Lower extremity artery disease in patients with type 2 diabetes

Surgical management of the diabetic foot: The current evidence

The prevalence of diabetes mellitus and its associated complications, particularly diabetic foot pathologies, poses significant healthcare challenges and economic burdens globally. This review synthesises current evidence on the surgical management of the diabetic foot, focusing on the interplay between neuropathy, ischemia, and infection that commonly culminates in ulcers, infections, and, in severe cases, amputations. The escalating incidence of diabetes mellitus underscores the urgency for effective management strategies, as diabetic foot complications are a leading cause of hospital admissions among diabetic patients, significantly impacting morbidity and mortality rates. This review explores the pathophysiological mechanisms underlying diabetic foot complications and further examines diabetic foot ulcers, infections, and skeletal pathologies such as Charcot arthropathy, emphasising the critical role of early diagnosis, comprehensive management strategies, and interdisciplinary care in mitigating adverse outcomes. In addressing surgical interventions, this review evaluates conservative surgeries, amputations, and reconstructive procedures, highlighting the importance of tailored approaches based on individual patient profiles and the specific characteristics of foot pathologies. The integration of advanced diagnostic tools, novel surgical techniques, and postoperative care, including offloading and infection control, are discussed in the context of optimising healing and preserving limb function.

Global epidemiology of lower extremity artery disease in the 21st century (2000-21): a systematic review and meta-analysis

AIMS

The epidemiology of lower extremity artery disease (LEAD) is evolving. This meta-analysis of aggregate data aimed to (i) determine the global prevalence of LEAD and by regions in the 21st century and (ii) update the associated risk factors in this period.

METHODS AND RESULTS

A systematic literature review was performed through PubMed, Cochrane, Scopus, Science Direct, and Google Scholar databases, restricted to general population studies between January 2000 and September 2021, with LEAD defined by a low (

CONCLUSION

This meta-analysis highlights a currently high prevalence of LEAD worldwide, with substantial differences in global regions and between sexes. The strongest associations were found with metabolic risk factors.

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Key Words

• Epidemiology
• General population
• Lower extremity artery disease
• Meta-analysis
• Prevalence
• Risk factors

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MESH Headings

• Humans
• Peripheral Arterial Disease/epidemiology
• Peripheral Arterial Disease/diagnosis
• Prevalence
• Risk Factors
• Global Health
• Lower Extremity/blood supply
• Risk Assessment
• Male
• Female
• Middle Aged
• Aged

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Grants

• Association limousine de Recherche et Développement en Cardiologie Interventionelle

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Affiliation(s)

Caroline Adou EpiMaCT Research Unit U1094 Inserm U270 IRD EpiMaCT, School of Medicine, Limoges University, 2ave Marcland, 87025 Limoges, France
Julien Magne EpiMaCT Research Unit U1094 Inserm U270 IRD EpiMaCT, School of Medicine, Limoges University, 2ave Marcland, 87025 Limoges, France
Nana Gazere EpiMaCT Research Unit U1094 Inserm U270 IRD EpiMaCT, School of Medicine, Limoges University, 2ave Marcland, 87025 Limoges, France
Maissa Aouida EpiMaCT Research Unit U1094 Inserm U270 IRD EpiMaCT, School of Medicine, Limoges University, 2ave Marcland, 87025 Limoges, France
Lucie Chastaingt EpiMaCT Research Unit U1094 Inserm U270 IRD EpiMaCT, School of Medicine, Limoges University, 2ave Marcland, 87025 Limoges, France Department of Vascular Surgery and Vascular Medicine, Dupuytren University Hospital, Limoges, France
Victor Aboyans EpiMaCT Research Unit U1094 Inserm U270 IRD EpiMaCT, School of Medicine, Limoges University, 2ave Marcland, 87025 Limoges, France Department of Cardiology, Dupuytren University Hospital, 16 rue Bernard Descottes, 87042 Limoges, France

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Revolutionizing Diabetic Foot Ulcer Care: The Senotherapeutic Approach

Diabetic foot ulcers (DFUs) are a prevalent and profoundly debilitating complication that afflicts individuals with diabetes mellitus (DM). These ulcers are associated with substantial morbidity, recurrence rates, disability, and mortality, imposing substantial economic, psychological, and medical burdens. Timely detection and intervention can mitigate the morbidity and disparities linked to DFU. Nevertheless, current therapeutic approaches for DFU continue to grapple with multifaceted limitations. A growing body of evidence emphasizes the crucial role of cellular senescence in the pathogenesis of chronic wounds. Interventions that try to delay cellular senescence, eliminate senescent cells (SnCs), or suppress the senescence-associated secretory phenotype (SASP) have shown promise for helping chronic wounds to heal. In this context, targeting cellular senescence emerges as a novel therapeutic strategy for DFU. In this comprehensive review, we look at the pathology and treatment of DFU in a systematic way. We also explain the growing importance of investigating SnCs in DFU and highlight the great potential of senotherapeutics that target SnCs in DFU treatment. The development of efficacious and safe senotherapeutics represents a pioneering therapeutic approach aimed at enhancing the quality of life for individuals affected by DFU.

Risk factors for peripheral artery disease and diabetic peripheral neuropathy among patients with type 2 diabetes

AIMS

To investigate the prevalence of peripheral artery disease (PAD) and diabetic peripheral neuropathy (DPN) and the associated risk factors among Chinese patients with type 2 diabetes mellitus.

METHODS

A cross-sectional study was conducted using data between November 1, 2018, and December 31, 2022. PAD was defined as ABI ≤ 0.9. DPN diagnosis involved specialized physician assessments using questionnaires and vibration perception threshold tests. Logistic regression analysis was used to identify related factors. We also evaluated the association between the clustering of risk factors and disease incidence.

RESULTS

The study population comprised 13,315 patients (mean age: 63.3 years). 4.9 % of the patients had PAD and 43.9 % had DPN. Multivariate regression analysis revealed advanced age, smoking, hypertension, coronary heart disease, dyslipidemia, elevated HbA1c, and uric acid levels as independent risk factors for PAD. For DPN, independent risk factors included advanced age, female gender, hypertension, coronary heart disease, elevated total cholesterol, triglycerides, lipoprotein(a), fasting plasma glucose, HbA1c, alkaline phosphatase, cystatin C, albumin-to-creatinine ratio, and elevated homocysteine levels, whereas apolipoprotein A was a protective factor. The clustering of risk factors was prevalent and associated with higher disease risk.

CONCLUSIONS

Our study contributed to identifying high-risk individuals and improving lower limb health among diabetic individuals.

Primary aldosteronism and lower-extremity arterial disease: a two-sample Mendelian randomization study

BACKGROUND AND AIMS

Primary aldosteronism (PA) is an adrenal disorder of autonomous aldosterone secretion which promotes arterial injury. We aimed to explore whether PA is causally associated with lower-extremity arterial disease (LEAD).

METHODS

We included 39,713 patients with diabetes and 419,312 participants without diabetes from UK Biobank. We derived a polygenic risk score (PRS) for PA based on previous genome-wide association studies (GWAS). Outcomes included LEAD and LEAD related gangrene or amputation. We conducted a two-sample Mendelian randomization analysis for PA and outcomes to explore their potential causal relationship.

RESULTS

In whole population, individuals with a higher PA PRS had an increased risk of LEAD. Among patients with diabetes, compared to the subjects in the first tertile of PA PRS, subjects in the third tertile showed a 1.24-fold higher risk of LEAD (OR 1.24, 95% CI 1.03-1.49) and a 2.09-fold higher risk of gangrene (OR 2.09, 95% CI 1.27-3.44), and 1.72-fold higher risk of amputation (OR 1.72, 95% CI 1.10-2.67). Among subjects without diabetes, there was no significant association between PA PRS and LEAD, gangrene or amputation. Two-sample Mendelian randomization analysis indicated that genetically predictors of PA was significantly associated with higher risks of LEAD and gangrene (inverse variance weighted OR 1.20 [95% CI 1.08-1.34]) for LEAD, 1.48 [95% CI 1.28-1.70] for gangrene), with no evidence of significant heterogeneity or directional pleiotropy.

CONCLUSIONS

Primary aldosteronism is genetically and causally associated with higher risks of LEAD and gangrene, especially among patients with diabetes. Targeting on the autonomous aldosterone secretion may prevent LEAD progression.

Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell's concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744-0.792) and 0.745 (95% CI, 0.669-0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30-54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.

Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling

BACKGROUND

This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with concomitant peripheral artery disease and diabetes mellitus.

METHODS

Cohort study using fee-for-service Medicare beneficiaries in 2015 who were newly diagnosed with peripheral artery disease and diabetes mellitus. Exposure variables include whether patients received preventive measures in the 6 months following their index date: HbA1c test, foot exam, or vascular imaging study. Outcomes include any reintervention, lower extremity amputation, and death. We fit both logistic regression models as well as random forest models.

RESULTS

There were 88,898 fee-for-service Medicare beneficiaries diagnosed with peripheral artery disease and diabetes mellitus in our cohort. The rate of preventative treatments in the first six months following diagnosis were 52% (n = 45,971) with foot exams, 43% (n = 38,393) had vascular imaging, and 50% (n = 44,181) had an HbA1c test. The directionality of the influence for all covariates considered matched those results found with the random forest and logistic regression models. The most predictive covariate in each approach differs as determined by the t-statistics from logistic regression and variable importance (VI) in the random forest model. For amputation we see age 85 + (t = 53.17) urban-residing (VI = 83.42), and for death (t = 65.84, VI = 88.76) and reintervention (t = 34.40, VI = 81.22) both models indicate age is most predictive.

CONCLUSIONS

The use of random forest models to analyze data and provide predictions for patients holds great potential in identifying modifiable patient-level and health-system factors and cohorts for increased surveillance and intervention to improve outcomes for patients. Random forests are incredibly high performing models with difficult interpretation most ideally suited for times when accurate prediction is most desirable and can be used in tandem with more common approaches to provide a more thorough analysis of observational data.

Evaluation of Short-Term Insulin Pump for Treatment of Patients with Type 2 Diabetes Mellitus Complicated with Lower Extremity Arterial Disease in Endocrinology by Ultrasonography

This research aimed to explore the curative effect of short-term insulin pump in the treatment of type 2 diabetes mellitus (T2DM) patients with lower extremity arterial disease (LEAD) based on ultrasonography. 422 patients (220 males and 202 females) with T2DM in the hospital were selected, and they were randomly divided into control group (n = 211, oral hypoglycemic drugs or diet control, appropriate exercise to lower blood glucose) and experimental group (n = 211, insulin pump was used to reduce blood glucose). After 2 weeks, the therapeutic effect was evaluated by ultrasonography. The results showed that after two weeks of treatment, the difference in lumen intima between the two groups was statistically significant (P < 0.05). The intima-media thickness (IMT) values of the experimental group were 0.83 ± 0.03 mm, 0.62 ± 0.03 mm, and 0.41 ± 0.04 mm, respectively, which were significantly different from those of the control group (1.62 ± 0.54 mm, 1.23 ± 0.14 mm, and 0.78 ± 0.11 mm) (P < 0.05). There was obvious difference in low-density lipoprotein cholesterol (LDL-C) level between the experimental group (2.22 ± 0.46 mmol/L) and the control group (3.21 ± 0.62 mmol/L) (P < 0.05). The LEAD score of the experimental group was 5.51 ± 1.11, which was significantly different from that of the control group (7.08 ± 2.73) (P < 0.05). There was clear difference in LEAD score between the two groups under different course of disease (CD) (P < 0.05). Studies indicated that short-term application of insulin pump therapy could effectively improve the pathological changes of lower limbs in patients with T2DM, which had clinical application value.

Development and validation of a nomogram to predict the risk of peripheral artery disease in patients with type 2 diabetes mellitus

OBJECTIVE

To develop and validate a nomogram for predicting the risk of peripheral artery disease (PAD) in patients with type 2 diabetes mellitus (T2DM) and assess its clinical application value.

METHODS

Clinical data were retrospectively collected from 474 patients with T2DM at the Air Force Medical Center between January 2019 and April 2022. The patients were divided into training and validation sets using the random number table method in a ratio of 7:3. Multivariate logistic regression analysis was performed to identify the independent risk factors for PAD in patients with T2DM. A nomogram prediction model was developed based on the independent risk factors. The predictive efficacy of the prediction model was evaluated using the consistency index (C-index), area under the curve (AUC), receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (HL) test, and calibration curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the prediction model's performance during clinical application.

RESULTS

Age, disease duration, blood urea nitrogen (BUN), and hemoglobin (P<0.05) were observed as independent risk factors for PAD in patients with T2DM. The C-index and the AUC were 0.765 (95% CI: 0.711-0.819) and 0.716 (95% CI: 0.619-0.813) for the training and validation sets, respectively, indicating that the model had good discriminatory power. The calibration curves showed good agreement between the predicted and actual probabilities for both the training and validation sets. In addition, the P-values of the HL test for the training and validation sets were 0.205 and 0.414, respectively, indicating that the model was well-calibrated. Finally, the DCA curve indicated that the model had good clinical utility.

CONCLUSION

A simple nomogram based on three independent factors-duration of diabetes, BUN, and hemoglobin levels-may help clinicians predict the risk of developing PAD in patients with T2DM.

Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China

OBJECTIVE

Aiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset.

DESIGN

Retrospective study based on a large sample and a high dimensional database.

SETTING

A Chinese central tertiary hospital in Beijing.

PARTICIPANTS

Information on 32 452 inpatients with type-2 diabetes mellitus (T2DM) were retrieved from the electronic medical record system from 1 January 2013 to 31 December 2017.

METHODS

Sixty variables (including demography information, physical and laboratory measurements, system diseases and insulin treatments) were retained for baseline analysis. The optimal 17 variables were selected by recursive feature elimination. The prediction model was built based on XGBoost algorithm, and it was compared with three other popular machine learning techniques: logistic regression, random forest and support vector machine. In order to explain the results of XGBoost model more visually, the Shapley Additive exPlanation (SHAP) method was used.

RESULTS

DR occurred in 2038 (6.28%) T2DM patients. The XGBoost model was identified as the best prediction model with the highest AUC (area under the curve value, 0.90) and showed that an HbA1c value greater than 8%, nephropathy, a serum creatinine value greater than 100 µmol/L, insulin treatment and diabetic lower extremity arterial disease were associated with an increased risk of DR. A patient's age over 65 was associated with a decreased risk of DR.

CONCLUSIONS

With better comprehensive performance, XGBoost model had high reliability to assess risk indicators of DR. The most critical risk factors of DR and the cut-off of risk factors can be found by SHAP method to render the output of the XGBoost model clinically interpretable.

Association of Lower Extremity Vascular Disease, Coronary Artery, and Carotid Artery Atherosclerosis in Patients with Type 2 Diabetes Mellitus

The motive of this article is to present the case study of patients to investigate the association between the ultrasonographic findings of lower extremity vascular disease (LEAD) and plaque formation. Secondly, to examine the association between the formation of coronary artery and carotid artery atherosclerosis in patients with type 2 diabetes mellitus. 124 patients with type 2 diabetes (64 males and 60 females with the age group 25-78 years) are considered for the research studies who have registered themselves in the Department of Endocrinology and Metabolism from April 2017 to February 2019. All participants have reported their clinical information regarding diabetes, alcohol consumption, smoking status, and medication. The blood samples from subjects are collected for measurement of HbA_1c, total cholesterol, triglycerides, HDL-c, and LDL-c levels. Two-dimensional ultrasound has been used to measure the inner diameter, peak flow velocity, blood flow, and spectral width of the femoral artery, pop artery, anterior iliac artery, posterior tibial artery, and dorsal artery and to calculate the artery stenosis degree. Independent factors of atherosclerosis are determined by multivariate logistic regression analysis. The results are evaluated within the control group and it is found that there is no significant impact of gender, age, and body mass index (P > 0.05) on the lower extremity vascular diseases. Those with smoking, alcohol consumption, hypertension, and dyslipidemia have higher positive rate (P < 0.05). The type 2 diabetes mellitus group has higher diastolic blood pressure and lower triglyceride (P < 0.05). Diastolic blood pressure, HbA_1C, total cholesterol, HDL-c, and LDL-C are not remarkably dissimilar between the type 2 diabetes mellitus group and the control group (P > 0.05). Compared with the control group, the type 2 diabetes mellitus group has higher frequency of lower extremity vascular diseases in the dorsal artery than in the pop artery (P < 0.05). The blood flow of type 2 diabetes mellitus group is found to be lower than that of the control group, especially in the dorsal artery (P < 0.05). The blood flow velocity of the dorsal artery is accelerated (P < 0.01). Among 117 patients of type 2 diabetes mellitus (94.35%) with a certain degree of injury, there are 72 cases of type I carotid stenosis (58.06%), 30 cases of type II carotid stenosis (24.19%), and 15 cases of type III carotid stenosis (12.10%). Out of 108 subjects in the control group, there are 84 cases of type 0 carotid stenosis (77.78%), 19 cases of type I carotid stenosis (17.59%), 5 cases of type II carotid stenosis (4.63%), and 0 case of type III carotid stenosis (0.00%). Compared with the control group, carotid stenosis is more common in patients with type 2 diabetes mellitus (P < 0.05). Age, smoking, duration of diseases, systolic blood pressure, and degree of carotid stenosis are found to be associated with atherosclerosis. The findings suggest that the color Doppler ultrasonography can give early warning when applied in patients with carotid and lower extremity vascular diseases to delay the incidence of diabetic macroangiopathy and to control the development of cerebral infarction, thus providing an important basis for clinical diagnosis and treatment.

The early diagnostic value of ankle-brachial index combined with feet electrochemical skin conductance for peripheral artery disease in type 2 diabetes

Aims/Introduction

In this paper, we focused on exploring the diagnostic and predictive clinical utility of ankle‐brachial index (ABI) in combination with feet electrochemical skin conductance (FESC) for peripheral artery disease (PAD) in Chinese patients with type 2 diabetes mellitus (T2DM).

Materials and Methods

Overall, 183 Chinese T2DM patients were enrolled in this study. The patients were classified into three groups: Group 1 comprised of uncomplicated type 2 diabetics (n = 36), Group 2 consisted of patients with diabetic peripheral neuropathy (n = 103) whereas Group 3 patients displayed peripheral artery disease (n = 44). All patients underwent Sudoscan test using a Sudoscan (Paris, France) and ABI assessment.

Results

Multivariate logistic regression models revealed that FESC was an independent risk factor of developing PAD in patients with type 2 diabetes. The AUC for diagnostic, positive predictive and negative predictive value of ABI in combination with FESC for PAD were 0.907, 0.733 and 0.920, respectively. The specificity and sensitivity of ABI in combination with FESC for PAD were 0.914 and 0.750, respectively.

Conclusions

Ankle‐brachial index in combination with FESC can accurately be used in early diagnosis of PAD.

Traditional and non-traditional risk factors for peripheral artery disease development/progression in patients with type 2 diabetes: the Rio de Janeiro type 2 diabetes cohort study

BACKGROUND

The prognostic importance of non-traditional risk factors for peripheral artery disease (PAD) development/progression is scarcely studied in diabetes. We investigated if carotid intima-media thickness (CIMT) and carotid-femoral pulse wave velocity (cf-PWV) added prognostic information beyond traditional cardiovascular risk markers for PAD outcomes.

METHODS

Ankle-brachial index (ABI) was measured at baseline and after a median of 91 months of follow-up in 681 individuals with type 2 diabetes. Multivariate Cox regressions examined the associations between the candidate variables and the outcome. PAD development/progression was defined by a reduction in ABI ≥ 0.15 (to a level < 0.9) or limb revascularization procedures, lower-extremity amputations or death due to PAD. The improvement in risk discrimination was assessed by increases in C-statistics of the models.

RESULTS

Seventy-seven patients developed/progressed PAD: 50 reduced ABI to < 0.9, seven had lower-limb revascularizations, and 20 had amputations or death. Age, male sex, diabetes duration, presence of microvascular complications (peripheral neuropathy and diabetic kidney disease), baseline HbA_1c, 24-h systolic BP (SBP) and mean cumulative office SBP and LDL-cholesterol were associated with PAD development/progression in several models. CIMT and cf-PWV were additionally associated with PAD outcomes, and their inclusion further improved risk discrimination (with C-statistic increases between 0.025 and 0.030). The inclusion of ambulatory 24-h SBP, instead of office SBP, also improved PAD risk discrimination.

CONCLUSIONS

Increased CIMT and aortic stiffness are associated with greater risks of developing/progressing PAD, beyond traditional risk factors, in type 2 diabetes.

Deep learning-based classification of lower extremity arterial stenosis in computed tomography angiography

PURPOSE

The purpose of this study is to develop and evaluate a deep learning model to assist radiologists in classifying lower extremity arteries based on the degree of arterial stenosis caused by plaque in lower extremity computed tomography angiography (CTA) of patients with peripheral artery disease.

METHODS

In this retrospective study, 265 patients who underwent lower-extremity CTA between January 1, 2016 and October 31, 2019 were selected. A total of 17050 axial images of iliac, femoropopliteal and infrapopliteal artery from these patients were used for the training and validation of the parallel efficient network (p-EffNet), a kind of supervised convolutional neural network, to classify the lower-extremity artery segments according to the degree of stenosis with digital subtraction angiography as reference standard. The classification results of the p-EffNet were then compared with those obtained from radiologists. Receiver operating characteristic curve (ROC) was used to evaluate the performance of the p-EffNet and accuracy, specificity, sensitivity and area under the curve (AUC) were used as measure metrics to compare the performance of the p-EffNet and that of radiologists.

RESULTS

The p-EffNet exhibited a good performance of 91.5 % accuracy, 0.987 AUC and 90.2 % sensitivity and 97.7 % specificity in classifying above-knee artery and 90.9 % accuracy, 0.981 AUC, 91.3 % sensitivity and 95.2 % specificity in classifying below-knee artery. When compared with human readers, for both above-knee and below-knee artery, the p-EffNet had comparable accuracy (p = 0.266 and p = 0.808, respectively) and specificity (p = 0.118 and p = 0.971, respectively) but lower sensitivity (p < 0.001 and p = 0.022, respectively).

CONCLUSIONS

The p-EffNet demonstrates promising diagnostic performance and has the potential to reduce the workload of radiologists and help to find the plaques that might otherwise have been missed or misjudged.