A New Inflammatory Marker of Clinical and Diagnostic Importance in Diabetic Foot Infection: Systemic Immune-Inflammation Index

Association between physical activity and diabetes mellitus: mediation analysis involving Systemic Immune-Inflammatory Index in a cross-sectional NHANES study

OBJECTIVES

In this study, we aimed to clarify the relationship between physical activity (PA) and diabetes mellitus (DM), as well as the mediating role of Systemic Immune-Inflammatory Index (SII) in the relationship.

DESIGN

A cross-sectional study.

SETTING

National Health and Nutrition Examination Survey (NHANES) data collection took place in the USA at participants' homes and mobile examination centres with specialised equipment.

PARTICIPANTS

The study population consisted of 9493 American adults aged 20 and above from the NHANES 2005 to 2018.

PRIMARY AND SECONDARY OUTCOME MEASURES

Information on the specific PA was reported through self-administered questionnaire by participants and we used this information to calculate a metabolic equivalent score for the particular PA. The calculation of SII follows a standard formula: SII=P (platelets)×N (neutrophils)/L (lymphocytes).

RESULTS

A total of 9493 participants were included, with 1672 diagnosed with DM. The participants with DM were more inclined to have lower levels of PA while having higher levels of SII. In all three models, high levels of PA were significantly negatively associated with the risk of DM compared with moderate levels of PA, and a non-linear association between natural logarithm-physical activity (Ln-PA) and DM was observed. Furthermore, there was a significant reduction in DM risk for Ln-PA >6.71 in all models. Mediation analysis showed that SII mediated the relationship between PA and DM, as well as between Ln-PA and DM, with respective mediation proportions of 4.32% and 12.141%, as well as 3.12% and 10.46% after adjusting for covariates.

CONCLUSION

This study investigated the relationship among PA, SII and DM. We provide robust evidence supporting the inverse association between PA and DM risk while highlighting the mediating role of inflammation, as reflected by SII. These findings contribute valuable insights to inform public health strategies and clinical interventions aimed at reducing the global burden of DM.

Predictive nomogram for risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy: development and performance evaluation

OBJECTIVE

To develop an accurate predictive model for identifying patients at high risk of pulmonary infection during radiochemotherapy.

METHODS

We retrospectively analyzed data from 544 lung cancer patients treated at Hubei Cancer Hospital between May 2019 and October 2022. The patients were divided into training and validation groups (7:3 ratio). An external validation cohort of 100 patients treated from November 2022 to January 2024 was also included. Feature selection and model development were performed using machine learning algorithms, including Lasso regression, Random Forest, XGBoost, and Support Vector Machine (SVM). Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis.

RESULTS

Key predictive factors for pulmonary infection risk were identified, including diabetes, chronic obstructive pulmonary disease, chemotherapy intensity, chemotherapy cycles, antibiotic use, age, Karnofsky Performance Status score, systemic inflammation index, prognostic nutritional index, and C-reactive protein. A nomogram-based prediction model was constructed, achieving ROC curve Area Under the Curve values of 0.889 in the training set, 0.897 in the validation set, and 0.875 in the external validation set, demonstrating strong classification ability and stability.

CONCLUSION

We developed a robust nomogram-based model incorporating eight key factors to predict the risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy. This model can assist clinicians in early identification of high-risk patients, enabling timely interventions to improve patient outcomes and quality of life.

Association between polychlorinated biphenyls and circulatory immune markers: results from NHANES 1999-2004

OBJECTIVES

Polychlorinated biphenyls (PCBs), a family of persistent toxic and organic environmental pollutants, were associated with multiple organ damages in humans once accumulating. However, association between PCBs exposure and circulatory immune markers were not clear.

METHODS

Data was collected from participants enrolled in the National Health and Nutrition Examination Survey in 1999-2004. PCBs were categorized by latent class analysis (LCA). Weighted quantile sum (WQS) regression was used to investigate effects of PCBs exposure on circulatory immune markers including leukocyte counts, monocyte-lymphocyte ratio (MLR), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII).

RESULTS

There were 3,109 participants included in the final analysis with blood PCBs levels presented as 3 classes. The high PCBs group had a higher rate of comorbidities. Leukocyte, lymphocyte and neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and system immune-inflammation index (SII) were significantly lower in the high PCBs group than in the low PCBs group (all p-values < 0.05). After adjusting for covariant variables, the low PCBs group was positively associated with SII (p = 0.021) and NLR (p = 0.006) in multivariate regression. Significantly negative correlations between PCBs classification and SII (β = -14.513, p = 0.047), and NLR (β = -0.035, p = 0.017) were found in WQS models. LBX028LA showed the most significant contribution in the associations between PCBs and SII, and LBX128LA contributed most significantly to associations with NLR.

CONCLUSION

Our study adds novel evidence that exposures to PCBs may be adversely associated with the circulatory immune markers, indicating the potential toxic effect of PCBs on the human immune system.

Can The Systemic Immune-Inflammation Index (SII) and Charlson Comorbidity Index (CCI) be used to predict mortality in patients with necrotizing fasciitis?

PURPOSE

This study aimed to determine the impact of mortality and morbidity indices on the diagnosis and prognosis of patients suffering from necrotizing fasciitis.

METHODS

A retrospective analysis was performed on 41 patients (26 females, 15 males) with necrotizing fasciitis (NF). The SII (Systemic Immune-Inflammation Index) was computed using the formula SII = (P × N)/L, where P, N, and L measure the counts of peripheral platelets, neutrophils, and lymphocytes, respectively. This study evaluated the clinicopathological characteristics and follow-up information to assess the comparative effectiveness of SII, CCI (Charlson Comorbidity Index), and LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) scores as mortality and morbidity indices for patients with NF.

RESULTS

The optimal cut-off for SII was determined to be 455. The SII value in the group with mortality was significantly higher compared to the group without mortality (p < 0.05). The CCI value in the group with mortality was significantly higher than the group without mortality (p < 0.05). The SII and CCI values were found to be effective in distinguishing between patients who suffered mortality and those who did not.

CONCLUSION

SII is a powerful tool for predicting mortality in patients with necrotizing fasciitis (NF). The SII index provides a novel, easily accessible, and inexpensive indicator for monitoring the progress and predicting the survival of patients with NF.

Update of biomarkers to diagnose diabetic foot osteomyelitis: A meta-analysis and systematic review

The aim of this study was to evaluate the diagnostic characteristics of biomarker for diabetic foot osteomyelitis (DFO). We searched PubMed, Scopus, Embase and Medline for studies who report serological markers and DFO before December 2022. Studies must include at least one of the following diagnostic parameters for biomarkers: area under the curve, sensitivities, specificities, positive predictive value, negative predictive value. Two authors evaluated quality using the Quality Assessment of Diagnostic Accuracy Studies tool. We included 19 papers. In this systematic review, there were 2854 subjects with 2134 (74.8%) of those patients being included in the meta-analysis. The most common biomarkers were erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and procalcitonin (PCT). A meta-analysis was then performed where data were evaluated with Forrest plots and receiver operating characteristic curves. The pooled sensitivity and specificity were 0.72 and 0.75 for PCT, 0.72 and 0.76 for CRP and 0.70 and 0.77 for ESR. Pooled area under the curves for ESR, CRP and PCT were 0.83, 0.77 and 0.71, respectfully. Average diagnostic odds ratios were 16.1 (range 3.6-55.4), 14.3 (range 2.7-48.7) and 6.7 (range 3.6-10.4) for ESR, CRP and PCT, respectfully. None of the biomarkers we evaluated could be rated as 'outstanding' to diagnose osteomyelitis. Based on the areas under the curve, ESR is an 'excellent' biomarker to detect osteomyelitis, and CRP and PCT are 'acceptable' biomarkers to diagnose osteomyelitis. Diagnostic odds ratios indicate that ESR, CRP and PCT are 'good' or 'very good' tools to identify osteomyelitis.

Association of systemic immune-inflammation index with insulin resistance and prediabetes: a cross-sectional study

Background and Objective

Previous research suggested a relationship between the Systemic Immune-Inflammation Index (SII) and multiple adverse health conditions. However, the role of SII in prediabetes and insulin resistance (IR) remains poorly understood. Therefore, this study aims to explore the potential relationship between SII and prediabetes and IR, providing data support for effective diabetes prevention by reducing systemic inflammation.

Methods

Linear regression models were used to assess the correlation between continuous SII and risk markers for type 2 diabetes (T2D). Subsequently, multivariate logistic regression models and subgroup analyses were employed to evaluate the association between SII tertiles and prediabetes and IR, controlling for various confounding factors. Finally, restricted cubic spline graphs were used to analyze the nonlinear relationship between SII and IR and prediabetes.

Results

After controlling for multiple potential confounders, SII was positively correlated with fasting blood glucose (FBG) (β: 0.100; 95% CI: 0.040 to 0.160), fasting serum insulin (FSI) (β: 1.042; 95% CI: 0.200 to 1.885), and homeostasis model assessment of insulin resistance (HOMA-IR) (β: 0.273; 95% CI: 0.022 to 0.523). Compared to participants with lower SII, those in the highest tertile had increased odds of prediabetes (OR: 1.17; 95% CI: 1.02-1.34; p for trend < 0.05) and IR (OR: 1.35; 95% CI: 1.18 to 1.51; p for trend<0.001).

Conclusions

Our study results demonstrate an elevated association between SII levels and both IR and prediabetes, indicating SII as a straightforward and cost-effective method identifying individuals with IR and prediabetes.

Assessment of the systemic immune-inflammation index in type 2 diabetic patients with and without dry eye disease: A case-control study

Background

The inflammation plays a role in the pathophysiology of type-2 diabetes progression, and the mechanism remains unclear. The systemic immune-inflammation index (SII) is a novel inflammatory marker for type 2 diabetes patients and integrates multiple indicators in complete blood counts and routine blood tests.

Aim

Since there is no international diagnostic standard for dry eye disease (DED), this study uses low-cost inflammatory blood biomarkers to investigate the correlation between SII and DM2-DED and determine the diagnosis indices of other biomarkers in DM2-DED.

Methodology

A case-control retrospective analysis of totel patients n = 293 randomly selected and categorized into four groups: DED, DM2, DM2-DED, and healthy subjects. Demographic and blood biomarker variables were classified as categorical and continuous variables. The platelet-to-lymphocyte ratio (PLR), lymphocytes-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio (NLR), and SII were calculated platelet count multiply by NLR and analyzed for their correlation for all groups.

Results

Focusing on DM2-DED patients was more common in females, 59.6%, than in males, 40.2%. The mean ages were 60.7 ± 11.85 years, a statistically significant difference with all groups. In the study group DM2-DED, there was an increase in all blood markers compared to all remaining groups except PLR. Only neutrophil, hemoglobin A1c (HbA1c), and fasting blood sugar levels were statistically significant differences in DM2-DED patients (p > 0.001, p < 0.001, and p < 0.001, respectively) compared to all groups. There was a positive correlation between HbA1c and PLR, HbA1c and NLR, and HbA1c and SII (r = 0.037, p = 0.705; r = 0.031, p = 0.754; and r = 0.066, p < 0.501, respectively) in the DM2-DED group.

Conclusion

This study demonstrated that elevated SII values were linked to elevated HbA1c in DM2-DED patients. The potential of SII and HbA1c as early diagnostic indicators for ocular problems associated with diabetes mellitus is highlighted by their favorable connection in diagnosing DM2-DED.

The impact of inflammatory biomarkers on amputation rates in patients with diabetic foot ulcers

Diabetic Foot Ulcers (DFUs) are a major complication of diabetes, often leading to amputation. Understanding the relationship between haematological inflammatory markers and the incidence of amputation in DFU patients with infectious complications is crucial for improving management and outcomes. This retrospective study, conducted from May 2020 to October 2022, involved 109 patients with DFUs, categorised into amputation (AM) and non-amputation (NAM) groups. Patients were evaluated for various factors, including demographic data, DFU duration, and blood parameters such as haemoglobin A1c (HbA1c), haemoglobin (Hb), albumin (ALB), white blood cell count (WBC), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR). Statistical analyses were performed using independent sample t-tests, Mann-Whitney U test and logistic regression. The univariate analysis showed no significant difference in BMI, DM duration or DFU duration between groups. However, significant differences were noted in PCT, Hb, ESR, ALB, HbA1c and WBC levels, and in inflammatory ratios (NLR, PLR and LMR). Multivariate logistic regression identified CRP, NLR and PLR as independent risk factors for amputation. The study highlights CRP, PLR and NLR as key independent risk factors for amputation in patients with DFUs. These easily obtainable markers from routine blood tests can effectively aid in predicting the risk of osteomyelitis and amputation, enhancing clinical decision making and patient care strategies.

The infected diabetic foot: Modulation of traditional biomarkers for osteomyelitis diagnosis in the setting of diabetic foot infection and renal impairment

The objective of this paper was to investigate erythrocyte sedimentation rate (ESR) and c-reactive protein (CRP) in diagnosing pedal osteomyelitis (OM) in patients with and without diabetes, and with and without severe renal impairment (SRI). This was a retrospective cohort study of patients with moderate and severe foot infections. We evaluated three groups: Subjects without diabetes (NDM), subjects with diabetes and without severe renal insufficiency (DM-NSRI), and patients with diabetes and SRI (DM-SRI). SRI was defined as eGFR <30. We evaluated area under the curve (AUC), cutoff point, sensitivity and specificity to characterize the accuracy of ESR and CRP to diagnose OM. A total of 408 patients were included in the analysis. ROC analysis in the NDM group revealed the AUC for ESR was 0.62, with a cutoff value of 46 mm/h (sensitivity, 49.0%; specificity, 76.0%). DM-NSRI subjects showed the AUC for ESR was 0.70 with the cutoff value of 61 mm/h (sensitivity, 68.9%; specificity 61.8%). In DM-SRI, the AUC for ESR was 0.67, with a cutoff value of 119 mm/h (sensitivity, 46.4%; specificity, 82.40%). In the NDM group, the AUC for CRP was 0.55, with a cutoff value of 6.4 mg/dL (sensitivity, 31.3%; specificity, 84.0%). For DM-NSRI, the AUC for CRP was 0.70, with a cutoff value of 8 mg/dL (sensitivity, 49.2%; specificity, 80.6%). In DM-SRI, the AUC for CRP was 0.62, with a cutoff value of 7 mg/dL (sensitivity, 57.1%; specificity, 67.7%). While CRP demonstrated relatively consistent utility, ESR's diagnostic cutoff points diverged significantly. These results highlight the necessity of considering patient-specific factors when interpreting ESR results in the context of OM diagnosis.

Association between systemic immune-inflammatory index and diabetes mellitus: mediation analysis involving obesity indicators in the NHANES

Background

Inflammation and obesity have been widely recognized to play a key role in Diabetes mellitus (DM), and there exists a complex interplay between them. We aimed to clarify the relationship between inflammation and DM, as well as the mediating role of obesity in the relationship.

Methods

Based on the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Univariate analyses of continuous and categorical variables were performed using t-test, linear regression, and χ2 test, respectively. Logistic regression was used to analyze the relationship between Systemic Immune-Inflammatory Index (SII) or natural logarithm (Ln)-SII and DM in three different models. Mediation analysis was used to determine whether four obesity indicators, including body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI) and lipid accumulation product index (LAP), mediated the relationship between SII and DM.

Results

A total of 9,301 participants were included, and the levels of SII and obesity indicators (BMI, WC, LAP, and VAI) were higher in individuals with DM (p < 0.001). In all three models, SII and Ln-SII demonstrated a positive correlation with the risk of DM and a significant dose-response relationship was found (p-trend <0.05). Furthermore, BMI and WC were associated with SII and the risk of DM in all three models (p < 0.001). Mediation analysis showed that BMI and WC mediated the relationship between SII with DM, as well as Ln-SII and DM, with respective mediation proportions of 9.34% and 12.14% for SII and 10.23% and 13.67% for Ln-SII (p < 0.001).

Conclusion

Our findings suggest that increased SII levels were associated with a higher risk of DM, and BMI and WC played a critical mediating role in the relationship between SII and DM.

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population

Objective

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population.

Methods

A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were divided into three groups based on estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR): non-DKD group, DKD stages 1-2 Alb group, and DKD-non-Alb+DKD stage 3 Alb group. The possible association of SII with DKD was investigated by correlation and multivariate logistic regression analysis, and receiver-operating characteristic (ROC) curves analysis.

Results

Moving from the non-DKD group to the DKD-non-Alb+DKD stage 3 Alb group, SII level was gradually increased (P for trend <0.01). Partial correlation analysis revealed that SII was positively associated with urinary ACR and prevalence of DKD, and negatively with eGFR (all P<0.01). Multivariate logistic regression analysis showed that SII remained independently significantly associated with the presence of DKD after adjustment for all confounding factors [(odds ratio (OR), 2.735; 95% confidence interval (CI), 1.840-4.063; P < 0.01)]. Moreover, compared with subjects in the lowest quartile of SII (Q1), the fully adjusted OR for presence of DKD was 1.060 (95% CI 0.773-1.455) in Q2, 1.167 (95% CI 0.995-1.368) in Q3, 1.266 (95% CI 1.129-1.420) in the highest quartile (Q4) (P for trend <0.01). Similar results were observed in presence of DKD stages 1-2 Alb or presence of DKD-non- Alb+DKD stage 3 Alb among SII quartiles. Last, the analysis of ROC curves revealed that the best cutoff values for SII to predict DKD, Alb DKD stages 1- 2, and DKD-non-Alb+ DKD stage 3 Alb were 609.85 (sensitivity: 48.3%; specificity: 72.8%), 601.71 (sensitivity: 43.9%; specificity: 72.3%), and 589.27 (sensitivity: 61.1%; specificity: 71.1%), respectively.

Conclusion

Higher SII is independently associated with an increased risk of the presence and severity of DKD, and SII might be a promising biomarker for DKD and its distinct phenotypes in Chinese population.

Increased Systemic Immune-Inflammation Index Was Associated with Type 2 Diabetic Peripheral Neuropathy: A Cross-Sectional Study in the Chinese Population

Background

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been demonstrated to be associated with type 2 diabetes mellitus (T2DM) and its vascular complications, however, the relation between SII and diabetic peripheral neuropathy (DPN) has been never reported. We aimed to explore whether SII is associated with DPN in Chinese population.

Methods

A cross-sectional study was conducted among 1460 hospitalized patients with T2DM. SII was calculated as the platelet count × neutrophil count/lymphocyte count, and its possible association with DPN was investigated by correlation and multivariate logistic regression analysis, and subgroup analyses.

Results

Patients with higher SII quartiles had higher vibration perception threshold and prevalence of DPN (all P<0.01), and SII was independently positively associated with the prevalence of DPN (P<0.01). Multivariate logistic regression analysis showed that the risk of prevalence of DPN increased progressively across SII quartiles (P for trend <0.01), and participants in the highest quartile of SII was at a significantly increased risk of prevalent DPN compared to those in the lowest quartile after adjustment for potential confounding factors (odds rate: 1.211, 95% confidence intervals 1.045-1.404, P<0.05). Stratified analysis revealed positive associations of SII quartiles with risk of prevalent DPN only in men, people less than 65 years old, with body mass index <24 kg/m2, duration of diabetes >5 years, hypertension, dyslipidaemia, poor glycaemic control, and estimated glomerular filtration rate <90 mL/min/1.73 m2 (P for trend <0.01 or P for trend <0.05). The receiver operating characteristic curve analysis revealed that the optimal cut-off point of SII for predicting DPN was 617.67 in patients with T2DM, with a sensitivity of 45.3% and a specificity of 73%.

Conclusion

The present study showed that higher SII is independently associated with increased risk of DPN, and SII might serve as a new risk biomarker for DPN in Chinese population.

Association between systemic immune-inflammation index and diabetes: a population-based study from the NHANES

Background

Systemic Immune-Inflammation Index (SII) has been reported to be associated with diabetes. We aimed to assess possible links between SII and diabetes.

Methods

Data were obtained from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) database. After removing missing data for SII and diabetes, we examined patients older than 20 years. Simultaneously, the relationship between SII and diabetes was examined using weighted multivariate regression analysis, subgroup analysis, and smooth curve fitting.

Results

There were 7877 subjects in this study, the average SII was 524.91 ± 358.90, and the prevalence of diabetes was 16.07%. Weighted multivariate regression analysis found that SII was positively associated with diabetes, and in model 3, this positive association remained stable (OR = 1.04; 95% CI: 1.02-1.06; p = 0.0006), indicating that each additional unit of SII, the possibility of having diabetes increased by 4%. Gender, age, BMI, regular exercise, high blood pressure, and smoking did not significantly affect this positive link, according to the interaction test (p for trend>0.05).

Discussion

Additional prospective studies are required to examine the precise connection between higher SII levels and diabetes, which may be associated with higher SII levels.

The superiority of immune-inflammation summary index for diagnosing periprosthetic joint infection

BACKGROUND

Accurate and rapid diagnosis of periprosthetic joint infections (PJI) is particularly challenging. This study aimed to evaluate the diagnostic value of a newly developed immune-inflammation summary index (IISI) for PJI.

METHODS

Our study enrolled 171 aseptic loosening (AL) and 172 PJI cases. Based on a biological-driven approach, the IISI was formulated as C-reaction protein (CRP) × globulin × neutrophil / [lymphocyte × albumin]. Receiver operating characteristic (ROC) curves were constructed to compare the diagnostic performance of IISI with other known serum markers. Subgroup analysis was also performed to explore the robustness of IISI. Restricted cubic splines were used to evaluate the dose-response association. Additionally, changes in IISI levels prior to reimplantation were investigated.

RESULTS

The levels of all tested biomarkers were significantly different between the PJI and AL groups (all P < 0.05). ROC analysis revealed that IISI outperformed any other marker in diagnosing PJI with an area under the curve (AUC) value of 0.890. The diagnostic performance of IISI was also optimal in the hip (0.898), knee (0.903), low-grade infection (0.841), and culture-negative (0.919) subgroups. The optimal cut-off value is stabilized at around 1.6. The nonlinear association between IISI scores and PJI was also confirmed (P < 0.001). The levels of IISI before reimplantation demonstrated a significant decrease (P < 0.001) and were comparable to those of the AL group (P = 0.143).

CONCLUSION

IISI can improve the utilization of serum indicators and is superior to other well-known biomarkers in diagnosing PJI. Further studies should evaluate its specific role in different infectious and inflammatory diseases.

The Course of Diabetic Foot Infection in Elderly Patients: Data of Patients From Turkey

With the aging population, it is expected that diabetes and related complications will increase rapidly. The aim of this study was to examine the signs and symptoms of diabetic foot infection in elderly individuals. Patients with diabetic foot infection were grouped as mild, moderate, and severe. Patients aged <65 years and those who did not meet the diagnosis of diabetic foot infection were excluded from the study. Only the first applications of patients who applied to the hospital multiple times with diabetic foot infection diagnosis were evaluated. 314 patients were included in the study. The mean age of the patients was 71.5 (±12). The number of patients aged 75 and over was 125 (39.8%). Of the patients, 25.7% had mild, 61.7% moderate, and 12.4% severe clinical forms. 131 (41.7%) of the patients had osteomyelitis. Amputation was performed in 112 of the patients. Antibiotic treatment was given to 102 patients only. While 89 patients died, a significant correlation was found between all groups between amputation rate and mortality frequency and clinical severity of diabetic foot infection (P < .001). In our study, it was observed that the clinical severity of diabetic foot infection was more severe and the overall mortality rate was higher in geriatric patients. In light of all these data, it can be concluded that an early and comprehensive roadmap should be followed in geriatric patients with diabetic foot infection who have an increased risk of morbidity and mortality.