Serum long noncoding RNA human plasmacytoma variant translocation 1 (PVT1) expression levels in Egyptians type 2 diabetic patients and its correlation with severity of diabetic nephropathy

Abstract

Background

Continuous rise in the global incidence of type 2 diabetes mellitus (T2DM) has been associated with a marked increase in diabetic kidney disease (DKD). Despite that, the clinical markers that are currently used for diagnosing diabetic nephropathy (DN) still not reliable. In recent years, long non-coding RNAs (lncRNAs) have drawn growing attention, with accumulating evidence linking their dysregulated expression to the onset and progression of DKD.

Aim of the study

This study aimed to assess serum lncRNA PVT1 expression in Egyptian patients with type 2 diabetes mellitus and evaluate its association with the progression and severity of diabetic kidney disease.

Patients and methods

A total of 80 adults were enrolled and divided into four groups (20 each): three type 2 diabetes mellitus groups stratified by urinary albumin-to-creatinine ratio (UACR; normal, moderately increased, and severely increased) and one healthy control group. Diabetic patients were further classified by fundus examination into those with and without retinopathy. All participants underwent clinical and laboratory evaluation, and serum lncRNA PVT1 expression was quantified using real-time PCR.

Results

Serum lncRNA PVT1 expression was markedly elevated in patients with type 2 diabetes compared to healthy controls, with significantly higher levels observed in G3 relative to G1. A significant positive correlation was found between PVT1 expression and UACR, serum creatinine, age, BMI, fasting blood glucose, 2-hour postprandial glucose, and HbA1c. Conversely, PVT1 demonstrated a significant negative correlation with eGFR, ALT, and platelet count. Notably, PVT1 emerged as the sole independent predictor of proteinuria among diabetic patients.

Conclusion

lncRNA PVT1 is significantly overexpressed in T2DM patients and correlates with glycemic status and renal dysfunction. It serves as a potential reliable biomarker for DKD severity and progression and represents a potential therapeutic target.

Clinical trial number

Not applicable.

Introduction

Diabetes mellitus is a globally prevalent and rapidly rising disease, currently estimated to affect around 537 million adults worldwide. It is characterized by chronic hyperglycemia, insulin resistance, and impaired glucose tolerance [1].

The development of type 2 diabetes mellitus (T2DM) is driven by a complex interplay of genetic predisposition, lifestyle habits, and environmental influences, with T2DM accounting for nearly 90% of all diabetes cases [2]. It is in deeded to mention that diabetic nephropathy (DN), referred to as diabetic kidney disease (DKD), which is the most common microvascular complication of diabetes and a major cause of end-stage renal disease [3].

Diabetic nephropathy (DN) is marked by both structural and functional renal alterations, including glomerular hypertrophy, thickening of the glomerular basement membrane, and accumulation of extracellular matrix proteins within the renal tubules [4]. It is estimated that 20–40% of individuals with diabetes develop DN, positioning it as a leading cause of morbidity and mortality among diabetic patients [5].

The current clinical diagnosis of diabetic nephropathy (DN) primarily depends on persistent albuminuria and reduced estimated glomerular filtration rate (eGFR) [6]. However, these markers have limited sensitivity and often fail to detect microvascular complications in diabetic patients prior to the onset of renal damage [7].

As a result, DN is frequently identified at advanced stages, and in some cases only confirmed postmortem [8]. This underscores the critical need for early detection and timely intervention, driving ongoing research into novel, reliable biomarkers and therapeutic targets for DN [6].

Recent advances in molecular biology have highlighted the regulatory functions of non-coding RNAs, particularly long non-coding RNAs (lncRNAs), in diverse physiological and pathological processes [9].

LncRNAs, although lacking protein-coding potential, play critical roles in gene regulation, chromatin remodeling, and epigenetic modification [10]. In recent years, growing attention has been directed toward the role of long non-coding RNAs (lncRNAs) in the pathogenesis of chronic diseases, including cancers, cardiovascular disorders, and immune-mediated conditions [11].

Emerging evidence indicates that lncRNAs also contribute to the development and progression of diabetic tissue injury, thereby supporting their potential as novel biomarkers for the diagnosis and evaluation of diabetic nephropathy [12].

Plasmacytoma variant translocation 1 (PVT1), a 1.9 kb long non-coding RNA, has been identified as a potential susceptibility locus for diabetic end-stage kidney disease (ESKD) through a pooling-based genome-wide polymorphism study. Subsequent studies demonstrated that PVT1 expression is upregulated in human podocytes and mouse podocyte clone 5 cells under high-glucose (HG) conditions, while PVT1 knockdown mitigated HG-induced injury and apoptosis. In addition, elevated PVT1 levels have been observed in HG-stimulated human mesangial cells (MCs), where its silencing reduced extracellular matrix (ECM) protein accumulation, highlighting the pivotal role of PVT1 in the pathogenesis of diabetic nephropathy [13–15].

Hanson et al. conducted a genome-wide analysis of 115,352 single nucleotide polymorphisms (SNPs) and identified PVT1 as a potential candidate gene contributing to end-stage renal disease (ESRD) susceptibility in patients with diabetes [16].

However, only a limited number of studies to date have investigated the relationship between PVT1 and diabetic nephropathy (DN), particularly through in-vivo models [13].

We aimed in this study to evaluate the serum expression level of lncRNA PVT1 using real-time quantitative PCR in Egyptian patients with type 2 diabetes mellitus, both with and without diabetic kidney disease, and to investigate its association with disease progression and severity.

Methodology

This cross-sectional study was performed at the outpatient Endocrinology and Diabetes Clinic of Kasr El Aini Hospital. A total of 80 individuals were enrolled. Patients with type 2 diabetes mellitus (T2DM) were diagnosed according to the American Diabetes Association (ADA) 2023 guidelines, while those with diabetic kidney disease (DKD) were identified based on a urinary albumin-to-creatinine ratio (UACR) greater than 30 mg/g in a 24-hour urine collection, following the KDIGO 2022 recommendations. Participants were divided into four equal groups of 20 subjects each:

• Group 1 (G1): T2DM patients with normal UACR (< 30 mg/g).

• Group 2 (G2): T2DM patients with moderately increased UACR (30–300 mg/g).

• Group 3 (G3): T2DM patients with severely increased UACR (> 300 mg/g).

• Group 4 (G4): Age- and sex-matched healthy controls.

Based on ophthalmological assessment, diabetic participants were further stratified as follows:

• Group A: Patients with diabetic retinal changes.

• Group B: Patients without diabetic retinal changes.

In total, 60 patients with established T2DM under regular follow-up were included, along with 20 healthy controls of comparable age and sex. Control subjects were asymptomatic, apparently healthy individuals between 35 and 65 years of age. Both male and female participants were recruited. A written informed consent was obtained from all study participants, or their legal representatives and the study was approved by The Cairo University Research Ethics Committee (REC) (Code: MD-197-2023).The study was conducted in accordance with the principles of the Declaration of Helsinki.

Exclusion criteria were history of acute kidney injury, renal stones, urinary tract infection, recent exposure to nephrotoxic drugs, non-diabetic CKD, or estimated GFR ≤ 30 ml/min/1.73 m². Patients with other types of diabetes, recent diabetic ketoacidosis or hypoglycemic coma (within 3 months), prior ischemic or hemorrhagic stroke, uncontrolled hypertension, congestive heart failure, congenital heart disease, established coronary artery disease (CAD), previous percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), or angina requiring further cardiac evaluation were excluded. Additional exclusions included peripheral vascular disease, fever, active inflammatory or infectious disease, autoimmune, hematological, rheumatological, or endocrine disorders, as well as current or past malignancy under chemotherapy or radiotherapy.

The diagnosis of fatty liver was based on imaging evidence of hepatic steatosis (abdominal ultrasound demonstrating increased hepatic echogenicity consistent with steatosis), in conjunction with the presence of metabolic risk factors (e.g., obesity, type 2 diabetes mellitus, or dyslipidemia), and after exclusion of secondary causes of hepatic steatosis, including significant alcohol intake and other chronic liver diseases.

All participants underwent the following assessments:

1. Clinical history:

• Age.

• Sex.

• Duration of diabetes.

• Current medications.

• Clinical, laboratory, and radiological evaluations.

• 2.Physical examination:

• Anthropometric measurements: waist circumference, height, weight.

• Body mass index (BMI), calculated as weight (kg) / height (m²).

• Blood pressure (systolic and diastolic) measured with a mercury sphygmomanometer after ≥ 5 min of rest.

• 3.Laboratory and imaging investigations:

• Complete blood count (CBC).

• Fasting plasma glucose (FPG).

• Two-hour postprandial glucose (2hPG).

• Glycated hemoglobin (HbA1c).

• Serum urea and creatinine with estimated GFR (eGFR).

• Lipid profile: total cholesterol, triglycerides, HDL-C, LDL-C (after ≥ 12 h of fasting).

• Liver function tests: ALT and AST.

• Measurement of serum LncRNA PVT1 using quantitative real-time PCR (5 ml venous sample).

• Urinary albumin-to-creatinine ratio (UACR) from an early morning urine sample.

• Fundus examination to detect diabetic retinopathy or other retinal changes.

• Abdominopelvic ultrasonography for related abnormalities.

Molecular biology techniques

Expression Analysis of Serum Long Non-Coding RNA PVT1.

• A)RNA Extraction.

Total RNA was extracted from 200 µL of serum using the Qiagen RNeasy Mini Kit (Valencia, CA, USA) following the manufacturer’s protocol. Samples were lysed with QIAzol reagent, followed by phase separation with chloroform and ethanol precipitation. RNA binding and sequential washes were performed on RNeasy Mini spin columns, and RNA was eluted in 50 µL of RNase-free water.

• B)Quantitation and assessment of RNA purity:

RNA concentration and purity were determined using a NanoDrop^® ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, USA). Absorbance was measured at 260 nm and 280 nm, and purity was assessed using the A260/A280 ratio, with acceptable values between 1.8 and 2.0. The Beer–Lambert law (A = ε × b × C) was applied for concentration determination, where ε represents molar absorptivity, b the path length, and C the analyte concentration.

• C)Reverse transcription (RT):

• D)Complementary DNA (cDNA) was synthesized from total RNA (60 µg) using the miScript II RT Kit (Qiagen, USA) in a 20 µL reaction volume. Reactions were incubated at 37 °C for 60 min, followed by enzyme inactivation at 95 °C for 5 min.
• E)Quantitative Real-time PCR (qPCR) for Detection of LNC PVT-1:
• F)qPCR amplification of lncRNA PVT1 and the internal control GAPDH was conducted using the SYBR Green qPCR Kit (Thermo Scientific, USA) and the QuantiTect Primer Assay (Qiagen, USA). Each 20 µL reaction contained 10 µL of SYBR Green Master Mix, 1 µL each of forward and reverse primers, 2 µL of cDNA, and RNase-free water. Cycling was performed on a Rotor-Gene thermocycler with an initial activation at 95 °C for 15 min, followed by 40 cycles of denaturation (94 °C, 15 s), annealing (55 °C, 30 s), and extension (70 °C, 30 s), with fluorescence recorded during the extension step.
• G)Data analysis:

Melting curve analysis confirmed amplification specificity. Relative PVT1 expression was quantified using the ΔΔCt method, with GAPDH as the reference gene. The calculations were as follows:

A fold change > 1 indicated upregulation, whereas < 1 denoted downregulation. Control samples were normalized to a fold change of 1.

Multivariable logistic regression analysis.

Variables demonstrated a significance level of P < 0.10 in the univariate analysis were considered candidates for the multivariable stepwise logistic regression model to identify independent predictors of proteinuria. Because proteinuria was defined according to the urinary albumin-to-creatinine ratio (UACR), UACR was deliberately excluded from the multivariable analysis to avoid circularity and collinearity between the outcome definition and predictor variables.

Results

Among the 80 participants, 60 were patients with type 2 diabetes mellitus and 20 were healthy controls, matched for sex (P = 0.401). More than half of the diabetic patients had fatty liver, whereas none of the controls exhibited this condition (P < 0.001). Diabetic retinopathy was present in 50% of the diabetic group. The control group was significantly younger (P < 0.001) and had a lower body mass index compared with the diabetic patients (P < 0.001). The median duration of diabetes was 9 years, ranging from 0.1 to 30 years, as shown in Table 1. Laboratory data of diabetic patients compared to healthy controls are shown in Table 2.

Table 1.

Demographic characteristics of diabetic patients compared to healthy controls

	Control	Diabetic
	n = 20 (%)	n = 60 (%)	P value
Gender
Female	16 (80)	41 (68.3)	0.401
Male	4 (20)	19 (31.7)
Funds Classification of Diabetic Patients
Group A	-	30 (50)	-
Group B	-	30 (50)
Fatty liver
Yes	0 (0)	34 (56.7)	< 0.001
No	20 (100)	26 (43.3)
	Mean ± SD	Mean ± SD
Age	42.6 ± 9.9	50.9 ± 8.3	< 0.001
BMI (Kg/m2)	23.7 ± 1.3	31.6 ± 3.4	< 0.001
		Median (range)
Duration of diabetes (years)	-	9 (0.1–30)	-

BMI: Body mass index

Table 2.

Laboratory data of diabetic patients compared to healthy controls

	Control	Diabetic
	Mean ± SD	Mean ± SD	P value
Hemoglobin (g/dl)	12.1 ± 1	12.2 ± 1.6	0.676
Total leucocytic count (cmm)	7.4 ± 2.1	7.8 ± 2.1	0.536
Platelets (mcL)	335.8 ± 51.9	273.7 ± 73.3	< 0.001
Fasting blood glucose (mg/dL)	84 ± 12.3	164 ± 67.9	< 0.001
2 h postprandial blood glucose (mg/dL)	109.6 ± 19	232.5 ± 83.2	< 0.001
HbA1c (mg/dL)	5.8 ± 0.4	8.7 ± 2.2	< 0.001
Albumin/creatinine ratio (mcg/mg creatinine)	17.5 ± 6.8	492.9 ± 879.1	< 0.001
LDL-C (mg/dL)	123 ± 34.2	120.4 ± 47.4	0.825
HDL-Cholesterol (mg/dL)	45.7 ± 7.7	47.7 ± 10.9	0.459
Total cholesterol (mg/dL)	203.1 ± 37.3	198 ± 52.2	0.688
Serum triglycerides (mg/dL)	118.1 ± 42.6	147.4 ± 68.7	0.076
e-GFR (mL/min/1.73m2)	102.5 ± 16	80.2 ± 29.7	< 0.001
	Median (range)	Median (range)
Alanine Aminotransferase (U/L)	20 (15–37)	17.5 (4–81)	0.067
Aspartate Amino transferase (U/L)	23.5 (16–35)	21.5 (11–62)	0.129
Urea(mg/dL)	29 (22–36)	28 (12.8–101)	0.661
Creatinine (mg/dL)	0.8 (0.6-1)	0.9 (0.5–2.4)	0.029
FC (PVT-1) (RQ OR FC)	1 (0.9–1.2)	11.6 (0-167.3)	< 0.001

LDL: low-density lipoprotein, HDL: high-density lipoprotein, e-GFR: estimated glomerular filtration rate, SD: Standard deviation, P value < 0.05 is considered significant

Among diabetic patients, those with macroalbuminuria had a significantly longer duration of diabetes compared with individuals with normoalbuminuria (P = 0.028) as shown in Table 3.

Table 3.

Demographic characteristics of study groups among diabetic patients

	Normoalbuminuria(G1)	Microalbuminuria(G2)	Macroalbuminuria(G3)
	n = 20 (%)	n = 20 (%)	n = 20 (%)	P value (Overall)
Gender
Female	15 (75)	14 (70)	12 (60)	0.692
Male	5 (25)	6 (30)	8 (40)
Funds Classification of Diabetic Patients
Group A	9 (45)	9 (45)	12 (60)	0.682
Group B	11 (55)	11 (55)	8 (40)
Fatty liver
Yes	11 (55)	12 (60)	11 (55)	0.934
No	9 (45)	8 (40)	9 (45)
	Mean ± SD	Mean ± SD	Mean ± SD
Age	50.2 ± 8.2	49.9 ± 7.6	52.5 ± 9.2	0.653
BMI (Kg/m2)	32.3 ± 2.2	31.6 ± 4.9	31 ± 2.4	0.466
	Median (range)	Median (range)	Median (range)
Duration of diabetes (years)	6 (0.1–20)	8.5 (2–30)	10.5 (3–30)	0.028

BMI: Body mass index, SD: Standard deviation, P value < 0.05 is considered significant

Hemoglobin levels were markedly lower in the macroalbuminuria group compared with both the normoalbuminuria and microalbuminuria groups (P = 0.009 and P < 0.001, respectively). The urinary albumin-to-creatinine ratio was significantly higher in patients with macroalbuminuria compared to all other groups (P < 0.001). In addition, estimated glomerular filtration rate (eGFR) was significantly reduced in the macroalbuminuria group (P < 0.001), whereas serum urea and creatinine levels were significantly elevated compared to patients in the other groups (P < 0.001). With respect to PVT1 expression, patients with macroalbuminuria showed significantly higher levels compared with those with normoalbuminuria (P < 0.001). On the other hand, there were no statistically significant differences among the groups in terms of total leukocyte count, fasting blood glucose, 2-hour postprandial glucose, HbA1c, lipid profile (LDL-C, HDL-C, total cholesterol, triglycerides), or liver enzymes (ALT, AST) as shown in Table 4.

Table 4.

Laboratory data of study groups among diabetic patients

	Normoalbuminuria (G1)	Microalbuminuria (G2)	Macroalbuminuria (G3)
	Mean ± SD	Mean ± SD	Mean ± SD	P value	P value (Pairwise comparison)
Hemoglobin (g/dl)	12.3 ± 1.4	13.3 ± 1.2	11.1 ± 1.2	< 0.001	G1 vs. G2: 0.058 G1 vs. G3: 0.009 G2 vs. G3:<0.001
Total leucocytic count (cmm)	7.5 ± 1.9	7.4 ± 2.1	8.4 ± 2.2	0.212
Platelets (mcL)	271.2 ± 72.5	268.4 ± 77.3	281.4 ± 73.1	0.846
Fasting blood glucose (mg/dL)	165.7 ± 49.9	162.7 ± 75.4	163.7 ± 78.3	0.991
2 h postprandial blood glucose (mg/dL)	218.2 ± 68.1	238.7 ± 100.5	240.8 ± 80.3	0.646
HbA1c (mg/dL)	8 ± 1.7	9.2 ± 2.5	8.7 ± 2.2	0.236
LDL-C (mg/dL)	121.7 ± 29.3	120.6 ± 38.2	118.9 ± 68.2	0.983
HDL-Cholesterol (mg/dL)	48.5 ± 11.7	51.3 ± 7	43.3 ± 12.2	0.059
Total cholesterol (mg/dL)	201.7 ± 30.9	198.4 ± 43.1	193.9 ± 75	0.897
Serum triglycerides (mg/dL)	152.9 ± 77.9	133.6 ± 55.1	155.8 ± 72.3	0.548
e-GFR (mL/min/1.73m2)	92.4 ± 22.5	94.8 ± 19.2	53.3 ± 26.7	< 0.001	G1 vs. G2: 1.000 G1 vs. G3: <0.001 G2 vs. G3:<0.001
	Median (range)	Median (range)	Median (range)
Alanine Aminotransferase (U/L)	18 (10–80)	19 (7–81)	16.5 (4–43)	0.696
Aspartate transferase (U/L)	18 (11–56)	21 (11–31)	22 (11–62)	0.294
Urea (mg/dL)	26 (12.8–54.7)	26.5 (16.5–59.9)	66 (23–101)	< 0.001	G1 vs. G2: 1.000 G1 vs. G3: <0.001 G2 vs. G3:<0.001
Creatinine (mg/dL)	0.8 (0.6–1.5)	0.8 (0.5–2.4)	1.8 (0.6–2.3)	< 0.001	G1 vs. G2: 1.000 G1 vs. G3: <0.001 G2 vs. G3:<0.001
FC (PVT-1) (RQ OR FC)	9.2 (0-39.4)	10.6 (0-52.6)	31.6 (0.5-167.3)	0.003	G1 vs. G2: 0.956 G1 vs. G3: <0.001 G2 vs. G3:0.064
Albumin/creatinine ratio (mcg/mg creatinine)	14.3 (2.9–26.2)	69.7 (31-254.5)	864.1 (309.3–4246)	< 0.001	G1 vs. G2: 1.000 G1 vs. G3: <0.001 G2 vs. G3:<0.001

LDL: low-density lipoprotein, HDL: high-density lipoprotein, e-GFR: estimated glomerular filtration rate, SD: Standard deviation, P value < 0.05 is considered significant

Fundus examination revealed no statistically significant difference in the occurrence of retinopathy between patients with normoalbuminuria and those with albuminuria (P = 0.785), as shown in Fig. 1. However, patients with albuminuria had a significantly longer duration of diabetes compared to those with normoalbuminuria (P = 0.020), as shown in Table 5; Fig. 2.

Fig. 1.

Bar graph representing distribution of retinopathy among study groups

Table 5.

Demographic characteristics of diabetic patients with and without proteinuria

	Normoalbuminuria	Albuminuria (micro or macro)
	n = 20 (%)	n = 40 (%)	P value
Gender
Female	15 (75)	26 (65)	0.560
Male	5 (25)	14 (35)
Funds Classification of Diabetic Patients
Group A	9 (45)	21 (52.5)	0.785
Group B	11 (55)	19 (47.5)
Fatty liver
Yes	11 (55)	23 (57.5)	0.854
No	9 (45)	17 (42.5)
	Mean ± SD	Mean ± SD
Age	50.2 ± 8.2	51.2 ± 8.4	0.664
BMI (Kg/m2)	32.3 ± 2.2	31.3 ± 3.8	0.268
	Median (range)	Median (range)
Duration of diabetes (years)	6 (0.1–20)	10 (2–30)	0.020

BMI: Body mass index, SD: Standard deviation, P value < 0.05 is considered significant

Fig. 2.

Boxplot representing duration of diabetes among study groups

Biochemical analysis demonstrated that the urinary albumin-to-creatinine ratio was markedly elevated in patients with albuminuria compared to normoalbuminuric patients (P < 0.001). Similarly, patients with albuminuria exhibited significantly lower eGFR (P = 0.022) and higher serum urea (P = 0.006) and creatinine levels (P = 0.016). In addition, PVT1 expression was significantly higher in patients with albuminuria compared to those with normoalbuminuria (P = 0.013), as shown in Table 6; Fig. 3.

Table 6.

Laboratory data of diabetic patients with and without proteinuria

	Normoalbuminuria	Albuminuria (micro or macro)
	Mean ± SD	Mean ± SD	P value
Hemoglobin (g/dl)	12.3 ± 1.4	12.2 ± 1.7	0.747
Total leucocytic count (cmm)	7.5 ± 1.9	7.9 ± 2.2	0.469
Platelets (mcL)	271.2 ± 72.5	274.9 ± 74.6	0.857
Fasting blood glucose (mg/dL)	165.7 ± 49.9	163.2 ± 75.9	0.895
2 h postprandial blood glucose (mg/dL)	218.2 ± 68.1	239.7 ± 89.8	0.350
HbA1c (mg/dL)	8 ± 1.7	9 ± 2.3	0.122
LDL-C (mg/dL)	121.7 ± 29.3	119.8 ± 54.6	0.883
HDL-Cholesterol (mg/dL)	48.5 ± 11.7	47.3 ± 10.7	0.698
Total cholesterol (mg/dL)	201.7 ± 30.9	196.1 ± 60.4	0.701
Serum triglycerides (mg/dL)	152.9 ± 77.9	144.7 ± 64.4	0.668
e-GFR (mL/min/1.73m2)	92.4 ± 22.5	74 ± 31.1	0.022
	Median (range)	Median (range)
Alanine Aminotransferase (U/L)	18 (10–80)	17 (4–81)	0.531
Aspartate transferase (U/L)	18 (11–56)	22 (11–62)	0.246
Urea(mg/dL)	26 (12.8–54.7)	32.5 (16.5–101)	0.006
Creatinine (mg/dL)	0.8 (0.6–1.5)	1 (0.5–2.4)	0.016
FC (PVT-1) (RQ OR FC)	9.2 (0-39.4)	25.5 (0-167.3)	0.013
Albumin/creatinine ratio (mcg/mg creatinine)	14.3 (2.9–26.2)	281.9 (31-4246)	< 0.001

LDL: low-density lipoprotein, HDL: high-density lipoprotein, e-GFR: estimated glomerular filtration rate, SD: Standard deviation, P value < 0.05 is considered significant

Fig. 3.

Boxplot representing PVT level among diabetic patients with and without albuminuria

According to Table 7, the four study groups were sex-matched (P = 0.614). Healthy controls were significantly younger than patients with normoalbuminuria, microalbuminuria, or macroalbuminuria (P = 0.004), and had a markedly lower body mass index compared with all diabetic groups (P < 0.001). The duration of diabetes was significantly longer in patients with macroalbuminuria compared to those with normoalbuminuria (P = 0.028). Abdominal ultrasonography revealed fatty liver in diabetic patients, whereas none of the controls exhibited fatty liver (P < 0.001). Fundus examination did not show a significant difference in the prevalence of retinopathy among the three diabetic subgroups (P = 0.682).

Table 7.

Demographic characteristics of study groups

	Control	Normoalbuminuria	Microalbuminuria	Macroalbuminuria
	n = 20 (%)	n = 20 (%)	n = 20 (%)	n = 20 (%)	P value
Gender
Female	16 (80)	15 (75)	14 (70)	12 (60)	0.614
Male	4 (20)	5 (25)	6 (30)	8 (40)
Funds Classification of Diabetic Patients
Group A	-	9 (45)	9 (45)	12 (60)	0.682
Group B	-	11 (55)	11 (55)	8 (40)
Fatty liver
Yes	0 (0)	11 (55)	12 (60)	11 (55)	< 0.001
No	20 (100)	9 (45)	8 (40)	9 (45)
	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD
Age	42.6 ± 9.9	50.2 ± 8.2	49.9 ± 7.6	52.5 ± 9.2	0.004
BMI (Kg/m2)	23.7 ± 1.3	32.3 ± 2.2	31.6 ± 4.9	31 ± 2.4	< 0.001
	Median (range)	Median (range)	Median (range)	Median (range)
Duration of diabetes (years)	-	6 (0.1–20)	8.5 (2–30)	10.5 (3–30)	0.028

BMI: Body mass index, SD: Standard deviation, P value < 0.05 is considered significant

Findings presented in Table 8 indicate that hemoglobin levels were significantly higher in patients with microalbuminuria compared to both controls and macroalbuminuric patients (P = 0.016 and < 0.001, respectively). Likewise, normoalbuminuric patients had significantly higher hemoglobin levels than those with macroalbuminuria (P = 0.010). Platelet counts were significantly greater in controls compared to patients with normo- and microalbuminuria (P = 0.026 and 0.018, respectively).

Table 8.

Laboratory data of study groups

	Control	Normoalbuminuria	Microalbuminuria	Macroalbuminuria
	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD	P value
Hemoglobin (g/dl)	12.1 ± 1	12.3 ± 1.4	13.3 ± 1.2	11.1 ± 1.2	< 0.001
Total leucocytic count (cmm)	7.4 ± 2.1	7.5 ± 1.9	7.4 ± 2.1	8.4 ± 2.2	0.324
Platelets (mcL)	335.8 ± 51.9	271.2 ± 72.5	268.4 ± 77.3	281.4 ± 73.1	0.009
Fasting blood glucose (mg/dL)	84 ± 12.3	165.7 ± 49.9	162.7 ± 75.4	163.7 ± 78.3	< 0.001
2 h postprandial blood glucose (mg/dL)	109.6 ± 19	218.2 ± 68.1	238.7 ± 100.5	240.8 ± 80.3	< 0.001
HbA1c (mg/dL)	5.8 ± 0.4	8 ± 1.7	9.2 ± 2.5	8.7 ± 2.2	< 0.001
Albumin/creatinine ratio (mcg/mg creatinine)	17.5 ± 6.8	12.9 ± 7.9	89.8 ± 67.2	1376.1 ± 1077.4	< 0.001
LDL-C (mg/dL)	123 ± 34.2	121.7 ± 29.3	120.6 ± 38.2	118.9 ± 68.2	0.993
HDL-Cholesterol (mg/dL)	45.7 ± 7.7	48.5 ± 11.7	51.3 ± 7	43.3 ± 12.2	0.071
Total cholesterol (mg/dL)	203.1 ± 37.3	201.7 ± 30.9	198.4 ± 43.1	193.9 ± 75	0.938
Serum triglycerides (mg/dL)	118.1 ± 42.6	152.9 ± 77.9	133.6 ± 55.1	155.8 ± 72.3	0.209
e-GFR (mL/min/1.73m2)	102.5 ± 16	92.4 ± 22.5	94.8 ± 19.2	53.3 ± 26.7	< 0.001
	Median (range)	Median (range)	Median (range)	Median (range)
Alanine Aminotransferase (U/L)	20 (15–37)	18 (10–80)	19 (7–81)	16.5 (4–43)	0.233
Aspartate transferase (U/L)	23.5 (16–35)	18 (11–56)	21 (11–31)	22 (11–62)	0.179
Urea (mg/dL)	29 (22–36)	26 (12.8–54.7)	26.5 (16.5–59.9)	66 (23–101)	< 0.001
Creatinine (mg/dL)	0.8 (0.6-1)	0.8 (0.6–1.5)	0.8 (0.5–2.4)	1.8 (0.6–2.3)	< 0.001
FC (PVT-1) (RQ OR FC)	1 (0.9–1.2)	9.2 (0-39.4)	10.6 (0-52.6)	31.6 (0.5-167.3)	< 0.001

LDL: low-density lipoprotein, HDL: high-density lipoprotein, e-GFR: estimated glomerular filtration rate, SD: Standard deviation, P value < 0.05 is considered significant

The urinary albumin-to-creatinine ratio was markedly elevated in patients with macroalbuminuria relative to all other groups (P < 0.001). In contrast, eGFR values were significantly reduced (P < 0.001), while serum urea and creatinine levels were significantly increased (P < 0.001) in this group. PVT1 expression was also higher in macroalbuminuric patients compared with normoalbuminuric patients and healthy controls (P = 0.047 and < 0.001, respectively). Furthermore, patients with microalbuminuria showed significantly higher PVT1 expression compared to healthy controls (P = 0.001) as shown in Table 8.

As displayed in Table 8, there were no significant differences among the groups with respect to total leukocyte count, lipid profile (LDL-C, HDL-C, total cholesterol, triglycerides), or liver enzymes (ALT, AST).

As illustrated in Table 9, the control group was sex-matched with both normoalbuminuric and albuminuric patients (P = 0.501). Fundus examination revealed no significant difference in the prevalence of retinopathy between patients with normoalbuminuria and those with albuminuria (P = 0.785). However, healthy controls were significantly younger than both normoalbuminuric and albuminuric patients (P = 0.022 and 0.002, respectively) and had a markedly lower body mass index compared to both groups (P < 0.001). Furthermore, the duration of diabetes was significantly longer in patients with albuminuria relative to those with normoalbuminuria (P = 0.020).

Table 9.

Demographic characteristics of patients with and without proteinuria

	Control	Normoalbuminuria	Albuminuria (micro or macro)
	n = 20 (%)	n = 20 (%)	n = 40 (%)	P value
Gender
Female	16 (80)	15 (75)	26 (65)	0.501
Male	4 (20)	5 (25)	14 (35)
Funds Classification of Diabetic Patients
Group A	-	9 (45)	21 (52.5)	0.785
Group B	-	11 (55)	19 (47.5)
Fatty liver
Yes	0 (0)	11 (55)	23 (57.5)	< 0.001
No	20 (100)	9 (45)	17 (42.5)
	Mean ± SD	Mean ± SD	Mean ± SD
Age	42.6 ± 9.9	50.2 ± 8.2	51.2 ± 8.4	0.002
BMI (Kg/m2)	23.7 ± 1.3	32.3 ± 2.2	31.3 ± 3.8	< 0.001
		Median (range)	Median (range)
Duration of diabetes (years)	-	6 (0.1–20)	10 (2–30)	0.020

BMI: Body mass index, SD: Standard deviation, P value < 0.05 is considered significant

According to Table 10, platelet counts were significantly higher in healthy controls compared to both diabetic groups (P = 0.006 and 0.012, respectively). Conversely, fasting blood glucose, 2-hour postprandial glucose, and HbA1c were all significantly lower among controls compared to patients with or without albuminuria (P < 0.001). The albumin-to-creatinine ratio was markedly elevated in albuminuric patients compared to both normoalbuminuric patients and controls (P = 0.001). Similarly, eGFR was significantly reduced in albuminuric patients relative to both comparison groups (P = 0.036 and < 0.001, respectively). Serum urea levels were significantly higher in albuminuric patients than in those with normoalbuminuria (P = 0.011), while creatinine was significantly elevated in albuminuric patients compared to controls (P = 0.013). Finally, PVT1 expression was significantly lower in controls compared to both diabetic groups (P < 0.001 and 0.006, respectively).

Table 10.

Laboratory data of patients with and without proteinuria

	Control	Normoalbuminuria	Albuminuria (micro or macro)
	Mean ± SD	Mean ± SD	Mean ± SD	P value
Hemoglobin (g/dl)	12.1 ± 1	12.3 ± 1.4	12.2 ± 1.7	0.889
Total leucocytic count (cmm)	7.4 ± 2.1	7.5 ± 1.9	7.9 ± 2.2	0.636
Platelets (mcL)	335.8 ± 51.9	271.2 ± 72.5	274.9 ± 74.6	0.004
Fasting blood glucose (mg/dL)	84 ± 12.3	165.7 ± 49.9	163.2 ± 75.9	< 0.001
2 h postprandial blood glucose (mg/dL)	109.6 ± 19	218.2 ± 68.1	239.7 ± 89.8	< 0.001
HbA1c (mg/dL)	5.8 ± 0.4	8 ± 1.7	9 ± 2.3	< 0.001
Albumin/creatinine ratio (mcg/mg creatinine)	17.5 ± 6.8	12.9 ± 7.9	732.9 ± 996	< 0.001
LDL-C (mg/dL)	123 ± 34.2	121.7 ± 29.3	119.8 ± 54.6	0.964
HDL-Cholesterol (mg/dL)	45.7 ± 7.7	48.5 ± 11.7	47.3 ± 10.7	0.968
Total cholesterol (mg/dL)	203.1 ± 37.3	201.7 ± 30.9	196.1 ± 60.4	0.848
Serum triglycerides (mg/dL)	118.1 ± 42.6	152.9 ± 77.9	144.7 ± 64.4	0.188
e-GFR (mL/min/1.73m2)	102.5 ± 16	92.4 ± 22.5	74 ± 31.1	< 0.001
	Median (range)	Median (range)	Median (range)
Alanine Aminotransferase (U/L)	20 (15–37)	18 (10–80)	17 (4–81)	0.145
Aspartate Aminotransferase (U/L)	23.5 (16–35)	18 (11–56)	22 (11–62)	0.158
Urea(mg/dL)	29 (22–36)	26 (12.8–54.7)	32.5 (16.5–101)	0.013
Creatinine (mg/dL)	0.8 (0.6-1)	0.8 (0.6–1.5)	1 (0.5–2.4)	0.006
FC (PVT-1) (RQ OR FC)	1 (0.9–1.2)	9.2 (0-39.4)	25.5 (0-167.3)	< 0.001

LDL: low-density lipoprotein, HDL: high-density lipoprotein, e-GFR: estimated glomerular filtration rate, SD: Standard deviation, P value < 0.05 is considered significant

Multivariate analysis

Variables showing an association with proteinuria in the univariate analysis at a significance level of P < 0.10 were considered for inclusion in the multivariable model. These variables included age, body mass index (BMI), fasting blood glucose, 2-hour postprandial glucose, HbA1c, serum creatinine, estimated glomerular filtration rate (eGFR), and PVT1 expression. Urinary albumin-to-creatinine ratio (UACR) was excluded a priori because proteinuria was defined using UACR values, thereby avoiding circularity in the analysis.

Given the limited sample size and number of outcome events, a parsimonious stepwise logistic regression model was constructed in accordance with the recommended events-per-variable principle, with priority given to clinically relevant variables. After adjustment for potential confounders, PVT1 expression remained the only independent predictor of proteinuria among patients with diabetes (Tables 11 and 12; Fig. 4). Each one-unit increase in PVT1 expression was associated with a 5% increase in the odds of proteinuria (OR = 1.05, 95% CI: 1.01–1.09, P = 0.043).

Table 11.

Multivariate analysis for prediction of proteinuria among diabetic patients

Variables		B	SE	P value	OR	95.0% CI for OR
PVT		0.05	0.02	0.043	1.05	1.01–1.09

B: regression coefficient, SE: standard error, OR; odds ratio, CI: confidence interval, P value < 0.05 is considered significant

Table 12.

ROC curve for prediction of proteinuria

Variable	Cutoff point	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	AUC	95% CI for AUC	P value
PVT	> 21.6	53	90	91	49	0.70	0.57–0.81	0.003

PPV: Positive predictive value, NPV: Negative predictive value, AUC: Area under ROC curve, P value < 0.05 is considered significant

Fig. 4.

ROC curve for PVT in diagnosis of proteinuria

As shown in Table 13, patients with diabetic retinopathy had a significantly longer duration of diabetes than those without retinopathy (P = 0.032). No significant differences were observed between the two groups in sex distribution, fatty liver disease, age, or BMI. Table 14 shows that serum creatinine levels were significantly higher in patients with diabetic retinopathy than in those without retinopathy (P = 0.040).

Table 13.

Demographic characteristics of diabetic patients with and without retinopathy

	Group A	Group B
	n = 30 (%)	n = 30 (%)	P value
Gender
Female	18 (60)	23 (76.7)	0.267
Male	12 (40)	7 (23.3)
Fatty liver
No	14 (46.7)	12 (40)	0.795
Yes	16 (53.3)	18 (60)
	Mean ± SD	Mean ± SD
Age	51.6 ± 8	50.1 ± 8.6	0.479
BMI (Kg/m2)	31 ± 2.7	32.3 ± 3.9	0.133
	Median (range)	Median (range)
Duration of diabetes (years)	10 (3–30)	6.5 (0.1–30)	0.032

SD: Standard deviation, P value < 0.05 is considered significant

Table 14.

Laboratory data of diabetic patients with and without diabetic retinopathy

	Group A	Group B
	Mean ± SD	Mean ± SD	P value
Hemoglobin (g/dl)	12.1 ± 1.6	12.3 ± 1.5	0.597
Total leucocytic count (cmm)	7.4 ± 2.5	8.1 ± 1.5	0.195
Platelets (mcL)	288.4 ± 82.4	258.9 ± 60.7	0.121
Fasting blood glucose (mg/dL)	163.9 ± 57.9	164.2 ± 77.7	0.984
2 h postprandial blood glucose (mg/dL)	244.3 ± 74.7	220.7 ± 90.7	0.276
HbA1c (mg/dL)	8.7 ± 1.8	8.6 ± 2.5	0.869
Albumin/creatinine ratio (mcg/mg creatinine)	708.8 ± 1090.6	277.1 ± 535.5	0.058
LDL-C (mg/dL)	118.8 ± 42.7	122 ± 52.3	0.793
HDL-Cholesterol (mg/dL)	47.1 ± 10.3	48.3 ± 11.7	0.674
Total cholesterol (mg/dL)	197.7 ± 44.6	198.3 ± 59.7	0.961
Serum triglycerides (mg/dL)	161.1 ± 68.7	133.7 ± 67	0.124
e-GFR (mL/min/1.73m2)	72.9 ± 30.2	87.4 ± 27.8	0.058
	Median (range)	Median (range)
Alanine Aminotransferase (U/L)	16.5 (4–81)	18.5 (10–80)	0.267
Aspartate transferase (U/L)	22 (11–39)	20.5 (11–62)	0.710
Urea(mg/dL)	31 (12.8–101)	28 (17–95)	0.429
Creatinine (mg/dL)	1 (0.5–2.4)	0.8 (0.6–2.3)	0.040
FC (PVT-1) (RQ OR FC)	18.2 (0.1-167.3)	9.8 (0-157.4)	0.105

LDL: low-density lipoprotein, HDL: high-density lipoprotein, e-GFR: estimated glomerular filtration rate, SD: Standard deviation, P value < 0.05 is considered significant

According to Table 15, PVT1 expression was positively correlated with age, BMI, fasting blood glucose, 2-hour postprandial glucose, HbA1c, UACR, and serum creatinine. In contrast, PVT1 expression was negatively correlated with platelet count, eGFR, and alanine aminotransferase. Figures 5 and 6 illustrate the correlations of PVT1 expression with eGFR and UACR, respectively.

Table 15.

Correlation between PVT with different factors

Factors	PVT
	r	P value	Interpretation
Age	0.34	0.002	Significant fair positive correlation
Body mass index	0.37	< 0.001	Significant fair positive correlation
Duration of diabetes	0.13	0.31	Non- significant correlation
Hemoglobin (g/dl)	-0.17	0.127	Non- significant correlation
Total leucocytic count (cmm)	0.03	0.801	Non- significant correlation
Platelets (mcL)	-0.23	0.043	Significant little negative correlation
Fasting blood glucose (mg/dL)	0.45	< 0.001	Significant fair positive correlation
2 h postprandial blood glucose (mg/dL)	0.43	< 0.001	Significant fair positive correlation
HbA1c (mg/dL)	0.42	< 0.001	Significant fair positive correlation
Albumin/creatinine ratio (mcg/mg creatinine)	0.47	< 0.001	Significant fair positive correlation
LDL-C (mg/dL)	-0.08	0.457	Non- significant correlation
HDL-Cholesterol (mg/dL)	-0.06	0.608	Non- significant correlation
Total cholesterol (mg/dL)	-0.08	0.469	Non- significant correlation
Serum triglycerides (mg/dL)	0.12	0.283	Non- significant correlation
e-GFR (mL/min/1.73m2)	-0.44	< 0.001	Significant fair negative correlation
Alanine Aminotransferase (U/L)	-0.23	0.037	Significant little negative correlation
Aspartate transferase (U/L)	-0.09	0.384	Non- significant correlation
Urea(mg/dL)	0.19	0.094	Non- significant correlation
Creatinine (mg/dL)	0.36	0.001	Significant fair positive correlation

LDL: low-density lipoprotein, HDL: high-density lipoprotein, e-GFR: estimated glomerular filtration rate, r is the correlation coefficient & it ranges from − 1 to + 1, p value < 0.05 is considered significant

Fig. 5.

Scatter plot diagram representing correlation between PVT and e-GFR

Fig. 6.

Scatter plot diagram representing correlation between PVT and albumin creatinine ratio

To determine whether differences in PVT1 expression among the study groups were influenced by age or BMI, an analysis of covariance was performed using age and BMI as covariates. After adjustment, the study group remained significantly associated with PVT1 expression (F = 11.66, P < 0.001). Neither age (F = 0.58, P = 0.447) nor BMI (F = 0.27, P = 0.602) was independently associated with PVT1 expression. These findings indicate that the higher PVT1 expression observed in patients with diabetes, particularly those with diabetic nephropathy, was independent of differences in age and BMI.

Statistical methods

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 27. Quantitative variables were summarized as means ± standard deviations (SD) or medians with ranges, depending on data distribution, while categorical variables were presented as frequencies and percentages.

The normality of numerical data was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. For group comparisons, the Student’s t-test was used for normally distributed continuous variables and the Mann–Whitney U test for non-normally distributed data. Comparisons among more than two groups were conducted using one-way ANOVA for normally distributed variables and the Kruskal–Wallis test for non-parametric data, followed by appropriate post hoc analyses with Bonferroni correction when required. Categorical variables were compared using the Chi-square or Fisher’s exact test, as appropriate.

Correlation between variables was assessed using Pearson’s correlation coefficient (r) for normally distributed data and Spearman’s rank correlation for non-normally distributed data. Correlation strength was interpreted as follows:

• r = 0–0.25 (or –0.25): little or no correlation

• r = 0.25–0.50 (or –0.25 to –0.50): fair correlation

• r = 0.50–0.75 (or –0.50 to –0.75): moderate to good correlation

• r > 0.75 (or < –0.75): very good to excellent correlation

Positive (+) and negative (–) signs indicated direct and inverse relationships, respectively.

Variables showing significance at p < 0.10 in univariate analysis were included in stepwise logistic regression to identify independent predictors of proteinuria. Results were expressed as adjusted odds ratios (ORs) with 95% confidence intervals (CIs); CIs not including 1.0 were considered statistically significant.

Receiver Operating Characteristic (ROC) curve analysis was used to determine the optimal cutoff point, sensitivity, specificity, and area under the curve (AUC) for diagnostic performance. Test accuracy was interpreted as follows:

• 0.90–1.00 = excellent

• 0.80–0.90 = good

• 0.70–0.80 = fair

• 0.60–0.70 = poor

• 0.50–0.60 = fail

The optimal cutoff value corresponded to the point closest to the upper left corner of the ROC plot, representing maximum test accuracy. All tests were two-tailed, and a p-value ≤ 0.05 was considered statistically significant [17].

Discussion

Type 2 diabetes mellitus (T2DM) is well-known as a chronic metabolic disorder that is characterized by impaired glucose, lipid, and protein metabolism, and it also ranks as the seventh leading cause of death worldwide [18].

Diabetic kidney disease (DKD) is considered as a frequent microvascular complication of type 2 diabetes mellitus (T2DM), and it is characterized by progressive structural and functional renal injury induced by chronic hyperglycemia [19]. Diabetic kidney disease represents a leading cause of chronic kidney disease (CKD) worldwide, which is defined as a persistent and irreversible decline in renal function, diagnosed when the estimated glomerular filtration rate (eGFR), derived from serum creatinine, remains below 60 mL/min/1.73 m² on at least two separate assessments [20].

Worldwide with the rising global prevalence of type 2 diabetes mellitus (T2DM), the incidence of diabetic kidney disease (DKD) has markedly increased, making it one of the leading causes of end-stage renal disease (ESRD) [21].

In diabetic patients, the early renal manifestations usually include glomerular hyperfiltration and hypertrophy, followed by thickening of the glomerular basement membrane, mesangial matrix expansion, and the development of nodular glomerulosclerosis. These structural changes are usually accompanied by increased urinary albumin excretion, that ultimately leading to progressive renal dysfunction and kidney failure [22].

Current clinical markers for diagnosing diabetic nephropathy (DN) have notable limitations, such as the delayed appearance of albuminuria during disease progression, the occurrence of proteinuria-negative cases despite typical DN pathology, and the limited accuracy of estimated glomerular filtration rate (eGFR). These shortcomings highlight the urgent need for novel biomarkers that can be detected earlier or alongside microalbuminuria to enable timely diagnosis and intervention [23].

In recent years, advances in microarray platforms and high-throughput sequencing technologies have enhanced the understanding of the biological roles and characteristics of long non-coding RNAs (lncRNAs). Growing evidence indicates that dysregulated lncRNA expression is closely associated with the pathogenesis and progression of diabetic kidney disease (DKD) [24].

Recently, long non-coding RNA plasmacytoma variant translocation 1 (PVT1), located on chromosome 8q24.21, has been identified as being closely associated with the development and progression of diabetic kidney disease (DKD) [25].

The primary objective of this study was to assess the serum expression levels of lncRNA PVT1 using real-time quantitative PCR in Egyptian patients with type 2 diabetes mellitus, both with and without diabetic kidney disease, meanwhile, the secondary objective was to investigate the association between circulating lncRNA PVT1 levels and the progression as well as the severity of the disease.

In the present study, the majority of participants were females. Among the 60 diabetic patients, 41 (68.3%) were females and 19 (31.7%) were males, while in the healthy control group, 16 (80%) were females and 4 (20%) were males, with no statistically significant difference between the groups (p = 0.401). When comparing the four study subgroups, sex distribution was as follows: normoalbuminuria (female: 15 [75%], male: 5 [25%]), microalbuminuria (female: 14 [70%], male: 6 [30%]), macroalbuminuria (female: 12 [60%], male: 8 [40%]), and controls (female: 16 [80%], male: 4 [20%]). These groups were also sex-matched, showing no significant differences (p = 0.614).

This result is consistent with a study conducted by Ragab and colleagues in 2024, who investigated urinary angiotensinogen as an indicator of the severity of diabetic nephropathy in type 2 diabetes on 88 individuals [30 males (34.1%) and 58 females (65.9%)]. Similar to our findings, patients and controls were sex-matched with no statistically significant difference (p = 0.24) [26].

Regarding age, healthy controls (42.6 ± 9.9 years) were significantly younger than diabetic patients (50.9 ± 8.3 years) (p < 0.001). This finding is consistent with the study by Essawi and colleagues (2023), who also reported a statistically significant age difference between diabetic patients and controls (p < 0.0001) [27]. Healthy controls were significantly younger than patients with normo-, micro-, and macroalbuminuria, who had mean ages of 50.2 ± 8.2, 49.9 ± 7.6, and 52.5 ± 9.2 years, respectively (p = 0.004). This finding is consistent with the study by Ahmed and colleagues (2022), who also reported significant age differences between controls and diabetic patients across normo-, micro-, and macroalbuminuria groups (p < 0.001) [28].

Regarding body mass index (BMI), healthy controls had a significantly lower mean BMI (23.7 ± 1.3) compared to diabetic patients (31.6 ± 3.4) (p < 0.001). This finding is in accordance with the study by Essawi and colleagues (2023), who also reported higher BMI values in diabetic patients compared to controls (p = 0.0504) [27]. Healthy controls had significantly lower body mass index (BMI) compared to patients with normo-, micro-, and macroalbuminuria, who had mean BMIs of 32.3 ± 2.2, 31.6 ± 4.9, and 31 ± 2.4, respectively (p < 0.001). This finding is consistent with the study by Li and colleagues (2024), who reported lower BMI in controls compared to diabetic patients across all albuminuria groups (p = 0.002) [29]. Similarly, Hliel and colleagues (2025) observed significantly higher BMI values in diabetic patients (macro-, micro-, and normoalbuminuria) compared to controls (p < 0.001) [30]. In contrast, a study by Ali and colleagues (2022) found no significant difference in BMI among controls, type 2 diabetic patients without nephropathy, and those with diabetic nephropathy (p > 0.05) [31].

Among diabetic patients with normo-, micro-, and macroalbuminuria, there were no significant differences in gender, age, or BMI (p = 0.692, 0.653, and 0.466, respectively). This finding is consistent with the study by Fang and colleagues (2024), which also reported no statistically significant differences in gender, age, or BMI among the groups (p > 0.05) [32].

Regarding gender, age, and BMI, there were no statistically significant differences between diabetic patients with retinopathy (group A) and those without retinopathy (group B). Gender distribution was similar between the groups (group A: female 18 [60%], male 12 [40%]; group B: female 23 [76.7%], male 7 [23.3%]; p = 0.267). Mean age was 51.6 ± 8 years in group A and 50.1 ± 8.6 years in group B (p = 0.479), and mean BMI was 31 ± 2.7 and 32.3 ± 3.9, respectively (p = 0.133). These results are consistent with Wu et al. (2024), who investigated the inter-relationships between dietary patterns and glycemic control-related biomarkers on the risk of retinopathy in type 2 diabetes (DR, n = 136; no DR, n = 466) and found no significant differences in age (p = 0.350) or BMI (p = 0.536) between groups [33]. Meanwhile, Casmuti et al. (2025) reported that gender was not associated with diabetic retinopathy in type 2 diabetic patients (p = 0.213) [34].

In our study, the median duration of diabetes among patients was 9 years (range: 0.1–30 years). Patients with macroalbuminuria had a significantly longer disease duration, with a median of 10.5 years (range: 3–30), compared to those with normoalbuminuria, who had a median duration of 6 years (range: 0.1–20; p = 0.028). These findings are consistent with Shahidur Rahman et al. (2024), who evaluated common risk factors of diabetic nephropathy among 128 adults with T2DM and reported that the mean duration of diabetes was significantly lowest in patients with ACR < 30 mg/g (2.5 ± 0.7 years) and highest in those with ACR ≥ 300 mg/g (8.5 ± 1.8 years; p < 0.001) [35].

The present study demonstrated that serum lncRNA PVT1 expression was significantly elevated in all patients with type 2 diabetes mellitus, with a median level of 11.6 (0–167.3), compared with underexpression in the healthy control group [median 1.1 (0.9–1.2); p < 0.001]. Among diabetic subgroups, PVT1 levels were highest in patients with macroalbuminuria [median 31.6 (0.5–167.3)], showing a significant increase compared with patients with normoalbuminuria [median 9.2 (0–39.4); p < 0.001]. No significant differences were observed between normoalbuminuria and microalbuminuria [median 10.6 (0–52.6); p = 0.956] or between microalbuminuria and macroalbuminuria (p = 0.064). Compared with healthy controls, PVT1 levels were significantly higher in patients with macroalbuminuria (p < 0.001), microalbuminuria (p = 0.001), and even in those without albuminuria [median 9.2 (0–39.4); p = 0.006].

Our results showed a significant positive correlation between serum lncRNA PVT1 levels and urinary albumin-to-creatinine ratio (r = 0.47, p < 0.001) as well as serum creatinine (r = 0.36, p = 0.001). These findings are in agreement with a study by Lv et al. (2024), who investigated the role of lncRNA PVT1 in diabetic kidney disease (DKD) and its effect on mitochondrial dysfunction of podocytes via TRIM56. Consistently, kidney tissue samples from DKD patients demonstrated significantly higher PVT1 expression [15].

Our results are also consistent with a study by Yu et al. (2021), who examined the effect of PVT1 knockdown on high glucose–induced proliferation and renal fibrosis in human renal mesangial cells (HRMCs) through the miR-23b-3p/early growth response factor 1 (EGR1) pathway. Their findings demonstrated that serum PVT1 levels were significantly elevated in patients with diabetic nephropathy (DN) (n = 90) compared with healthy controls (n = 90). In addition, PVT1 expression was higher in patients at clinical stages IV/V compared with those at stages I/III (p < 0.01), suggesting that PVT1 may play a pivotal role in the progression of DN [36]. These results are further supported by a study conducted by Abdel-Aty et al. (2022), who investigated serum long non-coding RNAs in diabetic kidney disease among 75 patients with type 2 diabetes mellitus and 25 healthy controls. Their main finding focused on lncRNA PVT1, which, when compared with the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH), was significantly underexpressed in controls and overexpressed across all T2DM patient groups (p = 0.033) [37].

Our findings are also in agreement with a study by Huang et al. (2023), who conducted a bioinformatics analysis of the inflammation-associated lncRNA–mRNA coexpression network in type 2 diabetes [38]. Many previous genetic studies have also highlighted the role of PVT1 in diabetic kidney disease. Such as a study published by Alvarez and DiStefano (2011) [39] that investigated PVT1 gene variants in ethnically diverse populations with diabetes and end-stage renal disease, reporting abundant expression of PVT1 in renal cells and a potential contribution to metabolic dysfunction of renal tissues, thereby promoting kidney-related complications. Similarly, another study done by Varghese and Kumar Subburaj (2021) [40] Which emphasized the role of PVT1 in the pathogenesis of diabetic renal injury. In addition, a genome-wide association study (GWAS) conducted by Gu et al. (2019) [41] that identified a significant association between the PVT1 polymorphism rs2648875 (G/A) and diabetic kidney disease.

In contrast to our findings, Varghese and Subburaj (2021) examined the association between Pentraxin 3 (PTX3) and PVT1 genetic polymorphisms with the risk of diabetic kidney disease in type 2 diabetes mellitus that showed that the analysis of genotypic frequencies for the PVT1 polymorphism (rs2648875) revealed no significant differences between T2DM patients with and without DKD when compared with controls (p > 0.05) [40]. Similarly, a previous study reported that PVT1 polymorphisms were not associated with end-stage renal disease in type 2 diabetes among Pima Indians [42].

Our results demonstrated a significant positive correlation between serum PVT1 levels and age (r = 0.34, p = 0.002), body mass index (BMI) (r = 0.37, p < 0.001), fasting blood glucose (r = 0.45, p < 0.001), two-hour postprandial glucose (r = 0.43, p < 0.001), and HbA1c (r = 0.42, p < 0.001). In contrast, significant negative correlations were observed between PVT1 and platelet count (r = − 0.23, p = 0.043), estimated glomerular filtration rate (eGFR) (r = − 0.44, p < 0.001), and alanine aminotransferase (ALT) (r = − 0.23, p = 0.037). To the best of our knowledge, this is the first study to report these associations by assessing PVT1 expression directly in patient sera and correlating it with albuminuria, in contrast to previous research that primarily relied on renal biopsies and experimental studies in human renal mesangial cells and podocytes. However, the study conducted by Abdel-Aty et al. (2022) reported no significant correlations between serum PVT1 levels and various clinical or biochemical parameters [37].

Our results demonstrated no significant correlation between serum PVT1 levels and duration of diabetes. In addition, no significant difference in PVT1 levels was observed between diabetic patients with retinopathy [Group A, median 18.2 (0.1–167.3)] and those without retinopathy [Group B, median 9.8 (0–157.4); p = 0.105]. These results are consistent with the findings of Abdel-Aty et al. (2022), who also reported no significant correlations between serum PVT1 levels and hemoglobin (r = − 0.05, p = 0.676), total leukocyte count (r = 0.116, p = 0.327), LDL-C (r = − 0.185, p = 0.118), HDL-C (r = − 0.044, p = 0.711), total cholesterol (r = − 0.204, p = 0.084), triglycerides (r = − 0.114, p = 0.338), AST (r = − 0.069, p = 0.564), urea (r = 0.008, p = 0.946), and diabetic retinopathy (p = 0.513) [37].

With regard to diabetic retinopathy, our findings contrast with those of Guo et al. (2022), who investigated the role of lncRNA PVT1 in human retinal pigment epithelial (ARPE-19) cells. The discrepancy between their results and ours may be attributed to methodological differences, as their study assessed PVT1 expression in vitro in glucose-treated ARPE-19 cells, whereas our study measured PVT1 levels in the sera of diabetic patients [43].

In the multivariate analysis of diabetic patients, our results demonstrated that PVT1 was the only independent predictor of proteinuria, with each unit increase in PVT1 expression associated with a 5% higher risk of proteinuria. In contrast, Abdel-Aty et al. (2022) reported that, in a similar multivariate model limited to diabetic patients, elevated serum creatinine was the only significant predictor of increased UACR [37].

The albumin-to-creatinine ratio (ACR) was significantly higher in patients with macroalbuminuria [864.1 (309.3–4246)] compared with those with normoalbuminuria [14.3 (2.9–26.2)] and microalbuminuria [69.7 (31–254.5); p < 0.001]. Similarly, eGFR was significantly lower in patients with macroalbuminuria (53.3 ± 26.7) compared with patients with normoalbuminuria (92.4 ± 22.5) and microalbuminuria (94.8 ± 19.2; p < 0.001). No significant differences were observed between normoalbuminuria and microalbuminuria groups for either ACR or eGFR (p = 1.000). In contrast, Mahmoud et al. (2024), who investigated the pro-inflammatory cytokine tumor necrosis factor-alpha (TNF-α) in diabetic nephropathy, reported no statistically significant change in eGFR (p > 0.05) between patients with diabetic nephropathy and those with diabetes alone [44].

Similarly, Ahmed et al. (2022), who studied the correlation between albuminuria levels and chitinase-3-like protein 1 in Iraqi patients with type 2 diabetes mellitus, demonstrated that the macroalbuminuria group (A3) had a highly significant increase in ACR compared with normoalbuminuria (A1), microalbuminuria (A2), and control (C) groups (p < 0.001). In addition, eGFR levels were significantly reduced in the macroalbuminuria group compared with A1, A2, and C groups (p < 0.001) [27].

In the same way, Shakh et al. (2025), in their investigation of the correlation between microalbuminuria and glycated hemoglobin in 50 patients with type 2 diabetes mellitus and 50 age- and sex-matched controls, demonstrated significantly higher creatinine levels among diabetics compared with controls (p < 0.001) [45].

In the study, urea and creatinine levels were significantly higher among patients with macroalbuminuria [median urea: 66 (23–101); creatinine: 1.8 (0.6–2.3)] compared to those with normoalbuminuria [26 (12.8–54.7) and 0.8 (0.6–1.5), respectively] and microalbuminuria [26.5 (16.5–59.9) and 0.8 (0.5–2.4), respectively] (p < 0.001). However, there was no significant difference between the normoalbuminuria and microalbuminuria groups with respect to urea and creatinine levels (p = 1.000). These findings are consistent with the results of Sobhy et al. (2025), who investigated urinary nephrin levels as an early predictor of diabetic nephropathy and reported that urea and creatinine were significantly higher in patients with macroalbuminuria compared to those with microalbuminuria and normoalbuminuria (p < 0.001), while no significant differences were observed between the latter two groups [46].

Analogously, Naiem et al. (2024), in their study on serum netrin-1 levels in 120 patients with type 2 diabetic nephropathy and controls, observed significantly higher serum creatinine and urea in patients with macroalbuminuria compared to controls (p < 0.001) [47].

Our results demonstrated no significant differences between type 2 diabetic patients and healthy controls with respect to LDL-C (120.4 ± 47.4 vs. 123 ± 34.2; p = 0.825), HDL-C (47.7 ± 10.9 vs. 45.7 ± 7.7; p = 0.459), total cholesterol (198 ± 52.2 vs. 203.1 ± 37.3; p = 0.688), and triglycerides (147.4 ± 68.7 vs. 118.1 ± 42.6; p = 0.076). These findings are consistent with Alkathiri et al. (2024), who examined the relationship between liver and kidney function and lipid profile in glycemic control among 129 diabetic patients and 130 non-diabetic controls, and reported no significant differences in LDL-C (p = 0.94), HDL-C (p = 0.21), and total cholesterol (p = 0.85) [48]. Equally, Ragheb et al. (2024) found no significant difference in triglyceride levels between diabetic patients and controls (p = 0.297) [49].

In contrast, Alapid et al. (2024), who investigated hypomagnesemia and its relationship with lipid profile in type 2 diabetes, reported significantly higher levels of serum cholesterol, triglycerides, and LDL-C in diabetic patients compared to controls, while HDL-C levels were significantly lower in the case group (p < 0.01). These findings differ from our results, which may be explained by the fact that many of our patients were receiving anti-dyslipidemic medications, thereby minimizing differences in lipid profiles between diabetic patients and controls [50].

Our results showed no statistically significant differences among the study groups (control, normoalbuminuria, microalbuminuria, and macroalbuminuria) with respect to lipid profile parameters, including LDL-C, HDL-C, total cholesterol (TC), and triglycerides (TG). The mean TG values were 118.1 ± 42.6, 152.9 ± 77.9, 133.6 ± 55.1, and 155.8 ± 72.3, respectively (p = 0.209). These findings are consistent with Zhu et al. (2022), who examined the circulating expression and clinical significance of lncRNA ANRIL in diabetic kidney disease and reported no significant differences in TG, TC, HDL-C, or LDL-C across groups (p > 0.05) [51].

Conversely, Sobhy et al. (2025) found that cholesterol, TG, and LDL-C were significantly higher in diabetic patients with macroalbuminuria compared with those with microalbuminuria, normoalbuminuria, and controls (p < 0.001), while HDL-C was significantly lower in the macroalbuminuria group (p < 0.001). The discrepancy between our findings and those of Sobhy et al. may be attributed to the use of anti-dyslipidemic medications in our cohort, which likely minimized intergroup differences in lipid profiles [46].

Our results showed no statistically significant differences in LDL-C, HDL-C, TC, or TG levels among diabetic patients with normoalbuminuria, microalbuminuria, and macroalbuminuria, with p values of 0.983, 0.059, 0.897, and 0.548, respectively. These findings are consistent with those of Liu et al. (2024), who examined the association of the systemic immune-inflammation index and systemic inflammation response index with diabetic kidney disease in 303 patients with T2DM and reported no significant differences in LDL-C (p = 0.909), HDL-C (p = 0.594), or TC (p = 0.289) among the T2DM, early DKD, and clinical DKD groups. Similarly, Lu et al. (2022), in their study comparing multiple lipid indices and their associations with diabetic kidney disease in patients with T2DM, found no significant difference in TG levels between patients with and without DKD (p = 0.357) [52].

In the analysis, fasting blood glucose, 2-hour postprandial blood glucose, and HbA1c were significantly lower among controls compared to the three diabetic subgroups. The mean values in the normoalbuminuria group were 165.7 ± 49.9 mg/dl, 218.2 ± 68.1 mg/dl, and 8 ± 1.7%, respectively; in the microalbuminuria group 162.7 ± 75.4 mg/dl, 238.7 ± 100.5 mg/dl, and 9.2 ± 2.5%, respectively; and in the macroalbuminuria group 163.7 ± 78.3 mg/dl, 240.8 ± 80.3 mg/dl, and 8.7 ± 2.2%, respectively (p < 0.001). These results are consistent with the findings of Hliel et al. (2025), who conducted a comparative study on nephrin, transforming growth factor-β, and selected biochemical markers for predicting type 2 diabetic nephropathy, and reported that FBS and HbA1c levels were significantly higher in patients (across macroalbuminuria, microalbuminuria, and normoalbuminuria groups) compared to controls (p < 0.001) [30].

Regarding postprandial blood glucose, our results are in agreement with those of Li et al. (2024), who investigated the association of serum Tsukushi levels with urinary albumin-creatinine ratio in patients with type 2 diabetes. Their study demonstrated that the 2-hour post–oral glucose tolerance test (2hPG) glucose levels were significantly lower among controls compared to diabetic patients with normoalbuminuria, microalbuminuria, and macroalbuminuria (p = 0.000) [53].

However, our results showed no significant differences between diabetic patients with normoalbuminuria, microalbuminuria, and macroalbuminuria regarding FBS, 2-hour postprandial blood glucose, and HbA1c (p = 0.991, 0.646, and 0.236, respectively). This finding is consistent with the study by Alirezaei et al. (2024), who compared complete blood count parameters across different severities of proteinuria in patients with type 2 diabetes mellitus and reported that the mean HbA1c (7.07 ± 2.36) and FPG (142.63 ± 74.36 mg/dl) did not differ significantly among the three groups (p = 0.699 and p = 0.373, respectively) [54].

Regarding postprandial blood glucose, our result is also in line with Murughesh et al. (2022), who investigated the clinical profile of diabetic nephropathy and its correlation with neutrophil-to-lymphocyte ratio in 105 healthy individuals and 105 patients with type 2 DM classified by albuminuria. They found no significant difference in postprandial glucose levels among the diabetic groups [55].

In contrast, Nabipoorashrafi et al. (2023), who evaluated insulin resistance indices in predicting albuminuria among patients with type 2 diabetes, reported that patients with albuminuria had significantly higher FBS, HbA1c, and 2-hour postprandial glucose levels compared with those without albuminuria (p < 0.001). The discrepancy with our findings could be explained by the fact that all our diabetic patients with varying degrees of proteinuria were receiving antihyperglycemic treatment, including insulin, while in the latter study, patients had not received insulin therapy. Additionally, HbA1c reflects glycemic control only over a relatively short period of time, which may account for the observed differences [56].

There was no statistically significant difference between diabetic patients and controls regarding alanine aminotransferase (ALT) levels (17.5 [4–81] vs. 20 [15–37], p = 0.067) or aspartate aminotransferase (AST) levels (21.5 [11–62] vs. 23.5 [16–35], p = 0.129). These results are consistent with the findings of Singh and colleagues (2023), who investigated the interplay between liver and kidney health in type 2 diabetes mellitus and reported no significant differences in ALT (p = 0.959) or AST (p = 0.450) between diabetic patients and healthy controls [57].

Regarding liver enzymes, no significant differences were observed between diabetic patients with retinopathy (group A) and those without retinopathy (group B) in ALT (16.5 [4–81] vs. 18.5 [10–80], p = 0.267) and AST (22 [11–39] vs. 20.5 [11–62], p = 0.710). This finding is supported by Wang et al. (2023), who investigated the correlation between the Systemic Immune-Inflammation Index (SII), Systemic Inflammatory Response Index (SIRI), and diabetic retinopathy in 500 patients with type 2 diabetes (NDR, n = 256; DR, n = 244), and reported no significant differences between the DR and NDR groups regarding ALT (p = 0.984) and AST (p = 0.424) [58].

Meanwhile regarding ALT, there was no statistically significant difference between the study groups: control, normoalbuminuria (18 [10–80]), microalbuminuria (19 [7–81]), and macroalbuminuria (16.5 [4–43]) (p = 0.233). Similarly, no significant difference was observed in AST levels across the groups: control, normoalbuminuria (18 [11–56]), microalbuminuria (18 [11–56]), and macroalbuminuria (22 [11–62]) (p = 0.179). These findings are consistent with the results of Murughesh (2022), who also reported no significant differences in ALT (p = 0.3191) and AST (p = 0.6202) values among the study groups [55].

No significant differences among diabetic groups with normo, micro and macroalbuminuria as regard ALT (p value = 0.696) and AST (p value = 0.294). This result agrees with the study done by Alirezaei. and colleagues in 2024 and found no significant differences among 3 groups as regards ALT and AST (p value = 0.663 and 0.929) respectively [54].

Abdominal ultrasound revealed that more than half of the diabetic patients had fatty liver (n = 34, 56.7%), while the remaining patients did not (n = 26, 43.3%). This finding is consistent with the study by Seo and colleagues (2022), who reported a prevalence of nonalcoholic fatty liver disease (NAFLD) of 54.2% among 3,188 patients with type 2 diabetes, compared to patients without NAFLD [59].

None of the healthy controls had fatty liver, showing a significant difference compared to diabetic patients (p < 0.001), which aligns with the results of Zahoor and colleagues (2023), who observed a MASLD frequency of 47.9% in cases versus 33.7% in controls (p = 0.005) [60].

Among the diabetic groups, the prevalence of fatty liver was similar: normoalbuminuria 11 (55%), microalbuminuria 12 (60%), and macroalbuminuria 11 (55%) (p < 0.001 versus controls). No significant difference in fatty liver prevalence was observed among the diabetic subgroups (normo-, micro-, and macroalbuminuria) (p = 0.934), which is consistent with the study by Jiang and colleagues (2024), who reported no significant difference in fatty liver between type 2 diabetes patients with and without diabetic kidney disease (p = 0.445) [61].

Regarding the presence of fatty liver, there was no significant difference between group A (patients with fatty liver: 16 [53.3%]; without fatty liver: 14 [46.7%]) and group B (patients with fatty liver: 18 [60%]; without fatty liver: 12 [40%]; p = 0.795). In contrast, Li et al. (2024) investigated the association between AST/ALT ratio and diabetic retinopathy risk in 3,002 patients with type 2 diabetes (1,787 without DR and 1,215 with DR) and reported a significant difference in the prevalence of fatty liver between patients with and without diabetic retinopathy (p < 0.001). This discrepancy could be explained by differences in study design: in the previous study, patients’ alcohol consumption was evaluated, and individuals with a history of recent ocular treatments or surgeries within the past three months were excluded [62].

Regarding diabetic retinopathy, half of the diabetic patients in our study had diabetic retinopathy (group A, n = 30, 50%). This finding aligns with the study by Casmuti and colleagues (2025), who reported that 50% of type 2 diabetes patients (n = 68/136) had diabetic retinopathy (DR) [34].

Following fundus examination, there was no statistically significant difference in the presence of DR among patients with normoalbuminuria, microalbuminuria, and macroalbuminuria (45%, 45%, and 60%, respectively; p = 0.682). This result is consistent with Jiang et al. (2023), who found no significant difference in DR prevalence between diabetic nephropathy (DN) and non-DN groups among females (p = 0.420) in a cohort of 595 Chinese older adults with type 2 diabetes. Similarly, Ding et al. (2023) reported that DR was not an independent factor influencing the occurrence of DN (p = 0.323) [61].

In contrast, Liu and colleagues (2024) observed a significant difference in DR prevalence among patients with type 2 diabetes, early DN (EDKD), and clinical DN (Cli-DKD) (p < 0.001). This discrepancy may be partially explained by the larger sample size in their study (n = 303; T2DM = 157, EDKD = 69, Cli-DKD = 77) compared to our smaller cohort of 20 patients per group [56].

Regarding diabetic retinopathy and disease duration, our results showed that patients with DR (group A) had a longer duration of diabetes, with a median of 10 years (range 3–30), compared to patients without DR (group B), who had a median duration of 6.5 years (range 0.1–30) (p = 0.032). This finding is consistent with Li et al. (2024), who reported a significant difference in diabetes duration between patients with and without DR (p < 0.001) [29].

Likewise, Zhong et al. (2023) found that patients with DR had a longer duration of type 2 diabetes compared to those without DR (p < 0.001), indicating that longer disease duration is associated with an increased risk of developing diabetic retinopathy [63].

Regarding renal function, creatinine levels were significantly higher in diabetic patients with retinopathy (group A) with a median of 1 (0.5–2.4) mg/dL compared to patients without retinopathy (group B), who had a median of 0.8 (0.6–2.3) mg/dL (p = 0.040). This finding aligns with Li et al. (2023), who reported that patients who developed diabetic retinopathy were more likely to have higher baseline creatinine levels (p = 0.001) [64].

However, no significant differences were observed between groups A and B regarding urinary albumin-to-creatinine ratio (A/C ratio: 708.8 ± 1090.6 vs. 277.1 ± 535.5, p = 0.058) and estimated glomerular filtration rate (eGFR: 72.9 ± 30.2 vs. 87.4 ± 27.8, p = 0.058). These results are consistent with Tomić et al. (2024), who found no significant differences in eGFR (p = 0.412) or A/C ratio (p = 0.772) among type 2 diabetic patients categorized by diabetic retinopathy status [65].

Urea levels were also not significantly different between the groups (31 [12.8–101] vs. 28 [17–95], p = 0.429), in agreement with Huang et al. (2024), who reported no significant differences in urea between DR and non-DR groups (p = 0.816) [66].

Regarding lipid profile: Our results showed no significant differences between group A and B regarding LDL (118.8 ± 42.7) and (122 ± 52.3) respectively (p value = 0.793) ,HDL(47.1 ± 10.3) and (48.3 ± 11.7) respectively (p value = 0.674), Total cholesterol (197.7 ± 44.6) and (198.3 ± 59.7) respectively (p value = 0.961) and Serum triglycerides (161.1 ± 68.7) and (133.7 ± 67) respectively (p value = 0.124).This results are in accordance with the study done by Huang and colleagues in 2024 and conducted no significant differences were observed between the DR and non-DR groups in LDL (p value = 0.986) ,HDL (p value = 0.588), TC (p value = 0.792) ,TG (p value = 0.971) [66].

Regarding 2-hour postprandial blood glucose (2hPPBG), no significant difference was observed between group A and B (244.3 ± 74.7 vs. 220.7 ± 90.7, p = 0.276). In contrast, Firouzabadi et al. (2024) reported significantly higher 2hPP levels in patients who developed retinopathy (p = 0.001). This discrepancy may be attributed to the fact that our patients received anti-hyperglycemic medications [67].

In the present study, serum PVT1 level was evaluated as a predictor of proteinuria in patients with T2DM. Our results showed that for every unit increase in PVT1, the risk of proteinuria increased by 5%. Using a cutoff value of > 21.6, PVT1 demonstrated a sensitivity of 53% and a specificity of 90%, with an area under the ROC curve of 0.70 (p = 0.003). These findings support the role of PVT1 in the progression of diabetic kidney disease in patients with T2DM.

The main limitations of our study include the relatively small sample size (n = 20 per subgroup) and the cross-sectional design, which precludes establishing a causal relationship between PVT1 levels and DKD progression.

Conclusion

The present study demonstrated that lncRNA PVT1 expression is significantly increased in patients with type 2 diabetes mellitus, with the highest levels observed among patients with advanced diabetic nephropathy characterized by macroalbuminuria and impaired renal function. These findings indicate that elevated PVT1 expression is associated with the severity of diabetic nephropathy and suggest that PVT1 may serve as a promising biomarker for identifying patients at increased risk of renal involvement. However, because of the nature of the study, the findings establish association rather than causation. Prospective longitudinal studies and mechanistic investigations are warranted to determine whether PVT1 contributes directly to the development and progression of diabetic nephropathy.

Author contributions

MA, NM read and revised the manuscript. OS revised the clinical data. AT, DA Collected data AA wrote the manuscript and submission procedure All authors read and approved the final manuscript.

Funding

Authors received no funding for this study.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and confidentiality considerations but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the institution’s ethical committee and the form review board of Kasr Al Ainy hospital (MD-197-2023). Oral and written informed consents were obtained from the patient or from his eligible relatives. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Consent for publication

Oral and written informed consents were obtained from the patient or from his eligible relatives.

Competing interests

The authors declare no competing interests.