Comparative analysis of triglyceride-glucose index, neutrophil-lymphocyte ratio and hs-CRP across albuminuria stages in type 2 diabetes

Comparative analysis of triglyceride-glucose index, neutrophil-lymphocyte ratio and hs-CRP across albuminuria stages in type 2 diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparative analysis of triglyceride-glucose index, neutrophil-lymphocyte ratio and hs-CRP across albuminuria stages in type 2 diabetes Marwa Mohamed Seyam, Hebatuallah Abdelhaleem Elhabiby, Zeinab Shafeek Hamed, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8866385/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Diabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM) and a leading cause of progressive renal failure worldwide. Conventional diagnostic methods have limitations, highlighting the need for simple, cost-effective, and widely accessible tools for early risk assessment. In this cross-sectional study, 300 patients with T2DM were categorized into three groups according to urinary albumin-to-creatinine ratio. We evaluated and compared the triglyceride-glucose (TyG) index, neutrophil-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) as markers of DKD across albuminuria stages. TyG index, NLR, and hs-CRP increased progressively from normoalbuminuria to macroalbuminuria. Receiver operating characteristic analysis demonstrated good diagnostic performance of all markers, with NLR showing high sensitivity and specificity. Multivariate logistic regression identified NLR as significantly associated with disease severity, whereas TyG index and hs-CRP were primarily linked to advanced DKD stages. These findings suggest that TyG index, NLR, and hs-CRP are simple, measurable markers associated with DKD in T2DM patients. NLR correlates with both early and advanced albuminuria stages, suggesting an association with disease severity, while TyG index and hs-CRP are more relevant for advanced stages. The combined evaluation of these markers could be explored in future studies for potential risk stratification across DKD stages. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Nephrology Diabetic kidney disease type 2 diabetes mellitus triglyceride-glucose index neutrophil-lymphocyte ratio high-sensitivity C-reactive protein. Figures Figure 1 Introduction Diabetes mellitus (DM) is a prevalent metabolic disorder characterized by prolonged hyperglycemia and chronic low-grade inflammation, both of which play a crucial role in the development of microvascular complications, particularly diabetic kidney disease (DKD). DKD represents one of the long-term consequences of diabetes and affects a substantial proportion of individuals with the condition. It is estimated that approximately 20–50% of patients with diabetes will develop some degree of renal involvement over their lifetime. Clinically, DKD is characterized by a progressive increase in urinary albumin excretion and a gradual decline in renal function, ultimately leading to advanced renal failure [ 1 ] . Early detection and timely intervention can prevent or slow the progression of DKD; however, fewer than 20% of patients are currently diagnosed and receive appropriate therapeutic management [ 2 ] . Early detection of DKD is critical for slowing disease progression and improving renal outcomes. Current screening approaches predominantly rely on urinary albumin excretion and renal histopathology. Although microalbuminuria has long been employed as an early marker of diabetic nephropathy, its limited diagnostic accuracy diminishes its utility as a reliable predictive tool. Renal biopsy, while providing definitive diagnostic information, is invasive, costly, and associated with procedural risks, which restricts its routine application. Consequently, dependence on these conventional methods may delay diagnosis and result in missed opportunities for timely intervention. This underscores the need for simple, cost-effective, and noninvasive biomarkers capable of identifying patients at higher risk of developing DKD [ 3 , 4 , 5 ] . The pathogenesis of microvascular complications in diabetes is strongly influenced by impaired insulin sensitivity. The triglyceride-glucose (TyG) index, calculated from fasting triglyceride and plasma glucose levels, is recognized as an effective indicator of reduced insulin sensitivity. In contrast to conventional approaches, the TyG index can be readily measured using standard laboratory tests, making it an accessible marker for routine clinical practice. Emerging evidence suggests that elevated TyG index values are associated with an increased risk of DKD and other diabetes-related microvascular complications [ 7 , 6 ] . Patients with diabetic microalbuminuria often display increased insulin resistance, indicating a direct role of reduced insulin sensitivity in the development of renal injury [ 7 ] . Although the hyperinsulinemic-euglycemic clamp technique remains the gold standard for evaluating impaired insulin sensitivity, its complexity and limited practicality restrict its application in research settings. Similarly, indices such as the homeostatic model assessment of insulin resistance (HOMA-IR) rely on endogenous insulin measurements, which are unreliable in patients receiving exogenous insulin therapy and are not routinely performed in clinical practice [ 8 ] . Consequently, the TyG index has emerged as a reliable and readily accessible alternative marker of reduced insulin sensitivity in patients with diabetes mellitus [ 9 , 10 ] . Chronic systemic inflammation is recognized as a key factor in the development and progression of DKD. The neutrophil-to-lymphocyte ratio (NLR), derived from standard complete blood count parameters, has been suggested as a marker of systemic inflammation. Elevated NLR values have been linked to various inflammatory and metabolic disorders, and recent evidence indicates that increased NLR may be associated with the onset of renal injury in patients with diabetes mellitus [ 11 , 12 , 13 ] . High-sensitivity C-reactive protein (hs-CRP) is a well-established inflammatory marker synthesized by hepatocytes in response to inflammatory stimuli [ 14 ] . Insulin resistance promotes hs-CRP production, which subsequently triggers inflammatory cascades, enhances oxidative stress, and induces endothelial dysfunction. These pathological processes contribute to increased glomerular permeability, albuminuria, and progressive renal impairment in patients with diabetes mellitus [ 15 , 16 ] . Results There were no significant differences in age or gender among the three groups. Body mass index (BMI) and diabetes duration showed a significant increase with the severity of albuminuria. Additionally, both systolic and diastolic blood pressures were significantly elevated in the microalbuminuria and macroalbuminuria groups compared to the normoalbuminuria group. Glycemic parameters, including fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), were also significantly higher in the microalbuminuria and macroalbuminuria groups relative to the normoalbuminuria group. Patients with macroalbuminuria exhibited significantly higher levels of total cholesterol, triglycerides, serum creatinine, and urinary albumin-to-creatinine ratio (UACR), along with lower estimated glomerular filtration rate (eGFR), compared to those with microalbuminuria and normoalbuminuria. There was a significant progressive increase in the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) in the microalbuminuria and macroalbuminuria groups compared to the normoalbuminuria group (Table 1 ). Table 1 Comparison between the groups of patients regarding demographic data and other studied parameters. Parameter patients p-value Group I Normoalbuminuria Group II Microalbuminuria Group III Macroalbuminuria Male/ Female Number (%) 43/57 (43/57) 47/53 (47/53) 48/52 (48/52) 0.754 d Age (years) Median (IQR) g 50.50 (25.00) 50.50 (22.50) 52.00 (24.25) 0.251 e Diabetes duration(years) Median (IQR) g 4.00 (6.00) 10.00 (4.00) 12.00 (6.00) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f BMI (kg/m 2 ) Median (IQR) g 2 7 . 0 0 ( 2.14 ) 32.65 (2.40) 36.05 (1.30) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f SBP (mmHg) Median (IQR) g 120.00 (20.00) 145.00 (10.00) 160.00 (20.00) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f DBP (mmHg) Median (IQR) g 80.00 (10.00) 90.00 (0.000) 90.00 (10.00) < 0.001 e p1 a < 0.00 f p2 b < 0.001 f p3 c < 0.001 f FPG (mg/dL) Median (IQR) g 1 20.65 (21. 45) 193.50 (28.85) 293.00 (20.00) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f HbA1c (%) Median (IQR) g 8 . 35 (1.2 5) 9.25 (0.80) 11. 20 (1. 5 0) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f Total cholesterol (mg/dL) Median (IQR) g 198.30 (23.35) 248.00 (9.20) 287.50 (18.25) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f Triglycerides (mg/dL) Median (IQR) g 145.00 (18. 62 ) 222.50 (38.83) 325.00 (43.38) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f SCr (mg/dL) Median (IQR) g 1.09 (0.23) 2.8 0 (0.4 2) 3. 71 ( 2 . 72) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f UACR (mg/g) Mean ± SD h 20 . 10 ± 5 . 35 15 3 . 35 ± 48 . 87 6 30 . 70 ± 210 . 65 < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f eGFR (mL/min) Mean ± SD h 95. 25 ± 10 . 1 2 6 8 . 15 ± 1 9 . 23 4 4 . 50 ± 1 4 .6 8 < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f TyG Index Median (IQR) g 9. 2 0 ( 1 . 80) 9.90 (0. 20) 10.70 (0. 2 0) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f NLR Median (IQR) g 1. 91 (0. 44) 3. 37 (0.5 2) 5.00 (0. 79) < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f HS-CRP (mg/L) Mean ± SD h 0. 96 ± 0. 44 1 . 97 ± 0. 83 4. 33 ± 1. 61 < 0.001 e p1 a < 0.001 f p2 b < 0.001 f p3 c < 0.001 f Two-tailed P-values are shown; pairwise comparisons Bonferroni-adjusted. α = 0.05; p < 0.05 considered significant. a comparison between normoalbuminuria and microalbuminuria groups. b comparison between normoalbuminuria and macroalbuminuria groups. c comparison between microalbuminuria and macroalbuminuria groups. d Chi-square test. e Kruskal–Wallis test. f Mann–Whitney U test g Interquartile Range h Standard deviation In the correlation analysis, the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) demonstrated significant positive correlations with the studied parameters, except for estimated glomerular filtration rate (eGFR), which exhibited significant negative correlations (Table 2). Table (2): Correlation of TyG Index, NLR and hs-CRP with the different studied parameters. Parameters TyG index NLR HS-CRP (mg/L) r s a p-value r s a p-value r s a p-value Diabetes duration (years) 0.268 < 0.001 0.448 < 0.001 0.508 < 0.001 BMI (kg/m²) 0.443 < 0.001 0.566 < 0.001 0.659 < 0.001 SBP (mmHg) 0.361 < 0.001 0.583 < 0.001 0.627 < 0.001 DBP (mmHg) 0.274 < 0.001 0.536 < 0.001 0.548 < 0.001 FPG (mg/dL) 0.416 < 0.001 0.646 < 0.001 0.626 < 0.001 HbA1c (%) 0.256 < 0.001 0.345 < 0.001 0.309 < 0.001 Total cholesterol (mg/dL) 0.356 < 0.001 0.594 < 0.001 0.638 < 0.001 Triglycerides (mg/dL) 0.411 < 0.001 0.588 < 0.001 0.664 < 0.001 SCr (mg/dL) 0.291 < 0.001 0.417 < 0.001 0.443 < 0.001 UACR (mg/g) 0.431 < 0.001 0.646 < 0.001 0.694 < 0.001 eGFR (mL/min) -0.395 < 0.001 -0.574 < 0.001 -0.626 < 0.001 NLR 0.343 < 0.001 – – 0.597 < 0.001 Hs-CRP (mg/L) 0.409 < 0.001 0.597 < 0.001 – – TyG index – – 0.343 < 0.001 0.409 < 0.001 Two-tailed P-values are shown. α = 0.05; P < 0.05 considered significant. a Spearman correlation Analysis of the receiver operating characteristic (ROC) curves revealed that the neutrophil-to-lymphocyte ratio (NLR) exhibited higher sensitivity and specificity compared to the triglyceride-glucose (TyG) index and high-sensitivity C-reactive protein (hs-CRP) (Table 3 ; Fig. 1 ). see figure legends section at the end of the manuscript Table 3 Interpreting diagnostic tests. Validity AUC d 95% CI a Cut-off Sensitivity (%) Specificity (%) PPV b (%) NPV c (%) TyG Index 0.678 0.594–0.762 9.5 92 71 86 81 NLR 0.830 0.767–0.887 2.5 93.5 85 93 87 HS-CRP 0.860 0.806–0.912 1.34 91 83 91 82 a confidence interval. b positive predictive value. c negative predictive value. d area Under the Curve Univariate and multivariate logistic regression analyses were performed for selected study parameters (Table 4 ). Table 4 Univariate and multivariate logistic regression model of some studied parameters Parameters Univariate Multivariate OR a (95% CI b ) p-value Group I & Group II Group I & Group III OR a (95% CI b ) P value OR a (95% CI b ) p-value Age (years) 1.01 (0.99–1.03) 0.151 1.03 (0.98–1.09) 0.257 1.04 (0.97–1.12) 0.300 Sex 1.20 (0.74–1.94) 0.461 0.30 (0.06–1.35) 0.204 0.20 (0.02–1.52) 0.120 Diabetes duration (years) 1.66 (1.48–1.86) < 0.001 2.00 (1.46–2.76) < 0.001 2.11 (1.47–3.03) < 0.001 HbA1c (%) 1.19 (1.07–1.34) 0.002 1.62 (0.97–2.70) 0.064 1.53 (0.85–2.75) 0.152 eGFR (mL/min) 0.84 (0.80–0.88) < 0.001 0.82 (0.76–0.89) 0.001 0.72 (0.66–0.80) < 0.001 TyG Index 2.73 (1.94–3.86) < 0.001 1.46 (0.59–3.56) 0.411 14.00 (2.85–68.67) < 0.001 NLR 3.13 (2.39–4.08) < 0.001 2.22 (1.27–3.92) 0.005 3.70 (1.65–8.28) < 0.001 HS-CRP (mg/L) 2.72 (2.08–3.57) < 0.001 1.55 (0.93–2.58) 0.091 5.78 (2.67–12.50) < 0.001 a Odds ratio. Two-tailed P-values are shown. α = 0.05; P < 0.05 considered significant. a Odds ratio. b confidence interval. Discussion There is a need for simple, cost-effective, and readily accessible alternative tests to identify individuals at risk of developing diabetic kidney disease (DKD). The present study aimed to evaluate and compare the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) as potential biomarkers of DKD across different stages of albuminuria in patients with type 2 diabetes mellitus (T2DM). A total of 300 patients with T2DM were enrolled and categorized into three groups (I, II, and III) based on their urinary albumin-to-creatinine ratio (UACR). Regarding the triglyceride-glucose (TyG) index, a progressive and significant increase was observed in the macroalbuminuria group compared to the microalbuminuria and normoalbuminuria groups. These findings are in agreement with the study by Ran L et al 2025 [ 20 ] , which reported a significant rise in TyG index values corresponding to the severity of diabetic nephropathy in patients with type 2 diabetes mellitus (T2DM). Regarding the neutrophil-to-lymphocyte ratio (NLR), values were significantly higher in diabetic patients with macroalbuminuria compared to those with microalbuminuria and normoalbuminuria. These results align with the findings of Pradesta R et al 2025 [ 21 ] , who evaluated 65 patients with type 2 diabetes mellitus (T2DM) and reported that NLR values increased progressively with the severity of albuminuria. Regarding high-sensitivity C-reactive protein (hs-CRP), levels were significantly higher in diabetic patients with macroalbuminuria compared to those with microalbuminuria and normoalbuminuria. These findings are consistent with the study by Hasan M et al 2024 [ 22 ] , who evaluated 75 patients with type 2 diabetes mellitus (T2DM) and reported that patients with microalbuminuria exhibited elevated hs-CRP values. Our results revealed strong positive correlations between the triglyceride-glucose (TyG) index and several parameters, including diabetes duration, body mass index (BMI), systolic and diastolic blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine, urinary albumin-to-creatinine ratio (UACR), neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP), along with negative correlations with estimated glomerular filtration rate (eGFR). These findings are partially consistent with the study by Duan S et al 2023 [ 23 ] , which reported positive correlations between the TyG index and BMI, fasting blood glucose (FBG), triglycerides, and total cholesterol in 179 patients with type 2 diabetes mellitus (T2DM). Our results revealed strong positive correlations between the neutrophil-to-lymphocyte ratio (NLR) and several parameters, including diabetes duration, body mass index (BMI), systolic and diastolic blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine, urinary albumin-to-creatinine ratio (UACR), triglyceride-glucose (TyG) index, and high-sensitivity C-reactive protein (hs-CRP), along with negative correlations with estimated glomerular filtration rate (eGFR). These findings are partially consistent with the study by Gurmu M et al 2023 [ 24 ] , which reported a significant increase in NLR in patients with diabetic nephropathy and demonstrated positive correlations between NLR and variables such as disease duration and systolic and diastolic blood pressure. Furthermore, the results align with those of Pradesta R et al 2025 [ 21 ] , who observed a statistically significant positive correlation between NLR and UACR in patients with type 2 diabetes mellitus (T2DM). Our results revealed strong positive correlations between high-sensitivity C-reactive protein (hs-CRP) and several parameters, including diabetes duration, body mass index (BMI), systolic and diastolic blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine, urinary albumin-to-creatinine ratio (UACR), neutrophil-to-lymphocyte ratio (NLR), and triglyceride-glucose (TyG) index, along with negative correlations with estimated glomerular filtration rate (eGFR). These findings are partially consistent with the study by Hasan M et al 2024 [ 22 ] , which reported that elevated hs-CRP levels were associated with microalbuminuria in patients with type 2 diabetes mellitus (T2DM). Additionally, microalbuminuria was correlated with older age, longer duration of diabetes, and higher triglyceride levels compared to patients without microalbuminuria. Our results revealed positive correlations between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), body mass index (BMI), and diabetes duration. These findings indicate that higher BMI and longer disease duration are associated with increased inflammatory activity (reflected by NLR and hs-CRP) and elevated insulin resistance (assessed by the TyG index). Furthermore, significant positive correlations were observed between the TyG index, NLR, hs-CRP, fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c), suggesting that poor glycemic control is closely linked to heightened inflammation and insulin resistance. Strong positive correlations were observed between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), and lipid parameters, including total cholesterol (TC) and triglycerides (TG). These findings underscore the potential of these markers to reflect metabolic alterations in patients with type 2 diabetes mellitus (T2DM), including dyslipidemia, which represents a major cardiovascular and renal risk factor. Significant positive correlations were observed between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), serum creatinine, and urinary albumin-to-creatinine ratio (UACR), along with a negative correlation with estimated glomerular filtration rate (eGFR). These findings indicate that heightened inflammation and insulin resistance are associated with declining renal function and increased albuminuria, suggesting their potential relevance as markers of disease severity. Significant positive correlations were observed between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), and both systolic and diastolic blood pressure. These findings suggest that inflammatory and metabolic dysregulation may play a role in the development of hypertension in patients with type 2 diabetes mellitus (T2DM). The optimal cut-off value of the serum TyG index in our study was 9.5. At this threshold, diagnostic sensitivity was 92%, diagnostic specificity was 71%, and positive predictive value was 86%. The optimal cut-off value for serum neutrophil-to-lymphocyte ratio (NLR) was determined to be 2.5, yielding a diagnostic sensitivity of 93.5%, specificity of 85%, and a positive predictive value of 93%. Similarly, the optimal cut-off value for serum high-sensitivity C-reactive protein (hs-CRP) was 1.34, with a diagnostic sensitivity of 91%, specificity of 83%, and a positive predictive value of 91%. In this study, logistic regression analysis was performed, incorporating variables selected based on the variance inflation factor (VIF) to reduce multicollinearity. Univariate logistic regression demonstrated that diabetes duration, glycated hemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) were significantly associated with disease severity. In contrast, multivariate logistic regression revealed independent associations of diabetes duration, eGFR, and NLR with disease severity, whereas hs-CRP and the TyG index were primarily linked to advanced stages. Age, sex, and HbA1c did not show significant effects. In conclusion, the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) represent simple and readily measurable parameters associated with diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). NLR was linked to both early and advanced stages of albuminuria, suggesting an association with disease severity, while TyG index and hs-CRP are more relevant for advanced stages. The combined evaluation of these markers could be explored in future studies for potential risk stratification across DKD stages. This study provides a comparative evaluation of three accessible biomarkers across albuminuria stages. Several limitations should be considered. The cross-sectional design precludes causal or predictive inferences, emphasizing the need for future longitudinal studies to validate these findings. Larger sample size is needed to more robustly evaluate the associations between these markers and diabetic kidney disease. Variations in pharmacological treatments and the presence of non-diabetic inflammatory conditions could have influenced inflammatory markers. Finally, being a single-center study, the findings may not be fully generalizable to other populations. Materials and methods This cross-sectional study included 300 patients with type 2 diabetes mellitus who were recruited from the Endocrinology Unit, Internal Medicine Department, Tanta University Hospital, from August 2025 to December 2025. The study protocol was approved by the Medical Ethical Committee of the Faculty of Medicine, Tanta University, approval number (36264PR1318/8/25). All procedures involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all patients included in the study. Patients were categorized into three groups based on their urinary albumin-to-creatinine ratio (UACR): Group I included 100 diabetic patients with normoalbuminuria, Group II comprised 100 diabetic patients with microalbuminuria, and Group III consisted of 100 diabetic patients with macroalbuminuria. All participants had a previously documented diagnosis of albuminuria prior to enrollment. The study enrolled patients with T2DM who fulfilled the World Health Organization (WHO) diagnostic criteria. Patients with severe cardiac, hepatic, renal, or other organ dysfunction; tumors; type 1 diabetes or other specific types of diabetes; gestational diabetes; acute complications such as diabetic ketoacidosis or hyperglycemic hyperosmolar state; conditions affecting high-sensitivity C-reactive protein (hs-CRP) and neutrophil-to-lymphocyte ratio (NLR), including acute infections, chronic inflammatory disorders, anemia, smoking, or use of medications such as steroids, statins, and sodium-glucose co-transporter 2 inhibitors; and conditions impacting urinary protein excretion, such as nephrotic or nephritic syndrome, urolithiasis, urinary tract infection, and other acute infections, were excluded from the study. All participants underwent comprehensive history taking, thorough clinical examination, body mass index (BMI) calculation, and laboratory investigations, including complete blood count, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine (SCr), estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and high-sensitivity C-reactive protein (hs-CRP). Calculations: BMI was calculated as weight in kilograms divided by height in meters squared. The CKD-EPI equation was used to estimate eGFR [ 17 ] . The TyG index was calculated using the following formula: ln [(TG (mg/dl) × FPG (mg/dl))/2]. The neutrophil-to-lymphocyte ratio (NLR) was determined as the ratio of the absolute neutrophil count to the absolute lymphocyte count obtained from a complete blood count. Blood sampling Two milliliters of fasting peripheral blood were collected in EDTA vacutainer tubes for complete blood count (CBC) and glycated hemoglobin (HbA1c) analysis using sterile disposable syringes under strict aseptic conditions. Additionally, three milliliters of blood were collected into sterile, plain tubes, allowed to clot, and the serum was separated for the measurement of fasting plasma glucose, total cholesterol, triglycerides, hs-CRP, and serum creatinine. Complete blood count (CBC) was obtained using an automated cell counter (ERMA PCE-210N). Glycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (HPLC) using the TOSOH G8 analyzer. High-sensitivity C-reactive protein (hs-CRP) was quantified by nephelometry (Atellica NEPH 630, Siemens). Serum triglycerides, total cholesterol, glucose, and creatinine were analyzed using an automated Konelab system (Thermo Fisher Scientific). Urine samples were collected in sterile, dry cups for the assessment of microalbuminuria using immunoturbidimetric latex (COD 31924 - BioSystem) and creatinine using the automated Konelab system to calculate the urinary albumin-to-creatinine ratio (UACR). Sample size The sample size was determined using a one-way ANOVA to compare the TyG index, NLR, and hs-CRP across the three groups. Standard deviations were obtained from previous studies. Considering a clinically meaningful difference of 0.3 between groups, with a significance level (α) of 0.05 and 80% statistical power, a minimum of 97 participants per group was required. To account for potential dropouts, the sample size was increased to 100 participants per group, resulting in a total of 300 participants. Statistical analysis Statistical analyses were performed using the Python programming language (v3.13), with Pandas (v2.3.3) and NumPy (v2.4.0) employed for data management. Core statistical computations were conducted using SciPy (v1.16.3) [ 18 ] , while diagnostic performance metrics were calculated with Scikit-learn (v1.8.0) [ 19 ] . The Shapiro-Wilk test was applied to assess the normality of data distribution. Continuous variables were expressed as mean ± standard deviation for parametric data and as median (interquartile range, IQR) for non-parametric data. Comparative Analysis Multi-group comparisons: One-way ANOVA was used for parametric data, whereas the Kruskal-Wallis test was applied for non-parametric data when comparing continuous variables across three or more independent groups. Pairwise comparisons: Following a significant Kruskal-Wallis test, post-hoc pairwise Mann-Whitney U tests were conducted. To control for Type I error inflation due to multiple comparisons, the Bonferroni correction was applied, with statistical significance set at an adjusted threshold of 𝑃 < 0.0167 (calculated as 𝛼 = 0.05/3). Categorical Variables: Associations between categorical variables were evaluated using the Chi-square test (𝜒²). When expected cell counts were less than 5, Fisher's Exact test was applied. Correlation and Diagnostic Performance: Spearman's correlation coefficient (rs) was used to assess non-parametric associations. Receiver Operating Characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated to evaluate diagnostic accuracy and determine optimal cutoff points. For all primary analyses not adjusted using the Bonferroni correction, a two-sided 𝑃 < 0.05 was considered statistically significant. Univariate and multivariate logistic regression analyses were performed to examine associations with disease outcomes. Declarations Competing interests The authors declare no competing interests. Funding This research did not receive any funding from any institution or organization. Author Contribution M. M. Seyam, H.A. Elhabiby, Z. S. Hamed, M. M. Elgebaly, and R. A. Aboeida contributed to the conception and design of the study, acquisition of data, analysis and/or interpretation of data, drafting the manuscript, and revising it critically for important intellectual content. E. R. El Fiky contributed to the analysis and/or interpretation of data and drafting of the manuscript. All authors approved the final version of the manuscript to be published. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Gross JL, de Azevedo MJ, Silveiro SP, Canani LH, Caramori ML, Zelmanovitz T. Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care 28(1):164–176 (2005). Lv L, Zhou Y, Chen X, Gong L, Wu J, Luo W, et al. Relationship between the TyG index and diabetic kidney disease in patients with type 2 diabetes mellitus. Diabetes Metab Syndr Obes 14:3299–3306 (2021). Garg P, Shetty M, Krishnamurthy V. Correlation of urinary neutrophil gelatinase with the histopathological extent of kidney damage in patients with diabetic nephropathy. Saudi J Kidney Dis Transpl 34(Suppl 1):S112–S121 (2023). Rakesh BM, Pradeep N, Nischal GJ. Correlation between neutrophil to lymphocyte ratio and urine albumin to creatinine ratio in diabetic nephropathy patients: a cross-sectional study. J Clin Diagn Res 18:OC01–OC05 (2024). Huang W, Huang J, Liu Q, Lin F, He Z, Zeng Z, et al. Neutrophil-lymphocyte ratio is a reliable predictive marker for early-stage diabetic nephropathy. Clin Endocrinol (Oxf) 82(2):229–233 (2015). Kassab HS, Osman NA, Elrahmany SM. Assessment of triglyceride-glucose index and ratio in patients with type 2 diabetes and their relation to microvascular complications. Endocr Res 48(4):94–100 (2023). Liu L, Xia R, Song X, Zhang B, He W, Zhou X, et al. Association between the triglyceride–glucose index and diabetic nephropathy in patients with type 2 diabetes: a cross-sectional study. J Diabetes Investig 12(4):557–565 (2021). Hong S, Han K, Park CY. The triglyceride-glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. BMC Med 18:361 (2020). Reckziegel MB, Nepomuceno P, Machado T, Renner JDP, Pohl HH, Nogueira de Almeida CA, et al. The triglyceride-glucose index as an indicator of insulin resistance and cardiometabolic risk in Brazilian adolescents. Arch Endocrinol Metab 67(2):153–161 (2023). Tam CS, Xie W, Johnson WD, Cefalu WT, Redman LM, Ravussin E. Defining insulin resistance from hyperinsulinemic euglycemic clamps. Diabetes Care 35(7):1605–1610 (2012). Khandare SA, Chittawar S, Nahar N, Dubey TN, Qureshi Z. Study of neutrophil lymphocyte ratio as a novel marker for diabetic nephropathy in type 2 diabetes. Indian J Endocrinol Metab 21(3):387–392 (2017). Sato H, Takeuchi Y, Matsuda K, Kagaya S, Saito A, Fukami H, et al. Pre-dialysis neutrophil-lymphocyte ratio, a novel and strong short-term predictor of all-cause mortality in patients with diabetic nephropathy. Ther Apher Dial 21(4):370–377 (2017). Zhang J, Zhang R, Wang Y, Wu Y, Li H, Han Q, et al. Effects of neutrophil-lymphocyte ratio on renal function and histologic lesions in patients with diabetic nephropathy. Nephrology (Carlton) 24(11):1115–1121 (2019). Tang Y, Fung E, Xu A, Lan H. C-reactive protein and ageing. Clin Exp Pharmacol Physiol 44(Suppl 1):9–14 (2017). Aryan Z, Ghajar A, Faghihi-Kashani S, Afarideh M, Nakhjavani M, Esteghamati A. Baseline high-sensitivity C-reactive protein predicts macrovascular and microvascular complications of type 2 diabetes: a population-based study. Ann Nutr Metab 72(4):287–295 (2018). Sproston NR, Ashworth JJ. Role of C-reactive protein at sites of inflammation and infection. Front Immunol 9:754 (2018). Levin A, Ahmed SB, Carrero JJ, Foster B, Francis A, Hall RK, et al. Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease: known knowns and known unknowns. Kidney Int 105(4):684–701 (2024). Virtanen P, Gommers R, Oliphant T, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17(3):261–272 (2020). Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res 12:2825–2830 (2011). Ran L, Han Y, Zhaohu H, Hailin S. Correlation Between Triglyceride-Glucose Index and Microvascular Complications in Patients With Early-Onset of Type 2 Diabetes Mellitus. Endocrinol Diabetes Metab 8(2) (2025). Pradesta R, Novadian, Kusnadi Y, Kurniati N, Husin S. Neutrophil-Lymphocyte Ratio as a Novel Biomarker for Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study. Bioscientia Medicina: J Biomed & Transl Res 9(3) (2025). Hasan M, Ray N, Bari M, Islam M, Ahmed I. Assessment of Microalbuminuria and Hs-CRP for Early Detection of Diabetic Nephropathy in Type 2 Diabetes. Glob Acad J Med Sci 6(4):161–168 (2025). Duan S, Zhou M, Lu F, Chen C, Chen S, Geng L, et al. Triglyceride-glucose index is associated with the risk of chronic kidney disease progression in type 2 diabetes. Endocrine 81(1):77–89 (2023). Gurmu M, Genet S, Gizaw S, Feyisa T, Gnanasekaran N. Neutrophil–lymphocyte ratio as an inflammatory biomarker of diabetic nephropathy among type 2 diabetes mellitus patients: A comparative cross-sectional study. SAGE Open Med 10:1–7 (2025). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 26 Feb, 2026 Editor invited by journal 25 Feb, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 17 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8866385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604027767,"identity":"5e105b1d-3fdc-4aa5-9ecd-a7e4fc0d2041","order_by":0,"name":"Marwa Mohamed Seyam","email":"data:image/png;base64,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","orcid":"","institution":"Internal Medicine Department, Faculty of Medicine, Tanta University","correspondingAuthor":true,"prefix":"","firstName":"Marwa","middleName":"Mohamed","lastName":"Seyam","suffix":""},{"id":604027768,"identity":"d8380b7b-71a3-4815-920a-31fefa33ff8a","order_by":1,"name":"Hebatuallah Abdelhaleem Elhabiby","email":"","orcid":"","institution":"Internal Medicine Department, Faculty of Medicine, Tanta University","correspondingAuthor":false,"prefix":"","firstName":"Hebatuallah","middleName":"Abdelhaleem","lastName":"Elhabiby","suffix":""},{"id":604027769,"identity":"df4d4b02-9181-436a-a94c-5dd2d95bbfa9","order_by":2,"name":"Zeinab Shafeek Hamed","email":"","orcid":"","institution":"Internal Medicine Department, Faculty of Medicine, Kafr El Sheikh University","correspondingAuthor":false,"prefix":"","firstName":"Zeinab","middleName":"Shafeek","lastName":"Hamed","suffix":""},{"id":604027770,"identity":"3e247e1c-2ef4-4d38-b86a-7dc9376356a8","order_by":3,"name":"Eman Rizk El Fiky","email":"","orcid":"","institution":"Clinical Pathology Department, Faculty of Medicine, Tanta University","correspondingAuthor":false,"prefix":"","firstName":"Eman","middleName":"Rizk El","lastName":"Fiky","suffix":""},{"id":604027771,"identity":"fc297535-cbca-4020-b366-497cbb93ff0c","order_by":4,"name":"Manar Mohammed Elgebaly","email":"","orcid":"","institution":"Internal Medicine Department, Faculty of Medicine, Tanta University","correspondingAuthor":false,"prefix":"","firstName":"Manar","middleName":"Mohammed","lastName":"Elgebaly","suffix":""},{"id":604027772,"identity":"c61bd2d5-7bff-4316-b572-c8583d630ab5","order_by":5,"name":"Reham Abdelmageed Aboeida","email":"","orcid":"","institution":"Internal Medicine Department, Faculty of Medicine, Tanta University","correspondingAuthor":false,"prefix":"","firstName":"Reham","middleName":"Abdelmageed","lastName":"Aboeida","suffix":""}],"badges":[],"createdAt":"2026-02-13 01:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8866385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8866385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104784518,"identity":"697499f8-bb23-44ad-8c5a-c5ee86dd77a4","added_by":"auto","created_at":"2026-03-17 08:08:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42557,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for TyG Index, NLR and HS-CRP\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8866385/v1/38ef53501eb9905db0853465.png"},{"id":104786600,"identity":"6eed9a6f-8d34-411d-8c72-d41b41c16f0f","added_by":"auto","created_at":"2026-03-17 08:17:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999603,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8866385/v1/ac003c05-6fe4-4143-ab0b-8d735e65cb4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative analysis of triglyceride-glucose index, neutrophil-lymphocyte ratio and hs-CRP across albuminuria stages in type 2 diabetes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus (DM) is a prevalent metabolic disorder characterized by prolonged hyperglycemia and chronic low-grade inflammation, both of which play a crucial role in the development of microvascular complications, particularly diabetic kidney disease (DKD). DKD represents one of the long-term consequences of diabetes and affects a substantial proportion of individuals with the condition. It is estimated that approximately 20\u0026ndash;50% of patients with diabetes will develop some degree of renal involvement over their lifetime. Clinically, DKD is characterized by a progressive increase in urinary albumin excretion and a gradual decline in renal function, ultimately leading to advanced renal failure \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEarly detection and timely intervention can prevent or slow the progression of DKD; however, fewer than 20% of patients are currently diagnosed and receive appropriate therapeutic management \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEarly detection of DKD is critical for slowing disease progression and improving renal outcomes. Current screening approaches predominantly rely on urinary albumin excretion and renal histopathology. Although microalbuminuria has long been employed as an early marker of diabetic nephropathy, its limited diagnostic accuracy diminishes its utility as a reliable predictive tool. Renal biopsy, while providing definitive diagnostic information, is invasive, costly, and associated with procedural risks, which restricts its routine application. Consequently, dependence on these conventional methods may delay diagnosis and result in missed opportunities for timely intervention. This underscores the need for simple, cost-effective, and noninvasive biomarkers capable of identifying patients at higher risk of developing DKD \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe pathogenesis of microvascular complications in diabetes is strongly influenced by impaired insulin sensitivity. The triglyceride-glucose (TyG) index, calculated from fasting triglyceride and plasma glucose levels, is recognized as an effective indicator of reduced insulin sensitivity. In contrast to conventional approaches, the TyG index can be readily measured using standard laboratory tests, making it an accessible marker for routine clinical practice. Emerging evidence suggests that elevated TyG index values are associated with an increased risk of DKD and other diabetes-related microvascular complications \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with diabetic microalbuminuria often display increased insulin resistance, indicating a direct role of reduced insulin sensitivity in the development of renal injury \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough the hyperinsulinemic-euglycemic clamp technique remains the gold standard for evaluating impaired insulin sensitivity, its complexity and limited practicality restrict its application in research settings. Similarly, indices such as the homeostatic model assessment of insulin resistance (HOMA-IR) rely on endogenous insulin measurements, which are unreliable in patients receiving exogenous insulin therapy and are not routinely performed in clinical practice \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsequently, the TyG index has emerged as a reliable and readily accessible alternative marker of reduced insulin sensitivity in patients with diabetes mellitus \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChronic systemic inflammation is recognized as a key factor in the development and progression of DKD. The neutrophil-to-lymphocyte ratio (NLR), derived from standard complete blood count parameters, has been suggested as a marker of systemic inflammation. Elevated NLR values have been linked to various inflammatory and metabolic disorders, and recent evidence indicates that increased NLR may be associated with the onset of renal injury in patients with diabetes mellitus \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHigh-sensitivity C-reactive protein (hs-CRP) is a well-established inflammatory marker synthesized by hepatocytes in response to inflammatory stimuli \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Insulin resistance promotes hs-CRP production, which subsequently triggers inflammatory cascades, enhances oxidative stress, and induces endothelial dysfunction. These pathological processes contribute to increased glomerular permeability, albuminuria, and progressive renal impairment in patients with diabetes mellitus \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThere were no significant differences in age or gender among the three groups. Body mass index (BMI) and diabetes duration showed a significant increase with the severity of albuminuria. Additionally, both systolic and diastolic blood pressures were significantly elevated in the microalbuminuria and macroalbuminuria groups compared to the normoalbuminuria group. Glycemic parameters, including fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), were also significantly higher in the microalbuminuria and macroalbuminuria groups relative to the normoalbuminuria group.\u003c/p\u003e \u003cp\u003ePatients with macroalbuminuria exhibited significantly higher levels of total cholesterol, triglycerides, serum creatinine, and urinary albumin-to-creatinine ratio (UACR), along with lower estimated glomerular filtration rate (eGFR), compared to those with microalbuminuria and normoalbuminuria.\u003c/p\u003e \u003cp\u003eThere was a significant progressive increase in the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) in the microalbuminuria and macroalbuminuria groups compared to the normoalbuminuria group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between the groups of patients regarding demographic data and other studied parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003epatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup I Normoalbuminuria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup II Microalbuminuria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup III Macroalbuminuria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale/ Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43/57 (43/57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47/53 (47/53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48/52 (48/52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.50 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.50 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.00 (24.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.251\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiabetes duration(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 (6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.00 (4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.00 (6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p3\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003cb\u003e7\u003c/b\u003e.\u003cb\u003e0\u003c/b\u003e0 (\u003cb\u003e2.14\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.65 (2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.05 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p3\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.00 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145.00 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160.00 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.00 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.00 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.00 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00 \u003csup\u003ef\u003c/sup\u003e p2\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p3\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFPG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003cb\u003e20.65\u003c/b\u003e (21.\u003cb\u003e45)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193.50 (28.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e293.00 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1 \u003csup\u003ea\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e.\u003cb\u003e35\u003c/b\u003e (1.2\u003cb\u003e5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.25 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.\u003cb\u003e20\u003c/b\u003e (1.\u003cb\u003e5\u003c/b\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1 \u003csup\u003ea\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198.30 (23.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248.00 (9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287.50 (18.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003cp\u003e(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.00 (18.\u003cb\u003e62\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222.50 (38.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325.00 (43.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSCr (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003cb\u003e0\u003c/b\u003e (0.4\u003cb\u003e2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.\u003cb\u003e71\u003c/b\u003e (\u003cb\u003e2\u003c/b\u003e.\u003cb\u003e72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUACR (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e.\u003cb\u003e10\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003e5\u003c/b\u003e.\u003cb\u003e35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003cb\u003e3\u003c/b\u003e.\u003cb\u003e35\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003e48\u003c/b\u003e.\u003cb\u003e87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003cb\u003e30\u003c/b\u003e.\u003cb\u003e70\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003e210\u003c/b\u003e.\u003cb\u003e65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eeGFR (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.\u003cb\u003e25\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003e10\u003c/b\u003e.\u003cb\u003e1\u003c/b\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003cb\u003e8\u003c/b\u003e.\u003cb\u003e15\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003cb\u003e9\u003c/b\u003e.\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003cb\u003e4\u003c/b\u003e.\u003cb\u003e50\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003cb\u003e4\u003c/b\u003e.6\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.\u003cb\u003e2\u003c/b\u003e0 (\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.90 (0.\u003cb\u003e20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.70 (0.\u003cb\u003e2\u003c/b\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedian (IQR) \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.\u003cb\u003e91\u003c/b\u003e (0.\u003cb\u003e44)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.\u003cb\u003e37\u003c/b\u003e (0.5\u003cb\u003e2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.00 (0.\u003cb\u003e79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHS-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.\u003cb\u003e96\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.\u003cb\u003e44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e97\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.\u003cb\u003e83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.\u003cb\u003e33\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.\u003cb\u003e61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ep1\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ef\u003c/sup\u003e p2 \u003csup\u003eb\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e p3 \u003csup\u003ec\u003c/sup\u003e \u0026lt; 0.001\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-tailed P-values are shown; pairwise comparisons Bonferroni-adjusted. α\u0026thinsp;=\u0026thinsp;0.05; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e comparison between normoalbuminuria and microalbuminuria groups.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e comparison between normoalbuminuria and macroalbuminuria groups.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e comparison between microalbuminuria and macroalbuminuria groups.\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e Chi-square test.\u003c/p\u003e \u003cp\u003e \u003csup\u003ee\u003c/sup\u003e Kruskal\u0026ndash;Wallis test.\u003c/p\u003e \u003cp\u003e \u003csup\u003ef\u003c/sup\u003e Mann\u0026ndash;Whitney U test\u003c/p\u003e \u003cp\u003e \u003csup\u003eg\u003c/sup\u003e Interquartile Range\u003c/p\u003e \u003cp\u003e \u003csup\u003eh\u003c/sup\u003e Standard deviation\u003c/p\u003e \u003cp\u003eIn the correlation analysis, the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) demonstrated significant positive correlations with the studied parameters, except for estimated glomerular filtration rate (eGFR), which exhibited significant negative correlations (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(2): Correlation of TyG Index, NLR and hs-CRP with the different studied parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHS-CRP (mg/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUACR (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-tailed P-values are shown. α\u0026thinsp;=\u0026thinsp;0.05; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Spearman correlation\u003c/p\u003e \u003cp\u003eAnalysis of the receiver operating characteristic (ROC) curves revealed that the neutrophil-to-lymphocyte ratio (NLR) exhibited higher sensitivity and specificity compared to the triglyceride-glucose (TyG) index and high-sensitivity C-reactive protein (hs-CRP) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003esee figure legends section at the end of the manuscript\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterpreting diagnostic tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPV \u003csup\u003eb\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNPV \u003csup\u003ec\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.594\u0026ndash;0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.767\u0026ndash;0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHS-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.806\u0026ndash;0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e confidence interval.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e positive predictive value.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e negative predictive value.\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e area Under the Curve\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses were performed for selected study parameters (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate logistic regression model of some studied parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR \u003csup\u003ea\u003c/sup\u003e (95% CI \u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGroup I \u0026amp; Group II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGroup I \u0026amp; Group III\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR \u003csup\u003ea\u003c/sup\u003e (95% CI \u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR \u003csup\u003ea\u003c/sup\u003e (95% CI \u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.98\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04 (0.97\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (0.74\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.06\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20 (0.02\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66 (1.48\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.46\u0026ndash;2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.11 (1.47\u0026ndash;3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.07\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (0.97\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53 (0.85\u0026ndash;2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.80\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.76\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72 (0.66\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73 (1.94\u0026ndash;3.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46 (0.59\u0026ndash;3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.00 (2.85\u0026ndash;68.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13 (2.39\u0026ndash;4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22 (1.27\u0026ndash;3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.70 (1.65\u0026ndash;8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHS-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.72 (2.08\u0026ndash;3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55 (0.93\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.78 (2.67\u0026ndash;12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Odds ratio.\u003c/p\u003e \u003cp\u003eTwo-tailed P-values are shown. α\u0026thinsp;=\u0026thinsp;0.05; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Odds ratio.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e confidence interval.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is a need for simple, cost-effective, and readily accessible alternative tests to identify individuals at risk of developing diabetic kidney disease (DKD). The present study aimed to evaluate and compare the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) as potential biomarkers of DKD across different stages of albuminuria in patients with type 2 diabetes mellitus (T2DM). A total of 300 patients with T2DM were enrolled and categorized into three groups (I, II, and III) based on their urinary albumin-to-creatinine ratio (UACR).\u003c/p\u003e \u003cp\u003eRegarding the triglyceride-glucose (TyG) index, a progressive and significant increase was observed in the macroalbuminuria group compared to the microalbuminuria and normoalbuminuria groups. These findings are in agreement with the study by Ran L et al 2025 \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, which reported a significant rise in TyG index values corresponding to the severity of diabetic nephropathy in patients with type 2 diabetes mellitus (T2DM).\u003c/p\u003e \u003cp\u003eRegarding the neutrophil-to-lymphocyte ratio (NLR), values were significantly higher in diabetic patients with macroalbuminuria compared to those with microalbuminuria and normoalbuminuria. These results align with the findings of Pradesta R et al 2025 \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, who evaluated 65 patients with type 2 diabetes mellitus (T2DM) and reported that NLR values increased progressively with the severity of albuminuria.\u003c/p\u003e \u003cp\u003eRegarding high-sensitivity C-reactive protein (hs-CRP), levels were significantly higher in diabetic patients with macroalbuminuria compared to those with microalbuminuria and normoalbuminuria. These findings are consistent with the study by Hasan M et al 2024 \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, who evaluated 75 patients with type 2 diabetes mellitus (T2DM) and reported that patients with microalbuminuria exhibited elevated hs-CRP values.\u003c/p\u003e \u003cp\u003eOur results revealed strong positive correlations between the triglyceride-glucose (TyG) index and several parameters, including diabetes duration, body mass index (BMI), systolic and diastolic blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine, urinary albumin-to-creatinine ratio (UACR), neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP), along with negative correlations with estimated glomerular filtration rate (eGFR). These findings are partially consistent with the study by Duan S et al 2023 \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, which reported positive correlations between the TyG index and BMI, fasting blood glucose (FBG), triglycerides, and total cholesterol in 179 patients with type 2 diabetes mellitus (T2DM).\u003c/p\u003e \u003cp\u003eOur results revealed strong positive correlations between the neutrophil-to-lymphocyte ratio (NLR) and several parameters, including diabetes duration, body mass index (BMI), systolic and diastolic blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine, urinary albumin-to-creatinine ratio (UACR), triglyceride-glucose (TyG) index, and high-sensitivity C-reactive protein (hs-CRP), along with negative correlations with estimated glomerular filtration rate (eGFR). These findings are partially consistent with the study by Gurmu M et al 2023 \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, which reported a significant increase in NLR in patients with diabetic nephropathy and demonstrated positive correlations between NLR and variables such as disease duration and systolic and diastolic blood pressure. Furthermore, the results align with those of Pradesta R et al 2025 \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, who observed a statistically significant positive correlation between NLR and UACR in patients with type 2 diabetes mellitus (T2DM).\u003c/p\u003e \u003cp\u003eOur results revealed strong positive correlations between high-sensitivity C-reactive protein (hs-CRP) and several parameters, including diabetes duration, body mass index (BMI), systolic and diastolic blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine, urinary albumin-to-creatinine ratio (UACR), neutrophil-to-lymphocyte ratio (NLR), and triglyceride-glucose (TyG) index, along with negative correlations with estimated glomerular filtration rate (eGFR). These findings are partially consistent with the study by Hasan M et al 2024 \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, which reported that elevated hs-CRP levels were associated with microalbuminuria in patients with type 2 diabetes mellitus (T2DM). Additionally, microalbuminuria was correlated with older age, longer duration of diabetes, and higher triglyceride levels compared to patients without microalbuminuria.\u003c/p\u003e \u003cp\u003eOur results revealed positive correlations between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), body mass index (BMI), and diabetes duration. These findings indicate that higher BMI and longer disease duration are associated with increased inflammatory activity (reflected by NLR and hs-CRP) and elevated insulin resistance (assessed by the TyG index). Furthermore, significant positive correlations were observed between the TyG index, NLR, hs-CRP, fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c), suggesting that poor glycemic control is closely linked to heightened inflammation and insulin resistance.\u003c/p\u003e \u003cp\u003eStrong positive correlations were observed between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), and lipid parameters, including total cholesterol (TC) and triglycerides (TG). These findings underscore the potential of these markers to reflect metabolic alterations in patients with type 2 diabetes mellitus (T2DM), including dyslipidemia, which represents a major cardiovascular and renal risk factor.\u003c/p\u003e \u003cp\u003eSignificant positive correlations were observed between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), serum creatinine, and urinary albumin-to-creatinine ratio (UACR), along with a negative correlation with estimated glomerular filtration rate (eGFR). These findings indicate that heightened inflammation and insulin resistance are associated with declining renal function and increased albuminuria, suggesting their potential relevance as markers of disease severity.\u003c/p\u003e \u003cp\u003eSignificant positive correlations were observed between the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), and both systolic and diastolic blood pressure. These findings suggest that inflammatory and metabolic dysregulation may play a role in the development of hypertension in patients with type 2 diabetes mellitus (T2DM).\u003c/p\u003e \u003cp\u003eThe optimal cut-off value of the serum TyG index in our study was 9.5. At this threshold, diagnostic sensitivity was 92%, diagnostic specificity was 71%, and positive predictive value was 86%.\u003c/p\u003e \u003cp\u003eThe optimal cut-off value for serum neutrophil-to-lymphocyte ratio (NLR) was determined to be 2.5, yielding a diagnostic sensitivity of 93.5%, specificity of 85%, and a positive predictive value of 93%. Similarly, the optimal cut-off value for serum high-sensitivity C-reactive protein (hs-CRP) was 1.34, with a diagnostic sensitivity of 91%, specificity of 83%, and a positive predictive value of 91%.\u003c/p\u003e \u003cp\u003eIn this study, logistic regression analysis was performed, incorporating variables selected based on the variance inflation factor (VIF) to reduce multicollinearity. Univariate logistic regression demonstrated that diabetes duration, glycated hemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) were significantly associated with disease severity. In contrast, multivariate logistic regression revealed independent associations of diabetes duration, eGFR, and NLR with disease severity, whereas hs-CRP and the TyG index were primarily linked to advanced stages. Age, sex, and HbA1c did not show significant effects.\u003c/p\u003e \u003cp\u003eIn conclusion, the triglyceride-glucose (TyG) index, neutrophil-to-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) represent simple and readily measurable parameters associated with diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). NLR was linked to both early and advanced stages of albuminuria, suggesting an association with disease severity, while TyG index and hs-CRP are more relevant for advanced stages. The combined evaluation of these markers could be explored in future studies for potential risk stratification across DKD stages. This study provides a comparative evaluation of three accessible biomarkers across albuminuria stages.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. The cross-sectional design precludes causal or predictive inferences, emphasizing the need for future longitudinal studies to validate these findings. Larger sample size is needed to more robustly evaluate the associations between these markers and diabetic kidney disease. Variations in pharmacological treatments and the presence of non-diabetic inflammatory conditions could have influenced inflammatory markers. Finally, being a single-center study, the findings may not be fully generalizable to other populations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis cross-sectional study included 300 patients with type 2 diabetes mellitus who were recruited from the Endocrinology Unit, Internal Medicine Department, Tanta University Hospital, from August 2025 to December 2025. The study protocol was approved by the Medical Ethical Committee of the Faculty of Medicine, Tanta University, approval number (36264PR1318/8/25).\u003c/p\u003e \u003cp\u003e All procedures involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments.\u003c/p\u003e \u003cp\u003e Written informed consent was obtained from all patients included in the study.\u003c/p\u003e \u003cp\u003ePatients were categorized into three groups based on their urinary albumin-to-creatinine ratio (UACR): Group I included 100 diabetic patients with normoalbuminuria, Group II comprised 100 diabetic patients with microalbuminuria, and Group III consisted of 100 diabetic patients with macroalbuminuria. All participants had a previously documented diagnosis of albuminuria prior to enrollment.\u003c/p\u003e \u003cp\u003eThe study enrolled patients with T2DM who fulfilled the World Health Organization (WHO) diagnostic criteria. Patients with severe cardiac, hepatic, renal, or other organ dysfunction; tumors; type 1 diabetes or other specific types of diabetes; gestational diabetes; acute complications such as diabetic ketoacidosis or hyperglycemic hyperosmolar state; conditions affecting high-sensitivity C-reactive protein (hs-CRP) and neutrophil-to-lymphocyte ratio (NLR), including acute infections, chronic inflammatory disorders, anemia, smoking, or use of medications such as steroids, statins, and sodium-glucose co-transporter 2 inhibitors; and conditions impacting urinary protein excretion, such as nephrotic or nephritic syndrome, urolithiasis, urinary tract infection, and other acute infections, were excluded from the study.\u003c/p\u003e \u003cp\u003eAll participants underwent comprehensive history taking, thorough clinical examination, body mass index (BMI) calculation, and laboratory investigations, including complete blood count, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), serum creatinine (SCr), estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and high-sensitivity C-reactive protein (hs-CRP).\u003c/p\u003e \u003cp\u003eCalculations:\u003c/p\u003e \u003cp\u003eBMI was calculated as weight in kilograms divided by height in meters squared.\u003c/p\u003e \u003cp\u003eThe CKD-EPI equation was used to estimate eGFR \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe TyG index was calculated using the following formula:\u003c/p\u003e \u003cp\u003eln [(TG (mg/dl) \u0026times; FPG (mg/dl))/2].\u003c/p\u003e \u003cp\u003eThe neutrophil-to-lymphocyte ratio (NLR) was determined as the ratio of the absolute neutrophil count to the absolute lymphocyte count obtained from a complete blood count.\u003c/p\u003e\n\u003ch3\u003eBlood sampling\u003c/h3\u003e\n\u003cp\u003eTwo milliliters of fasting peripheral blood were collected in EDTA vacutainer tubes for complete blood count (CBC) and glycated hemoglobin (HbA1c) analysis using sterile disposable syringes under strict aseptic conditions. Additionally, three milliliters of blood were collected into sterile, plain tubes, allowed to clot, and the serum was separated for the measurement of fasting plasma glucose, total cholesterol, triglycerides, hs-CRP, and serum creatinine.\u003c/p\u003e \u003cp\u003eComplete blood count (CBC) was obtained using an automated cell counter (ERMA PCE-210N). Glycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (HPLC) using the TOSOH G8 analyzer. High-sensitivity C-reactive protein (hs-CRP) was quantified by nephelometry (Atellica NEPH 630, Siemens). Serum triglycerides, total cholesterol, glucose, and creatinine were analyzed using an automated Konelab system (Thermo Fisher Scientific). Urine samples were collected in sterile, dry cups for the assessment of microalbuminuria using immunoturbidimetric latex (COD 31924 - BioSystem) and creatinine using the automated Konelab system to calculate the urinary albumin-to-creatinine ratio (UACR).\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe sample size was determined using a one-way ANOVA to compare the TyG index, NLR, and hs-CRP across the three groups. Standard deviations were obtained from previous studies. Considering a clinically meaningful difference of 0.3 between groups, with a significance level (α) of 0.05 and 80% statistical power, a minimum of 97 participants per group was required. To account for potential dropouts, the sample size was increased to 100 participants per group, resulting in a total of 300 participants.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using the Python programming language (v3.13), with Pandas (v2.3.3) and NumPy (v2.4.0) employed for data management. Core statistical computations were conducted using SciPy (v1.16.3) \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, while diagnostic performance metrics were calculated with Scikit-learn (v1.8.0) \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The Shapiro-Wilk test was applied to assess the normality of data distribution. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for parametric data and as median (interquartile range, IQR) for non-parametric data.\u003c/p\u003e \u003cp\u003eComparative Analysis\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMulti-group comparisons: One-way ANOVA was used for parametric data, whereas the Kruskal-Wallis test was applied for non-parametric data when comparing continuous variables across three or more independent groups.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePairwise comparisons: Following a significant Kruskal-Wallis test, post-hoc pairwise Mann-Whitney U tests were conducted. To control for Type I error inflation due to multiple comparisons, the Bonferroni correction was applied, with statistical significance set at an adjusted threshold of \u0026#119875; \u0026lt; 0.0167 (calculated as \u0026#120572; = 0.05/3).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCategorical Variables: Associations between categorical variables were evaluated using the Chi-square test (\u0026#120594;\u0026sup2;). When expected cell counts were less than 5, Fisher's Exact test was applied.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCorrelation and Diagnostic Performance: Spearman's correlation coefficient (rs) was used to assess non-parametric associations. Receiver Operating Characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated to evaluate diagnostic accuracy and determine optimal cutoff points. For all primary analyses not adjusted using the Bonferroni correction, a two-sided \u0026#119875; \u0026lt; 0.05 was considered statistically significant. Univariate and multivariate logistic regression analyses were performed to examine associations with disease outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any funding from any institution or organization.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM. M. Seyam, H.A. Elhabiby, Z. S. Hamed, M. M. Elgebaly, and R. A. Aboeida contributed to the conception and design of the study, acquisition of data, analysis and/or interpretation of data, drafting the manuscript, and revising it critically for important intellectual content. E. R. El Fiky contributed to the analysis and/or interpretation of data and drafting of the manuscript. All authors approved the final version of the manuscript to be published.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGross JL, de Azevedo MJ, Silveiro SP, Canani LH, Caramori ML, Zelmanovitz T. Diabetic nephropathy: diagnosis, prevention, and treatment. \u003cem\u003eDiabetes Care\u003c/em\u003e 28(1):164\u0026ndash;176 (2005).\u003c/li\u003e\n\u003cli\u003eLv L, Zhou Y, Chen X, Gong L, Wu J, Luo W, et al. Relationship between the TyG index and diabetic kidney disease in patients with type 2 diabetes mellitus. \u003cem\u003eDiabetes Metab Syndr Obes\u003c/em\u003e 14:3299\u0026ndash;3306 (2021).\u003c/li\u003e\n\u003cli\u003eGarg P, Shetty M, Krishnamurthy V. Correlation of urinary neutrophil gelatinase with the histopathological extent of kidney damage in patients with diabetic nephropathy. \u003cem\u003eSaudi J Kidney Dis Transpl\u003c/em\u003e 34(Suppl 1):S112\u0026ndash;S121 (2023).\u003c/li\u003e\n\u003cli\u003eRakesh BM, Pradeep N, Nischal GJ. Correlation between neutrophil to lymphocyte ratio and urine albumin to creatinine ratio in diabetic nephropathy patients: a cross-sectional study. \u003cem\u003eJ Clin Diagn Res\u003c/em\u003e 18:OC01\u0026ndash;OC05 (2024).\u003c/li\u003e\n\u003cli\u003eHuang W, Huang J, Liu Q, Lin F, He Z, Zeng Z, et al. Neutrophil-lymphocyte ratio is a reliable predictive marker for early-stage diabetic nephropathy. \u003cem\u003eClin Endocrinol (Oxf)\u003c/em\u003e 82(2):229\u0026ndash;233 (2015).\u003c/li\u003e\n\u003cli\u003eKassab HS, Osman NA, Elrahmany SM. Assessment of triglyceride-glucose index and ratio in patients with type 2 diabetes and their relation to microvascular complications. \u003cem\u003eEndocr Res\u003c/em\u003e 48(4):94\u0026ndash;100 (2023).\u003c/li\u003e\n\u003cli\u003eLiu L, Xia R, Song X, Zhang B, He W, Zhou X, et al. Association between the triglyceride\u0026ndash;glucose index and diabetic nephropathy in patients with type 2 diabetes: a cross-sectional study. \u003cem\u003eJ Diabetes Investig\u003c/em\u003e 12(4):557\u0026ndash;565 (2021).\u003c/li\u003e\n\u003cli\u003eHong S, Han K, Park CY. The triglyceride-glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. \u003cem\u003eBMC Med\u003c/em\u003e 18:361 (2020).\u003c/li\u003e\n\u003cli\u003eReckziegel MB, Nepomuceno P, Machado T, Renner JDP, Pohl HH, Nogueira de Almeida CA, et al. The triglyceride-glucose index as an indicator of insulin resistance and cardiometabolic risk in Brazilian adolescents. \u003cem\u003eArch Endocrinol Metab\u003c/em\u003e 67(2):153\u0026ndash;161 (2023).\u003c/li\u003e\n\u003cli\u003eTam CS, Xie W, Johnson WD, Cefalu WT, Redman LM, Ravussin E. Defining insulin resistance from hyperinsulinemic euglycemic clamps. \u003cem\u003eDiabetes Care\u003c/em\u003e 35(7):1605\u0026ndash;1610 (2012).\u003c/li\u003e\n\u003cli\u003eKhandare SA, Chittawar S, Nahar N, Dubey TN, Qureshi Z. Study of neutrophil lymphocyte ratio as a novel marker for diabetic nephropathy in type 2 diabetes. \u003cem\u003eIndian J Endocrinol Metab\u003c/em\u003e 21(3):387\u0026ndash;392 (2017).\u003c/li\u003e\n\u003cli\u003eSato H, Takeuchi Y, Matsuda K, Kagaya S, Saito A, Fukami H, et al. Pre-dialysis neutrophil-lymphocyte ratio, a novel and strong short-term predictor of all-cause mortality in patients with diabetic nephropathy. \u003cem\u003eTher Apher Dial\u003c/em\u003e 21(4):370\u0026ndash;377 (2017).\u003c/li\u003e\n\u003cli\u003eZhang J, Zhang R, Wang Y, Wu Y, Li H, Han Q, et al. Effects of neutrophil-lymphocyte ratio on renal function and histologic lesions in patients with diabetic nephropathy. \u003cem\u003eNephrology (Carlton)\u003c/em\u003e 24(11):1115\u0026ndash;1121 (2019).\u003c/li\u003e\n\u003cli\u003eTang Y, Fung E, Xu A, Lan H. C-reactive protein and ageing. \u003cem\u003eClin Exp Pharmacol Physiol\u003c/em\u003e 44(Suppl 1):9\u0026ndash;14 (2017).\u003c/li\u003e\n\u003cli\u003eAryan Z, Ghajar A, Faghihi-Kashani S, Afarideh M, Nakhjavani M, Esteghamati A. Baseline high-sensitivity C-reactive protein predicts macrovascular and microvascular complications of type 2 diabetes: a population-based study. \u003cem\u003eAnn Nutr Metab\u003c/em\u003e 72(4):287\u0026ndash;295 (2018).\u003c/li\u003e\n\u003cli\u003eSproston NR, Ashworth JJ. Role of C-reactive protein at sites of inflammation and infection. \u003cem\u003eFront Immunol\u003c/em\u003e 9:754 (2018).\u003c/li\u003e\n\u003cli\u003eLevin A, Ahmed SB, Carrero JJ, Foster B, Francis A, Hall RK, et al. Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease: known knowns and known unknowns. \u003cem\u003eKidney Int\u003c/em\u003e 105(4):684\u0026ndash;701 (2024).\u003c/li\u003e\n\u003cli\u003eVirtanen P, Gommers R, Oliphant T, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. \u003cem\u003eNat Methods\u003c/em\u003e 17(3):261\u0026ndash;272 (2020).\u003c/li\u003e\n\u003cli\u003ePedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. \u003cem\u003eJ Mach Learn Res\u003c/em\u003e 12:2825\u0026ndash;2830 (2011).\u003c/li\u003e\n\u003cli\u003eRan L, Han Y, Zhaohu H, Hailin S. Correlation Between Triglyceride-Glucose Index and Microvascular Complications in Patients With Early-Onset of Type 2 Diabetes Mellitus. \u003cem\u003eEndocrinol Diabetes Metab\u003c/em\u003e 8(2) (2025).\u003c/li\u003e\n\u003cli\u003ePradesta R, Novadian, Kusnadi Y, Kurniati N, Husin S. Neutrophil-Lymphocyte Ratio as a Novel Biomarker for Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study. \u003cem\u003eBioscientia Medicina: J Biomed \u0026amp; Transl Res\u003c/em\u003e 9(3) (2025).\u003c/li\u003e\n\u003cli\u003eHasan M, Ray N, Bari M, Islam M, Ahmed I. Assessment of Microalbuminuria and Hs-CRP for Early Detection of Diabetic Nephropathy in Type 2 Diabetes. \u003cem\u003eGlob Acad J Med Sci\u003c/em\u003e 6(4):161\u0026ndash;168 (2025).\u003c/li\u003e\n\u003cli\u003eDuan S, Zhou M, Lu F, Chen C, Chen S, Geng L, et al. Triglyceride-glucose index is associated with the risk of chronic kidney disease progression in type 2 diabetes. \u003cem\u003eEndocrine\u003c/em\u003e 81(1):77\u0026ndash;89 (2023).\u003c/li\u003e\n\u003cli\u003eGurmu M, Genet S, Gizaw S, Feyisa T, Gnanasekaran N. Neutrophil\u0026ndash;lymphocyte ratio as an inflammatory biomarker of diabetic nephropathy among type 2 diabetes mellitus patients: A comparative cross-sectional study. \u003cem\u003eSAGE Open Med\u003c/em\u003e 10:1\u0026ndash;7 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetic kidney disease, type 2 diabetes mellitus, triglyceride-glucose index, neutrophil-lymphocyte ratio, high-sensitivity C-reactive protein.","lastPublishedDoi":"10.21203/rs.3.rs-8866385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8866385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM) and a leading cause of progressive renal failure worldwide. Conventional diagnostic methods have limitations, highlighting the need for simple, cost-effective, and widely accessible tools for early risk assessment. In this cross-sectional study, 300 patients with T2DM were categorized into three groups according to urinary albumin-to-creatinine ratio. We evaluated and compared the triglyceride-glucose (TyG) index, neutrophil-lymphocyte ratio (NLR), and high-sensitivity C-reactive protein (hs-CRP) as markers of DKD across albuminuria stages. TyG index, NLR, and hs-CRP increased progressively from normoalbuminuria to macroalbuminuria. Receiver operating characteristic analysis demonstrated good diagnostic performance of all markers, with NLR showing high sensitivity and specificity.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression identified NLR as significantly associated with disease severity, whereas TyG index and hs-CRP were primarily linked to advanced DKD stages. These findings suggest that TyG index, NLR, and hs-CRP are simple, measurable markers associated with DKD in T2DM patients. NLR correlates with both early and advanced albuminuria stages, suggesting an association with disease severity, while TyG index and hs-CRP are more relevant for advanced stages. The combined evaluation of these markers could be explored in future studies for potential risk stratification across DKD stages.\u003c/p\u003e","manuscriptTitle":"Comparative analysis of triglyceride-glucose index, neutrophil-lymphocyte ratio and hs-CRP across albuminuria stages in type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:25:00","doi":"10.21203/rs.3.rs-8866385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"253327002418916057138135584309061894189","date":"2026-03-24T06:03:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T20:18:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111293388783289671660464843185242005010","date":"2026-03-08T19:00:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244106851567959365927785018289767438412","date":"2026-03-06T13:39:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T05:02:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T18:29:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T13:38:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T00:50:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-18T00:46:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69cf0552-2efb-45db-99fe-c1dee03b1769","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64278917,"name":"Health sciences/Biomarkers"},{"id":64278918,"name":"Health sciences/Diseases"},{"id":64278919,"name":"Health sciences/Endocrinology"},{"id":64278920,"name":"Health sciences/Medical research"},{"id":64278921,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2026-03-12T07:25:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:25:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8866385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8866385","identity":"rs-8866385","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}