevalence of DM is undergoing a persistent and marked upward trend.³ Statistical projections suggest that the global population living with DM is expected to surge to 643 million by 2030.⁴ Diabetic kidney disease (DKD) represents the most prevalent microvascular complication of DM, affects 20–40% of type 2 DM (T2DM) patients worldwide and has become the primary cause of end-stage renal disease (ESRD).⁵ Pathophysiologically, DKD is a progressive disorder initiated by glomerular hyperfiltration and mesangial matrix expansion under chronic hyperglycemia and hemodynamic disturbance, which further leads to glomerular hypertrophy, basement membrane thickening and podocyte loss; these structural injuries drive tubulointerstitial inflammation, fibrosis and renal microvascular dysfunction, ultimately resulting in irreversible renal damage,^6,7 with its pathogenesis also involving oxidative stress, inflammatory activation and structural remodeling.⁸ Clinically, DKD is defined by persistent albuminuria [urinary albumin-creatinine ratio (UACR) ≥30 mg/g] and progressive estimate glomerular filtration rate (eGFR) decline (<60 mL/min/1.73 m²) for over 3 months (excluding other renal pathologies).⁹ Patients with DKD experience progressive renal impairment that may progress to ESRD, posing significant risks for eventual kidney replacement therapy.¹⁰ It is therefore essential to identify a patient at the earliest in order to optimize clinical outcomes. Failure to intervene in a timely manner, persistent hyperglycaemia accelerates renal injury with proteinuria possibly leading to failure.

Clinical evaluation at present involves the use of primarily urinary albumin-creatinine ratio (UACR) as well as glomerular filtration rate (GFR), serum creatinine (SCr) and blood urea nitrogen (BUN). These remain established markers of renal function.^11–13 Percutaneous renal biopsy serves as the gold standard for the diagnosis of DKD.¹⁴ Nevertheless, given its invasive characteristic and the potential risks it entails, this procedure is not routinely recommended for diabetic patients complicated with chronic kidney disease (CKD), except in cases where high-risk factors suggestive of rapid disease progression or a non-diabetic renal origin are present.¹⁵ Accordingly, the screening and validation of novel biomarkers for the early detection of DKD offer pivotal theoretical underpinnings and clinical significance, which are essential for promoting precision treatment and prognostic assessment of this disease.

Protein Arginine Methyltransferase 1 (PRMT1), is the major isoform of the PRMT family. It is responsible for the contribution of 85% of all total cellular PRMT activity. The key biological processes include cellular signaling, DNA damage repair, and transcriptional regulation.^16,17 PRMT1 is expressed in the adrenal glands, brain, cerebellum, colon, esophagus, frontal cortex, heart, dendritic cells, kidneys, liver, lungs, ovaries, pancreas, retina, stomach, testes, thyroid, and bladder.¹⁸ Zhu et al¹⁹ revealed that inhibition of PRMT1 alleviates sepsis-induced acute kidney injury in mice by blocking the TGF-β1 and IL-6 trans-signaling pathways. Zhou et al²⁰ uncovered that PRMT4 could interact with NCOA4 to inhibit ferritinophagy in cisplatin-induced acute kidney injury. Interestingly, prior investigations have demonstrated that the upregulated expression of PRMT1 in proximal tubular cells exerts a pivotal regulatory role in the pathogenesis of DKD.²¹ Mesangial cells exhibit a high degree of sensitivity to lipotoxicity, and lipotoxicity-induced apoptosis of these cells is closely correlated with the progressive deterioration of renal function in patients with DKD. Park et al²² demonstrated that lipotoxicity-induced PRMT1 overexpression potentiates endoplasmic reticulum (ER) stress-mediated mesangial cell apoptosis in DKD, while PRMT1 gene knockout exerts a marked inhibitory effect on the activation of ER stress signaling pathways triggered by lipotoxicity. Meanwhile, PRMT1 is involved in the formation of the PRMT1-ADMA-DDAH1 metabolic axis, which exerts a prominent positive feedback regulatory association with oxidative stress. This axis acts as a key molecular underpinning for oxidative damage and inflammatory infiltration in the renal tissues of patients with DKD.²³ Importantly, clinical evidence from Zhang et al²⁴ indicates that PRMT1 expression is increased in patients with T2DM. There are no studies on the expression of PRMT1 in serum of patients with DKD and its correlation with disease severity.

This study was designed to explore the association between serum PRMT1 levels and renal function indicators in DKD. Specifically, we aimed to validate PRMT1 as a reliable biomarker for early diagnosis, which could offer a new avenue for screening and facilitate the optimization of early diagnostic and preventive systems for DKD.

Materials and Methods

Subjects

A cohort of 236 T2DM patients was enrolled in this study, all of whom were admitted to the Nephrology and Endocrinology Departments of Hebei Provincial People’s Hospital during the period from October 2023 to May 2025. The inclusion criteria were as follows: (1) Age over 18 years. (2) A confirmed diagnosis of T2DM according to the American Diabetes Association (ADA) criteria.²⁵ (3) DKD diagnostic criteria: after excluding interfering factors, microalbuminuria is confirmed if at least two of three samples collected over a 3–6 month period yield a UACR ≥ 30 mg/g or a UAER ≥ 30 mg/24 h (≥20 μg/min).²⁶ Among the patients who met the inclusion criteria, the following individuals were further excluded from the study: (1) Patients with changes in group classification during multiple UACR tests within 3–6 months or only one UACR test within this period. (2) Type 1 diabetes or other special forms of diabetes. (3) Pre-existing renal diseases, including primary glomerulonephritis or nephrotic syndrome. (4) Presence of secondary kidney damage conditions including systemic lupus erythematosus, psoriasis, or Henoch-Schonlein purpura nephritis. (5) Severe cardiac insufficiency or hepatic dysfunction. (6) Malignancies or active infections. (7) Acute diabetic complications.

The study divided participants into three groups based on UACR: normal urinary albumin group (Group D1, UACR<30mg/g), microalbuminuria group (Group D2, 30mg/g<UACR<300mg/g), and massive albuminuria group (Group D3, UACR>300mg/g). Group D1 included 93 patients (60 males, 33 females) aged 27–87 years (mean 55.34 ± 12.16 years). Group D2 comprised 72 patients (43 males, 29 females) aged 25–89 years (mean 57.75 ± 14.66 years). Group D3 consisted of 71 patients (45 males, 26 females) aged 23–85 years (mean 59.13 ± 14.41 years). The control group (NC group) included 93 healthy individuals (53 males, 40 females) aged 19–87 years (mean 54.94 ± 14.64 years) who underwent physical examinations at the same center during the same period. Analysis of group characteristics showed no statistically significant differences in gender composition or age distribution (P> 0.05), confirming comparable study groups. The flow diagram of this study is illustrated in Figure 1.

Figure 1.

Flow diagram of this study.

Method

Data Collection

Demographic parameters and baseline clinical indicators of all subjects covered sex, age, body mass index (BMI), systolic and diastolic blood pressure (SBP and DBP), and diabetes duration (DD), were collected for all eligible participants. Under the laboratory diagnostic department of Hebei provincial people’s hospital, the laboratory parameters were assessed using fully automated biochemical analyzers. These parameters include hemoglobin (Hb), total protein (TP), albumin (Alb), fasting blood glucose (FBG), glycated hemoglobin (HbA1c), SCr, BUN, eGFR, UA, UACR, total cholesterol (TC), triglycerides (TG), HDL, and LDL. We calculated BMI as the weight (kg) divided by height squared (m²), while eGFR was the 2021 CKD-EPI equation.

Sample Collection and Detection

After an overnight fast, 5 mL of venous blood was collected in the morning. To obtain clear serum, samples were allowed to stand at room temperature for 30 minutes before centrifugation at 3000 rpm for 10 minutes. The resulting supernatant was harvested and stored at −80°C until analysis. Serum PRMT1 concentrations were quantified using an ELISA kit purchased from Wuhan Yunke Long Technology Co., Ltd.

Statistical Processing

All statistical analyses were conducted with SPSS version 27.0. For continuous variables, the Kolmogorov–Smirnov test was used for normality testing (given the sample size of each group exceeded 50), and the Levene test for homogeneity of variance. Data with a normal distribution and homogeneous variance were presented as mean ± SD. One-way analysis of variance (ANOVA) was applied for multiple group comparisons, and the least significant difference (LSD) test for pairwise comparisons. For normally distributed data without heterogeneous variance, Tamhane’s T2 test was used for pairwise comparisons. Non-normally distributed data were presented as median (interquartile range), and the Kruskal–Wallis H-test was performed for multiple group comparisons. Spearman correlation analysis was used to evaluate the bivariate associations between the variables of interest. Multivariate logistic regression models were constructed to identify independent correlates of DKD. Receiver operating characteristic (ROC) curve analysis is a reliable statistical method for evaluating the diagnostic performance of a biomarker or test by plotting the true positive rate against the false positive rate across different cut-off values, and the area under the ROC curve (AUC) is used to quantify its discriminative ability, with a higher AUC indicating better diagnostic accuracy.²⁷ Herein, the diagnostic accuracy of PRMT1 was evaluated via ROC curve analysis. A two-tailed P value < 0.05 denoted statistical significance.

Ethical Statement

This study was conducted in accordance with the principles of the Declaration of Helsinki and has undergone ethical review. This project was approved by the Medical Ethics Committee of Hebei Provincial People’s Hospital. Ethics review number: (2023) Scientific Research Ethics Review No. (431). Derived from routine clinical practice conducted previously, the medical records and biological specimens used in this study were accompanied by full data anonymization, with the ethics committee waiving the requirement for informed consent.

Results

Comparison of Serum PRMT1 Expression Levels in Four Groups

The results showed that the serum PRMT1 levels of NC, D1, D2 and D3 groups increased successively, and the comparison between groups was statistically significant (P < 0.05) (Table 1 and Figure 2).

Table 1.

Comparison of PRMT1 Expression Levels Among the Four Groups

Groups	N	PRMT1 (pg/mL)
NC	93	163.097 (88.450)
D1	93	192.625 (165.845) ★
D2	72	233.022 (178.452) ★▲
D3	71	287.000 (324.290) ★▲#

Notes: Compared with the group NC, ^★P <0.05; compared with D1 group, ^▲P <0.05; compared with the D2 group, ^#P <0.05.

Figure 2.

Comparison of serum PRMT1 expression levels between NC, D1, D2 and D3 groups. Serum PRMT1 levels were measured by enzyme-linked immunosorbent assay (ELISA) in 93 D1 patients, 72 D2 patients, 71 D3 patients and 93 age- and gender-matched healthy volunteers (NC). *P<0.05, **P<0.001.

Comparison of General Clinical Data and Biochemical Indexes Such as Serum Glycolipid Metabolism and Renal Function Among the Four Groups

No significant differences were observed in age or sex among the four groups (P > 0.05). The patients in Groups D1, D2, and D3 had higher BMI, FBG, BUN and TG levels than the NC group. BUN was significantly higher in Group D3 than that in Groups D1, D2 (P <0.05). HDL levels were decreased in all three groups of diabetics compared to NC (P <0.05). DD and UACR levels increased progressively across Groups D1, D2, and D3 (P <0.05). Patients in Group D3 demonstrated higher SBP and SCr levels than the NC, D1, and D2 groups, while Group D2 showed a statistically significant increase in SBP compared to Group D1 (P <0.05). The levels of TP, Hb, Alb and eGFR in the D3 group were lower than those in the NC, D1, and D2 groups. Moreover, the level of Alb in the D2 group were lower than those in the NC group. And the differences were statistically significant (P < 0.05) (Table 2).

Table 2.

Comparison of Clinical Indicators Among the Four Groups

Groups	NC (n=93)	D1 (n=93)	D2 (n=72)	D3 (n=71)	F/H	P
Age (year)	54.94±14.64	55.34±12.16	57.75±14.66	59.13±14.41	1.635	0.181
M/F	53/40	60/33	43/29	45/26	1.331	0.722
BMI (Kg/m2)	25.17±3.35	26.33±2.57★	27.02±3.75★	27.19±3.14★	6.866	<0.001
DD (year)	–	7.00 (9.00)	10.00 (15.00) ▲	15.00 (14.00) ▲#	219.424	<0.001
SBP (mmHg)	129.49± 18.68	124.28± 17.18	130.81± 20.02▲	139.59± 20.10★▲#	8.934	<0.001
DBP (mmHg)	84.32±12.31	84.26±12.14	83.72±13.41	83.89±13.04	0.042	0.988
TP (g/L)	71.37± 5.62	69.59± 6.79	69.52± 7.63	66.05± 7.59★▲#	8.197	<0.001
Alb (g/L)	44.20 (5.00)	43.30 (6.00)	42.40 (7.00) ★	38.10 (7.00) ★▲#	44.278	<0.001
Hb (g/L)	137.39± 13.53	139.73± 19.31	138.31± 20.29	131.42± 23.80★▲#	2.743	0.043
FBG (mmol/L)	5.05 (0.80)	8.87 (4.76) ★	7.85 (5.62) ★	8.38 (4.73) ★	120.053	<0.001
HbA1C (%)	–	9.00 (2.85)	8.60 (2.74)	9.10 (2.90)	1.277	0.528
BUN (mmol/L)	4.80 (1.60)	5.80 (2.30) ★	5.65 (2.50) ★	7.00 (3.90) ★▲#	58.329	<0.001
SCr (μmol/L)	62.60 (18.80)	61.20 (16.85)	63.85 (29.35)	80.40 (54.60) ★▲#	44.046	<0.001
UA (μmol/L)	312.07± 83.78	305.27± 85.53	314.26± 100.04	343.51± 108.01	6.429	0.093
eGFR (mL/min)	105.00 (17.27)	105.13 (16.12)	102.57 (23.01)	87.10 (50.13) ★▲#	25.967	<0.001
UACR (mg/g)	–	9.80 (9.72)	68.03 (48.97) ▲	893.47 (914.82) ▲#	207.554	<0.001
TC (mmol/L)	4.60±1.17	4.94±1.45	4.73±1.23	4.84±1.40	1.113	0.344
TG (mmol/L)	1.21 (0.69)	1.46 (1.34) ★	1.38 (1.90) ★	1.56 (1.50) ★	17.406	<0.001
HDL (mmol/L)	1.34 (0.36)	1.13 (0.42) ★	1.14 (0.48) ★	1.18 (0.28) ★	16.761	<0.001
LDL (mmol/L)	2.66±0.85	2.90±0.99	2.60±0.95	2.64±1.04	1.701	0.167

Notes: Compared with NC group, ^★P <0.05; compared with group D1, ^▲P <0.05; compared with group D2,^#P <0.05.

Abbreviations: M/F, male/female; DD, duration of diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TP, total protein; Alb, albumin; Hb, hemoglobin; FBG, fasting blood glucose; HbA1c, glycated hemoglobin; BUN, blood urea nitrogen; SCr, serum creatinine; UA, uric acid; eGFR, estimate glomerular filtration rate; UACR, urinary albumin-creatinine ratio; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

Correlation Between UACR Level and Serum PRMT1 Level and Laboratory Related Indicators in T2DM Patients

The Spearman correlation analysis showed that in the T2DM patients included in our study, UACR level showed positive correlation with PRMT1 expression level, age, BMI, SBP, DD, BUN, SCr, and UA (r = 0.308, r=0.137, r=0.147, r=0.337, r=0.344, r=0.272, r=0.356, r=0.129, P < 0.05), and negative correlation with Hb, TP, Alb, and eGFR (r= −0.141, r= −0.198, r= −0.332, r= −0.361, P < 0.05) (Table 3).

Table 3.

Correlations of PRMT1 Expression Levels and Clinical Parameters with DKD Diagnosis

Indicator	r	p
PRMT1 (pg/mL)	0.308	<0.001
Age (year)	0.137	0.035
Gender	0.030	0.646
BMI (Kg/m2)	0.147	0.024
SBP (mmHg)	0.337	<0.001
DBP (mmHg)	0.011	0.873
DD (year)	0.344	<0.001
FBG (mmol/L)	0.042	0.522
HBA1C (%)	0.011	0.867
Hb (g/L)	−0.141	0.030
TP (g/L)	−0.198	0.002
Alb (g/L)	−0.332	<0.001
BUN (mmol/L)	0.272	<0.001
SCr (μmol/L)	0.356	<0.001
eGFR (mL/min)	−0.361	<0.001
UA (μmol/L)	0.129	0.048
TC (mmol/L)	0.013	0.844
TG (mmol/L)	0.083	0.204
HDL (mmol/L)	0.025	0.702
LDL (mmol/L)	−0.071	0.275

Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; DD, duration of diabetes mellitus; FBG, fasting blood glucose; HbA1c, glycated hemoglobin; Hb, hemoglobin; TP, total protein; Alb, albumin; BUN, blood urea nitrogen; SCr, serum creatinine; eGFR, estimate glomerular filtration rate; UA, uric acid; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

Influencing Factors in DKD Patients

Using the diagnostic status of DKD as the dependent variable (assigned: T2DM group = 1, early-stage DKD group = 2, clinical DKD group = 3), we performed correlation analysis and multivariate logistic regression with UACR, PRMT1 expression levels (grouped by interquartile range into high-level group (PRMT1>226.500pg/mL) and low-level group (PRMT1≤226.500pg/mL)), age, BMI, SBP, DD, Hb, TP, Alb, BUN, SCr, eGFR, and UA as independent variables. Results indicated that PRMT1 expression levels, DD, and Alb were factors influencing early DKD development, while PRMT1 expression levels, SBP, DD, Hb, and Alb were associated with clinical DKD. The findings demonstrated that high PRMT1 expression levels were independent risk factors for both early and clinical DKD in T2DM patients (P < 0.05), with relative risks increasing by 0.981 times and 4.692 times, respectively (Table 4).

Table 4.

Analysis of Factors Affecting DKD

	Early-Stage DKD Group			Clinical DKD Group
	OR	95% CI	P	OR	95% CI	P
PRMT1 (boxing)	1.981	1.002–3.916	0.049	5.692	2.409–13.447	<0.001
Age (year)	0.980	0.935–1.027	0.390	0.965	0.918–1.014	0.162
SBP (mmHg)	1.017	0.997–1.037	0.089	1.051	1.027–1.076	<0.001
DD (year)	1.083	1.035–1.134	<0.001	1.087	1.032–1.146	0.002
Hb (g/L)	1.009	0.985–1.033	0.470	1.032	1.003–1.062	0.028
TP (g/L)	1.072	0.993–1.158	0.076	1.083	0.990–1.184	0.082
Alb (g/L)	0.878	0.777–0.991	0.035	0.754	0.652–0.872	<0.001
BUN (mmol/L)	1.092	0.865–1.380	0.458	1.037	0.815–1.321	0.766
SCr (μmol/L)	1.003	0.971–1.037	0.854	1.007	0.974–1.041	0.700
eGFR (mL/min)	0.994	0.946–1.044	0.804	0.968	0.919–1.019	0.212
UA (μmol/L)	1.001	0.997–1.005	0.775	1.002	0.997–1.006	0.452

Abbreviations: SBP, systolic blood pressure; DD, duration of diabetes mellitus; Hb, hemoglobin; TP, total protein; Alb, albumin; BUN, blood urea nitrogen; SCr, serum creatinine; eGFR, estimate glomerular filtration rate; UA, uric acid.

Evaluation of PRMT1 as a Potential Biomarker for DKD Using ROC Curve Analysis

The results demonstrated that in the T2DM patients analyzed, PRMT1 exhibited an AUC of 0.687 for the prediction of DKD, and the optimal cutoff value was 181.919, when the specificity and sensitivity were best, with a sensitivity of 0.818 and a specificity of 0.462. For SBP, the AUC of DKD prediction was 0.656, with the optimal cutoff at 132.5, showing sensitivity of 0.538 and specificity of 0.731. DD had an AUC of 0.699, with the cutoff at 13.5, demonstrating sensitivity of 0.462 and specificity of 0.849. 1/Alb exhibited an AUC of 0.636, with the cutoff at 0.026, revealing sensitivity of 0.371 and specificity of 0.860. The AUC of PRMT1 combined with DD, SBP, and 1/Alb for predicting DKD was 0.809, with a sensitivity of 0.636 and specificity of 0.860 (Table 5 and Figure 3).

Table 5.

The Cutoff Values of PRMT1, SBP, DD and Alb for DKD Prediction

Indicator	AUC	P	Cut-off	Sensitivity	Specificity
PRMT1	0.687	<0.001	181.919	0.818	0.462
SBP	0.656	<0.001	132.500	0.538	0.731
DD	0.699	<0.001	13.500	0.462	0.849
1/Hb	0.569	0.072	0.008	0.385	0.785
1/Alb	0.636	<0.001	0.026	0.371	0.860
PRMT1+DD+SBP+1/Alb	0.809	<0.001	-	0.636	0.860

Abbreviations: SBP, systolic blood pressure; DD, duration of diabetes mellitus; Hb, hemoglobin; Alb, albumin; AUC, area under the curve.

Figure 3.

Evaluation of PRMT1 as a potential biomarker for DKD using ROC Curve analysis.

Discussion

Diabetes, as a typical representative of metabolic diseases, is characterized by long-term hyperglycemia. This pathological state causes persistent inflammatory damage to multiple organs, significantly increasing the risk of complications.²⁸ Among the various complications, DKD is recognized as one of the most widespread and life-impacting ones.⁵ Its early manifestation is abnormal hyperfunction of glomerular filtration, which is caused by abnormal proliferation and expansion of mesangial matrix, resulting in enlargement of kidney size and gradual thickening of glomerular basement membrane. The disease progresses as they die off, and the glomerular and tubular structures breaks down which leads to glomerulosclerosis, tubulointerstitial fibrosis, and irreversible kidney damage.^6,7 DKD is progressive. With the continuous progress of these pathological changes, if timely scientific and effective intervention measures are not taken, the kidney will gradually undergo irreversible structural remodeling and functional decline, eventually leading to progressive deterioration of renal function, and even develop to the stage of renal failure, seriously threatening the life and health of patients.

To detect and assess the extent of renal damage caused by DM at an early stage, clinicians typically employ a series of sensitive biochemical markers for testing. A sustained reduction in eGFR not only acts as a direct indicator of the progressive deterioration of glomerular filtration capacity but also represents one of the most characteristic biomarkers for the progression of DKD. The dynamic changes in eGFR are routinely adopted to evaluate the disease severity and clinical prognosis of affected patients.²⁹ Under sustained hyperglycemic conditions, dilation of the glomerular arterioles induces a state of glomerular hyperfiltration. Simultaneously, advanced glycation end products (AGEs) accumulate and compromise the charge-based and size-based selective barriers of the glomerular filtration membrane, which in turn causes a rise in urinary albumin leakage. When the UACR reaches 30–300 mg/g, this marks the onset of microalbuminuria, a key manifestation that facilitates the early diagnosis of diabetic nephropathy.³⁰ Thus, measurement of urinary microalbumin serves as a valuable adjunctive tool for the early detection of DKD.³¹ Due to the complicated and multi-factorial pathogenesis of DKD, it is of great theoretical and practical significance to identify novel biomarkers for early diagnosis for accurate and active management.

In the early stage of DKD, renal function impairment is occult, with no significant reduction in glomerular filtration function; instead, glomerular hyperfiltration may occur due to the body’s compensatory mechanisms, leading to a transient compensatory elevation of filtration capacity.³² In this phase, SCr and BUN—the routine clinical indicators—mostly remain within the normal reference range of conventional tests, making it difficult to detect latent renal dysfunction in a timely manner and thus increasing the risk of missed or delayed diagnosis.³³ In contrast, eGFR calculated by the internationally standardized CKD-EPI formula may be in the upper normal range or slightly elevated at this stage, and this subtle change can effectively indicate early compensatory alterations in glomerular filtration function, serving as a key reference for the early clinical identification of DKD.³⁴ With the further progression of DKD, glomerular basement membrane injury is aggravated, and renal filtration function exhibits an irreversible decline, accompanied by gradual changes in core renal function indicators. Specifically, eGFR shows a progressive decrease, and its decline rate is closely correlated with patients’ glycemic and blood pressure control status, with a faster decline observed in patients with poor control of blood glucose and pressure. Meanwhile, SCr and BUN levels increase gradually with the reduction of eGFR. This dynamic variation law of renal function indicators was consistent with the findings of the present study. Our results revealed distinct characteristic changes in renal function-related indices with the progression of DKD: no significant differences in SCr, BUN and eGFR levels were found between patients with early DKD and those with T2DM alone; while compared with the T2DM-only group and early DKD group, patients with clinical-stage DKD had significantly elevated SCr and BUN levels and a marked reduction in eGFR, which directly reflects the progressive impact of DKD pathological development on renal function indices.

The PRMT family consists of 3 isoforms. These include 6 type I PRMTs, which are PRMT1, PRMT2, PRMT3, PRMT4, PRMT6, and PRMT8, with PRMT1 being the most abundant.³⁵ The diverse physiological functions of PRMT1 include cell signaling, DNA damage repair, transcriptional and epigenetic regulation, and plays important roles in cancer and neurodegenerative diseases.^16,17,36,37 Serum asymmetric dimethylarginine (ADMA), a hydrolysis product derived from arginine-methylated proteins, can suppress the biosynthesis of nitric oxide (NO) and impair the preservation of vascular tone and structural stability.³⁸ Approximately 80% of ADMA undergoes metabolic degradation via type I dimethylarginine dimethylaminohydrolase (DDAH1), whereas its de novo synthesis is predominantly catalyzed by PRMT1, accounting for around 85% of total ADMA production.³⁹ These proteins form the PRMT1-ADMA-DDAH1 metabolic axis, which is closely associated with oxidative stress, modulates ADMA concentrations in the circulation and tissues, and thereby constitutes a renal local metabolic regulatory axis.²³ Emerging evidence has demonstrated that the PRMT1-ADMA-DDAH1 pathway exhibits dysregulated expression in multiple organs of diabetic rats, including the pancreas, kidney and eye.^23,40 When metabolic homeostasis is perturbed, the hyperoxidative microenvironment of diabetes upregulates PRMT1 expression while downregulating that of DDAH1, thereby elevating ADMA concentrations.²³ These increased ADMA levels suppress NO biosynthesis to induce vascular endothelial dysfunction and, at the same time, attenuate SIRT1-FoxO1 pathway-driven autophagic activity, which impairs metabolic waste elimination in podocytes and further aggravates renal damage.⁴¹ At the level of cumulative cellular injury, a lipotoxic microenvironment can induce the upregulation of PRMT1 expression in mesangial cells, which promotes cellular apoptosis by activating the ER stress pathway mediated by PERK-ATF6.²² Meanwhile, its aberrant methylation modification represses the nuclear translocation of FoxO1 and reduces the expression of autophagy-related genes LC3 and Beclin-1, which leads to the accumulation of damaged organelles and accelerates the injury of podocytes and mesangial cells.^42,43 During the induction of renal fibrosis, PRMT1 functions through the indirect modulation of the TGF-β1/Smad signaling axis, with its upregulated expression mediating increased deposition of collagen and fibronectin in renal parenchyma; in contrast, the suppression of PRMT1 can reduce the expression of fibrosis-associated proteins and mitigate glomerulosclerotic lesions.⁴⁴ In terms of inflammatory amplification, PRMT1 can selectively activate pro-inflammatory and pro-apoptotic signaling pathways including p38 MAPK and NF-κB, which further exacerbates oxidative damage and accelerates cellular apoptosis, thereby deteriorating the microenvironment of renal tissues.^45,46 A study investigating the correlation between PRMT1 expression levels and insulin resistance in patients with T2DM demonstrated that PRMT1 expression is elevated in T2DM patients and positively correlated with the homeostasis model assessment of insulin resistance (HOMA-IR), suggesting that PRMT1 is a contributing factor to the development of insulin resistance (IR) in these patients.²⁴ In vitro studies demonstrate that high glucose significantly upregulates PRMT1 expression in podocytes from mice with diabetic nephropathy. PRMT1 inhibition reduces podocyte apoptosis and mitigates podocyte injury.⁴⁷ In line with this, the present study stratified T2DM patients based on UACR and measured serum PRMT1 levels. Our study results showed that compared with T2DM patients without nephropathy and healthy individuals, PRMT1 expression in DKD patients was significantly higher.

The findings identified DD and Alb as independent risk factors for early-stage DKD, and SBP, Alb, DD and Hb as independent risk factors for clinical DKD; however, these factors (DD, Alb, SBP, Hb) presented weak correlation characteristics with DKD (OR values close to 1). Also, PRMT1 was a key independent risk factor for both early-stage and clinical DKD, and showed a gradual increase in effect size with the progression of DKD. Hypertension primarily damages kidneys. Moreover, a persistently higher blood pressure causes damage of structure and function of the organ.⁴⁸ Studies have revealed that mild elevation of blood pressure (not exceeding the upper limit of the normal range) serves as an independent predictive indicator for the progression to microalbuminuria in patients with early-stage diabetes.⁴⁸ The evolution of DKD is closely associated with the duration and severity of diabetes. Persistent hyperglycemia further aggravates microvascular injury and metabolic disturbance. Clinically, early DKD is characterized by microalbuminuria which adds up to the glomerular filtration burden initiating a vicious cycle of “hyperglycemia-induced kidney damage”. Serum albumin makes up roughly 50–60% of plasma protein which maintains osmotic pressure-the pressure which is responsible for the influx of nutrients across the membranes of cells. Hypoalbuminemia lowers plasma colloid osmotic pressure and aggravates renal hyperfiltration. It accelerates glomerulosclerosis. Numerous studies show that decreased Alb correlates with a lower eGFR and high UACR, which reflects more severe DKD and poorer prognosis.^49–51 The kidneys also produce erythropoietin (EPO) that stimulates erythrocyte production. The EPO production is reduced due to a decrease in EPO-producing cells in the kidney. Anemia is not just a complication of DKD, it leads to renal hypoxia, renal vasoconstriction, activation of the renin–angiotensin system, and glomerulosclerosis and tubulointerstitial fibrosis. Data of the past suggested that low Hb levels in patients suffering from T2DM are independently associated with high DKD risk, regardless of the anemic state.⁵² Interestingly, van Beek et al⁵³ developed a clinical prediction model for predicting quality of recovery up to 1 week after surgery, consisting of age, sex, previous surgery, BMI, ASA classification, duration of surgery, Hospital anxiety and Depression Scale and preoperative QoR-40 score. Thus, the weak association of DD, Alb, SBP and Hb with DKD indicates that a single factor has limited independent predictive value for DKD, while the combined consideration of these weak factors with PRMT1 has important practical significance for DKD detection and risk management: the combined assessment of weak factors can realize the stratification of early screening population for diabetic patients, make up for the deficiency of single strong factor screening; the dynamic monitoring of weak factors can supplement the evaluation of DKD progression risk; and the combined intervention of weak factors and targeted intervention of PRMT1 can guide the individualized clinical management of DKD. In addition, the inclusion of these weak factors in the DKD risk prediction model can improve the comprehensiveness and clinical applicability of the model.

Correlation analysis demonstrated that serum PRMT1 expression increased progressively with rising UACR values. PRMT1 expression was significantly positively correlated with the diagnosis of DKD. High PRMT1 levels were independently associated with early DKD (microalbuminuria) and clinical DKD (macroalbuminuria) in patients with T2DM, and is correlated with worsening renal function, suggesting it is involved in the pathogenesis of DKD. ROC analysis revealed that either PRMT1, DD, SBP, or Alb could be successfully used as a predictor for DKD. The combination of the four resulted in an AUC value greater than that of all four individuals indicating an improvement in diagnostic performance. However, DD on its own is inadequate in differentiating between the long-term duration of T2DM patients without renal damage and the short-term duration of patients with early damage. In early DKD, the SBP may be only mildly elevated and may even be normal in some patients. Furthermore, the BP lowering effect of antihypertensive therapy may mask the true value of BP, thus limiting its utility as a marker. Serum Alb is usually normal in the early stages of DKD, while elevated PRMT1 levels may found in other chronic diseases. Thus, their diagnostic specificity when used alone is low. Combining DD, SBP, Alb and PRMT1 could improve the sensitivity and specificity of diagnosis through the assessment of molecular pathology (PRMT1), disease progression (DD) and functional outcomes (SBP, Alb). This multi-dimensional approach offers a more comprehensive framework for accurate early diagnosis risk stratification and management of DKD-overcoming limitation of single marker.

Conclusion

The serum PRMT1 level of DKD patients was higher than that of T2DM patients and healthy people, and it was closely related to the severity of DKD, which could be used as an indicator to predict DKD. As a single biomarker, PRMT1 has moderate to strong independent predictive value for DKD, being an independent risk factor for early-stage and clinical DKD with enhanced efficacy in advanced stages, and ROC analysis confirmed its good discriminative ability for DKD detection. Combining PRMT1 with conventional clinical factors (DD, Alb, SBP, Hb) further improves DKD predictive efficacy, as these weakly correlated factors complement the single biomarker to enhance early screening sensitivity and clinical risk stratification accuracy.

Funding Statement

This work was supported by the following projects: 2025 Hebei Provincial Medical Applicable Technology Follow-up Project entitled (Project No. ZG20250088); 2024 Government-Sponsored Clinical Medicine Talent Cultivation Project jointly funded by the Finance Department of Hebei Province and the Health Commission of Hebei Province (Project No. ZF2024024); 2026 Hebei Provincial Medical Science Research Program sponsored by the Health Commission of Hebei Province (Project No. 20260069).

Abbreviations

PRMT1, Protein Arginine Methyltransferase 1; DKD, diabetic kidney disease; T2DM, Type 2 Diabetes Mellitus; UACR, urinary albumin–creatinine ratio; eGFR, estimate glomerular filtration rate; ELISA, Enzyme-Linked Immunosorbent Assay; ESRD, end-stage renal disease; OR, Odds ratio; GFR, glomerular filtration rate; SCr, serum creatinine; BUN, blood urea nitrogen; ER, endoplasmic reticulum; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; DD, diabetes duration; Hb, hemoglobin; TP, total protein; Alb, albumin; FBG, fasting blood glucose; TC, total cholesterol; TG, triglycerides; AGEs, advanced glycation end products; ADMA, asymmetric dimethylarginine; NO, nitric oxide; DDAH1, type I dimethylarginine dimethylaminohydrolase; HOMA-IR, homeostasis model assessment of insulin resistance; IR, insulin resistance; EPO, erythropoietin.

Data Sharing Statement

The data that support the findings of this study are openly available in Mendeley Data at https://data.mendeley.com/, reference number: DOI:10.17632/nb2hvczkcv.1, Additionally, the data used or generated in this work is available from the corresponding author when reasonably requested.

Author Contributions

Chunxia Dong: Writing – review & editing, Validation, Supervision. Jing Liu: Writing – original draft, Writing – review & editing, Validation, Supervision, Funding acquisition. Rutong Zhang: Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Formal analysis. Kexin Gan: Writing – review & editing, Visualization. Yueying Zhang: Writing – original draft, Methodology, Investigation. Kai Niu: Writing – review & editing, Supervision, Investigation, Validation. Zhijuan Hu: Writing – review & editing, Investigation, Formal analysis. Hao Li: Writing – review & editing, Methodology, Investigation. Yuling Xing: Writing – review and editing, Validation. Boqing Ma: Writing – review & editing, Supervision, Validation.

All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare no conflicts of interest in this work.