Abstract
Type 2 diabetic foot ulcer (DFU) is closely linked to disorders of bone metabolism and abnormal bone mineral density (BMD), while serum 25-hydroxyvitamin D [25(OH)D], the gold standard for evaluating Vit D nutritional status, acts as an independent protective factor for DFU and is implicated in skeletal health regulation in diabetic patients. This cross-sectional study aimed to investigate biochemical markers of bone metabolism and BMD in patients with type 2 diabetic foot ulcer, and to identify risk factors for diabetic foot and accompanying gangrene. A total of 400 patients with type 2 diabetes mellitus (T2DM) admitted to the Endocrinology Ward of Affiliated Hospital of Nantong University from December 2021 to May 2023 were enrolled, including 155 in the DF group and 245 in the non-diabetic foot (NDF) group. Clinical data, bone metabolism markers and BMD measured by dual-energy X-ray absorptiometry (DXA) were collected, and data were analyzed using SPSS 25.0 software. Compared with the NDF group, the DF group had older age, longer diabetes duration, higher systolic blood pressure (SBP), urinary albumin creatinine ratio (UACR) and type I collagen N-terminal propeptide (PINP) level, but lower levels of serum albumin (ALB), glycemic and lipid indicators, 25(OH)D, total BMD and hip regional B < 0.05), with a significantly higher prevalence of low bone mass and osteoporosis. Binary logistic regression revealed that elevated SBP, reduced ALB, lower total T-score and total body fat content were independent risk factors for DF, with optimal cutoff values of 140 mmHg, 34.9 g/L, − 0.5 and 27.5% respectively. Additionally, lower BMD at the femoral shaft, femoral neck and greater trochanter was significantly associated with an increased risk of gangrene in DF patients. In conclusion, patients with type 2 diabetic foot ulcer present significant bone metabolism disorders and reduced BMD, and decreased BMD and related metabolic indicators can serve as valuable markers for predicting DF and its gangrene progression, providing clinical evidence for the early prevention and targeted treatment of this condition.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12902-026-02328-5.
Keywords: Type 2 diabetes, Diabetic foot, Bone density, Bone metabolism, Correlation
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12902-026-02328-5.
Introduction
Diabetes Mellitus (DM) is a common chronic metabolic disorder, with DF representing one of its most serious complications. Globally, the overall prevalence of DFU is 6.3%.The incidence is slightly higher in patients with type 2 diabetes (6.4%) than in those with type 1 diabetes (5.5%), and higher in males (4.5%) than in females (3.5%) [1]. DF arises from the combined effects of peripheral neuropathy, peripheral vascular lesions and secondary infection, ultimately leading to foot ulceration, soft tissue destruction and even limb damage [2, 3]. Diabetic foot ulceration is the leading cause of lower-extremity amputation in diabetic patients, with amputation rates reaching up to 85% in severe cases [4, 5]. It markedly increases long-term morbidity and mortality [6, 7] and imposes a heavy socioeconomic and family economic burden worldwide [8]. The onset and progression of DF are affected by multiple clinical risk factors, including advanced age, long diabetes duration, poor glycemic control, peripheral neuropathy, peripheral arterial disease (PAD) and structural foot deformities [9–12].
Accumulating clinical evidence demonstrates that 50%–67% of diabetic patients present with low bone mass, while only one-third meet the diagnostic criteria for osteoporosis [13]. Individuals with diabetes have a 2–3 fold higher risk of hip and femoral neck fractures compared with age-matched non-diabetic populations, and elderly diabetic patients with fractures show a significantly increased one-year mortality risk due to multiple complications [14]. Clinically, reduced BMD and fragility fracture history are the core diagnostic indicators for osteoporosis [15]. BMD accounts for approximately 70% of bone strength and is defined as bone mineral content per unit area or volume, serving as the most reliable quantitative index for evaluating bone status in clinical practice [16]. DXA is the gold-standard non-invasive technique for BMD measurement, and results are universally reported as T-scores. In accordance with WHO criteria: a T-score of − 1.0 to + 1.0 indicates normal bone mass; a T-score of − 2.5 to − 1.0 defines low bone mass; and a T-score ≤ − 2.5 confirms osteoporosis [17].
Vitamin D is an essential regulatory factor in bone metabolism. It not only participates in calcium and phosphorus homeostasis but also closely correlates with diabetic microvascular complications, including retinopathy and peripheral neuropathy. Serum 25(OH)D is the major circulating form of vitamin D and is widely recognized as the optimal biomarker for evaluating systemic vitamin D nutritional status [18]. Relevant clinical trials have confirmed that 12-week vitamin D supplementation effectively promotes diabetic foot ulcer healing by reducing ulcer size, depth and local inflammatory erythema [19]. However, existing studies have reported inconsistent results regarding peripheral 25(OH)D levels between DF and NDF patients [20]. Most current viewpoints support a high prevalence of vitamin D deficiency in DF populations, recommending routine 25(OH)D detection and targeted supplementation for high-risk individuals [21]. A previous cross-sectional study measured femoral neck, greater trochanter and Ward’s triangle BMD via DXA, and confirmed significantly decreased bone density in DF patients compared with T2DM controls [22].
Nevertheless, high-quality evidence focusing on bone metabolic markers, skeletal characteristics and related influencing factors in DF patients remains limited. This study aimed to compare clinical and skeletal parameters between hospitalized DF and NDF patients, screen independent risk factors for DF, and provide practical evidence for early prevention and individualized treatment of diabetic foot, so as to improve long-term prognosis in diabetic populations.
All NDF patients were hospitalized for poor glycemic control or routine chronic disease evaluation. Patients with severe acute illnesses or organ dysfunction that may potentially affect bone metabolism and biochemical parameters were strictly excluded to ensure the reliability of the control group.
Patients and methods
A retrospective analysis was conducted on 400 patients with T2DM and DF, who were hospitalized in the Endocrinology Ward of University Affiliated Hospital from December 2021 to May 2023. Of these, 155 were classified into the DF group and 245 into the NDF group. Diabetes was diagnosed according to the 1999 World Health Organization (WHO) criteria, which include: [23] (1) classic symptoms of diabetes plus a random blood glucose level ≥ 11.1 mmol/L; (2) fasting plasma glucose (FPG) ≥ 7.0 mmol/L; or (3) two-hour plasma glucose ≥ 11.1 mmol/L during an oral glucose tolerance test (OGTT). If classic symptoms are absent, a repeat test is necessary to confirm the diagnosis. DF is defined as: foot infections, ulcers, or (potentially) deeper tissue damage caused by distal peripheral nerve abnormalities and varying degrees of vascular complications, encompassing DFU, PAD, and foot infections (DFI) [24]. All participants were required to meet the following criteria: (1) aged over 18 years; (2) diagnosed with DM. Participants were grouped into DF and NDF based on the presence or absence of DF. Exclusion criteria were as follows: (1) severe cognitive impairment or poor overall condition; (2) pregnant or breastfeeding women; (3) secondary diabetes; (4) patients with malignancies, mental disorders, cognitive impairments, severe infections, recent trauma, surgeries, or other emergent conditions; (5) history of diseases affecting bone metabolism, such as thyroid and parathyroid disorders, osteoporosis, and fractures; (6) history of medication use that affects bone metabolism markers, such as calcium supplements, Vit D, corticosteroids, or bisphosphonates; (7) missing clinical, laboratory biochemical indicators, or bone density data.
This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of the Affiliated Hospital of Nantong University (Approval No.: 2018-K016).All data were analyzed anonymously to ensure patient confidentiality.
Data collection
For this retrospective analysis, we collected a range of clinical data from patients, including: gender, age, BMI, SBP, DBP, duration of diabetes, usage of antidiabetic drugs, glycated hemoglobin (HbA1c), fasting insulin (FINS), fasting C-peptide (FCP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Scr), serum uric acid (SUA), ALB, and various bone metabolism markers including 25(OH)D, serum calcium, phosphorus, Osteocalcin(OC), procollagen type I N-terminal propeptide(PINP) and β-C-terminal telopeptide of type I collagen(β-CTX). The severity of DFU was evaluated using the Wagner classification system.
Blood samples were collected after an eight-hour fast. Liver and kidney function, lipid profile, and electrolytes were analyzed using an Advia-2400 automatic biochemistry analyzer (Siemens, Germany); HbA1c levels were measured using a Bio-Rad VARIANTTM hemoglobin analyzer (Bio-Rad, USA); Fasting glucose, insulin, and C-peptide were assessed using a Roche Cobas E411 automated chemiluminescence analyzer; Bone metabolism biochemical markers were analyzed using a Roche e602 automated electrochemiluminescence immunoassay analyzer, and urinary albumin to creatinine ratio (UACR) was determined using a BioSystems A25 automated special protein analyzer (BioSystems, Spain). Bone density was measured using a DXA scanner (Prodigy, GE). All tests were performed in a medical center laboratory with regular calibration and maintenance of laboratory equipment by professionals to ensure accuracy and reliability of the results.
Statistical analysis
All data analyses were performed using Statistical Product and Service Solutions version 23.0 (SPSS 23.0) or GraphPad Prism 8. Quantitative data were tested for normal distribution and presented as mean ± standard deviation (x ± s) for normally or approximately normally distributed data, ‾and as median and interquartile range (M (Q1, Q3)) for non-normally distributed data. Categorical variables were presented as percentages (n%). Comparisons between groups: were made using the Chi-square test for categorical variables. T-tests or one-way ANOVA were used for normally or approximately normally distributed data, while the Kruskal-Wallis H or Mann-Whitney tests were applied for non-normally distributed data. Binary logistic regression models were used to analyze related risk factors. Initial univariate logistic regression analyses were performed, and variables with P < 0.05 underwent collinearity diagnostics to exclude indices with strong collinearity (VIF > 10). The remaining indices were included in a multivariate binary logistic regression analysis. Receiver operating characteristic (ROC) curves were used to calculate optimal cutoff values, selecting the most sensitive diagnostic standards. A P-value of less than 0.05 was considered statistically significant.
Results
General characteristics of the study subjects
A total of 400 hospitalized patients with type 2 diabetes enrolled in this study, including 155 cases in the DF group (38.7%) and 245 cases in the NDF group (61.3%). Within the DF group, 68 patients (43.9%) were complicated with gangrene, and 87 patients (56.1%) had no gangrene.
The male proportion was 71.0% (110/155) in the DF group and 62.5% (151/245) in the NDF group, with no significant inter-group difference in gender distribution (P > 0.05).
Patients in the DF group were older (68.0 ± 11.0 years vs. 63.0 ± 11.0 years) and had a longer diabetes duration (median: 12.0 years vs. 10.0 years), and these two indicators showed significant statistical differences between the two groups.
Comparison between DF and NDF groups
General clinic indexes
As summarized in Table 1, the DF group had advanced age, prolonged diabetes duration, elevated SBP and higher whole-body muscle mass ratio. Meanwhile, BMI, visceral adipose tissue (VAT) volume and whole-body fat percentage were notably lower in the DF group. No significant differences in baseline gender and DBP were observed between the two groups.
Table 1.
General comparison of patient characteristics
| DF Group (n = 155) | NDF Group (n = 245) | P Value | |
|---|---|---|---|
| Age (years) | 68༎0 ± 11.0 | 63༎0 ± 11.0 | < 0.001 |
| Course of disease (years) | 12.0 (8.0,20.0) | 10 (4.0,17.0) | < 0.001 |
| Male (n, %) | 110 (71.0) | 151 (62.5) | 0.056 |
| BMI(kg/m2) | 23.6 (21.3,26.0) | 24.2 (21.8,26.7) | 0.092 |
| SBP (mmHg) | 143 ± 20 | 134 ± 17 | < 0.001 |
| DBP (mmHg) | 77 ± 11 | 78 ± 11 | 0.167 |
| VAT Volume (cm3) | 1093 (633,1466) | 1218 (854,1692) | 0.009 |
| Total body fat (%) | 29.1 (24.7,34.5) | 31.6 (24.7,34.5) | 0.001 |
| Muscle/Body mass | 0.68 (0.63,0.72) | 0.66 (0.61,0.69) | 0.004 |
| Drug Use (n, %) | |||
| INS | 100 (64.5) | 110 (44.9) | < 0.001 |
| SU | 26 (16.8) | 49 (20.0) | 0.421 |
| SGLT-2i | 20 (12.9) | 35 (14.3) | 0.696 |
| GLP-1RA | 4 (2.6) | 11 (4.5) | 0.328 |
| TZD | 4 (2.6) | 4 (1.6) | 0.509 |
Note: Data: Data presented as mean ± standard deviation (x ± s), median (Q1, Q3), or number and percentage (n, %)
Abbreviations: BMI: Body Mass Index; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; VAT: Visceral Adipose Tissue; INS: Insulin; SU: Sulfonylureas; SGLT-2i: Sodium-Glucose Co-Transporter 2 Inhibitor; GLP-1RA: Glucagon-Like Peptide-1 Receptor Agonist; TZD: Thiazolidinediones
In terms of hypoglycemic treatment, the insulin application rate was 65.5% in the DF group, which was significantly higher than that in the NDF group (P < 0.05). The usage of other oral antidiabetic agents showed no statistical difference.
Comparison of bone density classification
As shown in Table 2, the proportion of normal bone mass in the DF group was 73.5%, which was markedly lower than 91.0% in the NDF group.
Table 2.
Comparison of bone density conditions between groups
| DF Group (n = 155) | NDF Group (n = 245) | P Value | |
|---|---|---|---|
| Normal Bone Mass (%) | 114(73.5) | 223(91.0) | < 0.001 |
| Osteopenia | 38(24.5) | 22 (9.0) | |
| Osteoporosis | 3(1.9) | 0(0) |
The prevalence of low bone mass (24.5%) and osteoporosis (1.9%) in DF patients was significantly higher than that in NDF patients, with statistical differences (P < 0.05).
Comparison of biochemical indicators
As presented in Table 3, ALB, eGFR, TC, TG, HDL, HbA1c, FPG and FCP were significantly decreased in the DF group.
Table 3.
Comparison of biochemical markers between groups
| DF Group (n = 155) | NDF Group (n = 245) | P Value | |
|---|---|---|---|
| ALB (g/L) | 34.3 ± 5.7 | 39.1 ± 4.2 | < 0.001 |
| UA (umol/L) | 317.0 (248.0,394.0) | 317 (257.0,389.5) | 0.807 |
| eGFR | 93.4 (67.7,118.4) | 112 (89.0,130.6) | < 0.001 |
| UACR(mg/mmolCr) | 10.1 (3.6,63.9) | 1.42 (0.7,5.6) | < 0.001 |
| TC (mmol/L) | 0.94 (0.7,1.1) | 1.1 (0.9,1.2) | < 0.001 |
| TG (mmol/L) | 2.7 (2.1,3.3) | 3.0 (2.5,3.5) | 0.001 |
| HDL (mmol/L) | 1.1 ± 0.1 | 1.2 ± 0.2 | 0.016 |
| LDL (mmol/L) | 2.1 ± 0.1 | 2.3 ± 0.1 | < 0.001 |
| HbA1c% | 8.8 ± 1.7 | 9.2 ± 2.1 | 0.021 |
| FPG (mmol/L) | 7.8 ± 2.6 | 8.5 ± 2.4 | 0.006 |
| FINS (mIU/L) | 4.6 (1.4,9.8) | 4.4 (1.9,8.0) | < 0.001 |
| FCP (ug/L) | 1.3 (0.8,2.2) | 1.5 (0.9,2.3) | < 0.001 |
Note: Data: `x ± s, M (Q1,Q3)
Abbreviations: ALB: Serum albumin; UA: Uric acid; eGFR: GFR glomerular filtration rate; UACR: Urine microalbumin/creatinine; TC: Cholesterol; TG: Triglyceride; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; HbA1c: Glycosylated hemoglobin; FPG: Fasting blood glucose; FINS: Fasting insulin; FCP: Fasting C-peptide
By contrast, UACR and FINS levels were increased. UA showed no remarkable difference between the two groups (P > 0.05).
Comparison of bone metabolism markers
Table 4 shows that serum calcium, phosphorus, 25(OH)D, overall BMD, total T-score, femoral shaft, femoral neck and greater trochanter density were significantly reduced in the DF group, while serum PINP was obviously elevated (all P < 0.05).
Table 4.
Comparison of bone density and metabolism markers between groups
| DF Group (n = 155) | NDF Group (n = 245) | P Value | |
|---|---|---|---|
| P (mmol/L) | 1.1 ± 0.1 | 1.2 ± 0.2 | 0.016 |
| Ca (mmol/L) | 2.1 ± 0.1 | 2.3 ± 0.1 | < 0.001 |
| OC (ng/ml) | 9.88 (7.21,15.19) | 9.85 (7.8,13.37) | 0.702 |
| PINP (ng/ml) | 38.61 (26.52,60.36) | 34.19 (24.91,47.04) | 0.027 |
| β-CTX (ng/ml) | 366.7 (253.9,513.4) | 357.7 (258.3,524.6) | 0.818 |
| 25-OH Vitamin D (ng/ml) | 17.5 (12.69,25.46) | 21.6 (16.41,27.02) | 0.001 |
| Overall Bone Density (g/cm²) | 0.996 ± 0.199 | 1.114 ± 0.13 | < 0.001 |
| Total T-score | -0.3 ± 1.17 | 0.27 ± 0.98 | < 0.001 |
| L1-L4 (g/cm²) | 1.112 (0.992,1.283) | 1.129 (1.032,1.222) | 0.445 |
| Femoral Shaft (g/cm²) | 1.049 ± 0.183 | 1.130 ± 0.169 | < 0.001 |
| Femoral Neck (g/cm²) | 0.871 ± 0.159 | 0.908 ± 0.137 | 0.013 |
| Greater Trochanter(g/cm²) | 0.755 (0.649, 0.837) | 0.780 (0.691,0.869) | 0.015 |
| Ward Triangle (g/cm²) | 0.642 (0.545,0.764) | 0.689 (0.596,0.782) | 0.026 |
Note: Data: `x ± s, M (Q1,Q3)
Abbreviations: P: serum phosphate; Ca: serum calcium; OC: osteocalcin; PINP: total type I collagen N-terminal propeptide; β-CTX: beta-C-terminal telopeptide
No significant differences in OC, β-CTX and lumbar spine (L1–L4) BMD were found between the two groups (P > 0.05).
Risk factor analysis for DF
Univariate logistic regression analysis demonstrated that age, diabetes duration, SBP, BMI, ALB, eGFR, UACR, calcium, phosphorus, blood lipid indexes, HbA1c, multiple skeletal density parameters, whole-body fat percentage, PINP and 25(OH)D were closely correlated with DF occurrence (P < 0.05), as detailed in Table 5.
Table 5.
Univariate binary logistic regression analysis of factors related to DF occurrence
| OR | 95%CI | P Value | |
|---|---|---|---|
| Age | 1.039 | 1.019–1.06 | < 0.001 |
| Course of disease (years) | 1.046 | 1.021–1.073 | < 0.001 |
| SBP (mmHg) | 1.028 | 1.016–1.04 | < 0.001 |
| BMI (kg/m2) | 0.953 | 0.912–0.997 | 0.037 |
| ALB(g/L) | 0.807 | 0.765–0.851 | < 0.001 |
| eGFR | 0.988 | 0.982–0.993 | < 0.001 |
| UACR (mg/mmolCr) | 1.011 | 1.006–1.016 | < 0.001 |
| P (mmol/L) | 0.291 | 0.105–0.804 | 0.017 |
| Ca (mmol/L) | 0.03 | 0.001–0.019 | < 0.001 |
| TC (mmol/L) | 0.689 | 0.571–0.831 | < 0.001 |
| TG (mmol/L) | 0.61 | 0.472–0.789 | < 0.001 |
| HDL (mmol/L) | 0.151 | 0.068–0.335 | < 0.001 |
| LDL (mmol/L) | 0.691 | 0.545–0.878 | 0.002 |
| Overall Bone Density | 0.008 | 0.002–0.034 | < 0.001 |
| Total T-score | 0.593 | 0.484–0.726 | < 0.001 |
| Femoral Shaft Density | 0.067 | 0.02–0.229 | < 0.001 |
| Greater Trochanter Density | 0.185 | 0.044–0.786 | 0.022 |
| Femoral Neck Density | 0.169 | 0.041–0.701 | 0.014 |
| Ward Triangle Density | 0.235 | 0.062–0.882 | 0.032 |
| Total Body Fat (%) | 0.953 | 0.925–0.992 | < 0.001 |
| PINP (ng/ml) | 1.007 | 1.001–1.013 | 0.018 |
| 25-OH Vitamin D (ng/ml) | 0.962 | 0.938–0.987 | 0.003 |
| HbA1c% | 0.882 | 0.792–0.982 | 0.022 |
| FPG (mmol/L) | 0.889 | 0.816–0.967 | 0.006 |
| INS (mIU/L) | 1.001 | 1.001–1.021 | 0.033 |
Note: Significance: Significance set at P < 0.05
Abbreviations: P: serum phosphate; Ca: serum calcium; PINP: total type I collagen N-terminal propeptide; ALB: Serum albumin; EGFR: GFR glomerular filtration rate; UACR: Urine microalbumin/creatinine; TC: Cholesterol; TG: Triglyceride; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; HbA1c: Glycosylated hemoglobin; FPG: Fasting blood glucose; INS: Fasting insulin. BMI: Body Mass Index; SBP: Systolic Blood Pressure
After adjustment for confounding factors in multivariate logistic regression, elevated SBP, decreased ALB, reduced total T-score and declined whole-body fat content were identified as independent risk factors for DF (all P < 0.05), as presented in Table 6.
Table 6.
Multivariate logistic regression analysis for risk factors associated with DF occurrence
| OR | 95%Cl | P Value | |
|---|---|---|---|
| SBP(mmHg) | 1.033 | 1.018–1.048 | 0.001 |
| ALB (g/L) | 0.883 | 0.791–0.92 | 0.001 |
| Total T-score | 0.484 | 0.316–0.743 | 0.001 |
| Total body fat % | 0.942 | 0.906–0.979 | 0.003 |
ROC curve analysis
ROC analysis was performed to evaluate the predictive value of SBP, ALB, total T-score and whole-body fat percentage for DF (Table 7). The corresponding AUC values were 0.637, 0.769, 0.652 and 0.597, respectively (all P < 0.05).
Table 7.
Predictive value of independent risk factors for DF
| AUC | 95%CI | P Value | |
|---|---|---|---|
| Total T-score | 0.652 | 0.661–0.768 | < 0.001 |
| ALB mmol/L | 0.769 | 0.720–0.818 | < 0.001 |
| SBP mmHg | 0.637 | 0.58–0.693 | < 0.001 |
| Total Body Fat % | 0.597 | 0.539–0.656 | < 0.001 |
According to the Youden index, the optimal cut-off values were determined as follows: SBP 140 mmHg, ALB 34.9 g/L, total T-score − 0.5, and whole-body fat percentage 27.5%(Fig. 1).
Fig. 1.
ROC curves for predicting DF using SBP, ALB, total T-scores, and total body fat
Bone parameters in DF patients with or without gangrene
As illustrated in Table 8, patients with gangrene had significantly lower whole-body BMD, femoral shaft, femoral neck and greater trochanter density than those without gangrene (P < 0.05).
Table 8.
Comparison of bone metabolism and density markers between gangrene and non-gangrene groups
| Gangrene Group (n = 68) | Non-Gangrene Group (n = 87) | P Value | |
|---|---|---|---|
| P (mmol/L) | 1.15 ± 0.20 | 1.16 ± 0.19 | 0.717 |
| Ca (mmol/L) | 2.19 (2.08,2.29) | 2.2 (2.13,2.27) | 0.648 |
| OC (ng/ml) | 9.225 (7.77,15.69) | 9.245 (7.77,15.69) | 0.122 |
| PINP (ng/ml) | 34.62 (25.01,59.40) | 41.27 (28.14,60.73) | 0.295 |
| β-CTX (ng/ml) | 365.2 (278.6,529.2) | 378.7 (233.8,505.1) | 0.224 |
| 25-OH Vitamin D (ng/ml) | 17.06 (13.09,24.27) | 18.22 (12.00,26.79] | 0.75 |
| Overall Bone Density | 0.960 ± 0.175 | 1.024 ± 0.21 | 0.046 |
| Total T-score | -0.51 ± 1.14 | -0.1 ± 1.18 | 0.068 |
| L1-L4 | 1.078 (0.976, 1.246) | 1.138 (1.012,1.296) | 0.112 |
| Femoral Shaft Density | 1.007 ± 0.168 | 1.081 ± 0.188 | 0.012 |
| Femoral Neck Density | 0.841 ± 0.156 | 0.895 ± 0.159 | 0.035 |
| Greater Trochanter Density | 0.706 (0.621, 0.826) | 0.772 (0.662,0.858) | 0.015 |
| Ward Triangle Density | 0.645 ± 0.171 | 0.681 ± 0.173 | 0.191 |
Note: Data: `x ± s, M (Q1,Q3)
Abbreviations: P: serum phosphate; Ca: serum calcium; OC: osteocalcin; PINP: total type I collagen N-terminal propeptide; β-CTX: beta-C-terminal telopeptide
No significant differences in calcium, phosphorus, bone turnover markers, 25(OH)D, total T-score, lumbar spine BMD and Ward’s triangle density were detected between the two subgroups (P > 0.05).
Multivariate regression analysis further indicated that decreased femoral neck, femoral shaft and greater trochanter density were independent risk factors for gangrene in DF patients (Table 9, P < 0.05).
Table 9.
Binary logistic regression analysis of factors related to the occurrence of gangrene
| OR | 95%Cl | P Value | |
|---|---|---|---|
| Femoral Shaft Density | 0.096 | 0.015–0.622 | 0.014 |
| Femoral Neck Density | 0.104 | 0.012–0.881 | 0.038 |
| Greater Trochanter Density | 0.079 | 0.01–0.654 | 0.019 |
To clarify the correlation between ulcer severity and bone status, we analyzed the relationship between Wagner grade and skeletal indicators. Wagner grade was negatively correlated with femoral shaft, femoral neck and greater trochanter BMD (Table 10).
Table 10.
Correlation analysis of wagner grade and bone indicators
| R Value | P Value | |
|---|---|---|
| P (mmol/L) | -0.040 | 0.624 |
| Ca (mmol/L) | -0.087 | 0.282 |
| OC (ng/ml) | 0.052 | 0.519 |
| PINP (ng/ml) | -0.033 | 0.686 |
| β-CTX (ng/ml) | 0.145 | 0.073 |
| 25-OH Vitamin D (ng/ml) | -0.086 | 0.285 |
| Overall Bone Density | -0.144 | 0.074 |
| Total T-score | -0.138 | 0.086 |
| L1-L4 | -0.136 | 0.091 |
| Femoral Shaft Density | -0.202 | 0.012 |
| Femoral Neck Density | -0.200 | 0.012 |
| Greater Trochanter Density | -0.161 | 0.046 |
| Ward Triangle Density | -0.109 | 0.178 |
Discussion
Bone density and diabetic foot (DF)
This study has revealed that patients with DF are more prone to reduced bone mass or osteoporosis compared to those NDF, and a decrease in total T-scores is an independent risk factor for the occurrence of DF in patients with T2DM. A total T-score lower than − 0.5 serves as a predictive marker for DF. According to the Wagner classification, the lower the bone density values of the femoral shaft, femoral neck, and greater trochanter, the higher the likelihood of gangrene.
Previous research has indicated that diabetes patients are more susceptible to osteoporosis. Factors such as free radicals, hyperlipidemia, increased ambient glucose concentrations, and the presence of advanced glycation end products can further enhance bone resorption pathways [25]. Studies have shown that the prevalence of reduced bone density and osteoporosis in elderly T2DM patients is similar to that in healthy elderly individuals. However, patients with elderly diabetic foot have significantly lower bone density at the femur and lumbar spine, and a notably higher rate of osteoporosis compared to healthy elderly individuals [26, 27], which strongly correlates with the onset of DF, aligning with the findings of this study. Gaolu et al. [28] found that DM combined with osteoporosis more readily leads to pathological fractures, with complications of osteoporosis related to glycemic control, microvascular neuropathy, calcium metabolism, 25(OH)D, and C-reactive protein [29]. Lower bone density indicates a higher incidence of foot and ankle fractures related to neuroarthropathy. The femoral T-score can serve as a predictive factor for the risk of diabetic neuroarthropathy in patients with diabetic foot [30]. Wang Li et al. [31] have suggested that monitoring and treatment of osteoporosis in diabetic patients could prevent the onset of diabetic foot. However, previous domestic studies have not comprehensively analyzed DXA-related indicators in DF patients, often with limited cases and low credibility, and have not examined the correlation between bone density indicators and the severity of DF. There is a lack of related international research, and further exploration into the mechanisms by which reduced bone density leads to DF is needed.
Notably, the bone–vascular axis plays an important role in diabetic complications. Chronic hyperglycemia accelerates vascular calcification, which may interfere with bone mineral density measurement and aggravate peripheral arterial insufficiency, further increasing the susceptibility to diabetic foot lesions.
Bone metabolism markers and diabetic foot (DF)
The results of this study indicate that patients with DF have higher levels of β-CTX and P1NP compared to those NDF. In patients with DF, those who developed gangrene showed lower levels of OC, P1NP, and β-CTX compared to those without gangrene, although the differences were not statistically significant.
Previous studies have identified osteoporosis [32, 33], lower limb ischemia [34, 35], and infection [36, 37] as independent risk factors for poor outcomes in DF patients. However, the impact of OC, P1NP, and β-CTX on osteoporosis, ischemia, and infection is not consistent. In DF patients with concurrent osteoporosis, sugar and lipid toxicity are known to impair osteoblast differentiation [38, 39] and increase osteoclast activity [40–42], leading to elevated levels of P1NP and β-CTX, which is consistent with the findings of this study. In DM patients with microvascular complications, lower levels of β-CTX, P1NP, and OC [43–47] are observed; however, these markers significantly increase when there is a concomitant infection. Hayeso G and others [48] found that patients with DF and osteomyelitis showed significantly higher serum P1NP levels. Infection also promotes apoptosis of osteoblasts [49] and increases osteoclast activity, raising β-CTX levels.
25(OH)D is the predominant form of Vit D in the body. Unlike parathyroid hormone and calcitonin, 25(OH)D levels are not regulated by these hormones and more accurately reflect the body’s vitamin D reserves.
In this study, the DF group had lower levels of 25(OH)D with significant statistical differences. Within the DF patients, those with gangrene had lower vitamin D levels compared to those without, although the differences were not statistically significant.
Research has shown that supplementing Vit D can restore the production of antimicrobial peptides (AMPs) in primary DFU cells, thus improving wound healing in vitro, highlighting its potential therapeutic use in DFU treatment. Furthermore, vitamin D inhibits the secretion of T-helper type 1 (Th1) cytokines like IFN-Y and IL-2 while stimulating the production of Th2 cytokines, thus promoting wound healing [50]. A deficiency in 25(OH)D can lead to insufficient insulin secretion and insulin resistance [51–53], posing a risk factor for complications such as diabetic nephropathy and DFU [54]. Some studies suggest that supplementing 25(OH)D can prevent and improve complications associated with diabetes [55]. The level of 25(OH)D is inversely related to the severity of DF [21]. Patients with moderate to severe deficiencies in 25(OH)D have lower ulcer healing rates and higher recurrence rates compared to those with mild deficiencies [56], contrasting with the findings of this study, possibly due to the smaller sample size of DF patients in this study. Future research could use a prospective approach to assess the correlation between serum 25-hydroxyvitamin D levels and the onset of DF in NDF patients.
Beyond its classical bone-protective effect, vitamin D exerts crucial anti-inflammatory and immunomodulatory effects. It inhibits excessive chronic inflammation, improves endothelial function and tissue repair capacity, and therefore contributes independently to preventing the progression of diabetic foot ulcer, rather than merely regulating bone metabolism.
Total body fat and diabetic foot ulcer
Although ROC curve analysis showed that elevated total body fat percentage had a certain predictive effect on diabetic foot ulcers in this study, its practical value in routine clinical screening is relatively limited. Relevant studies have confirmed that high body fat percentage is closely related to chronic inflammation, peripheral vascular endothelial dysfunction, and poor wound repair ability, which may indirectly increase the risk of foot ulceration in diabetic patients [57]. However, body fat percentage is easily affected by hydration status, detection instruments, and daily physical activity, resulting in unstable repeatability in clinical practice [58]. In addition, current domestic and international diabetic foot prevention guidelines mainly focus on nerve function detection, ankle-brachial index, and foot deformity evaluation as core routine screening indicators, and body fat percentage has not been included in the standardized foot risk assessment system [59]. Therefore, although total body fat percentage has statistical predictive significance in research settings, it is not suitable for independent clinical application as a conventional predictive marker for diabetic foot ulcers.
Based on the multivariate regression analysis in this study, independent influencing factors for diabetic foot were further identified. After adjusting for confounding factors, elevated systolic blood pressure, decreased serum albumin and reduced total body fat were independently correlated with the occurrence of diabetic foot. In the subgroup stratified by Wagner classification, decreased bone mineral density at the femoral shaft, femoral neck and greater trochanter were independent risk factors for gangrene formation in diabetic foot patients. These regression findings further supplement our above results, indicating that poor nutritional status, abnormal body composition, uncontrolled blood pressure and decreased bone density are key high-risk factors for diabetic foot and its severe gangrene complication.
This study has several limitations. Firstly, as a single-center, cross-sectional study, the source of patients being solely hospital-based may introduce selection bias. Secondly, data collection was not comprehensive, and strong correlations between some markers might bias the results.Thirdly, the overall sample size was relatively limited. In particular, further subgroup analysis based on Wagner classification and gangrene status further reduced the sample number of the gangrene and non-gangrene groups, which might lead to insufficient statistical power and fail to detect potential differences in some bone metabolism markers.Lastly, the small sample size limits the generalizability of the results, calling for larger-scale, long-term prospective studies and further basic research to confirm these findings.
Conclusion
Older patients with a longer history of T2DM are more prone to developing DF, which tends to have poorer metabolic control compared to NDF cases. These patients have lower levels of body fat and 25(OH)D, and a higher incidence of reduced bone mass and osteoporosis.
Elevated SBP, reduced ALB, lower total T-scores, and decreased total body adipose content are independent risk factors for the onset of DF, with critical thresholds set at SBP of 140mmHg, ALB of 34.9 g/L, a T-score of -0.5, and a body fat content of 27.5%.
Reduced bone density values in the femoral shaft, femoral neck, and greater trochanter are significant risk factors for the development of gangrene in DF patients.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors gratefully acknowledge the support from the funding sources. We also thank all participants and colleagues for their assistance in this study.
Abbreviations
- ALB
Albumin
- BMI
Body Mass Index
- β-CTX
β-C-terminal telopeptide of type I collagen
- BMD
Bone mineral density
- CI
Confidence interval
- DBP
Diastolic blood pressure
- DF
Diabetic Foot
- DFI
Diabetic Foot Infection
- DFU
Diabetic Foot Ulcer
- eGFR
Estimated Glomerular Filtration Rate
- Hb
Haemoglobin
- HDL-C
High-density lipoprotein-cholesterol
- LDL-C
Low-density lipoprotein-cholesterol
- OC
Osteocalcin
- P1NP
Procollagen type 1 N-terminal propeptide
- PLT
Platelet
- SBP
Systolic blood pressure
- T2DM
Type 2 Diabetes mellitus
- TC
Total cholesterol
- TG
Triglyceride
- UA
Uric acid
- UACR
Urinary albumin-to-creatinine ratio
- WBC
White blood cell
Author contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all authors. The first draft of the manuscript was written by the corresponding author, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Tongtong Xu: Completed data collection and sorting, carried out statistical analysis, and drafted the initial manuscript. Yi Sun: Provided guidance on research design and analytical thinking. Ping Huang: Offered suggestions on statistical methods and analytical strategies. Rongfeng Shi: Provided clinical support related to diabetic foot intervention. Jianjun Yang: Performed bone mineral density examinations and provided corresponding data. Yunjuan Gu: Provided funding support, proposed statistical ideas, revised and refined the manuscript, supervised the entire study, and finalized the paper.
Funding
This study was supported by the following grants: National Science and Technology Major Project (No.2024ZD0523300); Nantong Municipal Science and Technology Project: Standardized Multidisciplinary Diagnosis and Treatment of Diabetic Foot(No. MSZ2023155); Nantong Municipal Science and Technology Project: Study on the Mechanism of Oxidative Stress-induced Adipose Stem Cell Exosomes in Improving Wound Microenvironment and Promoting Diabetic Foot Ulcer Healing(No. JCZ2023004).
Data availability
The raw data used to support the findings of this study are included in the article and its supplementary material files.
Declarations
Ethics approval and consent to participate
All procedures performed in this study were conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Nantong University (Approval No.: 2018-K016). Written informed consent was obtained from all individual participants included in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
The raw data used to support the findings of this study are included in the article and its supplementary material files.