Patients in the study had both lower extremity arterial disease, involving stenosis or occlusion of arteries supplying the legs, and diabetes. Their mean age was 72.7 years, and 65.7% were men. The investigators divided the cohort into 967 training participants, 414 internal-validation participants and 538 external-validation participants.

During the standardized 12-month observation period, 269 patients underwent major or minor amputation, 118 died before amputation without limb loss, and 1,532 were censored. Amputation occurred in 14.6%, 13.3% and 13.6% of the training, internal-validation and external-validation groups, respectively. A competing event matters because a conventional binary model may treat death before amputation as simply a non-amputation outcome, potentially distorting the estimated probability that a patient actually undergoes limb loss while remaining at risk.

To reduce information leakage, candidate predictors were screened using only the training cohort. A systematic literature review initially identified 75 potential amputation-related factors, after which clinical accessibility and data completeness reduced the set to 48 variables. Three complementary methods—LASSO logistic regression, a random forest classifier and the Boruta algorithm—then ranked or selected those candidates.

A variable was retained when at least two methods supported it, yielding 14 predictors: Rutherford classification, financial burden, age, Barthel Index, diabetic foot, statin use, fibrinogen, neutrophil count, residence, prealbumin, C-reactive protein, activated partial thromboplastin time, body-mass-index classification and the presence of a wound. The same predictor set was used for both the machine-learning and competing-risk models, allowing their performance to be compared under a common population and 12-month prediction window. Ten algorithms were then trained with hyperparameter tuning and evaluated on discrimination, calibration and related classification measures.

The candidates were random forest, logistic regression, decision tree, naive Bayes, artificial neural network, k-nearest neighbors, XGBoost, gradient boosting, CatBoost and light gradient boosting machine. The selected GraBoost model, a gradient-boosting algorithm, performed best in internal validation: its AUC was 0.860, accuracy was 0.787, F1 score was 0.782 and Brier score was 0.156. In the geographically separate external cohort, it retained an AUC of 0.828, accuracy of 0.760, F1 score of 0.695 and Brier score of 0.178.

Eight algorithms had external AUC values above 0.70, while decision tree and k-nearest neighbors performed less consistently. DeLong tests found statistically significant AUC differences between GraBoost and most competing models in internal validation, although some external differences were not significant. These results describe discrimination and calibration in the studied cohorts; they do not establish that the model can improve clinical decisions or patient outcomes.

SHAP analysis, which decomposes each prediction into the directional contribution of its inputs, showed that Rutherford classification had the strongest influence, followed by financial burden and age. Barthel Index, diabetic foot, statin use, fibrinogen, neutrophil count, residence and prealbumin had moderate effects, while C-reactive protein, activated partial thromboplastin time, body-mass-index classification and wounds had comparatively weaker associations. Higher Rutherford grades, lower Barthel Index scores, younger age, wounds and diabetic foot were associated with higher predicted amputation risk.

Higher fibrinogen, activated partial thromboplastin time, C-reactive protein and neutrophil values also contributed to higher predictions, whereas higher prealbumin was associated with lower risk. Urban residence, lower financial burden, normal body-mass-index classification and statin use contributed in the protective direction. These are predictive associations in an observational cohort, not evidence that changing any one factor would cause a reduction in amputation.

The Fine-Gray competing-risk regression used the same 14 inputs and treated amputation as the index event and all-cause death before amputation as the competing event. Its 12-month time-dependent AUC was 0.762 in the training cohort, 0.711 in internal validation and 0.811 in external validation. Corresponding Brier scores were 0.092, 0.106 and 0.085.

Calibration curves for 3-, 6- and 12-month endpoints indicated agreement between predicted and observed cumulative incidence across risk strata, while Gray’s test found no significant difference in 12-month cumulative amputation incidence among the three cohorts, with p = 0.722. The relatively small internal-validation group produced wide confidence intervals for the time-dependent AUC, adding uncertainty to the estimate of discrimination. The authors also generated a nomogram that assigns points to the 14 predictors and translates total points into estimated 3-, 6- and 12-month cumulative incidence under the competing-risk framework.

Several patterns challenge simple assumptions about risk. Younger age was associated with greater predicted amputation despite the general tendency for arterial disease to become more prevalent with age. The investigators proposed possible explanations, including more aggressive surgical decisions among younger patients with severe limb complications and a more aggressive disease phenotype in some younger patients, but emphasized that these ideas are speculative.

They also noted that residual confounding could not be excluded. Similarly, the observed association between rural or town residence, greater financial burden and amputation may reflect differences in access to care, delayed intervention or adherence, although the retrospective data cannot establish those mechanisms. The model also identified a U-shaped pattern in which both underweight and higher body-mass-index classifications were associated with greater predicted risk.

Underweight may reflect malnutrition, sarcopenia or underlying disease burden, while higher body mass index may accompany metabolic disturbance and inflammation, but the study did not test those pathways as causes of amputation. Wounds and diabetic foot were positively associated with predicted risk, but their SHAP effects were weaker than those of Rutherford classification, financial burden and age. Their presence can be consistent with tissue ischemia, impaired healing and infection, but the model’s outputs do not determine why an individual patient developed either condition.

Statin use was associated with lower predicted risk, while higher fibrinogen and prolonged activated partial thromboplastin time were associated with higher risk. The authors interpreted these laboratory patterns in relation to inflammation, coagulation and tissue healing. They also emphasized the role of limited self-care reflected by a lower Barthel Index: reduced mobility and poorer self-management might delay recognition or treatment of foot injury, although the study did not directly test that pathway.

These biological and social interpretations remain explanations for model associations rather than demonstrated causal effects. The researchers deployed both models in a Shiny-based web calculator that accepts patient information and displays synchronized 12-month risk estimates from the binary GraBoost and Fine-Gray frameworks. Dropdown menus are provided for categorical predictors and sliders for continuous variables, allowing a user to enter clinical data and calculate the two outputs.

The 14 inputs were described by the authors as routinely measurable across different hospital tiers, supporting the practical feasibility of collecting the required data. The paired outputs are intended to present two perspectives: a binary machine-learning estimate and a competing-risk estimate that explicitly accounts for pre-amputation death. Nevertheless, the web implementation has not undergone prospective validation in routine care.

It cannot replace comprehensive clinical assessment, and the study did not test whether using it changes surveillance, revascularization, medication prescribing, limb outcomes or survival. Important limitations constrain how the findings should be interpreted. The study was retrospective and observational, so selection bias, incomplete records and unmeasured confounding remain possible.

Baseline characteristics differed among the three cohorts, including differences in Rutherford classification, Barthel Index, residence, statin use and other variables. All participants were Chinese patients treated in Chinese hospitals, limiting extrapolation to other ethnic populations and healthcare systems. Multiple imputation was performed on the pooled dataset before partitioning, which may have introduced some data leakage even though feature selection was restricted to the training cohort.

Patients lost to follow-up also differed significantly from the overall cohort on most baseline variables, indicating non-random attrition. The models were designed specifically for 12-month amputation and cannot be assumed to predict longer-term outcomes. The observed 14.0% amputation rate was also within the 13%–48% range reported for comparable populations, although variations among studies may reflect differences in inclusion criteria, disease severity, healthcare systems and follow-up definitions.

Taken together, the study demonstrates promising external discrimination in a defined clinical population, while prospective multicenter testing remains necessary before the tools can support routine clinical use.

Subject of Research: Development and external validation of 12-month amputation risk models for patients with lower extremity arterial disease and diabetes

Article Title: Development and validation of a machine learning-based model for predicting amputation risk in patients with lower extremity arterial disease and diabetes

Article References: Tang, L., Wang, L., Li, J., Liu, X., Chen, Q., Song, P., Quan, X., Li, Q., Liu, G., Liu, Y., Cui, C., & Shi, M. (2026). Development and validation of a machine learning-based model for predicting amputation risk in patients with lower extremity arterial disease and diabetes. Frontiers in Cardiovascular Medicine, 13, Article 1893875. https://doi.org/10.3389/fcvm.2026.1893875

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1893875

Keywords: amputation risk, lower extremity arterial disease, diabetes, machine learning, gradient boosting, competing risks, external validation, clinical prediction, SHAP interpretability, web calculator, Development, validation

The single-center cohort, which included 100 infected limbs from 98 patients, establishes associations within this hospital population but does not show that particular organisms caused the clinical outcomes. The researchers consecutively enrolled adults with diabetes who were admitted with a foot ulcer and a WIfI foot infection grade from 1 to 3 between February 2017 and March 2019. Deep tissue cultures yielded 133 isolates: 54.1% were Gram-negative, 43.6% Gram-positive and 2.3% fungi.

Staphylococcus aureus was the most frequent species, accounting for 23.3% of isolates. Cultures were monomicrobial in 71 of 100 limbs, meaning that only one species was identified in 71.0% of the sampled infected limbs. Several Enterobacterales were more frequent in grade 3 infection than in grade 2 infection.

Proteus mirabilis had an odds ratio of 15.00, a 95% confidence interval of 1.98 to 112.03 and a p-value of 0.011. Klebsiella pneumoniae and Klebsiella aerogenes each had an odds ratio of 16.78, with p-values of 0.039. The broad confidence interval for Proteus and the small study population indicate substantial uncertainty around these estimates.

When results were grouped by WIfI amputation risk stage, Enterobacterales rose from 13.6% of isolates in the very-low-risk stage to 40.5% in the high-risk stage. Enterococcus faecalis declined from 31.8% to 7.1% across the same stages. The broader decrease in Gram-positive bacteria did not reach statistical significance, so the findings support a species-level pattern rather than a general claim that all Gram-positive organisms became less common as risk increased.

Carbapenem resistance followed a different and non-linear pattern. Organisms with carbapenem resistance were isolated from 25.7% of limbs with grade 1 infection, 8.3% with grade 2 infection and 40.0% with grade 3 infection. Among the nine carbapenem-resistant isolates from grade 1 limbs, eight were non-fermenting Gram-negative bacilli.

The study authors concluded that this pattern does not indicate that carbapenem resistance was associated with greater infection severity. Clinical outcomes differed across the WIfI amputation risk stages. Major amputation occurred in 15 of the 100 infected limbs, or 15.0%, and the proportion increased from 0.0% in the lowest stage to 32.4% in the highest; the test for trend produced a p-value of 0.003.

Two of the 98 patients died in hospital, corresponding to 2.0%. Overall, the findings offer a detailed microbiological snapshot of infected diabetic foot ulcers treated at one Brazilian tertiary hospital. They should not be assumed to represent every hospital or region, and the observational design, relatively small cohort and low number of deaths limit broader generalization.

Subject of Research: Microbial profiles, antibiotic resistance and clinical outcomes in diabetic foot ulcers stratified by WIfI classification

Article Title: Microbial profiles, resistance patterns, and clinical outcomes in diabetic foot ulcers: a prospective cohort study using the WIfI classification

Article References: Cardoso, N. A., Cisneros, L. D. L., Machado, C. J., Procópio, R. J., Navarro, T. P., & Duani, H. (2026). Microbial profiles, resistance patterns, and clinical outcomes in diabetic foot ulcers: a prospective cohort study using the WIfI classification. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14404-1

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14404-1

Keywords: diabetic foot ulcers, foot infection, WIfI classification, Enterobacterales, Gram-negative bacteria, carbapenem resistance, amputation, clinical microbiology, prospective cohort, Brazil, Microbial, profiles

The research, led by Stefan Koning and colleagues at Leiden University Medical Center together with collaborators in The Hague and Copenhagen, enrolled 128 patients with peripheral artery disease, covering 144 treated limbs. Of these, 42 patients, or roughly a third, had diabetes mellitus, accounting for 48 limbs. The team performed quantitative indocyanine green near-infrared fluorescence imaging both before and after revascularization, measuring three time-based perfusion parameters that describe how quickly the fluorescent tracer arrives in the tissue of the foot, how fast it leaves, and how the signal builds up over time. In a subset of 67 patients suffering from chronic limb-threatening ischaemia, the most severe form of the disease, the researchers then checked whether the imaging findings predicted clinical improvement at six weeks.

Indocyanine green is a well-established fluorescent tracer that binds to plasma proteins and circulates with the blood. When illuminated by a near-infrared light source, it emits fluorescence that can be captured by specialized cameras, allowing clinicians to visualize perfusion beneath the skin surface without ionizing radiation. Because the technique is dynamic, it does not merely show whether blood vessels are open, as an angiogram does, but whether blood is actually reaching and washing through the microcirculation of the foot, the capillary beds where oxygen delivery ultimately matters. This distinction is crucial, because a technically successful operation on a large artery does not guarantee that the smallest vessels of a diabetic foot can respond.

The preoperative findings were already revealing. Before any intervention, patients with diabetes showed faster inflow and outflow of the tracer than patients without diabetes at most levels of measurement, with the notable exception of the crural level, the region of the lower leg where the arteries branch toward the ankle and foot. This counterintuitive observation suggests that the diabetic foot microcirculation behaves differently even at baseline, a phenomenon the authors link to the complex interplay of neuropathy, microvascular dysfunction and calcified, often rigid vessels that characterize diabetic vascular disease. Rather than simply being more blocked, the diabetic circulation appears to be differently regulated, which may explain why conventional anatomical assessments so often fail to predict outcomes in this group.

The postoperative picture was where the divide became dramatic. After revascularization, only two of the nine perfusion parameters improved significantly in the diabetes group, with p-values below 0.001, whereas six of nine parameters improved in the non-diabetes group, with p-values ranging from 0.027 to below 0.001. In plain terms, opening the arteries produced a far richer physiological response in patients without diabetes. Their feet flushed with restored blood flow across nearly every measurable dimension, while the diabetic feet showed a muted, partial recovery. The imaging was, in effect, capturing the difference between a circulation that can respond to restored supply and one whose microvascular machinery has been damaged by years of metabolic disease.

The clinical correlation strengthened the case. Among the 67 patients with chronic limb-threatening ischaemia, 50 experienced clinical improvement at six weeks, 34 without diabetes and 16 with diabetes, while 14 did not improve, including 8 with diabetes and 6 without, and 3 were lost to follow-up. In the patients who did improve, perfusion rose significantly across all measured parameters in the non-diabetic group, with p-values from 0.08 to below 0.001, but not in the diabetic group. Overall, 85 percent of non-diabetic chronic limb-threatening ischaemia patients improved clinically, compared with 66 percent of diabetic patients, a trend that approached but did not reach statistical significance with a p-value of 0.073. The numbers are small, and the authors are careful to describe the difference as a trend, but the direction is consistent with everything the perfusion imaging showed.

These findings matter because current decision-making in limb-threatening ischaemia relies heavily on the Wound, Ischemia and foot Infection classification system maintained by the Society for Vascular Surgery, which grades the severity of wounds, the degree of ischemia and the presence of infection to predict the likelihood of limb salvage. What that system cannot do is tell a surgeon whether an individual diabetic foot, once reperfused, has enough residual microvascular capacity to heal. The Leiden study suggests that quantitative near-infrared fluorescence imaging could fill that gap, offering a functional, dynamic readout of tissue-level perfusion both before the operation, to set expectations, and immediately after, to verify whether the revascularization has actually changed anything at the capillary level.

The implications for personalized treatment are considerable. If diabetic patients demonstrate reduced perfusion responsiveness after technically successful revascularization, surgeons might use that information to plan adjunctive therapies, to select different revascularization targets, to intensify wound care and offloading, or to have earlier and more honest conversations about prognosis and the risk of amputation. Conversely, a diabetic patient whose postoperative imaging shows a robust perfusion response might be reassured and followed less intensively. The technology itself is relatively practical: indocyanine green is already approved and widely used, the imaging sessions are quick, and the radiation-free nature of near-infrared imaging makes repeated measurements feasible, even at the bedside.

The study, approved by the Medical Research Ethics Committee of Leiden University Medical Center under number G21.087 and conducted in accordance with the Declaration of Helsinki, was co-funded through the Dutch public-private partnership program Health~Holland under the RULER and IMPULSE projects, with additional support from the European Union’s Horizon Europe program through the RETINA project. The work, published open access on 9 September 2026, arrives amid growing recognition that diabetes transforms peripheral artery disease from a plumbing problem into a disease of the smallest vessels and the nerves that regulate them. Diabetic patients face higher rates of neuropathy, more extensive ischemia and dramatically elevated amputation risk, and the study provides direct visual evidence of why.

For now, the authors position their results as a step toward tailored strategies for the highest-risk group rather than a finished clinical tool. The message, however, is unmistakable: restoring flow through a large artery is only half the battle. What ultimately saves a limb is blood arriving where it is needed, in the skin and tissue of the foot, and for the first time surgeons can watch that happen, in glowing green, seconds after injecting a tracer. As near-infrared fluorescence imaging matures, the day may come when every revascularization is followed by a real-time perfusion check, turning an invisible microvascular failure into something a surgeon can see, measure and act upon before the wound, or the limb, is lost.

Subject of Research: Quantitative ICG near-infrared fluorescence imaging of foot perfusion before and after revascularization in peripheral artery disease patients with and without diabetes mellitus

Article Title: Assessment of foot perfusion before and after revascularization using quantitative ICG near-infrared fluorescence imaging in patients with and without diabetes mellitus

Article References: Koning, S., Peul, R. C., Kruiswijk, M. W., Tange, F. P., van Schaik, J., Schepers, A., Brouwers, J. J. W. M., van Rijswijk, C. S. P., van der Bogt, K. E. A., Eiberg, J. P., Vahrmeijer, A. L., Hamming, J. F., van den Hoven, P., & van der Vorst, J. R. (2026). Assessment of foot perfusion before and after revascularization using quantitative ICG near-infrared fluorescence imaging in patients with and without diabetes mellitus. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02682-9

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02682-9

Keywords: peripheral artery disease, diabetes mellitus, indocyanine green, near-infrared fluorescence imaging, foot perfusion, revascularization, chronic limb-threatening ischaemia, microcirculation, vascular surgery, WIfI classification, amputation risk, BMC Medical Imaging

The scale of the problem documented in the study is striking. Among the 462 participants, 28.9 percent exhibited established factors associated with increased odds of ulceration, such as peripheral neuropathy, impaired arterial supply, or structural foot deformity. A further 16.6 percent presented with active diabetic foot ulcers, meaning open lesions that had already breached the skin. Taken together, 32.5 percent of the cohort demonstrated either risk factors or existing ulceration. For clinicians, these numbers translate into a continuous stream of patients whose feet are one unnoticed injury away from infection, gangrene, and amputation. For public health planners, they signal that foot pathology in Pakistani diabetes clinics is not an occasional crisis but a routine, systemic feature of the disease landscape.

Diabetic foot ulceration is widely regarded as one of the most feared consequences of type 2 diabetes because it sits at the intersection of two parallel degenerative processes. Chronic hyperglycemia damages the long nerves of the lower limbs, producing peripheral neuropathy in which patients progressively lose protective sensation. A person with neuropathic feet may step on a sharp object, develop a blister from ill-fitting shoes, or sustain a minor burn without feeling any pain, and the wound therefore goes untreated until it becomes deeply infected. Simultaneously, elevated glucose accelerates atherosclerosis of the small and medium arteries of the leg, a condition known as peripheral arterial disease or angiopathy, which starves the tissues of the oxygen needed for healing. When numbness and poor blood supply coexist, even a trivial lesion can progress to a limb-threatening ulcer within days.

The Karachi study was designed to quantify exactly how these processes manifest in a real clinical population and to identify which factors independently predict who ends up in the danger zone. Following written informed consent, the researchers documented each participant’s demographic characteristics, diabetes history, and anthropometric measurements, and then performed standardized clinical foot examinations. Neuropathy was assessed using 10-g monofilament testing, a technique in which a nylon filament is pressed against specific points on the sole until it bows; a patient who cannot feel the filament at a tested site is classified as having lost protective sensation. Peripheral arterial disease was evaluated through the ankle-brachial index, a noninvasive measurement that compares blood pressure at the ankle with blood pressure in the arm, with low values indicating compromised arterial flow to the feet. Fasting blood samples were also obtained to calculate HOMA-IR, the homeostatic model assessment of insulin resistance, which estimates how strongly the body’s tissues resist the glucose-lowering action of insulin.

Participants were then sorted into three clinically meaningful categories: those with no detectable foot risk, those classified as at-risk because of neuropathy, angiopathy, or foot deformity, and those with an active ulcer. Binary logistic regression, a statistical technique that estimates the odds of an outcome as a function of multiple explanatory variables, was used to identify which factors were independently associated with risk or ulceration, with statistical significance set at a p-value below 0.05. This two-stage analytical approach matters because many variables that appear related to foot complications in simple comparisons are actually proxies for one another; multivariable modeling strips away those overlaps and isolates the factors that carry independent predictive weight.

The univariate analysis, which examined each factor in isolation, revealed significant positive associations between risk or ulcer status and a broad range of characteristics: advancing age, elevated body mass index, raised diastolic blood pressure, smoking, a prior history of ulceration or amputation, insulin resistance as measured by HOMA-IR, dry skin, hyperkeratotic lesions, fissures, and nail deformities. Each of these associations tells a mechanistic story. Smoking worsens arterial disease and impairs tissue oxygenation. Elevated BMI and blood pressure track with metabolic syndrome and its vascular consequences. Dry skin, calluses, corns, and fissures represent the mechanical and dermatological consequences of neuropathy, because denervated skin loses its normal sweating and its ability to distribute pressure evenly, causing thickened, cracked patches that readily break down into ulcers.

When the researchers moved to multivariable analysis, a tighter set of independent predictors emerged. HOMA-IR, indicating insulin resistance, remained significantly associated with foot risk or ulceration, as did the presence of calluses, corns, or fissures, nail deformity, onychomycosis, a history of previous amputation, and daily foot inspection behavior. The inclusion of onychomycosis, a fungal infection of the toenails, is particularly noteworthy because thickened, dystrophic nails can create pressure points, harbor pathogens, and serve as entry sites for bacteria, yet fungal nail disease is often dismissed as a cosmetic nuisance rather than a genuine limb-threat marker. The appearance of previous amputation as an independent predictor underscores a sobering clinical reality: patients who have already lost part of a foot remain at high risk of losing more, because the biomechanics of the remaining foot are altered and pressure distribution becomes even more hazardous.

One of the most provocative findings concerns foot inspection behavior, which appeared as an independent factor in the multivariable model. Daily self-examination of the feet is the cornerstone of diabetic foot prevention, recommended by every major clinical guideline precisely because it allows patients to catch blisters, cuts, and color changes before they escalate. Its emergence in the analysis highlights a double-edged reality: patients who inspect their feet daily are often those who have already been educated about their risk or who have experienced a complication, while those who do not inspect may be accumulating silent damage. The authors emphasize that this pattern points toward systematic patient education as a low-cost, high-yield intervention, particularly in settings where specialist podiatry services are scarce and where a single clinic visit may be the only structured foot assessment a patient ever receives.

The study’s implications extend well beyond Karachi. Pakistan carries one of the world’s heaviest diabetes burdens, and diabetic foot complications contribute substantially to morbidity, lower-extremity amputation, and healthcare expenditure, with costs that cascade through families and already strained health systems. The authors argue that the substantial burden of risk factors and active ulceration observed in their cohort underscores the imperative for systematic foot surveillance, patient education, and early intervention strategies targeting modifiable metabolic and dermatological precursors. In practical terms, that means integrating monofilament testing and ankle-brachial index measurement into routine diabetes visits, treating calluses and fungal nail infections before they ulcerate, aggressively managing insulin resistance and vascular risk factors, and teaching every patient to look at their feet every day. The study, approved by the institutional review boards of the University of Karachi, the Aga Khan Health Service Centre, and the Tabba Heart Institute, and conducted in accordance with the Declaration of Helsinki, received no external funding. Its message is ultimately an optimistic one: the factors that most strongly predict limb loss in this population are visible, measurable, and modifiable, which means that with systematic screening and education, a large share of these amputations and ulcers could be prevented before they ever begin.

Subject of Research: Prevalence and risk factors for diabetic foot complications among type 2 diabetes patients in Pakistan

Article Title: Prevalence and factors associated with diabetic foot complications among type 2 diabetes patients in Pakistan: a cross-sectional analysis of clinical risk factors and preventive opportunities

Article References: Riaz, N., Yasmeen, G., & Khan, M. A. (2026). Prevalence and factors associated with diabetic foot complications among type 2 diabetes patients in Pakistan: a cross-sectional analysis of clinical risk factors and preventive opportunities. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02531-4

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02531-4

Keywords: diabetic foot ulcer, type 2 diabetes, peripheral neuropathy, insulin resistance, HOMA-IR, foot care behavior, onychomycosis, peripheral arterial disease, amputation risk, Pakistan, cross-sectional study, preventive care