Introduction

Chronic wound healing, regardless of etiological origin, is a complex pathological process in which normal wound-healing stages fail to be completed within the expected timeframe, and such wounds are often prone to infection and biofilm formation (1). This condition, increasingly recognized as a global health concern, affects approximately 40 million people and, despite advances in modern medical technologies, imposes a difficult-to-sustain economic burden on healthcare systems due to its treatment-resistant nature. In particular, the rapidly increasing aging population, together with accompanying comorbidities such as diabetes and hypertension and broader demographic changes, is driving the prevalence of chronic wounds toward what has been described as a "silent ­pandemic" (2).

Chronic wounds are defined as wounds that do not follow the normal healing process and generally fail to heal within four to six weeks (3). These wounds often remain in the inflammatory phase (4). It is estimated that 1–2% of the global population will experience a chronic wound during their lifetime (5). Factors such as diabetes, venous hypertension, peripheral artery disease, poor nutrition, wound bed hypoxia, and infection contribute to the chronicity of wounds (6). While the normal healing process consists of hemostasis, inflammation, proliferation, and maturation phases, this balance is disrupted in chronic wounds (7). In chronic wound management, debridement of necrotic tissue, infection control, maintenance of appropriate moisture balance, and pressure relief are fundamental steps (8). Treatment may involve modern dressing materials, such as hydrocolloids, alginates, and hydrogels, or advanced methods, such as negative-pressure wound therapy, depending on the wound characteristics. One of the key factors exacerbating the problem is the reduction of chronic wound management to simple wound dressing and the lack of a multidisciplinary approach. Although primary healthcare services represent a strategic component in managing this problem, the competence of family physicians in current debridement techniques, wound-bed preparation (TIME [Tissue, Infection/Inflammation, Moisture, Edge] and TIMERS [Tissue, Infection/Inflammation, Moisture, Edge, Repair/Regeneration, Social factors] frameworks), and modern dressing materials remains a subject of debate. Nonstandardized diagnosis and treatment approaches may result in prolonged healing times, costly complications, and deterioration in patients' quality of life.

Primary healthcare services play a critical role in the early diagnosis, treatment, and patient education processes of chronic wounds. Improving family physicians' knowledge in this area may enhance patient outcomes and help reduce the burden on the healthcare system (9).

This study aimed to evaluate the knowledge levels and clinical approaches of family physicians regarding chronic wound care, including debridement management, infection control, and current dressing materials.

Materials and Methods

The population of this descriptive study consisted of 470 contracted family physicians working in Manisa province, Türkiye. During the research process, we aimed to recruit as many family physicians as possible who agreed to participate in the study within the specified dates.

Some of the family physicians declined to participate, and the research was completed with the participation of family physicians who volunteered for the study. The overall participation rate was 86.2%. The study protocol was approved by the Manisa Celal Bayar University Faculty of Medicine Health Sciences Ethics Committee on September 6, 2023 (No: 20.478.486/987).

Data were collected using a 93-item knowledge questionnaire. The first section included questions on the demographic and occupational characteristics of the participants, and the second section covered the knowledge of chronic wound management, including the TIMERS framework, general wound care, dressing, and wound types (diabetic, venous, ischemic, and pressure ulcers).

In evaluating the knowledge questionnaire, 1 point was awarded for each correct response, and 0 points were assigned for incorrect or unanswered items. The raw scores were converted to a 100-point scale (number of correct responses / total number of questions × 100) and standardized as the "knowledge success score" to facilitate comparative analysis. Data were collected after obtaining written informed consent from the participants.

Data were analyzed using IBM SPSS Statistics version 27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were expressed as numbers (n), percentages (%), means ± standard deviations (SD), and medians (minimum–maximum). The normality of numerical data was assessed using the Shapiro-Wilk test.

For comparisons between two groups, the independent samples t-test was used when the data were normally distributed, and the Mann-Whitney U test was used when they were not. Cronbach’s α coefficients were calculated to assess scale reliability and internal consistency. The competency levels and knowledge gaps of physicians in chronic wound management were visualized using a radar chart (spider chart). A p-value < 0.05 was considered statistically significant.

Results

Variable	n (%)
Sex
Female	77 (38.5)
Male	123 (61.5)
Income status
Income less than expenses.	35 (17.5)
Income equal to expenses	94 (47)
Income greater than expenses	71 (35.5)
Marital status
Married	146 (73.0)
Not married	54 (27.0)
Having children
Yes	150 (75.0)
No	50 (25.0)
Educational status
Bachelor’s degree	170 (85.0)
Specialist (residency-trained)	30 (15.0)
Years in medical practice
≤20 years	100 (50.0)
>20 years	100 (50.0)
Years working as a family physician
≤10 years	93 (46.5)
>10 years	107 (53.5)
Work setting
City center	80 (40.0)
District	114 (57.0)
Rural	6 (3.0)
Chronic wound care training
Yes	26 (13.0)
No	174 (87.0)

Table 1:

Sociodemographic characteristics of family physicians.

Table 1:

Sociodemographic characteristics of family physicians.

The study was conducted with 200 family physicians among the 470 working in Manisa province. Of the participants, 123 (61.5%) were male, 146 (73%) were married, 150 (75%) had children, and 156 (78%) lived with their spouse or children. Regarding economic status, 94 (47%) reported that their income was equal to their expenses. In terms of professional status, 157 (78.5%) were general practitioners, 13 (6.5%) were family medicine residents working under contract, and 30 (15%) were family medicine specialists. Only 26 (13%) physicians had received training in wound care (Table 1).

Women physicians had a higher mean number of correct responses (61.25 ± 18.41) compared with male physicians (56.93 ± 21.26). Physicians with a bachelor's degree answered 57.81 ± 20.32 questions correctly, whereas those with specialist training answered 63.03 ± 19.73 questions correctly. Physicians working in the city center answered 56.56 ± 20.40 questions correctly, while those working in districts and rural areas answered 59.95 ± 20.16 questions correctly.

When years of professional experience were examined, physicians with ≤20 years of experience answered 59.31 ± 20.04 questions correctly, while those with >20 years of experience answered 57.89 ± 20.57 questions correctly. Similarly, based on years of experience in family medicine, physicians with ≤10 years of experience answered 57.87 ± 21.66 questions correctly, while those with >10 years of experience answered 59.23 ± 19.07 questions correctly.

Variable	Mean ± SD	p-value
Sex
Female	61.25 ± 18.41	0.236 †
Male	56.93 ± 21.26
Specialist Training
Yes	63.03 ± 19.73	0.095 †
No	57.81 ± 20.32
Work setting
Town center	56.56 ± 20.40	0.484 †
District-Rural	59.95 ± 20.16
Years in medical practice
≤20 years	59.31 ± 20.04	0.656 †
>20 years	57.89 ± 20.57
Years in family medicine practice
≤10 years	57.87 ± 21.66	0.773 †
>10 years	59.23 ± 19.07
Chronic wound care training
Yes	72.53 ± 18.96	<0.001 †
No	56.51 ± 19.68

Table 2:

Comparison of variables according to the mean number of correct responses.

Table 2:

Comparison of variables according to the mean number of correct responses.

Among all variables examined, only the difference associated with wound care training was statistically significant (p < 0.001). Physicians who had received chronic wound care training answered 72.53 ± 18.96 questions correctly, whereas those without such training answered 56.51 ± 19.68 questions correctly.

Question 26, regarding indicators of wound infection, had the highest number of correct responses (n = 185). This was followed by Question 8, which assessed knowledge of the purpose of debridement (n = 184). Question 2, related to the stages of wound healing, ranked third with 181 correct responses (Table 3).

In contrast, Question 6, which addressed the types of debridement that can be performed in primary healthcare settings, had the lowest number of correct responses (n = 60). Question 11, concerning the most commonly used agent for enzymatic debridement, had the second lowest number of correct responses (n = 65). Question 50, regarding the replacement period of alginates, ranked third lowest with 75 correct responses (Table 4).

Category	Number of items	Mean ± SD	Median (Min–Max)	Cronbach α
Chronic wound	3	77.83 ± 26.37	66.67 (0–100)	0.387
Debridement management	11	62.05 ± 22.13	63.64 (0–100)	0.696
Moisture balance	10	66.95 ± 25.35	70.0 (0–100)	0.751
Infection and inflammation	11	63.91 ± 23.82	63.64 (0–100)	0.745
Wound care and dressing	28	54.05 ± 28.33	53.57 (0–100)	0.929
Pressure ulcer	7	70.93 ± 31.97	85.71 (0–100)	0.839
Ischemic wounds	8	64.63 ± 27.26	75.0 (0–100)	0.732
Venous ulcer	12	67.67 ± 28.25	75.0 (0–100)	0.850
Diabetic wound	3	77.5 ± 28.73	100. (0–100)	0.505

Table 3:

Questions with the highest number of correct responses.

Table 3:

Questions with the highest number of correct responses.

Reliability analyses of the subdimensions of the knowledge questionnaire used in the study are presented in Table 5. Cronbach’s α coefficients demonstrated high reliability for the subdimensions of wound care and dressing (0.929), venous wounds (0.850), and pressure ulcers (0.839). Acceptable reliability was observed for moisture balance (0.751), infection and inflammation (0.745), ischemic wounds (0.732), and debridement management (0.696).

Questions	n (%)
Increased erythema and induration around the wound, increased temperature, and purulent, foul-smelling discharge indicate a wound infection (Question 26).	185 (92.5)
Debridement aims to remove hyperkeratotic epidermis, necrotic dermal tissue, foreign bodies, and bacterial load from the wound bed (Question 8).	184 (92.0)
The stages of wound healing are four: hemostasis, inflammation, proliferation, and maturation (Question 2).	181 (90.5)
Ideal wound dressing removes excess exudate from the wound. It should absorb moisture but prevent the wound from drying out by maintaining moisture balance, completely cover the wound bed, not stick to the wound, reduce pain and odor, be easily available in the clinic, and be easy to learn how to use (Question 36)	181 (90.5)
Patients with diabetic neuropathy should check their feet daily for any lesions (Question 93)	177 (88.5)

Table 4:

Questions with the lowest number of correct responses.

Table 4:

Questions with the lowest number of correct responses.

Lower reliability values were observed for the chronic wound (0.387) and diabetic wound (0.505) subdimensions, likely due to the limited number of items in these categories. When the knowledge categories were examined, family physicians achieved the highest success rate in the chronic wound category (77.83%), whereas the lowest success rate was observed in the wound care and dressing category (54.05%).

Questions	n (%)
Mechanical debridement and otolytic procedures in primary healthcare settings. Debridement and enzymatic Debridement can be performed (Question 6).	60 (30.0)
Clostridial that does not harm healthy tissue. Collagenase-containing enzymatic ointment is the most commonly used agent for debridement (Question 11).	65 (32.5)
Alginates require less frequent dressing changes because they can remain on the wound for days (Question 50).	75 (37.5)
Hydrofibers. They retain approximately three times as much fluid as alginates. Their use is recommended in wounds with moderate to severe exudate (Question 52).	78 (39.0)
Thick polymeric tissue in wounds with abundant exudate. The use of membrane coverings is suitable (Question 56).	79 (39.5)

Table 5:

Knowledge scores by category.

Table 5:

Knowledge scores by category.

The radar (spider) chart presented in Figure 1 illustrates the distribution of family physicians’ competencies and knowledge gaps in chronic wound management. The chart demonstrates an asymmetrical distribution of knowledge across categories. The chronic wound category is positioned closest to the outer edge of the chart, indicating the highest level of knowledge. Conversely, the wound care and dressing category lies closest to the center, indicating the lowest level of performance.

The relationship between receiving chronic wound care training and knowledge scores is presented in Table 6. Physicians who had received training (n = 26) demonstrated significantly higher scores than those without training (n = 174) across nearly all categories.

Category	Trained (n = 26)	Untrained (n = 174)	p-value
Chronic wound	83.33 ± 27.08	77.01 ± 26.24	0.139 †
	100.0 (0–100)	66.67 (0–100)
Debridement management	76.57 ± 20.76	59.87 ± 21.55	<0.001 †
	77.27 (27.27–100)	63.64 (0–100)
Moisture balance	83.85 ± 16.99	64.43 ± 25.45	<0.001 †
	90.0 (40–100)	70.0 (0–100)
Infection and inflammation	76.57 ± 24.42	62.02 ± 23.21	0.003 †
	81.82 (18.18–100)	63.64 (0–100)
Wound care and dressing	73.35 ± 28.05	51.17 ± 27.3	<0.001 †
	83.93 (3.57–100)	53.57 (0–100)
Pressure ulcer	85.71 ± 27.7	68.72 ± 32.05	<0.001 †
	100.0 (0–100)	85.71 (0–100)
Ischemic wounds	74.52 ± 25.37	63.15 ± 27.29	0.034 †
	81.25 (0–100)	62.50 (0–100)
Venous ulcer	80.13 ± 24.62	65.8 ± 28.35	0.005 †
	83.33 (16.67–100)	66.67 (0–100)
Diabetic wound	89.74 ± 20.59	75.67 ± 29.37	0.011 †
	100.0 (33.33–100)	66.67 (0–100)

Table 6:

Knowledge scores by category.

Table 6:

Knowledge scores by category.

In the debridement management category, the success rate was 76.57% in the trained group and 59.87% in the untrained group (p < 0.001). In the moisture balance category, success rates were 83.85% in the trained group and 64.43% in the untrained group (p < 0.001).

In the wound care and dressing category, which showed the lowest overall success rate, the trained group achieved 73.35%, whereas the untrained group achieved 51.17% (p < 0.001). In the pressure ulcer category, the success rate in the trained group (85.71%) was also significantly higher than in the untrained group (68.72%) (p < 0.001).

Similarly, in the infection and inflammation category, physicians who received training scored significantly higher (76.57%) than those without training (62.02%) (p = 0.003). In the vascular and diabetic wound categories, trained physicians also demonstrated significantly higher knowledge levels in venous wounds (p = 0.005), ischemic wounds (p = 0.034), and diabetic wounds (p = 0.011).

However, in the chronic wound definition category, the difference between the trained group (83.33%) and the untrained group (77.01%) was not statistically significant (p = 0.139).

Discussion

This study evaluated the competencies and clinical approaches of family physicians, who play a central role in primary healthcare services, in chronic wound care, debridement management, infection control, and the use of current dressing materials. The findings indicate that family physicians demonstrate a high level of awareness regarding the recognition and the general definition of chronic wounds, but substantial knowledge gaps remain in practical management and technical interventions.

Chronic wounds represent a major clinical and economic burden for healthcare systems and significantly impair patients' quality of life. Since no single medical specialty is responsible for the management of chronic wounds, their care requires a multidisciplinary approach involving multiple levels of the healthcare system.

Family medicine is a comprehensive medical specialty that provides continuous, person-centered care across all age groups and health conditions, integrating clinical and behavioral sciences while remaining easily accessible within the community (10). Within this framework, family physicians often represent the first point of contact for patients presenting with chronic wounds, placing them in a key position for early assessment and initial management. Therefore, strengthening wound care competencies in primary care is essential and necessitates the development of structured training programs.

Adequate wound care training enables physicians to perform appropriate wound assessment, initiate evidence-based treatment, and identify cases requiring referral to specialized care. Timely and effective wound management at the primary care level may reduce unnecessary referrals to secondary and tertiary healthcare services while improving patient outcomes.

In this study, the knowledge levels of family physicians regarding chronic wound management were examined, and the significant impact of training status on knowledge and competency levels was demonstrated. The finding that only 13% of participants had received wound care training highlights a substantial educational gap in primary healthcare settings.

Consistent with our findings, the study by Şahan et al. (11) reported that only 19 (10.67%) participants had received wound care training within the previous four years. In the present study, wound care training was associated with significantly higher knowledge scores, particularly in areas requiring technical competence such as debridement management (p < 0.001), moisture balance (p < 0.001), and wound care and dressing practices (p < 0.001).

Similarly, the study by Fernández-Araque et al. (12) demonstrated that greater exposure to wound care training was associated with improved knowledge levels. Şahan et al. (11) also reported a statistically significant relationship between wound care training and knowledge test performance.

The finding that physicians without training achieved only a 51.17% success rate in the dressing materials category suggests that modern wound management approaches, such as hydrocolloids and silver-containing dressings, may not be sufficiently integrated into routine clinical practice (11).

This study has several limitations. First, it was conducted among family physicians working in a single province, and therefore, the findings may not be generalizable to all primary care physicians in Türkiye. Second, data were collected using a self-report questionnaire that primarily reflects participants' theoretical knowledge rather than their clinical practice. Finally, the cross-sectional study design prevents the establishment of causal relationships between the variables examined.

In conclusion, effective chronic wound management in primary healthcare requires not only the ability to recognize and classify wounds but also sufficient knowledge of modern wound care interventions and dressing materials. The findings of this study indicate that important knowledge gaps persist in the practical management of chronic wounds among family physicians. Expanding wound care training programs and integrating structured wound management education into undergraduate medical curricula and family medicine residency training may help strengthen physicians’ competencies and improve the quality of chronic wound care in primary healthcare settings.

Introduction

Diabetic foot ulceration (DFU) is one of the most common and debilitating complications of diabetes mellitus (DM). The lifetime incidence of DFU in individuals with DM has been reported to be as high as 25%. Diabetic foot ulceration represents a major public health concern due to its association with impaired quality of life, high recurrence rates, increased risk of amputation, and elevated mortality (1–3).

The management of DFU is often complex and costly, frequently requiring prolonged hospitalization and multidisciplinary treatment approaches. In the United States, approximately one-third of the direct healthcare costs associated with DM and its complications are attributed to diabetic foot ulcers (4). With the global prevalence of DM increasing rapidly, the incidence of DFU is also expected to increase further in the coming years.

Effective prevention and management of DFU require careful clinical evaluation and comprehensive treatment strategies. Patient education, optimal metabolic control, identification of risk factors, and early intervention are critical components in preventing DFU progression (3). Multidisciplinary management, including medical therapy, surgical intervention, infection control, vascular assessment, and nutritional support, is recommended to improve clinical outcomes (5).

Malnutrition is recognized as an important systemic factor affecting wound healing in chronic wounds such as DFU. Adequate nutritional support may improve immune response, reduce infection risk, and promote tissue repair, particularly in older adults. Monitoring nutritional biomarkers, such as serum albumin and prealbumin, may provide valuable information on the nutritional status of patients with chronic wounds (6,7).

The present study aimed to evaluate the association between routine clinical use of an oral immunonutrition supplement and changes in serum albumin and total protein levels, as well as wound measurements in hospitalized patients with DFU.

Materials and Methods

This study was designed as a prospective cohort study conducted between May 2023 and October 2023 at the Diabetic Foot Service of Hitit University Erol Olçok Training and Research Hospital. Adult patients hospitalized with a diagnosis of DFU who agreed to participate in the study were included. Ethical approval for the study was obtained from the Hitit University Faculty of Medicine Clinical Research Ethics Committee on March 29, 2023, with decision no. 2023-44.

Within the routine clinical nutritional support protocol of the service, some patients received an oral immunonutrition supplement containing arginine, omega-3 fatty acids, and RNA based on their clinical condition and laboratory findings. Each 237 mL serving of the supplement contained 18 g of protein, 4.3 g arginine, 1.4 g omega-3 fatty acids, and 0.43 g RNA.

Patients with normal renal function parameters (urea and creatinine levels) received the supplement twice daily for 30 days as part of routine clinical care. Patients with elevated renal function parameters did not receive immunonutrition supplementation. Accordingly, patients were categorized into two cohorts: those receiving oral immunonutrition and those not receiving it.

Diabetic foot ulcers were classified according to the Wagner classification system. Daily dietary intake records were collected for 15 days, and average daily protein intake was calculated using the Turkish National Food Composition Database. Clinical data, including serum albumin, total protein, and creatinine levels, were obtained from the hospital information system. Ulcer measurements were performed at baseline and on day 30 using a sterile disposable ruler. No modifications were made to systemic or local treatment protocols routinely applied in the diabetic foot unit during the study period.

Statistical analyses were performed using IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, NY, USA). The normality of data distribution was assessed using the Shapiro-Wilk test. Continuous variables were compared using the independent samples t test or the Mann-Whitney U test, depending on the distribution. Categorical variables were compared using the Pearson’s chi-square test or Fisher’s exact test when appropriate. Multivariate linear regression analysis was conducted to evaluate the association between immunonutrition supplementation and changes in serum albumin, total protein levels, and wound size, while accounting for potential confounding variables, including renal function parameters. A p-value < 0.05 was considered statistically significant.

Results

Parameter, mean ± SD	Immunonutrition group (n = 36)	Control group (n = 36)	p-value
Mean serum albumin level on day 30 (g/dL)	3.5 ± 0.4	2.5 ± 0.5	<0.001
Mean total protein level on day 30 (g/dL)	6.9 ± 0.6	5.9 ± 0.7	<0.001
Mean change in serum albumin over 30 days (g/dL)	+0.8 ± 0.3	−0.4 ± 0.05	<0.001
Mean change in total protein over 30 days (g/dL)	+0.6 ± 0.5	−0.2 ± 0.01	<0.001

Table 1:

Comparison of biochemical parameters between patients receiving and not receiving immunonutrition support.

Table 1:

Comparison of biochemical parameters between patients receiving and not receiving immunonutrition support.

A total of 72 patients hospitalized with diabetic foot ulcers were included in the study and divided into the intervention group (n = 36) and the control group (n = 36). Among these patients, 36.9% (n = 24) were female, and the mean age was 65.1 ± 9.2 years. The mean daily dietary protein intake was 95 ± 5.9 g/day.

A statistically significant association was observed between the use of the oral immunonutrition supplementation and both serum albumin and total protein levels on day 30, as well as changes in these parameters during the 30-day follow-up period (p < 0.001 for all comparisons) (Table 1).

Wagner grade	Immunonutrition group (n = 36), n (%)	Control group (n = 36), n (%)
Grade 1	6 (16.7)	5 (13.9)
Grade 2	14 (38.9)	15 (41.7)
Grade 3	12 (33.3)	11 (30.6)
Grade 4	4 (11.1)	5 (13.9)

Table 2:

Wagner classification distribution between the groups.

Table 2:

Wagner classification distribution between the groups.

On day 30, the mean serum albumin and total protein levels in the intervention group were 3.5 ± 0.4 g/dL and 6.9 ± 0.6 g/dL, respectively. In contrast, the control group had mean levels of 2.5 ± 0.5 g/dL and 5.9 ± 0.7 g/dL, respectively (Table 1).

During the 30-day follow-up period, serum albumin and total protein levels increased by 0.8 ± 0.3 g/dL and 0.6 ± 0.5 g/dL, respectively, in the intervention group. In the control group, these parameters decreased by 0.4 ± 0.05 g/dL and 0.2 ± 0.01 g/dL, respectively (Table 1).

Ulcer measurement, mean ± SD	Immunonutrition group (n = 36)	Control group (n = 36)	p-value
Baseline ulcer length (cm)	4.2 ± 1.1	4.0 ± 1.2	0.62
Ulcer length at day 30 (cm)	2.9 ± 1.0	3.6 ± 1.1	0.5
Change in ulcer length (cm)	-1.3 ± 0.6	-0.4 ± 0.5	0.008

Table 3:

Comparison of ulcer size changes between the groups.

Table 3:

Comparison of ulcer size changes between the groups.

No statistically significant difference was observed between the Wagner ulcer stages of the two groups (p > 0.05) (Table 2). However, a statistically significant difference was found in ulcer length reduction between the groups (p = 0.008) (­Table 3).

Correlation analysis revealed significant correlations between changes in serum albumin and total protein levels and dietary protein intake variables (Table 4).

Variable	30-day change in albumin	30-day change in total protein	Mean dietary protein intake, g/day	Mean enteral product protein intake, g/day
30-day change in albumin	1	r = 0.701; p < 0.001	r = 0.392; p = 0.001	r = 0.797; p < 0.001
30-day change in total protein	r = 0.701; p < 0.001	1	r = 0.284; p = 0.022	r = 0.479; p < 0.001
Mean dietary protein intake	r = 0.392; p = 0.001	r = 0.284; p = 0.022	1	r = 0.272; p = 0.029
Mean enteral product protein intake	r = 0.797; p < 0.001	r = 0.479; p < 0.001	r = 0.272; p = 0.029	1

Table 4:

Correlation analysis between changes in nutritional biomarkers and protein intake (n = 65).

Table 4:

Correlation analysis between changes in nutritional biomarkers and protein intake (n = 65).

Discussion

With the global increase in DM cases, inadequate long-term glycemic control in patients with DM causes damage to the distal microvasculature and eventually leads to the development of diabetic foot ulcers (8). Diabetic foot ulcers are one of the most important chronic complications in patients with DM, often requiring surgical intervention and imposing substantial morbidity and economic burden on patients (9).

Systemic factors play an important role in the wound-healing process in the management of chronic wounds. Diabetic foot ulcers generally begin with minor injuries that go unnoticed due to diabetic neuropathy, and the combination of immunological, vascular, nutritional, glycemic, and infectious factors may affect wound healing (9).

In this study, the association between routine immunonutrition support administered as part of routine clinical practice in our hospital and changes in serum albumin and total protein levels, as well as wound measurements, in patients with DFU was evaluated. The findings demonstrated a statistically significant association between changes in serum albumin and total protein levels and the reduction in ulcer length in patients receiving oral immunonutrition support.

Several studies have reported an inverse relationship between serum albumin level, an important indicator of nutritional status, and the risk of amputation in patients with DFU (10–12). It has also been stated that a decrease in albumin level may not only reflect nutritional status but may also be associated with the clinical severity of the disease, including infectious and inflammatory processes (13).

In conclusion, oral immunonutrition supplementation in hospitalized patients with DFU was associated with increased serum albumin and total protein levels. In addition, a significant association was observed between oral immunonutrition supplementation and a reduction in ulcer length during the follow-up period. However, no statistically significant difference was found in ulcer stage between the two groups. Larger prospective studies with longer follow-up periods are needed to further clarify the potential effects of immunonutrition support on wound healing in patients with DFU.

Introduction

Diabetes mellitus is a major global health concern whose prevalence continues to increase steadily. One of its most serious complications is the development of foot ulcers, which represent a substantial cause of morbidity among affected individuals. Worldwide, a diabetes-related foot ulcer develops every 1–2 seconds, and a major amputation attributable to these wounds occurs approximately every 20 seconds (1). Among diabetes-related complications requiring hospitalization, diabetes-related foot infections remain the most common and continue to be the leading cause of lower-extremity amputation (2). Follow-up data from patients with diabetes-related foot infections indicate that, after 1 year, only 46% of ulcers have healed (with 10% of these subsequently recurring). In contrast, 15% of patients have died, and 17% have required lower-extremity amputation (3). These outcomes underscore that timely and appropriate infection management remains one of the most critical challenges in this patient population.

The spectrum of microorganisms responsible for diabetes-related foot infections can often be anticipated based on specific clinical factors. Superficial and mild infections are predominantly caused by Gram-positive bacteria, whereas advanced infections are more frequently associated with Gram-negative organisms; in cases accompanied by gangrene, anaerobic pathogens must also be considered (2,4). Particularly in advanced infections, Gram-positive, Gram-negative, and anaerobic organisms may coexist in a polymicrobial pattern (4). Moreover, interactions among these pathogens are clinically important, as they are associated with biofilm formation and the development of antimicrobial resistance (5).

In the absence of a life-threatening infection, the preferred approach is not to initiate empirical antibiotic therapy immediately, but rather to obtain appropriate tissue specimens and await microbiological results before selecting targeted antimicrobial treatment. Conversely, when a life-threatening infection is present, prompt empirical therapy should be initiated based on the most likely pathogens (6). When selecting empirical antibiotics, several factors must be carefully considered, including the availability of previous culture and susceptibility results from the same wound, the clinical severity of the infection, potential adverse effects of the selected agent, the need for dose adjustment according to renal and hepatic function, possible drug-drug interactions (particularly relevant given the high burden of comorbidities and concomitant medications in patients with diabetes), and overall treatment cost (7). In addition, knowledge of the regional distribution of causative pathogens, derived from prior studies, can substantially increase the likelihood of successful empirical therapy. For this reason, studies reporting the local microbiological profile of diabetes-related foot infections are of considerable importance, as they provide clinicians with essential data to guide treatment decisions.

The present study aimed to determine the distribution of causative pathogens among patients with diabetes-related foot infections treated as outpatients or inpatients at a secondary care hospital.

Materials and Methods

Study Design and Patient Selection

This retrospective study included patients who presented to the outpatient clinic or were hospitalized with a diagnosis of diabetes-related foot infection between May 1, 2025, and February 1, 2026.

Patients were eligible for inclusion if they met all of the following criteria:

• Age ≥ 18 years.
• Presentation to our hospital with a diagnosis of diabetes-related foot infection between May 1, 2025, and February 1, 2026.
• A diagnosis of infection was established according to the criteria defined by the International Working Group on the Diabetic Foot (IWGDF), including local signs of infection (swelling, erythema, increased local temperature, pain, purulent discharge) or systemic findings.
• Availability of tissue or aspiration samples obtained from the wound and sent to the microbiology laboratory for analysis.
• Availability of complete clinical and laboratory data in the electronic medical records.

Patients were excluded if they met any of the following ­criteria:

• Age < 18 years.
• Absence of tissue or aspiration sampling from the wound site.
• Incomplete or erroneous clinical or laboratory data.
• Presence of infections or foot lesions other than diabetes-related foot infections.

The study was approved by the Non-Interventional Clinical Research Ethics Committee of Aydın Adnan Menderes University Faculty of Medicine on March 26, 2026, with decision no. 2026/117.

Definition of Diabetes-related Foot Infection

The diagnosis of diabetes-related foot infection was established according to the criteria defined in the IWGDF guidelines (4). According to these criteria, infection was diagnosed in the presence of local signs of inflammation—such as swelling, erythema, increased local temperature, and pain—or systemic findings including fever, leukocytosis, and elevated C-reactive protein levels. The presence of purulent discharge from the wound and the clinician’s judgment that the lesion was infected were also considered diagnostic criteria.

Surgical Sampling Procedure

Under sterile conditions, any necrotic tissue present in the wound area was debrided using appropriate surgical techniques. Following adequate debridement, an adequate tissue specimen was obtained from the infected area and transported to the microbiology laboratory under sterile conditions for further analysis.

Microbiological Analyses

From the prepared fluid suspension of the specimen, 0.05 mL was inoculated onto 5% sheep blood agar and MacConkey agar plates using a sterile loop under aseptic conditions. The inoculated plates were incubated aerobically at 35°C and examined for microbial growth after 24 hours. If no growth was observed, the plates were re-incubated and reassessed at 48 hours post-inoculation.

Bacterial identification was performed using conventional biochemical methods. Upon detection of microbial growth, isolates were initially evaluated by Gram staining. Gram-negative organisms were identified based on standard biochemical characteristics, including glucose, sucrose, and lactose fermentation; citrate utilization; motility; urease and indole production; ornithine decarboxylase activity; and oxidase reaction. Gram-positive organisms were identified according to catalase activity, hemolytic patterns, coagulase production, susceptibility to optochin, bacitracin, and trimethoprim-sulfamethoxazole, as well as growth on bile esculin agar and in media containing 6.5% sodium chloride.

Antimicrobial susceptibility testing was performed and interpreted according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines. Identification of methicillin-resistant Staphylococcus aureus (MRSA) was performed using the cefoxitin disk diffusion method in accordance with EUCAST criteria. Each infection episode was analyzed as an independent event.

Results

A total of 197 patients were included in the study. Of these, 137 (69.5%) were male, and 60 (30.5%) were female. During the study period, 11 patients experienced a second infection episode. Overall, 208 wound specimens were obtained. No microbial growth was detected in 12 samples. Among the culture-positive specimens, 38 (18.3%) yielded two distinct infectious agents, indicating polymicrobial infection. In total, 196 culture-positive samples yielded 230 microbial isolates. Of these, 109 (47.4%) were Gram-positive organisms, and 119 (51.7%) were Gram-negative organisms (Table 1).

Infectious isolates	n (%)
Gram-positive organisms	109 (47.4)
Staphylococcus aureus MSSA (methicillin-susceptible) MRSA (methicillin-resistant)	63 40 23
Coagulase-negative staphylococcus MSSA (methicillin-susceptible) MRSA (methicillin-resistant)	12 2 10
Enterococcus spp.	21
Streptococcus spp.	13
Gram-negative organisms	119 (51.7)
Escherichia coli	30
Klebsiellaspp. Klebsiella pneumoniae Klebsiella oxytoca	32 22 10
Pseudomonas aeruginosa	14
Proteus spp. Proteus mirabilis Proteus vulgaris	17 13 4
Acinetobacter spp.	10
Enterobacter spp.	7
Citrobacter spp.	2
Serratia marcescens	3
Morganella morganii	3
Stenotrophomonas maltophilia	1
Candida spp.	2 (0.9)
Total	230 (100)

Table 1:

Distribution of infectious microorganisms.

Table 1:

Distribution of infectious microorganisms.

The most frequently isolated pathogen was S. aureus (n = 63, 27.4%), including 23 (10%) MRSA isolates. The second and third most common pathogens were Klebsiella spp. (n = 32, 13.9%) and Escherichia coli (n = 30, 13%), respectively.

Discussion

Studies conducted over the past several decades have demonstrated that the distribution of pathogens in patients with diabetes-related foot infections varies not only by region but also by country. When these studies are examined collectively, the world can be broadly categorized into two epidemiological regions. The first includes Europe and North America, where Gram-positive organisms predominate. The second comprises temperate regions encompassing Africa and Asia, where Gram-negative organisms are more prevalent, and the proportion of Pseudomonas aeruginosa is notably high (8). Studies conducted in Türkiye have generally indicated that the country aligns more closely with the second epidemiological pattern. For example, in a study covering a 15-year period, Gram-positive organisms accounted for 45.8% of isolates, whereas Gram-negative organisms accounted for 53.7% (9). In the same study, a statistically significant trend was observed over time, with Gram-positive pathogens increasing and Gram-negative pathogens decreasing. The distribution observed in our study was similar to these previously reported findings. Regarding specific pathogens, S. aureus, P. aeruginosa, and E. coli were consistently among the most frequently encountered organisms over the 15-year period, although their relative ranking varied by year (9). In a multicenter study by Acar et al. (10), which compared the pre–COVID-19 period with the pandemic period, a significant reduction in the number of specimens obtained for microbiological analysis during the pandemic was observed; however, no substantial change in pathogen distribution was identified. In that study, the most frequently isolated organisms were Pseudomonas spp. (16.4%) and S. aureus (15%) (10).

The findings of our study warrant particular discussion regarding P. aeruginosa. In the majority of studies conducted in Türkiye on diabetes-related foot infections, this pathogen has ranked either first or second among the most frequently isolated organisms (11–14). In contrast, in our study, P. aeruginosa ranked fifth. One important limitation of the existing literature in this field is that most studies have been conducted in tertiary-care hospitals, which may substantially influence pathogen distribution. Because our study was performed in a secondary-care state hospital, other Gram-negative organisms such as Klebsiella spp. (13.9%), E. coli (13%), and Proteus spp. (7.4%) may have become more prominent. Furthermore, Enterococcus spp. (9.1%), which has not been reported as a leading pathogen in several previous studies, was among the most frequently isolated organisms in our cohort.

Although an overall increase in Gram-positive pathogens has been observed in recent years in Türkiye, the proportion of S. aureus and, consequently, MRSA has reportedly declined. In a meta-analysis of studies conducted in Türkiye over the past two decades, the prevalence of S. aureus decreased from 29.4% to 18.1%, while the prevalence of MRSA declined from 12.5% to 5.5% (9). In that meta-analysis, the observed increase in Gram-positive organisms was primarily attributed to rising rates of Enterococcus spp. and Streptococcus spp. (9). In another recently published study presenting one-year data from five centers, the proportion of S. aureus was 14.6%, and the overall MRSA rate among all isolates was approximately 3% (15). In contrast to these reports, the proportion of S. aureus in our study was 27.4%, and the MRSA rate was 10%, both of which are considerably high. The elevated rates of S. aureus and MRSA observed in our cohort may indicate that pathogen distribution patterns in Türkiye are continuing to evolve. Nevertheless, our findings require confirmation through additional large-scale and multicenter studies.

Although relatively uncommon, fungal pathogens may also be implicated in diabetes-related foot infections, and pre-existing fungal infection of the foot or nails has been identified as a significant risk factor (16). In our study, Candida spp. was isolated from only two specimens, resulting in a very low prevalence.

The most important limitation of our study is the inability to evaluate anaerobic bacteria. Due to inadequate laboratory infrastructure, we were unable to assess the presence and distribution of anaerobic pathogens, which may represent a significant component of polymicrobial diabetes-related foot infections.

Conclusion

Our study revealed two key observations. First, the proportion of S. aureus and MRSA among patients presenting to our hospital with diabetes-related foot infections was relatively high. Second, although many studies conducted in Türkiye have reported a high prevalence of P. aeruginosa, this pathogen was not among the most frequently isolated organisms in our cohort. These results underscore the importance of local microbiological surveillance in guiding empirical antimicrobial therapy for diabetes-related foot infections. Further multicenter studies conducted in different regions are needed to confirm and expand upon these findings.

Introduction

Diabetes mellitus (DM) is recognized as a major public health issue due to its rapidly increasing prevalence worldwide and the chronic complications it causes. Recent Global Burden of Disease analyses indicate that the prevalence of diabetes has increased substantially in recent decades and is projected to continue rising in the coming years (1). Data from the International Diabetes Federation (IDF) also highlight that diabetes imposes a significant clinical and economic burden on healthcare systems, in addition to its impact on individual health (2). Diabetic foot disease, one of the most serious complications of diabetes, is associated with increased morbidity, mortality, and healthcare costs. Among these complications, diabetic foot ulcers (DFUs) represent a major cause of prolonged hospitalization and permanent functional impairment. According to the current international guidelines, the management of such wounds requires a holistic approach that considers not only clinical healing but also the patient’s psychosocial well-being (3).

Diabetic foot ulcers are defined as chronic wounds that develop in individuals with diabetes, resulting from factors such as peripheral neuropathy and peripheral arterial disease. It is reported that approximately 19–34% of individuals with diabetes are at risk of developing DFUs during their lifetime. Current data indicate that DFUs affect millions of people worldwide (3). Diabetic foot disease is not merely a local wound problem but also carries serious systemic consequences. Indeed, five-year mortality rates among individuals with diabetic foot complications have been reported to be comparable to those observed in certain types of cancer (4). Furthermore, diabetic foot disease is one of the primary causes of non-traumatic lower-extremity amputations worldwide (2,5).

Diabetic foot ulcers are a significant problem not only in terms of clinical outcomes but also due to their negative impact on patients’ quality of life. Prolonged wound healing, pain, restricted mobility, recurrent infections, and repeated hospitalizations can significantly affect patients’ physical, psychological, and social well-being (6). Studies in the literature indicate that health-related quality of life (HRQoL) is markedly lower in individuals with DFUs (7–9). Furthermore, it has been reported that HRQoL may function not only as an outcome measure but also as a predictive factor of adverse clinical outcomes, including amputation and mortality (10).

Among the factors affecting quality of life in individuals with DFUs are numerous clinical and patient-related variables, such as ulcer severity, pain, infection, neuropathy, and metabolic control. In particular, loss of physical function and limitations in activities of daily living are major contributors to reduced quality of life (6,11). Therefore, patients with diabetic foot disease should be evaluated using a holistic approach that extends beyond wound management and incorporates quality-of-life assessment as an important clinical outcome.

There are limited studies examining the quality of life in patients with diabetic foot disease in Türkiye. Existing studies indicate that quality of life is low in patients with DFUs, particularly regarding physical functioning. A study conducted in Türkiye reported that patients with DFUs had significantly lower physical functioning scores compared with other individuals with diabetes, and that their overall quality-of-life levels were below societal norms (12). However, studies comprehensively examining the factors influencing quality of life in patients with diabetic foot disease remain limited.

Assessing quality of life in patients with diabetic foot disease is important not only for understanding patient well-being but also for predicting clinical outcomes and planning care pathways. Previous research has shown that poor quality of life may be associated with adverse outcomes, including amputation and mortality (10). Identifying the level of quality of life and its associated factors in patients with diabetic foot ulcers is important for developing patient-centered care strategies and improving clinical outcomes. Therefore, this study aimed to evaluate health-related quality of life (HRQoL) in patients with DFUs and to identify factors independently associated with HRQoL.

Materials and Methods

This multicenter, descriptive, cross-sectional study was conducted between August 2021 and August 2022 at the wound care clinic of a university hospital in the Southeastern Anatolia Region of Türkiye and the wound care unit of a private hospital in İstanbul.

The study population consisted of patients with DFUs who attended these wound care clinics during the specified period. A power analysis was performed to determine the sample size. Post-hoc power analysis was conducted for the final multiple linear regression model. Using the R² value of 0.158, the effect size (f²) was calculated as 0.188. Considering five independent variables, a significance level of α = 0.05, and the number of participants, the statistical power of the study was approximately 99%, indicating sufficient power to detect moderate effect sizes.

The inclusion criteria were age ≥18 years, a confirmed diagnosis of DFU, willingness to participate, and the cognitive capacity to provide informed consent. The exclusion criteria included a known diagnosis of psychotic disorder, schizophrenia, dementia, bipolar disorder, or intellectual disability; the presence of additional chronic conditions that could significantly affect quality of life and functional status; pregnancy; age <18 years; and refusal to participate. Written informed consent was obtained from all participants prior to data collection.

Data were collected by researchers through face-to-face interviews. Each interview lasted approximately 15–20 minutes and was completed in a single session.

The study was approved by the Clinical Non-Interventional Research Ethics Committee of SANKO University on July 7, 2021 with decision number 07/5.

Data Collection Tools

The Patient Information Form, developed by the researchers based on the literature, consisted of two sections. The first section included seven questions on sociodemographic characteristics, including gender, age, place of residence, educational status, marital status, employment status, and economic status (7,10).

The second section included 13 questions related to clinical and disease characteristics, such as type and duration of diabetes, duration of DFU treatment, history of previous DFUs, hemoglobin A1c level, presence of other chronic complications, Wagner classification stage, and presence of infection (7–10,13).

Ulcer severity was assessed using the Wagner Diabetic Foot Ulcer Classification, which categorizes ulcers from grade 0 to 5 based on ulcer depth, infection, and the presence of gangrene. Higher grades indicate greater ulcer depth and tissue damage (5).

Health-related quality of life was assessed using the Diabetic Foot Ulcer Scale–Short Form (DFS-SF), developed by Bann et al. (14). The Turkish validity and reliability study of the instrument was conducted by Kılıç et al. (15).

The DFS-SF consists of 29 items across six subscales: leisure time (L), physical health (PH), dependence/daily living (D/DL), negative emotions (NE), worry about feet/ulcers (WU/F), and discomfort regarding ulcer care.

It is a five-point Likert-type instrument ranging from “never” to “always.” Scores are transformed to a standardized 0–100 scale, with higher scores indicating better quality of life. No established cut-off value exists for the scale. In the Turkish validation study, the Cronbach’s alpha (α) coefficients for the subscales ranged from 0.93 to 0.97.

Statistical Analysis

Data were analyzed using SPSS Statistics version 22 (IBM Corp., Armonk, NY, USA). Descriptive statistics were used to summarize the data. The normality of continuous variables was assessed using skewness and kurtosis values, with values between ±1.5 considered acceptable for normal distribution. For comparisons between two groups, the independent samples t-test was used for normally distributed data, while the Mann-Whitney U test was applied for non-normally distributed variables. For comparisons involving more than two groups, one-way analysis of variance (ANOVA) or the Kruskal-Wallis test was used, depending on the distribution of the data. Post-hoc analyses were performed using the Tukey HSD test.

To identify factors associated with the total DFS-SF score, multivariate linear regression analysis was performed. Variables that were statistically significant in univariate analyses or considered clinically relevant were included in the initial model. A backward elimination procedure was used to remove nonsignificant variables and obtain the most parsimonious model.

Multicollinearity was assessed using the variance inflation factor (VIF) values. Model adequacy was evaluated using the coefficient of determination ( R² ) and the F test. A p-value of <0.05 was considered statistically significant.

Results

Characteristics		n (%)
Age, years (mean ± SD, range)		61.59 ± 12.48 (29–90)
Sex	Male	136 (63.8)
	Female	77 (36.2)
Marital status	Married	175 (82.2)
	Single	38 (17.8)
Education level	Literate	58 (27.2)
	Primary school	80 (37.6)
	High school	40 (18.8)
	University	35 (16.4)
Urbanization level	Urban	127 (59.6)
	Rural	86 (40.4)
Place of residence	Metropolis	77 (36.2)
	Small town	136 (63.8)
Employment status	Yes	128 (60.1)
	No	85 (39.9)
Economic status	Income less than expenses	126 (59.2)
	Income equals expenses	51(23.9)
	Income more than expenses	36 (16.9)
Duration of diabetes, years (mean ± SD, range)		16.92 ± 8.05 (2–40)
HbA1c, mean ± SD (range)		8.44 ± 1.98 (5.30–15.20)
Treatment setting	Inpatient	101 (47.4)
	Outpatient	112 (52.6)
Type of diabetes	Type 1	10 (4.7)
	Type 2	203 (95.3)
BMI	Underweight (<18.5)	5 (2.3)
	Normal weight (18.5–24.9)	46 (21.6)
	Overweight (25.0–29.9)	114 (53.5)
	Obesity (≥30.0)	48 (22.6)
Diabetes treatment regimens	OAD	64 (30.0)
	Insulin	56 (26.3)
	OAD + Insulin	93 (43.7)
Comorbidities*	Retinopathy	35 (16.4)
	Nephropathy	49 (23.0)
	Periferal neuropathy	166 (77.9)
	Cerebrovascular disease	13 (6.1)
	Peripheral artery disease	110 (51.6)
	Cardiovascular disease	127 (59.6)
History of DFUs	Yes	50 (23.5)
	No	163 (76.5)
History of amputation	Yes (major/minor)	18 (9.4)
	No	195 (91.5)
Wagner classification	Grade 1	65 (30.5)
	Grade 2	112 (52.6)
	Grade 3	24 (11.3)
	Grade 4	12 (5.6)
Duration of wound treatment	< 1 week (a)	106 (49.8)
	1 week–3 months (b)	80 (37.6)
	>3 months (c)	27 (12.7)
Wound infection status	Yes	147 (69.0)
	No	66 (31.0)
Pain related to DFUs	Yes	92 (43.2)
	No	121 (56.8)
Physical mobility limitation	Yes	161 (75.6)
	No	52 (24.4)

Table 1:

Sociodemographic and clinical characteristics of participants (n=213).

Table 1:

Sociodemographic and clinical characteristics of participants (n=213).

The mean age of the participants was 61.59 ± 12.48 years (range: 29–90), and 59.6% (n = 127) were younger than 65 years. Most participants were male (63.8%, n = 136) and married (82.2%, n = 175). The largest proportion of participants had a primary school education (37.6%; n = 80). In addition, 59.6% (n = 127) lived in urban areas, while 63.8% (n = 136) resided in small towns. More than half of the participants were employed (60.1%, n = 128), and 59.2% (n = 126) reported that their income was lower than their expenses (Table 1).

More than half of the participants received outpatient care (52.6%, n = 112), and the majority had type 2 diabetes (95.3%, n = 203). The mean duration of diabetes was 16.92 ± 8.05 years (range: 2–40). In terms of body mass index (BMI), 53.5% (n = 114) were classified as overweight. Regarding diabetes treatment, 43.7% (n = 93) were receiving a combination of oral antidiabetic drugs and insulin therapy.

Among comorbidities, peripheral neuropathy (77.9%; n = 166) and cardiovascular disease (59.6%; n = 127) were the most common. A history of diabetic foot ulcer was reported by 23.5% (n = 50) of participants, and 9.4% (n = 18) had a history of amputation. According to the Wagner classification, most participants were classified as grade 2 (52.6%; n = 112).

Regarding wound-related characteristics, 49.8% (n = 106) of the ulcers had been present for less than 1 week, and 69.0% (n = 147) were associated with infection. In addition, 43.2% (n = 92) of participants reported pain related to DFUs, and 75.6% (n = 161) had limitations in physical mobility.

The mean DFS-SF total score was 21.37 ± 16.54, indicating substantially impaired HRQoL among patients with DFUs. Among the DFS-SF subscales, the highest mean score was observed in PH (34.06 ± 26.66), followed by WU/F (25.00 ± 28.05) and NE (22.61 ± 23.96). The lowest score was observed in the L subscale (10.99 ± 18.56) (Table 2).

DFS-SF ­subscales	Minimum	Maximum	Mean ± SD
L	0	75.00	10.99 ± 18.56
PH	0	100.00	34.06 ± 26.66
D/DL	0	100.00	16.46 ± 24.33
NE	0	91.67	22.61 ± 23.96
WU/F	0	100.00	25.00 ± 28.05
BUC	0	100.00	19.10 ± 23.36
DFS-SF total score	0	70.69	21.37 ± 16.54

Table 2:

Descriptive statistics of DFS-SF total and subscale scores.

Table 2:

Descriptive statistics of DFS-SF total and subscale scores.

When DFS-SF scores were analyzed according to sociodemographic characteristics, a statistically significant difference in total DFS-SF scores was found according to educational level (p = 0.036). Among the subscales, only the WU/F score differed significantly across educational groups (p = 0.015). Post-hoc Tukey analysis revealed that participants with primary school education had higher WU/F scores compared to literate individuals.

Significant differences were also observed according to place of residence, with differences detected in NE scores (p < 0.001) and DFS-SF total scores (p = 0.006). Similarly, urbanization level was associated with significant differences in NE, WU/F, and DFS-SF total scores (p = 0.018, p = 0.002, and p = 0.010, respectively).

Characteristics	L (Mean ± SD)	PH (Mean ± SD)	D/DL (Mean ± SD)	NE (Mean ± SD)	WU/F (Mean ± SD)	BUC (Mean ± SD)	Total Score (Mean ± SD)
Education level
Literate (a)	11.03 ± 15.66	29.56 ± 24.01	13.70 ± 21.49	16.73 ± 23.47	16.91 ± 24.83	17.88 ± 24.84	17.62 ± 15.34
Primary school (b)	11.93 ± 21.10	38.31 ± 29.02	17.93 ± 24.28	25.93 ± 23.94	31.87 ± 29.62	22.26 ± 24.33	24.59 ± 17.18
High school (c)	12.50 ± 19.54	38.50 ± 24.10	20.50 ± 30.27	23.54 ± 23.46	26.25 ± 29.15	20.00 ± 24.80	23.57 ± 16.97
University (d)	7.00 ± 15.53	26.71 ± 27.65	13.00 ± 21.11	23.69 ± 24.56	21.25 ± 24.87	12.85 ± 14.77	17.66 ± 15.06
Test statistic¹	F = 0.694 p = 0.556	F = 2.536 p = 0.058	F = 0.949 p = 0.418	F = 1.738 p = 0.160	F = 3.567 p = 0.015 a	F = 1.403 p = 0.243	F = 2.900 p = 0.036
Place of residence
Metropolis	8.76 ± 17.28	37.53 ± 31.09	13.18 ± 22.43	32.95 ± 25.92	41.88 ± 32.01	19.48 ± 19.68	25.53 ± 16.42
Small town	12.24 ± 19.18	32.09 ± 23.69	18.30 ± 25.23	16.75 ± 20.67	15.44 ± 20.09	18.88 ± 25.27	19.00 ± 16.19
Test statistic²	t = -1.316 p = 0.177	t = 1.331 p = 0.186	t = -1.482 p = 0.140	t = 4.701 p < 0.001	t = 6.653 p < 0.001	t = 0.190 p = 0.850	t = 2.814 p = 0.006
Urbanization level
Urban	12.63 ± 20.15	35.15 ± 28.14	17.95 ± 26.09	25.78 ± 24.40	29.77 ± 29.68	21.77 ± 22.65	23.67 ± 17.31
Rural	8.54 ± 15.69	32.44 ± 24.38	14.24 ± 21.41	17.92 ± 22.60	17.95 ± 23.90	16.27 ± 24.22	17.95 ± 14.77
Test statistic²	t = 1.661 p = 0.098	t = 0.729 p = 0.467	t = 1.092 p = 0.276	t = 2.375 p = 0.018	t = 3.208 p = 0.002	t = 1.455 p = 0.147	t = 2.586 p = 0.010
Economic status
Income < expenses (a)	11.03 ± 18.00	33.29 ± 24.79	16.34 ± 21.53	19.90 ± 23.05	22.47 ± 27.43	17.36 ± 23.29	20.07 ± 15.55
Income = expenses (b)	14.80 ± 22.35	39.41 ± 30.35	20.19 ± 29.30	29.82 ± 24.27	33.70 ± 29.66	28.30 ± 25.62	27.55 ± 17.69
Income > expenses (c)	5.41 ± 12.67	29.16 ± 26.92	11.52 ± 25.62	21.87 ± 25.18	21.52 ± 25.98	12.15 ± 15.66	17.12 ± 16.31
Test statistic¹	F = 2.746 p = 0.066	F = 1.697 p = 0.186	F = 1.347 p = 0.262	F = 3.193 p = 0.043	F = 3.313 p = 0.038	F = 6.194 p = 0.002 b	F = 5.346 p = 0.005 b

Table 3:

Comparison of DFS-SF scores according to sociodemographic characteristics.

Table 3:

Comparison of DFS-SF scores according to sociodemographic characteristics.

Regarding economic status, statistically significant differences were found in NE, WU/F, bothered by ulcer care (BUC), and total DFS-SF scores (p = 0.043, p = 0.038, p = 0.002, and p = 0.005, respectively). Post-hoc analysis demonstrated that participants whose income equaled their expenses had higher WU/F and BUC scores compared with those whose income exceeded their expenses. Additionally, participants whose income equaled their expenses had higher total DFS-SF scores than those whose income was lower than their expenses (­Table 3).

As presented in Table 4, comparisons based on treatment setting showed significant differences in L, NE, WU/F, and BUC scores (p = 0.001, p = 0.004, p < 0.001, and p = 0.042, respectively). Patients without nephropathy demonstrated significantly higher scores in PH, NE, WU/F, and total DFS-SF scores (p = 0.033, p = 0.023, p < 0.001, and p = 0.002, respectively).

Characteristics	L (Mean ± SD)	PH (Mean ± SD)	D/DL (Mean ± SD)	NE (Mean ± SD)	WU/F (Mean ± SD)	BUC (Mean ± SD)	Total Score (Mean ± SD)
Treatment setting
Inpatient	6.87 ± 15.61	31.33 ± 21.84	17.75 ± 24.91	17.69 ± 21.35	16.89 ± 20.75	22.58 ± 26.89	20.59 ± 16.30
Outpatient	15.54 ± 20.48	36.51 ± 30.21	13.48 ± 23.50	27.04 ± 25.36	32.31 ± 31.63	15.95 ± 19.21	22.05 ± 16.79
Test statistic¹	t = 3.446 p = 0.001	t = -1.443 p = 0.151	t = 1.889 p = 0.060	t = -2.289 p = 0.004	t = -4.243 p < 0.001	t = 2.049 p = 0.042	t = -0.644 p = 0.520
Body mass index
Underweight + Healthy (a)	14.60 ± 20.49	32.74 ± 28.04	19.80 ± 26.19	28.26 ± 23.50	30.88 ± 29.64	22.79 ± 23.11	24.83 ± 16.34
Overweight (b)	11.31 ± 19.64	36.97 ± 25.89	16.31 ± 23.11	21.89 ± 22.52	27.24 ± 28.11	17.70 ± 22.02	21.86 ± 15.85
Obesity (c)	6.35 ± 11.92	28.54 ± 26.53	13.22 ± 25.16	18.31 ± 26.95	13.41 ± 22.95	18.48 ± 26.57	16.48 ± 17.55
Test statistic²	F = 2.521 p = 0.083	F = 1.784 p = 0.171	F = 0.906 p = 0.406	F = 2.271 p = 0.106	F = 5.839 p = 0.003 a	F = 0.855 p = 0.427	F = 3.331 p = 0.038 a
Nephropathy
Yes	7.65 ± 16.20	26.93 ± 22.09	12.04 ± 20.30	15.81 ± 21.86	14.03 ± 20.94	17.09 ± 21.67	15.60 ± 13.69
No	11.98 ± 19.13	36.18 ± 27.58	17.77 ± 25.31	24.64 ± 24.24	28.27 ± 29.10	19.70 ± 23.86	23.08 ± 16.96
Test statistic¹	t = -1.571 p = 0.120	t = -2.149 p = 0.033	t = -1.451 p = 0.148	t = -2.286 p = 0.023	t = -3.792 p < 0.001	t = -0.686 p = 0.494	t = -3.167 p = 0.002
Cardiovascular Disease
Yes	7.32 ± 14.52	34.72 ± 28.18	12.12 ± 21.91	19.91 ± 22.63	24.26 ± 28.28	14.91 ± 19.25	18.86 ± 15.38
No	16.39 ± 22.27	33.08 ± 24.37	22.84 ± 26.36	26.59 ± 25.39	26.09 ± 27.28	25.29 ± 27.31	25.06 ± 17.56
Test statistic¹	t = -3.328 p = 0.001	t = 0.440 p = 0.660	t = -3.113 p = 0.002	t = -2.012 p = 0.045	t = -0.466 p = 0.642	t = -3.048 p = 0.003	t = -2.654 p = 0.009
Wagner classification
Wagner 1 (a)	13.76 ± 21.39	36.69 ± 27.40	22.15 ± 28.74	28.06 ± 26.08	28.65 ± 27.55	23.84 ± 27.39	25.61 ± 18.31
Wagner 2 (b)	11.42 ± 17.58	33.34 ± 26.85	15.93 ± 22.82	21.91 ± 22.99	24.49 ± 27.23	17.91 ± 21.24	20.85 ± 15.56
Wagner 3–4 (c)	4.58 ± 14.50	31.52 ± 24.98	7.77 ± 16.83	14.58 ± 20.68	19.96 ± 31.18	14.23 ± 20.73	15.30 ± 14.26
Test statistic²	F = 2.960 p = 0.054	F = 0.517 p = 0.597	F = 4.222 p = 0.016 a	F = 3.991 p = 0.020 a	F = 1.151 p = 0.318	F = 2.294 p = 0.103	F = 4.777 p = 0.009 a

Table 4:

Comparison of DFS-SF Scores According to Clinical Characteristics

Table 4:

Comparison of DFS-SF Scores According to Clinical Characteristics

With respect to cardiovascular disease, significant differences were observed in L, D/DL, NE, BUC, and DFS-SF total scores (p = 0.001, p = 0.002, p = 0.045, p = 0.003, and p = 0.009, respectively).

According to the Wagner classification, statistically significant differences were found in D/DL, NE, and total DFS-SF scores (p = 0.016, p = 0.020, and p = 0.009, respectively). Post-hoc Tukey analysis indicated that patients in Wagner stage 1 ulcers had higher scores compared to those with stages 2 and 3–4.

As presented in Table 5, comparisons according to ulcer- and symptom-related characteristics showed statistically significant differences in WU/F and BUC scores according to a history of DFUs (p = 0.002 and p = 0.011, respectively).

Characteristics	L (Mean ± SD)	PH (Mean ± SD)	D/DL (Mean ± SD)	NE (Mean ± SD)	WU/F (Mean ± SD)	BUC (Mean ± SD)	Total Score (Mean ± SD)
History of DFUs
Yes	14.50 ± 19.03	34.10 ± 22.87	19.40 ± 28.70	19.83 ± 23.69	17.75 ± 20.01	23.12 ± 27.41	21.46 ± 17.40
No	9.90 ± 18.32	34.04 ± 28.78	15.55 ± 22.84	23.46 ± 24.04	27.22 ± 29.78	17.86 ± 21.91	21.33 ± 16.32
Test statistic¹	t = 1.397 p = 0.126	t = 0.044 p = 0.990	t = 1.134 p = 0.329	t = 0.794 p = 0.349	t = -3.174 p = 0.002	t = 2.002 p = 0.011	t = 0.548 p = 0.961
Wound infection status
Yes	9.59 ± 17.75	32.38 ± 26.34	14.18 ± 22.61	20.20 ± 22.95	23.38 ± 28.16	16.41 ± 20.31	19.35 ± 15.89
No	14.09 ± 20.01	37.80 ± 27.17	21.51 ± 27.27	27.96 ± 25.42	28.59 ± 27.67	25.09 ± 28.28	25.84 ± 17.19
Test statistic¹	t = -1.643 p = 0.102	t = -1.375 p = 0.170	t = -2.049 p = 0.042	t = -2.205 p = 0.029	t = -1.256 p = 0.210	t = -2.247 p = 0.027	t = -2.689 p = 0.008
Pain related to DFUs
Yes	15.76 ± 20.87	25.10 ± 2.75	19.72 ± 24.42	22.87 ± 24.47	22.28 ± 23.93	20.17 ± 24.23	21.03 ± 17.12
No	7.35 ± 15.71	40.86 ± 28.08	13.96 ± 24.06	22.41 ± 23.65	27.06 ± 30.75	18.28 ± 22.73	21.61 ± 16.14
Test statistic¹	t = 3.229 p = 0.002	t = -4.601 p < 0.001	t = 1.720 p = 0.087	t = 0.137 p = 0.891	t = -1.277 p = 0.203	t = 0.584 p = 0.560	t = -0.253 p = 0.801
Physical mobility limitation
Yes	7.98 ± 17.04	35.18 ± 27.45	12.67 ± 21.00	21.99 ± 24.40	25.34 ± 29.48	15.37 ± 19.41	19.79 ± 15.67
No	20.28 ± 20.08	30.57 ± 23.94	28.17 ± 29.85	24.51 ± 22.62	23.91 ± 23.27	30.64 ± 30.09	26.22 ± 18.29
Test statistic¹	t = -3.980 p < 0.001	t = 1.084 p = 0.279	t = -3.477 p = 0.001	t = -0.659 p = 0.511	t = 0.360 p = 0.720	t = -3.437 p = 0.001	t = -2.467 p = 0.025
Duration of wound treatment
<1 week (a)	11.17 ± 19.81	39.66 ± 29.15	16.03 ± 25.25	27.00 ± 25.23	31.78 ± 29.88	19.81 ± 23.50	24.23 ± 17.78
1 week–3 months (b)	11.00 ± 16.99	29.31 ± 22.34	16.31 ± 21.78	16.97 ± 21.19	17.81 ± 23.21	18.67 ± 25.16	18.30 ± 14.45
>3 months (c)	10.18 ± 18.52	26.11 ± 24.15	18.51 ± 28.34	22.06 ± 23.79	19.67 ± 28.20	17.59 ± 16.98	19.15 ± 15.87
Test statistic²	F = 0.031 p = 0.970	F = 4.996 p = 0.008 a	F = 0.113 p = 0.893	F = 4.118 p = 0.018 b	F = 6.534 p = 0.002 b	F = 0.118 p = 0.889	F = 3.271 p = 0.040 b

Table 5:

Comparison of DFS-SF scores according to ulcer-related characteristics.

Table 5:

Comparison of DFS-SF scores according to ulcer-related characteristics.

Significant differences were also observed in D/DL, NE, BUC, and total DFS-SF scores according to the presence of wound infection (p = 0.042, p = 0.029, p = 0.027, and p = 0.008, respectively).

In terms of pain related to DFUs, significant differences were found in L and PH scores (p = 0.002 and p < 0.001, respectively). Similarly, physical mobility limitation was associated with significant differences in L, D/DL, BUC, and total DFS-SF scores (p < 0.001, p = 0.001, p = 0.001, and p = 0.025, respectively).

Regarding duration of wound treatment, statistically significant differences were found in PH, NE, WU/F, and DFS-SF total scores ( p = 0.008, p = 0.018, p = 0.002, and p = 0.040, respectively). Post-hoc analysis indicated that participants with wound durations of less than 1 week had higher scores than those with longer wound durations.

Finally, multivariate linear regression analysis was performed to identify independent factors associated with the DFS-SF total score. The regression model was statistically significant (F = 7.390, p < 0.001) and explained 15.8% of the variance in the DFS-SF total score (R² = 0.158).

The analysis revealed that place of residence (β = -0.243, p = 0.001), BMI (β=-0.161, p = 0.016), presence of diabetic foot infection (β=-0.180, p = 0.012), and presence of cardiovascular disease (β = 0.160, p = 0.018) were independent factors associated with the DFS-SF total score (Table 6).

Variables	B	SE	β	t	p	95% CI for B
Constant	30.126	7.142	–	4.218	<0.001	16.041 to 44.211
Residence (ref: small town)	-8.073	2.294	-0.243	-3.519	0.001	-12.596 to -3.549
BMI	-3.822	1.578	-0.161	-2.422	0.016	-6.934 to -0.710
Physical mobility limitation (yes)	3.430	2.852	0.089	1.202	0.231	-2.195 to 9.055
Diabetic foot infection (yes)	-6.220	2.456	-0.180	-2.533	0.012	-11.064 to -1.377
Cardiovascular disease (yes)	5.286	2.221	0.160	2.380	0.018	0.906 to 9.667

Table 6:

Multivariate linear regression analysis of factors associated with DFS-SF total score.

Table 6:

Multivariate linear regression analysis of factors associated with DFS-SF total score.

Discussion

This study demonstrated that HRQoL is markedly impaired in individuals with DFUs, indicating that DFUs represent not only a clinical condition but also a multidimensional burden affecting physical, functional, and psychosocial domains. The low DFS-SF scores observed across all subscales, particularly in L and D/DL, highlight the substantial restrictions imposed by the disease on patients’ everyday functioning.

A prospective cohort study conducted in Singapore reported that HRQoL was low at baseline in patients with DFUs, with an EuroQol 5-Dimension (EQ-5D) utility value of 0.736 (9). Similarly, the Eurodiale study demonstrated that quality of life in patients presenting with DFUs was significantly affected, particularly in the areas of mobility and pain/discomfort (10). In a study conducted by Lima Neto et al. (16) in Brazil, scores on most subscales of the SF-36 quality of life questionnaire were reported to be low in individuals with DFUs, with the “physical role limitations” domain being the most affected. Likewise, Sekhar et al. (11) showed that both physical and mental quality-of-life scores were significantly lower in patients with DFUs compared with individuals without diabetic foot. Furthermore, a meta-analysis involving adults with DFUs reported markedly lower quality of life, particularly in the domains of physical functioning, physical role, general health, and vitality (7). Together, these findings consistently demonstrate that DFUs negatively affect quality of life across multiple dimensions.

In the present study, the relatively low quality-of-life scores may be explained by the clinical characteristics of the study population. A considerable proportion of participants experienced mobility limitation (75.6%), wound infection (69.0%), and DFU-related pain (43.2%), all of which are known to negatively affect physical functioning and daily activities. Additionally, the relatively high mean age of the participants (61.59 ± 12.48), with approximately 40% aged ≥65 years, may have contributed to the lower HRQoL observed. Advanced age is often associated with increased comorbidity burden, reduced physical capacity, and delayed wound healing, which may lead to a greater impact on quality of life in patients with DFU (8,17).

Consistent with previous studies, ulcer severity emerged as a key determinant of HRQoL in this study. The progressive decline in quality-of-life scores with increasing Wagner stage suggests that disease progression is closely linked to worsening functional status and symptom burden. Advanced-stage ulcers are typically associated with higher infection risk, prolonged treatment duration, and increased likelihood of hospitalization or amputation, all of which may impair patients’ physical and psychological well-being.

Similar findings have been reported in previous research. Alrub et al. (18) demonstrated that patients with lower Wagner stages reported significantly higher quality-of-life scores. Likewise, Kudlová and Kočvarová (8) reported that ulcer depth and clinical severity are associated with quality of life in individuals with DFUs. A systematic review examining the impact of diabetic foot complications also concluded that advanced-stage ulcers cause more pronounced deterioration in quality of life (19). Furthermore, a meta-analysis of DFU studies reported that larger wounds were associated with a significant decline in quality of life (7). These findings support the notion that ulcer severity is one of the key clinical indicators determining quality of life in patients with DFUs.

Mobility limitation was another critical factor associated with poorer HRQoL. A large proportion of participants experienced restricted physical movement, suggesting that loss of independence is a central component of the disease burden in DFUs. Reduced mobility may limit participation in daily and social activities, thereby negatively affecting both physical and emotional aspects of quality of life. Previous research also highlights the importance of mobility in determining HRQoL. The Eurodiale study reported that inability to walk or dependence on others for daily activities was associated with a marked decline in quality of life across many domains (10). Similarly, studies have shown that mobility limitations negatively affect physical functioning and activities of daily living in patients with diabetic foot problems (16,20,21). These findings highlight the importance of incorporating rehabilitation strategies and mobility-preserving interventions into DFU management.

This study also highlights the role of clinical complications, particularly infection and comorbid conditions. Diabetic foot infection was identified as an independent predictor of lower HRQoL, likely reflecting its association with pain, inflammation, and treatment complexity. Similarly, the presence of cardiovascular disease was associated with reduced quality of life, possibly due to decreased physical capacity and increased overall disease burden.

Multivariate regression analysis identified place of residence, BMI, diabetic foot infection, and cardiovascular disease as independent predictors of quality of life. In particular, place of residence was found to be one of the strongest predictors (β = -0.243, p = 0.001). This finding indicates that HRQoL in patients with diabetic foot disease is influenced not only by clinical factors but also by socioeconomic and environmental conditions, such as access to healthcare services, availability of social support, and healthcare infrastructure (21).

Individuals living in large cities had lower quality-of-life scores in this study. This finding may reflect differences in living conditions, the burden of disease, social support networks, and access to healthcare services in large cities. However, this finding needs to be clarified by further studies. Furthermore, lower educational and economic status were associated with poorer quality of life, which may be explained by differences in health literacy, self-care behaviors, and access to healthcare resources (13).

Body mass index was also negatively associated with HRQoL (β = -0.161, p = 0.016). Higher BMI may worsen outcomes in patients with DFUs through mechanisms such as reduced mobility, increased plantar pressure, delayed wound healing, and difficulties in implementing effective off-loading strategies (7). Consequently, obesity may exacerbate both ulcer formation and the wound-healing process while simultaneously limiting physical functioning and independence. These findings underscore the importance of integrating weight management strategies into comprehensive DFU care.

Diabetic foot infection was also identified as an independent determinant of HRQoL (β = -0.180, p = 0.012). Infection may negatively affect patients’ physical and psychosocial well-being through pain, inflammation, prolonged treatment duration, and increased need for hospitalization (22). Similarly, the decisive role of infection on quality of life is also emphasized in the literature (10,23). Consequently, early diagnosis and effective management of infection are essential not only for clinical recovery but also for preserving patients’ quality of life.

The presence of cardiovascular disease was identified as an independent determinant of quality of life. Cardiovascular diseases can negatively affect quality of life in patients with diabetic foot through mechanisms such as peripheral circulatory disorders, reduced physical capacity and functional limitations (14,23). The fact that cardiovascular diseases reduce physical capacity and lead to functional limitations may contribute to a decline in quality of life in patients with diabetic foot. Furthermore, the identification of the presence of cardiovascular disease as one of the independent determinants of quality of life in the regression analysis in this study supports the importance of this relationship.

Although the regression model explained a modest proportion of variance in HRQoL, this finding suggests that additional psychological, behavioral, and healthcare-related factors may also contribute to patient outcomes. Nevertheless, the predictors identified in this study provide clinically relevant targets for intervention.

Overall, these findings indicate that improving HRQoL in patients with DFUs requires a comprehensive and multidisciplinary approach. Beyond wound care, effective management should include infection control, management of comorbidities, mobility support, and attention to socioenvironmental determinants of health. Such integrated care strategies may enhance both clinical outcomes and patient-reported quality of life.

This study has several limitations. First, the cross-sectional design limits the ability to establish causal relationships between variables. Second, the sample was recruited from two healthcare centers, which may limit the generalizability of the findings. Third, quality of life was assessed using self-reported measures, which may introduce response bias. In addition, the cross-sectional design did not allow evaluation of longitudinal changes in HRQoL over time. Despite these limitations, the inclusion of participants from two different clinical centers enhances the clinical relevance of the findings. Future multicenter and longitudinal studies are needed to better understand the determinants of HRQoL in patients with DFUs.

In conclusion, this study demonstrates that HRQoL is generally impaired among individuals with DFUs and is influenced by both clinical and sociodemographic factors. Ulcer severity, diabetic foot infection, mobility limitations, BMI, and comorbid cardiovascular disease were associated with poorer HRQoL, while place of residence emerged as an important socioenvironmental determinant. Our findings highlight the multidimensional burden of DFUs and emphasize the need for early identification and management of modifiable risk factors. Further longitudinal research is needed to better understand the long-term impact of these interventions on quality of life.