Abstract
Background
This study aimed to investigate the correlation between six surrogate indexes of insulin resistance (IR) and six inflammatory markers with diabetic peripheral arterial disease (DPAD) in patients with type 2 diabetes mellitus (T2DM).
Methods
Clinical and hematological data were retrospectively collected from 300 patients with T2DM admitted to the Endocrinology Department of Wuhu Hospital of Traditional Chinese Medicine (Anhui, China) between January 2024 and May 2025. Patients were divided into two groups: DPAD (n = 207) and T2DM (without DPAD (n = 93)). IR surrogate indexes analyzed included estimated glucose disposal rate (eGDR), Chinese Visceral Adiposity Index (CVAI), triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), metabolic score for insulin resistance (METS-IR), and atherogenic index of plasma (AIP). Inflammatory markers assessed included neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), total cholesterol-to-lymphocyte ratio (TCLR), fibrinogen-to-lymphocyte ratio (FLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). Factors associated with DPAD were identified and the discriminative ability of IR surrogate indexes and inflammatory markers for DPAD was explored.
Results
Compared with the T2DM group, the DPAD group exhibited significantly higher CVAI, TyG-BMI, METS-IR, AIP, NLR, PLR, TCLR, FLR, and SII (p < 0.05), while eGDR and LMR were significantly lower (p < 0.05). Logistic regression analysis revealed that CVAI, TyG-BMI, METS-IR, AIP, NLR, PLR, FLR, and SII were positively associated with DPAD, whereas eGDR and LMR were inversely associated. eGDR and FLR exhibited significant associations with DPAD in sex and age subgroups. Receiver operating characteristic (ROC) curve analysis revealed that the combined eGDR+CVAI+FLR+LMR model yielded the highest area under the ROC curve (AUC) of 0.742 (95% CI [0.681–0.803]).
Conclusion
IR surrogate indexes and inflammatory markers are closely associated with DPAD risk in T2DM patients. The combined eGDR+CVAI+FLR+LMR model showed the best discriminative ability for DPAD, offering a potential reference for early clinical identification. These findings, however, are hypothesis-generating and require validation in prospective studies.
Keywords: Type 2 diabetes mellitus, Diabetic peripheral arterial disease, Insulin resistance, Inflammation, Surrogate indexes
Introduction
The epidemiological incidence of diabetes mellitus is increasing with accelerating global aging. Diabetic peripheral arterial disease (DPAD) is one of the most prevalent arterial complications in patients diagnosed with type 2 diabetes mellitus (T2DM) (Bonora et al., 2020), which can manifest as a diabetic foot syndrome or peripheral artery disease (PAD). Comorbid PAD in patients diagnosed with T2DM constitutes a primary contributor to lower-extremity and cardio-arterial complications, as well as elevated all-cause and cardio-arterial mortality rates (Bonaca et al., 2018; Bonaca et al., 2020; Hess et al., 2021; Verma et al., 2022; Verma et al., 2023). Epidemiological studies have indicated that PAD affects >230 million middle-age and elderly individuals worldwide, demonstrating strong correlations with increased amputation and mortality rates (Barnes et al., 2020; Mandaglio-Collados, Marin & Rivera-Caravaca, 2023). Approximately 20% of patients with T2DM present with peripheral arterial complications (Aboyans et al., 2025), with an incidence exceeding 90% among those with diabetes duration >5 years, compared with only 10% in non-diabetic populations (Jia et al., 2022). Patients diagnosed with DPAD exhibit a five-year mortality rate of 23%, representing more than double the risk in patients with PAD alone (Mueller et al., 2014). Moreover, amputation risk in patients with DPAD increases exponentially compared with that in patients with single-system diseases (Barnes et al., 2020). The characteristic clinical manifestations of PAD encompass intermittent claudication (IC)—defined as lower limb pain during physical activity that subsides with rest—and rest pain. However, approximately 50% of diabetic patients present with “silent PAD”, attributable to concurrent peripheral neuropathy. In certain cases of DPAD, patients may exhibit atypical symptoms, including diminished exercise tolerance, lower limb numbness, or delayed wound healing. When individuals with DPAD begin to display either typical or atypical symptoms, their arterial structures frequently demonstrate pathological alterations, such as intimal thickening, plaque formation, luminal stenosis, and occlusion (Shou et al., 2020; Wang et al., 2020). Therefore, identifying arterial lesions at an early stage may help improve risk stratification and guide further clinical evaluation.
The ankle–brachial index (ABI) and toe–brachial index (TBI) currently serve as primary diagnostic tools for PAD (AbuRahma et al., 2020; Gerhard-Herman et al., 2017). However, these indexes primarily stem from studies involving symptomatic patients (Dachun et al., 2010; Friberg et al., 2022), and their performance in individuals with atypical or absent limb symptoms remains poorly understood. Invasive angiography remains the gold standard for PAD diagnosis, although its application is now largely confined to patients requiring endo-arterial re-arterialization (Gerhard-Herman et al., 2017). Duplex ultrasonography is a safe diagnostic modality for all patients; however, its efficacy depends on the technical proficiency of the operator. Moreover, its sensitivity and specificity are influenced by multiple factors including arterial wall calcification, vessel location and depth, and the presence of multilevel occlusions (Allard et al., 1994). Currently, there is a lack of reliable blood-based biomarkers for the accurate assessment of DPAD. Therefore, evaluating multiple biomarkers for the early identification of DPAD has significant clinical utility in reducing mortality risk and improving patient quality of life.
The pathogenesis of DPAD overlaps with those of atherosclerosis (AS) and micro- arterial endothelial injury (Li et al., 2023), with insulin resistance (IR) and chronic inflammation playing crucial roles in the development and progression of DPAD (Athavale, Fukaya & Leeper, 2024; Mitrea et al., 2025). Although the hyperinsulinemic-euglycemic clamp technique is recognized to be the gold standard for IR assessment (DeFronzo, Tobin & Andres, 1979), its complexity, invasiveness, and high cost render it unsuitable for clinical and epidemiological studies. Therefore, various IR surrogate markers, including the estimated glucose disposal rate (eGDR) (Yi et al., 2024), Chinese visceral adiposity index (CVAI) (Qiao et al., 2022), triglyceride-glucose (TyG) index (Cui et al., 2024), TyG-body mass index (TyG-BMI)(Cui et al., 2024), metabolic score for insulin resistance (METS-IR) (Bello-Chavolla et al., 2018), and atherogenic index of plasma (AIP) (Yin et al., 2023), have garnered increasing attention and have been demonstrated to be closely associated with the risk and prognosis of cardio-arterial diseases. Novel inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), total cholesterol-to-lymphocyte ratio (TCLR), fibrinogen-to-lymphocyte ratio (FLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII), which are easily accessible, have significant clinical utiity (Bai et al., 2023; Su et al., 2025). However, few studies have compared the discriminative abilities of various IR surrogate markers and multiple inflammatory indicators for PAD in patients with T2DM. As such, the present study aimed to investigate the relationship between six IR surrogate markers and six inflammatory indicators with the occurrence of PAD in patients with T2DM, and to evaluate their ability to discriminate DPAD risk.
Materials and Methods
Study population
This single-center, retrospective, cross-sectional study included data from 300 patients diagnosed with T2DM, hospitalized in the Endocrinology Department of Wuhu Traditional Chinese Medicine Hospital (Anhui, China) between January 2024 and May 2025. Among these, 207 cases (140 male, 67 female; 33–88 years of age (median, 59 years)) were complicated with DPAD, while 93 (48 male, 45 female; 18–75 years of age median, 45 years) were simple cases of T2DM.
The inclusion criteria were as follows: fulfilled the T2DM international criteria (American Diabetes Association) (American Diabetes Association, 2021). The diagnostic criteria for T2DM included the following: fasting plasma glucose ≥7.0 mmol/L; 2 h plasma glucose ≥ 11.1 mmol/L during the oral glucose tolerance test; or glycated hemoglobin A1c (HbA1c) ≥ 6.5%. All enrolled patients underwent PAD assessment, which involved either documentation of PAD-related symptoms or confirmation of PAD diagnosis based on standardized criteria developed by the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines (Gornik et al., 2024). PAD is diagnosed based on a combination of clinical features, functional testing, and imaging confirmation. Clinically, patients may present with symptoms such as limb pain, numbness, temperature abnormalities (coldness or a sensation of heat), weakness, intermittent claudication, or rest pain. Physical signs include cool skin, trophic changes, such as skin thinning and hair loss, diminished or absent peripheral pulses, and reduced systolic blood pressure in the affected limb. A resting ABI ≤ 0.9 is diagnostic, as is a normal resting ABI that drops by 15%–20% after exercise. Finally, imaging modalities, such as color Doppler ultrasound, computed tomography angiography, magnetic resonance angiography, or digital subtraction angiography can provide direct evidence of atherosclerotic plaques, arterial stenosis, or occlusion. In this study, patients were enrolled in the PAD group if they fulfilled any of the following criteria: ABI < 0.9; TBI < 0.6; history of vascular surgery or endovascular intervention for PAD; or a confirmed diagnosis by medical imaging or clinical grading.
The exclusion criteria were as follows: type 1 diabetes mellitus or other types of diabetes; acute diabetic complications (diabetic ketoacidosis, lactic acidosis, and hyperglycemic hyperosmolar state); concurrent acute or chronic infectious diseases; comorbid severe organic disease(s), malignant tumors or hematological diseases; use of immunosuppressive drugs, glucocorticoids, or anticoagulants; leukocytosis (>10 × 109/L), leukopenia (<4 × 109/L), thrombocytosis (>450 × 109/L), or thrombocytopenia (<100 × 109/L); pancreatic insufficiency, acute or chronic pancreatitis, or previous pancreatic surgery; psychiatric disorders; impaired liver and kidney functions; pregnancy and/or lactation; arterial function damage in the lower limbs due to other diseases (e.g., trauma, vasculitis, congenital vascular malformations); and abnormal coagulation function or a tendency to bleed within the previous half month. All participants were educated about the study in detail and signed an informed consent form before inclusion. This study was approved by the Ethics Committee of Wuhu Hospital of Traditional Chinese Medicine (Ethics Number: YW-2025-082). Given the retrospective nature of the data collection, the Ethics Committee granted a waiver for the requirement of informed consent; nonetheless, written informed consent was obtained from all participants prior to their enrollment in the study, in accordance with the institutional review board’s requirements and the Declaration of Helsinki.
Data and protocol
General clinical data were collected from the patients, and electronic medical records were collected from the relevant departments, including sex, age, duration of diabetes, histories cerebrovascular disease, coronary heart disease, and hypertension, blood pressure, height, weight, smoking and alcohol consumption habits, waist circumference (WC), and hip circumference. Smoking history was defined as smoking >1 cigarette(s) per day for >1 year and continued to smoke within 1 month before the visit. Study participants were classified into three groups according to drinking habits: never drinkers (less than monthly consumption); former drinkers (consumed monthly but quit in the preceding year); and current drinkers (consumed at least monthly). All patients underwent fasting venous blood collection the morning after admission for blood index testing, including complete blood count, biochemistry, coagulation, and HbA1c. Biochemical indicators were assessed using a fully automated biochemical analyzer (7600, Hitachi, Tokyo, Japan); HbA1c was tested using a fully automated HbA1c detector (D-100, Bio-Rad, Hercules, CA, USA); fibrinogen was detected using point-of-care analyzer (Diagnostica Stago Ltd., Asnières sur Seine, France); and routine blood tests were performed using a laboratory analyzer (Mindray, Shenzhen, China).Information on medication use (including antidiabetic, lipid-lowering, and antiplatelet agents) was collected from electronic medical records but was not included in the regression models due to the high variability and incomplete documentation, which is acknowledged as a limitation.
The following equations were used to calculate the various indexes:
BMI = weight (kg)/height (m2)
eGDR = 21.158−0.09 × WC (cm)−3.407 × hypertension (yes=1/no=0)−0.551 × HbA1c (%)
CVAI (male) = −267.93 + 0.68 × age (years) + 0.03 × BMI (kg/m2) + 4 × WC (cm) + 22 × log10(TG [mmol/L]) –16.32 × High-density lipoprotein cholesterol (HDL-c) (mmol/L)
CVAI (female) = −187.32+1.71 × age (years) + 4.23 × BMI (kg/m2) + 1.12 × WC (cm) + 39.76 × log10 (TG [mmol/L])−11.66 × HDL-c (mmol/L)
TyG = ln (TG [mg/dl] × fasting plasma glucose [FPG] mg/dl / 2)
TyG-BMI = TyG × BMI (kg/m2)
AIP = log (TG [mg/dl] /HDL-c [mg/dl]);
METS-IR = ln (2 × FPG [mg/dl] +TG [mg/dl]) × BMI (kg/m2) /In (HDL-C [mg/dl])
NLR = Neutrophil count (×109/L)/Lymphocyte count (×109/L)
PLR = Platelet count (×109/L)/Lymphocyte count (×109/L)
TCLR = TC (mmol/L)/Lymphocyte count (×109/L)
FLR = Fibrinogen (Fib) (g/L)/Lymphocyte count (×109/L)
LMR = Lymphocyte count (×109/L)/Monocyte count (×109/L)
SII = Neutrophil count (×109/L) × Platelet count (× 109/L)/Lymphocyte count (×109/L)
Because some units of the test values are in mmol/L, they were converted to mg/dL when calculating the indicators; more specifically, mg/dL = mmol/L × molar mass:
TG (mg/dl) = TG (mmol/L) × 88.5; FPG (mg/dl) = FPG (mmol/L) × 18;
HDL-c (mg/dl) = HDL-c (mmol/L) × 38.67.
Statistical analysis
Statistical analyses were performed using SPSS version 23.0 (IBM Corporation, Armonk, NY, USA). Plots were generated using Prism version 10.0 (GraphPad Inc., San Jose, CA, USA) and Origin software (OriginLab, Northampton, MA, USA), and normality tests were performed on the measured data. Data that conformed to a normal distribution are expressed as mean ± standard deviation (SD), and the independent samples t-test was used for between-group comparisons. Data with a skewed distribution are expressed as median with interquartile range (IQR i.e., P25, P75), and the Mann–Whitney U test was used for between-group comparisons. Categorical variables are expressed as rate or proportion and compared using the chi-squared test. Spearman correlation analysis was used to examine the relationship between different variables. Logistic regression analysis was used to assess the associations between surrogate indices of IR, inflammatory markers, and DPAD. Variables for multivariate models were selected based on clinical relevance and univariate screening (p < 0.10), and stepwise forward selection was applied. Multicollinearity among predictors was assessed using the variance inflation factor (VIF); all VIF values were < 5.0, indicating no severe collinearity. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative ability of the indexes for DPAD. The combined model was constructed using logistic regression with the selected markers (eGDR, CVAI, FLR, LMR), and internal validation was performed using the bootstrap method (1,000 resamples) to estimate optimism-corrected C-statistics. Calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test. All statistical tests were two-sided and differences with p < 0.05 were considered to be significant.
Results
Baseline characteristics of the T2DM and DPAD groups
Demographic and clinical characteristics of the T2DM and DPAD groups are summarized in Table 1. Among the 300 patients diagnosed with T2DM enrolled in the study, 207 presented with DPAD (DPAD group) and 93 did not (T2DM group). Compared with the T2DM group, the DPAD group exhibited significantly higher values for proportion of males, age, diabetes duration, history of cerebrovascular disease, history of hypertension, systolic blood pressure, WC, HC, BMI, CVAI, TyG-BMI, METS-IR, AIP index, NLR, PLR, TCLR, FLR, and SII (p < 0.05). Conversely, eGDR, lymphocyte count, and LMR were significantly lower in the DPAD group, with statistically significant differences (p < 0.05).
Table 1. Comparison of baseline characteristics between T2DM group and DPAD group.
CHD, coronary heart disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference; HC,hip circumference; BMI, body mass index; HbA1c, glycosylated hemoglobin A1c; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C,low-density lipoprotein cholesterol; ALB, serum albumin; PLT, platelet; Fib, fibrinogen; eGDR, estimated glucose disposal rate; CVAI, Chinese visceral adiposity index; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; METS-IR, metabolic score for insulin resistance; AIP, atherogenic index of plasma; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; TCLR, total cholesterol-to-lymphocyte ratio; FLR, fibrinogen-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflammation index. Variables with p < 0.05 are represented in bold.
| Variable | TD2M (N = 93) |
DPAD (N = 207) |
t/χ2/Z | p value |
|---|---|---|---|---|
| Gender (male,%) | 48 (51.6%) | 140 (67.6%) | 7.039 | 0.008 |
| Age (years) | 45 (34.5, 55.5) | 59 (52.0, 67.0) | −7.987 | <0.001 |
| Diabetes duration (years) | 3 (0.7, 8.0) | 5 (2.0, 10.0) | −3.149 | 0.002 |
| CHD n (%) | 3 (3.23%) | 6 (2.90%) | 0.024 | 0.878 |
| Cerebroarterial disease n (%) | 2 (2.15%) | 18 (8.70%) | 4.418 | 0.036 |
| Smoking, n (%) | 32 (33.40%) | 79 (38.20%) | 0.388 | 0.533 |
| Drinking status, n (%) | 0.764 | 0.714 | ||
| Never | 53 (56.98%) | 109 (52.66%) | ||
| Former | 8 (8.60%) | 23 (11.11%) | ||
| Current | 32 (34.41%) | 75 (36.23%) | ||
| Hypertension, n (%) | 25 (26.88%) | 118 (57.00%) | 23.342 | <0.001 |
| SBP (mmHg) | 130 (122,142) | 135 (123,148) | −2.058 | 0.040 |
| DBP (mmHg) | 82 (73,89) | 79 (72,86) | −1.564 | 0.118 |
| WC (cm) | 90.74 ± 10.29 | 93.97 ± 10.18 | −2.533 | 0.012 |
| HC (cm) | 98.00 ± 7.94 | 100.13 ± 7.86 | −2.164 | 0.031 |
| BMI (kg/m 2 ) | 24.51 ± 4.236 | 25.68 ± 3.84 | −2.372 | 0.018 |
| HbA1C(%) | 7.9 (6.9, 10.2) | 8.0 (6.8, 10.1) | −0.299 | 0.765 |
| FPG (mmol/L) | 7.2 (5.7, 8.5) | 7.0 (6.1, 8.8) | −0.520 | 0.603 |
| TC (mmol/L) | 4.55 (4.14, 5.12) | 4.50 (3.70, 5.37) | −0.619 | 0.536 |
| TG (mmol/L) | 1.27 (0.98, 1.81) | 1.42 (1.05, 2.29) | −1.892 | 0.059 |
| HDL-C (mmol/L) | 1.18 (0.94, 1.46) | 1.13 (0.96, 1.35) | −0.550 | 0.582 |
| LDL-C (mmol/L) | 2.57 ± 0.77 | 2.76 ± 0.95 | −1.729 | 0.085 |
| ALB (g/L) | 41.4 (39.0, 44.3) | 40.5 (38.8, 42.8) | −0.984 | 0.325 |
| Neutrophil (109/L) | 3.51 (3.04, 4.22) | 3.45 (2.78, 4.42) | −0.583 | 0.560 |
| Lymphocyte (109/L) | 2.13 (1.79, 2.86) | 1.90 (1.51, 2.29) | −3.959 | <0.001 |
| Monocyte (109/L) | 0.37 (0.30, 0.49) | 0.41 (0.32, 0.48) | −1.343 | 0.179 |
| PLT (109/L) | 194 (156, 241) | 186 (161, 228) | −0.773 | 0.440 |
| Fib (g/L) | 3.13 (2.70, 3.64) | 3.28 (2.87, 3.71) | −1.337 | 0.169 |
| eGDR | 7.45 ± 1.98 | 6.02 ± 2.40 | 4.992 | <0.001 |
| CVAI | 103.81 ± 42.11 | 131.89 ± 40.01 | −5.53 | <0.001 |
| TyG | 8.87 (8.45, 9.35) | 9.00 (8.58, 9.63) | −1.751 | 0.080 |
| TyG-BMI | 219.22 ± 40.21 | 234.40 ± 42.02 | −2.93 | 0.004 |
| METS-IR | 37.61 (31.63, 42.83) | 40.86 (35.47, 46.58) | −3.034 | 0.002 |
| AIP | 0.37 (0.21, 0.59) | 0.46 (0.28, 0.70) | −1.970 | 0.049 |
| NLR | 1.63 (1.25, 1.94) | 1.83 (1.41, 2.35) | −2.978 | 0.003 |
| PLR | 83.51 (66.62, 124.06) | 98.59 (78.26, 129.41) | −2.817 | 0.005 |
| TCLR | 2.14 (1.64, 2.71) | 2.33 (1.75, 3.20) | −1.972 | 0.049 |
| FLR | 1.46 (1.06, 1.85) | 1.75 (1.31, 2.23) | −3.860 | <0.001 |
| LMR | 5.91 (4.36, 7.89) | 4.82 (3.64, 6.20) | −4.057 | <0.001 |
| SII | 280.19 (224.50, 443.47) | 347.82 (248.70, 482.49) | −2.089 | 0.027 |
Associations between IR surrogate indexes, inflammatory markers, and DPAD
The associations between IR surrogate indexes, inflammatory markers, and peripheral arterial complications in T2DM are reported in Table 2. Results indicated that, in Models 1 and 2, the eGDR and LMR were inversely associated with DPAD, whereas other IR surrogate indexes (CVAI, TyG-BMI, METS-IR, and AIP) and inflammatory markers (NLR, PLR, FLR, and SII) were positively associated.After adjusting for all confounding factors in Model 3, the associations of the 5 IR surrogate indexes and the 5 inflammatory markers with DPAD remained statistically significant. After full adjustment (Model 3), the TyG index was significantly associated with DPAD risk (OR = 1.273, 95% CI [1.048–1.495], p = 0.030), whereas TCLR was not.
Table 2. Multivariate regression analysis of the associations between IR surrogate indexes, inflammatory markers, and DPAD risk.
Model 1 was unadjusted; Model 2 was adjusted for gender, age, diabetes duration, CHD, cerebroarterial disease, smoking, alcohol, hypertension, SBP, DBP, WC, HC, BMI; Model 3 was adjusted for Model 2+TG, TC, HDL-C, LDL-C,ALB, and HbA1c. ORs are presented as per 1 SD increase in variables for DPAD risk. OR, odds ratio; CI, confidence interval; eGDR, estimated glucose disposal rate; CVAI, Chinese visceral adiposity index; TyG, triglyceride - glucose; TyG-BMI, TyG-body mass index; METS-IR, metabolic score for insulin resist ance; AIP, atherogenic index of plasma; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; TCLR, total cholesterol-to-lymphocyte ratio; FLR, fibrinogen-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflamm ation index; BMI, body mass index; WC, waist circumference; HC, hip circumference; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; ALB, serum albumin; HbA1c, glycosylated hemoglobin A1c.
| Variable | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | OR (95% CI) | p value | |
| eGDR | 0.756 (0.672, 0.851) | <0.001 | 0.786 (0.686, 0.901) | 0.001 | 0.806 (0.691, 0.940) | 0.006 |
| CVAI | 1.117 (1.011, 1.224) | <0.001 | 1.110 (1.001, 1.218) | 0.024 | 1.109 (1.001, 1.218) | 0.025 |
| TyG | 1.205 (0.974, 1.441) | 0.070 | 1.235 (1.101, 1.569) | 0.016 | 1.273 (1.048, 1.495) | 0.030 |
| TyG-BMI | 1.015 (1.007, 1.023) | <0.001 | 1.009 (1.003, 1.015) | 0.004 | 1.015 (1.004, 1.026) | 0.007 |
| METS-IR | 1.053 (1.019, 1.087) | 0.002 | 1.086 (1.041, 1.134) | <0.001 | 1.123 (1.038, 1.215) | 0.004 |
| AIP | 1.151 (1.062, 1.336) | 0.037 | 1.184 (1.002, 1.387) | 0.049 | 1.167 (1.095, 1.441) | 0.032 |
| NLR | 1.686 (1.177, 2.415) | 0.004 | 1.691 (1.183, 2.430) | 0.008 | 1.775 (1.226, 2.552) | 0.026 |
| PLR | 1.006 (1.001, 1.014) | 0.001 | 1.008 (1.005, 1.013) | 0.002 | 1.010 (1.005, 1.014) | 0.003 |
| TCLR | 1.298 (0.997, 1.690) | 0.053 | 0.997 (0.737, 1.351) | 0.987 | 1.029 (0.754, 1.406) | 0.855 |
| FLR | 1.939 (1.306, 2.877) | 0.001 | 1.946 (1.315, 2.880) | 0.001 | 2.006 (1.331, 3.024) | 0.001 |
| LMR | 0.808 (0.791, 0.905) | 0.020 | 0.814 (0.795, 0.908) | 0.030 | 0.862 (0.803, 0.910) | 0.030 |
| SII | 1.001 (1.001, 1.003) | 0.001 | 1.001 (1.001, 1.002) | 0.001 | 1.002 (1.001, 1.003) | 0.002 |
Subgroup analyses examining the associations between IR surrogate indexes, inflammatory markers, and DPAD
To further investigate the association between IR surrogate indexes, inflammatory markers, and DPAD, subgroup analyses were performed based on sex and age. Using a median age of 55 years in the total study population as the cut-off (Fig. 1), the associations between IR surrogate indexes, inflammatory markers, and DPAD varied according to sex and age. eGDR and FLR were significantly associated with DPAD risk across both sexes and age groups. Other IR surrogate indexes primarily exhibited associations with DPAD in female and older patients with T2DM (>55 years of age) (Fig. 1A). Significant associations between inflammatory markers and DPAD were observed for the NLR among older males, PLR in middle-age females (<55 years), LMR in middle-age females, and SII in middle-age females (Fig. 1B). The TyG index and TCLR did not show statistically significant associations with DPAD in sex and age subgroup analyses.
Figure 1. Subgroup analyses of the associations between IR surrogate indexes, inflammatory markers, and DPAD risk.
Each subgroup was adjusted for gender, age, CHD, cerebroarterial disease, smoking, alcohol, diabetes duration, hypertension, BMI, WC, HC, TG, TC, HDL-C, LDL-C, ALB, and HbA1c, except for stratification variables. ORs are presented as per 1 SD increase in variables for DPAD risk. OR, odds ratio; CI, confidence interval; eGDR, estimated glucose disposal rate; CVAI, Chinese visceral adiposity index; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; METS-IR, metabolic score for insulin resistance; AIP, atherogenic index of plasma; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; TCLR, total cholesterol-to-lymphocyte ratio; FLR, fibrinogen-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflammation index; BMI, body mass index; WC, waist circumference; HC, hip circumference; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; ALB, serum albumin; HbA1c, glycosylated hemoglobin A1c.
Analysis of the correlation between IR surrogate indexes, and inflammatory markers and DPAD in patients with T2DM
Spearman’s correlation analysis was used to evaluate the correlation between IR surrogate indexes, inflammatory markers, and DPAD in patients with T2DM. As shown in Fig. 2, except for TyG, which exhibited no significant correlation with DPAD, the remaining five IR surrogate markers and six inflammatory indicators were significantly correlated with DPAD. Specifically, eGDR (r = − 0.28) and LMR (r = − 0.23) exhibited negative correlations with DPAD, while CVAI (r = 0.30), TyG-BMI (r = 0.16), METS-IR (r = 0.18), AIP (r = 0.11), NLR (r = 0.17), PLR (r = 0.16), TCLR (r = 0.11), FLR (r = 0.22) and SII (r = 0.12) exhibited positive correlations (all p < 0.05). Additionally, among the IR surrogate indexes and inflammatory markers, eGDR was significantly positively correlated with LMR (r = 0.13), CVAI was significantly positively correlated with FLR (r = 0.14) and SII (r = 0.13), and TyG was significantly positively correlated with TCLR (r = 0.13); CVAI was significantly negatively correlated with LMR (r = − 0.22) (all p < 0.05).
Figure 2. Heatmap of the correlation between IR surrogate indexes, inflammatory marker, and DPAD in patients with type 2 diabetes mellitus.
eGDR, estimated glucose disposal rate; CVAI, Chinese visceral adiposity index; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; METS-IR, metabolic score for insulin resistance; AIP, atherogenic index of plasma; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; TCLR, total cholesterol-to-lymphocyte ratio; FLR, fibrinogen-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII , systemic immune-inflammation index; DPAD, diabetic peripheral arterial disease.
Discriminative ability of IR surrogate indexes, inflammatory markers, and combined markers for DPAD
The ability of the IR surrogate indexes, inflammatory markers, and combined markers to discriminate DPAD was evaluated using ROC curve analysis, as shown in Table 3 and Fig. 3. Among the IR surrogate indicators, CVAI demonstrated superior discriminative ability for DPAD, with the highest AUC (0.685; 95% CI [0.619–0.750]), followed by eGDR (AUC, 0.677; 95% CI [0.615–0.740]). Among the inflammatory markers, LMR exhibited the best discriminative performance for DPAD, with the highest AUC (0.646; 95% CI [0.580–0.713]), followed by FLR (0.639; 95% CI [0.572–0.707]). Based on previous findings, the eGDR and FLR were significantly associated with DPAD across both sexes and age subgroups. Here, two markers, eGDR and FLR, were combined (AUC, 0.706; 95% CI [0.643–0.770]). Furthermore, by incorporating the top-performing markers, eGDR+CVAI+FLR+LMR (AUC, 0.742; 95% CI [0.681–0.803]), the combined model achieved the highest discriminative accuracy for DPAD. The discriminative ability of the remaining indexes for DPAD is reported in Table 3. The TyG index did not show statistically significant discriminative ability for DPAD (AUC 0.563, 95% CI [0.495–0.632], p = 0.080). Internal validation using bootstrap resampling (1,000 replicates) yielded an optimism-corrected C-statistic of 0.731 for the combined model, indicating acceptable stability. The Hosmer–Lemeshow test showed good calibration (χ2 = 8.24, p = 0.411).
Table 3. ROC curves of various indexes and DPAD risk.
ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval; eGDR, estimated glucose disposal rate; CVAI, Chinese visceral adiposity index; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; METS-IR, metabolic score for insulin resistance; AIP, atherogenic index of plasma; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; TCLR, total cholesterol-to-lymphocyte ratio; FLR, fibrinogen-to- lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflammation index.
| Type | Variable | AUC | 95% CI | P value | Cutoff value | Sensitivity | Specificity | Youden index |
|---|---|---|---|---|---|---|---|---|
| IR surrogate indexes | eGDR | 0.677 | 0.615∼0.740 | <0.001 | 5.884 | 0.828 | 0.502 | 0.330 |
| CVAI | 0.685 | 0.619∼0.750 | <0.001 | 127.495 | 0.560 | 0.731 | 0.291 | |
| TyG | 0.563 | 0.495∼0.632 | 0.080 | – | – | – | – | |
| TyG-BMI | 0.598 | 0.528∼0.668 | 0.007 | 189.865 | 0.860 | 0.301 | 0.161 | |
| METS-IR | 0.610 | 0.540∼0.679 | 0.002 | 42.554 | 0.440 | 0.742 | 0.182 | |
| AIP | 0.571 | 0.502∼0.640 | 0.049 | 0.331 | 0.710 | 0.441 | 0.151 | |
| Inflammatory markers | NLR | 0.608 | 0.540∼0.675 | 0.003 | 2.014 | 0.415 | 0.817 | 0.232 |
| PLR | 0.602 | 0.530∼0.673 | 0.005 | 80.054 | 0.744 | 0.484 | 0.228 | |
| TCLR | 0.571 | 0.503∼0.640 | 0.049 | 2.535 | 0.444 | 0.720 | 0.164 | |
| FLR | 0.639 | 0.572∼0.707 | <0.001 | 1.748 | 0.502 | 0.720 | 0.222 | |
| LMR | 0.646 | 0.580∼0.713 | <0.001 | 5.747 | 0.538 | 0.705 | 0.243 | |
| SII | 0.575 | 0.505∼0.646 | 0.037 | 281.083 | 0.686 | 0.505 | 0.191 | |
| Combined markers | eGDR+FLR | 0.706 | 0.643∼0.770 | <0.001 | – | 0.758 | 0.602 | 0.360 |
| eGDR+CVAI +FLR+LMR |
0.742 | 0.681∼0.803 | <0.001 | – | 0.725 | 0.667 | 0.392 |
Figure 3. ROC curve analysis of the ability of these indexes to discriminate DPAD.
ROC, receiver operating characteristic; AUC, area under the curve; eGDR, estimated glucose disposal rate; CVAI, Chinese visceral adiposity index; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; METS-IR, metabolic score for insulin resistance; AIP, atherogenic index of plasma; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; TCLR, total cholesterol-to-lymphocyte ratio; FLR, fibrinogen-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflammation index.
Discussion
T2DM is a chronic metabolic disease characterized by hyperglycemia, which is associated with relative insulin deficiency and IR (Wondmkun, 2020). DPAD is a common vascular complication of T2DM, and can manifest as diabetic foot syndrome and PAD, significantly increasing the risk for diabetic foot ulcers, gangrene, and even lower limb amputation, seriously affecting patient quality of life. The traditional view holds that PAD is mainly dominated by AS of the large arteries (Aday & Matsushita, 2021); however, PAD is often accompanied by local and systemic micro-arterial lesions (Mohammedi et al., 2016), such as thickening of the capillary basement membrane, endothelial proliferation, decreased oxygen tension, and hypoxia, which in turn affect peripheral nerve function (Ergul, 2011). The chronic course of T2DM may have adverse effects when blood glucose control is poor (Nanayakkara et al., 2021). The pathogenesis of DPAD overlaps with those of other AS and micro-arterial endothelial injuries. Inflammation, oxidative stress, insulin resistance, AGEs, nerve growth factor, activation of the polyol pathway, and activation of the hexosamine and PKC pathways are core pathological factors and processes (Li et al., 2023). However, the specific markers of DPAD are not yet clear. To identify a rapid and easy-to-use laboratory indicator, we evaluated the discriminative ability of six IR surrogate indices and six inflammatory markers for DPAD in patients with T2DM.
The typical characteristics of IR include hyperglycemia, abnormal lipid levels, hypertension and obesity (Uehara et al., 2023). The IR substitution indexes derived from different combinations of these characteristics (i.e., eGDR, CVAI, TyG, TyG-BMI, METS-IR, and AIP) may better reflect the degree of IR in different ways. The eGDR integrates WC, hypertension, and HbA1c, and this index has been validated through the high insulin-normal blood glucose clamp technique and can accurately reflect insulin sensitivity. The CVAI integrates sex, age, BMI, WC, and lipid levels and is a reliable indicator of visceral fat distribution in the Chinese population; it has been widely used to assess the risk of cardio-arterial and metabolic diseases (Qiao et al., 2022; Xiao et al., 2024). TyG, based on TG and FPG, has been widely used to predict diabetes and cardio-arterial diseases (Tian et al., 2022; Zhang et al., 2017). TyG-BMI may provide a more comprehensive perspective for evaluating IR and may be superior to TyG index for predicting IR (Lim et al., 2019). METS-IR and AIP are also been identified as strong independent predictors of cardiovascular and cerebro-arterial events (Yang et al., 2023; Zheng et al., 2023). This study found that eGDR had a significant negative correlation with the occurrence of DPAD and was an independent protective factor, and its significance was not affected in subgroup analyses according to sex and age. CVAI, TyG-BMI, METS-IR, and AIP were all independently associated with DPAD, and CVAI had the highest diagnostic efficacy in discriminating T2DM combined with DPAD. The TyG index showed a statistically significant association in the fully adjusted model (Model 3) but did not demonstrate significant discriminative ability in ROC analysis, suggesting that its association may be modest and influenced by other covariates.
Chronic inflammation is a common feature of both AS and type T2DM (Poznyak et al., 2020). IR promotes oxidative stress and the inflammatory response. Monocytes initiate and accelerate the progression of AS by releasing proinflammatory cytokines, inducing production of reactive oxygen species and proteolytic enzymes (Gratchev et al., 2012). Neutrophils, the most abundant leukocyte subtype, exacerbate arterial wall inflammation by inducing apoptosis of small muscle cells (Fernandez-Ruiz, 2019). By contrast, lymphocytes may impede the progression of AS (Biscetti et al., 2019). Fibrinogen (Fib), a glycoprotein complex and marker of the thrombotic cascade, acts as an inflammatory molecule and participates in the formation and maintenance of atherosclerotic plaques (Zaib et al., 2024). Platelets play a dual role in AS: their adhesion to the arterial wall promotes plaque formation (Massberg et al., 2002), while their activation facilitates inflammation and thrombosis (Gawaz, Langer & May, 2005). NLR, PLR, TCLR, FLR, LMR, and SII, combinations of these factors, have emerged as novel markers of inflammation. This study found no statistically significant differences in HDL and low-density lipoprotein (LDL) levels between the two groups, which is consistent with a previous report (Jia et al., 2022), indicating that HDL and LDL are not be strong discriminators of PAD risk in patients with T2DM. In contrast, significant differences were observed in the six inflammatory indicators between the T2DM and DPAD groups. After adjusting for confounding factors, NLR, PLR, FLR, LMR, and SII demonstrated significant associations with DPAD, with FLR maintaining significance across sex and age subgroups. LMR was significantly negatively correlated with the occurrence of DPAD and was an independent protective factor. Moreover, it had the highest diagnostic efficacy for discriminating T2DM complicated by DPAD.
The present study found that the incidence of PAD in males with T2DM was higher than that in females. This difference may be attributed to sex disparities in arterial stress responses and sequelae (Pabon et al., 2022). Disparities in fat distribution, hormone levels, and metabolic variations between the sexes, as well as differences in metabolic functions across age groups, may affect the performance of IR surrogate indexes and inflammatory markers in assessing DPAD risk. Accordingly, we performed subgroup analyses stratified according to sex and age, which revealed that the associations between various markers and DPAD risk differed at the subgroup level, with only eGDR and FLR remaining unaffected by subgroup stratification. Based on the Spearman correlation results, significant correlations were identified between IR surrogate markers and inflammatory indicators, specifically eGDR and LMR and CVAI and FLR. By integrating these four indicators, we derived an optimal combined AUC (0.742) that achieved the highest discriminative accuracy for DPAD. IR and inflammatory markers may serve as useful tools for risk stratification (Wei et al., 2025). Recent evidence suggests that early improvement in glycemic status may reduce the risk of subsequent vascular complications (Vazquez Arreola et al., 2026). In this context, IR and inflammatory markers may serve as useful tools for risk stratification rather than definitive diagnostic indicators.
Conclusions
In summary, the surrogate indexes of IR and inflammatory markers in patients with T2DM were significantly correlated with DPAD. The combination eGDR+CVAI+FLR+LMR model demonstrated moderate discriminative ability for peripheral arterial complications in patients with T2DM. Further multicenter, prospective studies are needed to confirm its clinical utility. However, this study had several limitations. First, its single-center, retrospective, cross-sectional design precludes the establishment of causal relationships between the disease and indicators.Second, the indicators were not dynamically monitored, and it remains unknown whether these changes are related to DPAD progression. Third, the sample size was relatively small, and the DPAD group was larger than the T2DM group, which may have introduced selection bias, as DPAD patients may be more likely to be hospitalized due to symptoms. A formal power analysis was not performed, which is a limitation. Fourth, potential confounding factors, such as genetic predisposition, dietary habits, environmental variations, and, importantly, medication use (including antidiabetic, lipid-lowering, and antiplatelet agents), remain unaccounted for due to incomplete documentation. The lack of medication data is a significant limitation that may have influenced the observed associations. Fifth, the study did not include a comparison with other traditional clinical indicators (e.g., ABI, TBI, or cardiovascular history) to assess the incremental diagnostic value of the tested markers. Future research should expand the sample size, incorporate medication profiles and other relevant factors, conduct multicenter prospective studies, and include head-to-head comparisons with established clinical markers to validate the clinical utility of these indexes in DPAD risk assessment.
Supplemental Information
Acknowledgments
The authors appreciate the valuable comments from reviewers.
Funding Statement
This work was supported by Science and Technology Innovation Joint Fund project of Fujian province (2024Y9407), Quanzhou Science and Technology Program (2023N055S), the Second Affiliated Hospital of Fujian Medical University Doctoral Nursery Project (BS202324), and Wuhu Science and Technology Program (2025kj024). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Additional Information and Declarations
Competing Interests
The authors declare there are no competing interests.
Author Contributions
Tingting Liu conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Yong Ni performed the experiments, prepared figures and/or tables, and approved the final draft.
Ping Wang conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.
Jingying Wu conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.
Clinical Trial Ethics
The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):
The Ethics Committee of Wuhu Hospital of Traditional Chinese Medicine granted Ethical approval to carry out the study within its facilities (Ethics Number: YW-2025-082).
Data Availability
The following information was supplied regarding data availability:
The raw data are available in the Supplemental Files.
Clinical Trial Registration
The following information was supplied regarding Clinical Trial registration:
YW-2025-082.
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