Skin characterization of diabetes mellitus revealed by polarization-sensitive optical coherence tomography imaging.

Significance: Diabetes can lead to the glycation of proteins and dysfunction of skin collagen. Skin lesions are a prevalent clinical symptom of diabetes mellitus (DM). Early diagnosis and assessing the efficacy of treatment for DM are crucial for patient health management. However, performing a non-invasive skin assessment in the early stages of DM is challenging. Aim: By using the polarization-sensitive optical coherent tomography (PS-OCT) imaging technique, it is possible to noninvasively assess the skin changes caused by diabetes. Approach: The PS-OCT was used to monitor the polarization characteristics of mouse skin at different stages of diabetes. Results: Based on a multi-layered adhesive tape model, we found that the polarization characteristics (retardation, optic axis, and polarization uniformity) were sensitive to the microstructure changes in the samples. Through this method, we observed significant changes in the polarization states of the skin as diabetes progressed. This was in line with the detected microstructure changes in skin collagen fibers using scanning electron microscopy. Conclusions: This study presents a highly useful approach for non-invasive skin assessment of diabetes.

Universal Fasting Glucose Screening Before Gastric Emptying Scintigraphy and the High Prevalence of Undiagnosed Diabetes and Prediabetes.

The aim of this study was to assess the rates of undiagnosed diabetes mellitus (DM) and pre-DM in patients undergoing gastric emptying scintigraphy (GES). Diabetes is an epidemic in the United States, and the disease is associated with altered gut motility. As a result, we suspected that a significant number of patients referred for GES may have undiagnosed DM or pre-DM. Given that established procedure standards for GES require all patients to prepare with an 8-h fast, an opportunity is provided to measure the fasting blood glucose (FBG) in all individuals before they undergo the examination. Methods: The charts of patients undergoing GES were reviewed for a history of DM and correlated with FBG and GES results. FBG values, obtained by point-of-care testing, were categorized as normal, pre-DM, or DM. Results: Patients with known DM made up 23% of those referred for GES, and most (55%) had a normal FBG. In those without a history of DM, there were a significant number with undiagnosed pre-DM (12%) and DM (33%). Conclusion: Our study provides the first measure of the likely prevalence of undiagnosed DM and pre-DM and characterizes the different gastric emptying patterns among patients with normal FBG, likely undiagnosed pre-DM, likely undiagnosed DM, and known DM.

Chest CT Radiomics is Feasible in Evaluating Muscle Change in Diabetes Patients.

BACKGROUND: Non-invasive imaging methods are still lacking for the evaluation of muscle changes in diabetes. PURPOSE: To investigate the feasibility of muscle CT radiomics in evaluating muscle changes in diabetes. MATERIALS AND METHODS: 60 diabetics and 60 health controls (HC) were assessed with the method of muscle CT radiomics. 93 CT images of radiomics features of the pectoralis major muscle (PMM) were obtained by using the software 3D Slicer and were then compared between diabetics and HC cases. The least absolute shrinkage and selection operator (LASSO) regression method was used to establish a prediction model. The receiver operating characteristic (ROC) curve was used to determine the performance of the model. RESULTS: Diabetics and HC cases differed in 19 radiomics features (P<0.05). By using the LASSO method, 6 features were finally selected. The AUC of the model in the discrimination of diabetics and HC were 0.92 and 0.90, respectively, for the training cohort and validation cohort. CONCLUSION: Muscle CT radiomics is feasible in evaluating muscle changes in diabetes.

Toward Ultrasound Molecular Imaging of Endothelial Dysfunction in Diabetes: Targets, Strategies, and Challenges.

Diabetes vasculopathy is a significant complication of diabetes mellitus (DM), and early identification and timely intervention can effectively slow the progression. Accumulating studies have shown that diabetes causes vascular complications directly or indirectly through a variety of mechanisms. Direct imaging of the endothelial molecular changes not only identifies the early stage of diabetes vasculopathy but also sheds light on the precise treatment. Targeted ultrasound contrast agent (UCA)-based ultrasound molecular imaging (UMI) can noninvasively detect the expression status of molecular biomarkers overexpressed in the vasculature, thereby being a potential strategy for the diagnosis and treatment response evaluation of DM. Amounts of efforts have been focused on identification of the molecular targets expressed in the vasculature, manufacturing strategies of the targeted UCA, and the clinical translation for the diagnosis and evaluation of therapeutic efficacy in both micro- and macrovasculopathy in DM. This review summarizes the latest research progress on endothelium-targeted UCA and discusses their promising future and challenges in diabetes vasculopathy theranostics.

Mechanistic analysis of viscosity-sensitive fluorescent probes for applications in diabetes detection.

Diabetes is one of the most detrimental diseases affecting the human life because it can initiate several other afflictions such as liver damage, kidney malfunctioning, and cardiac inflammation. The primary method for diabetes diagnosis involves the analysis of blood samples to quantify the level of glucose, while secondary diagnostic methods involve the qualitative analysis of obesity, fatigue, etc. However, all these symptoms start showing up only when the patient has been suffering from diabetes for a certain period of time. In order to avoid such delay in diagnosis, the development of specific fluorescent probes has attracted considerable attention. Prominent biomarkers for diabetes include abundance of certain analytes in blood serum, e.g., glucose, methylglyoxal, albumin, and reactive oxygen species; high intracellular viscosity; alteration of enzyme functionality, etc. Among these, high viscosity can greatly affect the fluorescence properties of various chromophores owing to the environment sensitivity of fluorescence spectra. In this review article, we have illustrated the application of some prominent fluorophores such as coumarin, BODIPY, xanthene, and rhodamine in the development of viscosity-dependent fluorescent probes. Detailed mechanistic aspects determining the influence of viscosity on the fluorescent properties of the probes have also been elaborated. Fluorescence mechanisms that are directly affected by the high-viscosity heterogeneous microenvironment are based on intramolecular rotations like twisted intramolecular charge transfer (TICT), aggregation-induced emission (AIE), and through-bond energy transfer (TBET). In this regard, this review article will be highly useful for researchers working in the field of diabetes treatment and fluorescent probes. It also provides a platform for the planning of futuristic clinical translation of fluorescent probes for the early-stage diagnosis and therapy of diabetes.

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Analysis of microvascular pattern in diabetes mellitus condition using the nailfold capillaroscopy images.

Diabetes is often considered a vascular disease due to its impact on blood vessels, it is a complex condition with various metabolic and autoimmune factors involved. One of the long term comorbidities of diabetes includes microvascular complications. The microvascular complications can be analyzed using the Nailfold capillaroscopy, a non-invasive technique that allows for the visualization and analysis of capillaries in the proximal nailfold area. Using advanced video capillaroscopy with high magnification, capillary images can be captured from and processed to analyze their morphology. The capillary images of normal group and diabetic group are acquired from 118 participants using nailfold capillaroscopy and the obtained images are preprocessed using image processing filters. The identification and segmentation of the capillaries are the challenges to be addressed in the processing of the images. Hence segmentation of capillaries is done using morphological operations, thresholding and convolutional neural networks. The performance of the filters and segmentation methods are evaluated using Mean Square Error (MSE), Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Jaccard Index and Sorensen coefficient. By analyzing the morphological features namely the capillary diameter, density, distribution, presence of hemorrhage and the shape of the capillaries from both the groups, the capillary changes associated with diabetic condition were studied. It was found that the non diabetic participants considered in this study has capillary diameter in the range of 8-14 µm and the capillary density in the range of 10-30 capillaries per mm2 whereas the diabetic participants has capillary diameter greater than 30 µm and the capillary density is less than 10 capillaries per mm2. In addition to capillary density and diameter, the presence of hemorrhage, the orientation and distribution of the capillaries are also considered to differentiate the diabetic group from the non diabetic group. The classification of the participants are validated with the clinical history of the participants.

Noninvasive detection of diabetes mellitus based on skin fluorescence and diffuse reflectance spectroscopy.

There is an urgent need for a mass population screening tool for diabetes. Skin tissue contains a large number of endogenous fluorophores and physiological parameter markers related to diabetes. We built an excitation-emission spectrum measurement system with the excited light sources of 365, 395, 415, 430, and 455 nm to extract skin characteristics. The modeling experiment was carried out to design and verify the accuracy of the recovery of tissue intrinsic discrete three-dimensional fluorescence spectrum. Blood oxygen modeling experiment results indicated the accuracy of the physiological parameter extraction algorithm based on the diffuse reflectance spectrum. A community population cohort study was carried out. The tissue-reduced scattering coefficient and scattering power of the diabetes were significantly higher than normal control groups. The Gaussian multi-peak fitting was performed on each excitation-emission spectrum of the subject. A total of 63 fluorescence features containing information such as Gaussian spectral curve intensity, central wavelength position, and variance were obtained from each person. Logistic regression was used to construct the diabetes screening model. The results showed that the area under the receiver operating characteristic curve of the model for predicting diabetes was 0.816, indicating a high diagnostic value. As a rapid and non-invasive detection method, it is expected to have high clinical value.

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group.

Non-Invasive Retinal Blood Vessel Wall Measurements with Polarization-Sensitive Optical Coherence Tomography for Diabetes Assessment: A Quantitative Study.

Diabetes affects the structure of the blood vessel walls. Since the blood vessel walls are made of birefringent organized tissue, any change or damage to this organization can be evaluated using polarization-sensitive optical coherence tomography (PS-OCT). In this paper, we used PS-OCT along with the blood vessel wall birefringence index (BBI = thickness/birefringence2) to non-invasively assess the structural integrity of the human retinal blood vessel walls in patients with diabetes and compared the results to those of healthy subjects. PS-OCT measurements revealed that blood vessel walls of diabetic patients exhibit a much higher birefringence while having the same wall thickness and therefore lower BBI values. Applying BBI to diagnose diabetes demonstrated high accuracy (93%), sensitivity (93%) and specificity (93%). PS-OCT measurements can quantify small changes in the polarization properties of retinal vessel walls associated with diabetes, which provides researchers with a new imaging tool to determine the effects of exercise, medication, and alternative diets on the development of diabetes.

The Role of Echocardiography in Evaluating Cardiovascular Diseases in Patients with Diabetes Mellitus.

Patients with diabetes mellitus are highly susceptible to cardiovascular complications, which are directly correlated with cardiovascular morbidity and mortality. In addition to coronary artery disease, there is growing awareness of the risk and prevalence of heart failure (HF) in patients with diabetes. Echocardiography is an essential diagnostic modality commonly performed in patients with symptoms suggestive of cardiovascular diseases (CVD), such as dyspnea or chest pain, to establish or rule out the cause of symptoms. Conventional echocardiographic parameters, such as left ventricular ejection fraction, are helpful not only for diagnosing CVD but also for determining severity, treatment strategy, prognosis, and response to treatment. Echocardiographic myocardial strain, a novel echocardiographic technique, enables the detection of early changes in ventricular dysfunction before HF symptoms develop. This article aims to review the role of echocardiography in evaluating CVD in patients with diabetes mellitus and how to use it in patients with suspected cardiac diseases.

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.

Significance: Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finger-prick methods. Aim: We aim to report a noncontact speckle-based blood glucose measurement system that utilizes artificial intelligence (AI) data processing to improve glucose detection accuracy. The study also explores the influence of an alternating current (AC) induced magnetic field on the sensitivity and selectivity of blood glucose detection. Approach: The proposed blood glucose sensor consists of a digital camera, an AC-generated magnetic field source, a laser illuminating the subject's finger, and a computer. A magnetic field is applied to the finger, and a camera records the speckle patterns generated by the laser light reflected from the finger. The acquired video data are preprocessed for machine learning (ML) and deep neural networks (DNNs) to classify blood plasma glucose levels. The standard finger-prick method is used as a reference for blood glucose level classification. Results: The study found that the noncontact speckle-based blood glucose measurement system with AI data processing allows for the detection of blood plasma glucose levels with high accuracy. The ML approach gives better results than the tested DNNs as the proposed data preprocessing is highly selective and efficient. Conclusions: The proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.

The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.

Artificial intelligence (AI)-based diagnostic technology using medical images can be used to increase examination accessibility and support clinical decision-making for screening and diagnosis. To determine a machine learning algorithm for diabetes complications, a literature review of studies using medical image-based AI technology was conducted using the National Library of Medicine PubMed, and the Excerpta Medica databases. Lists of studies using diabetes diagnostic images and AI as keywords were combined. In total, 227 appropriate studies were selected. Diabetic retinopathy studies using the AI model were the most frequent (85.0%, 193/227 cases), followed by diabetic foot (7.9%, 18/227 cases) and diabetic neuropathy (2.7%, 6/227 cases). The studies used open datasets (42.3%, 96/227 cases) or directly constructed data from fundoscopy or optical coherence tomography (57.7%, 131/227 cases). Major limitations in AI-based detection of diabetes complications using medical images were the lack of datasets (36.1%, 82/227 cases) and severity misclassification (26.4%, 60/227 cases). Although it remains difficult to use and fully trust AI-based imaging analysis technology clinically, it reduces clinicians' time and labor, and the expectations from its decision-support roles are high. Various data collection and synthesis data technology developments according to the disease severity are required to solve data imbalance.

Characterization of left ventricular diastolic parameters of gated-single-photon emission computed tomography myocardial perfusion imaging in patients with diabetes and normal myocardial perfusion and systolic function.

BACKGROUND: Diabetic cardiomyopathy is defined as an independent entity with a specified pathological progression from diastolic dysfunction with preserved ejection fraction to overt heart failure. Myocardial perfusion imaging (MPI) with gated-single-photon emission computed tomography (G-SPECT) has been introduced as a feasible tool to evaluate left ventricular (LV) diastolic function. The aim of this study was to investigate the characteristics of diastolic parameters derived from G-SPECT MPI in diabetic patients compared to patients at very low risk of coronary artery disease (CAD) and with no other CAD risk factors. METHODS: This cross-sectional study was performed on patients referred to the nuclear medicine department for G-SPECT MPI. Demographic and clinical data, as well as medical history, were extracted from a digital registry system including 4447 patients. Then, two matched groups of patients with only diabetes as cardiac risk factor ( n = 126) and those without any identifiable CAD risk factors ( n = 126) were selected. Diastolic parameters of MPI, including peak filling rate, time to peak filling rate, mean filling rate at the first third of diastole and second peak filling rate, were derived using quantitative software for eligible cases. RESULTS: The mean age of the diabetic and nondiabetic groups was 57.1 ± 14.9 and 56.7 ± 10.6 years, respectively ( P = 0.823). Comparison of quantitative SPECT MPI parameters between the two groups showed a statistically significant difference only in total perfusion deficit scores, whereas none of the functional parameters, including diastolic and dyssynchrony indices and the shape index, were significantly different. There were also no significant differences in diastolic function parameters between diabetes and nondiabetes patients in the age and gender subgroups. CONCLUSION: Based on the G-SPECT MPI findings, there is a comparable prevalence of diastolic dysfunction in patients with only diabetes as a cardiovascular risk factor and low-risk patients with no cardiovascular risk factors in the setting of normal myocardial perfusion and systolic function.

CT imaging shows specific pancreatic abnormalities in persons with cystic fibrosis related diabetes.

Cystic fibrosis related diabetes (CFRD) is observed in 20-50% of adults with cystic fibrosis (CF). Pancreas abnormalities on imaging, e.g. pancreas lipomatosis, are frequent in subjects with CF. We hypothesized that specific abnormalities may characterize patients with CFRD. We performed a retrospective study comparing the computed tomography (CT) of participants with CF with or without diabetes ("CFRD" and "CF control" groups). We classified the pancreas on imaging according to 3 categories: normal, partial lipomatosis and complete lipomatosis of the pancreas. We also assessed the presence or absence of pancreatic calcifications. Forty-one CFRD and 53 CF control participants were included. Only 2% of the patients with CFRD had a normal pancreas, as compared with 30% of the participants from the CF control group (p = 0.0016). Lipomatosis was more frequent in subjects with CFRD and was related to exocrine pancreatic insufficiency (EPI) and to severe CFTR mutations (classes I to III). Nine participants with diabetes (22%) presented with pancreatic calcifications, versus none of the control participants (p = 0.0003). In conclusion, pancreas imaging was almost always abnormal in subjects with CFRD, while it was normal in a third of the CF control subjects. Pancreatic calcifications were specific of subjects with CFRD.

Macular optical coherence tomography angiography analysis in diabetes mellitus patients with a history of Covid-19.

PURPOSE: There is evidence of decreased vessel density in optical coherence tomography angiography (OCTA) after Covid-19. We aimed to investigate whether the outcome of retinal vasculopathy would be worse if patients with diabetes mellitus (DM) were infected with coronavirus using OCTA to assess retinal vessels. METHODS: One eye of each subject was included in the study. Diabetic patients without retinopathy and non-diabetic controls were divided into four groups according to their Covid-19 history: group 1=DM(-)Covid-19(-); group 2=DM(+)Covid-19(-); group 3=DM(-)Covid-19(+); and group 4=DM(+)Covid-19(+). All Covid-19 patients were not hospitalised. Macular OCTA scans were performed in a 6 × 6 mm area. RESULTS: Diabetes had no effect on the area of the foveal avascular zone (FAZ), but Covid-19 caused an increase in FAZ area. Diabetes and Covid-19 had an effect on both the superficial capillary plexus (SCP) and the deep capillary plexus (DCP) in the fovea. Eta squared (ƞ2) is a measure of effect size. The effect size of Covid-19 (ƞ2=0.180) was found to be greater than that of diabetes (ƞ2=0.158) on the SCP, whereas the effect size of diabetes (ƞ2=0.159) was found to be greater than that of Covid-19 (ƞ2=0.091) on the DCP. CONCLUSIONS: The percentage of vessel density was lower in the fovea and the FAZ area was enlarged in the diabetic patients who recovered from Covid-19. In diabetic patients Covid-19 may lead to deterioration of vascular metrics.

Diffusion-weighted MR imaging and utility of ADC measurements in characterizing nerve and muscle changes in diabetic patients on ankle DWI studies: a cross-sectional study.

OBJECTIVE: To evaluate the utility of apparent diffusion coefficient (ADC) measurements from ankle MRI diffusion-weighted imaging (DWI) studies in identifying neuropathic changes in diabetic patients. METHODS: In total, 109 consecutive ankle MRI scans (n = 101 patients) at a single tertiary care county hospital from November 1, 2019, to July 11, 2021, who met the inclusion criteria were identified. Patients were divided into 2 cohorts: diabetic (n = 62) and non-diabetic (n = 39). Demographics, HgbA1c, neuropathy diagnosis, and image quality data were collected. Abductor hallucis (AH) ADC mean and minimum (min) values and posterior tibial nerve (PTN) ADC mean and minimum values were measured. Student t-test and Pearson's correlation coefficient analysis were performed using R. RESULTS: Diabetic patients had significantly higher mean and min ADC values (× 10-3 mm2/s) of the AH muscle (mean: 1.77 vs 1.39, p < 0.001; min: 1.51 vs 1.06, p < 0.001) and PTN (mean: 1.65 vs 1.18, p < 0.001; min: 1.33 vs 0.95, p < 0.001) compared to non-diabetic patients. HgbA1c positively correlated with AH and PTN ADC mean values (AH: p = 0.036; PTN: p = 0.004). CONCLUSION: Our data suggests that an increasing diffusivity of water as quantified by ADC across neuronal and muscular membranes is a consequence of the pathophysiology of the disease. Thus, ankle MRI-DWI studies are useful in identifying neuropathic changes in diabetic patients and quantifying the severity noninvasively. KEY POINTS: • Diabetic patients had significantly higher mean and minimum ADC values of the abductor hallucis muscle and posterior tibial nerve compared to non-diabetic patients. • HgbA1c positively correlated with ADC mean values (AH: p = 0.036; PTN: p = 0.004) suggesting that an increasing diffusivity of water across neuronal and muscular membranes is a consequence of the pathophysiology of diabetic neuropathy. • Ankle MRI DWI can be used clinically to non-invasively identify neuropathic changes due to diabetes mellitus.

Quantitative model for assessment of lower-extremity perfusion in patients with diabetes.

BACKGROUND: Although diabetic and atherosclerotic vascular diseases have different pathophysiological mechanisms, the screening methods currently used for diabetic lower-extremity vascular diseases are mainly based on the evaluation methods used for atherosclerotic vascular diseases. Thus, assessment of microvascular perfusion is of great importance in early detection of lower-extremity ischemia in diabetes. PURPOSE: This cross-sectional study aimed to develop a quantitative model for evaluating lower-extremity perfusion. METHODS: We recruited 57 participants (14 healthy participants and 43 diabetes patients, of which 16 had lower-extremity arterial disease [LEAD]). All participants underwent technetium-99 m sestamibi (99mTc-MIBI) scintigraphy and ankle-brachial index (ABI) examination. We derived two key perfusion kinetics indices named activity perfusion index (API) and basal perfusion index (BPI). This study was registered in ClinicalTrials.gov (URL: https://www. CLINICALTRIALS: gov, NCT02752100). RESULTS: The estimated limb perfusion values in our lower-extremity perfusion assessment (LEPA) model showed excellent consistency with the actual measured data. Diabetes patients showed reduced lower-extremity perfusion in comparison with the control group (BPI: 106.21 ± 11.99 vs. 141.56 ± 17.38, p < 0.05; API: 12.34 ± 3.27 vs. 14.56 ± 3.12, p < 0.05). Using our model, the reductions in lower-extremity perfusion could be detected early in approximately 96.30% of diabetes patients. Patients with LEAD showed more severe reductions in lower-extremity perfusion than diabetes patients without LEAD (BPI: 47.85 ± 20.30 vs. 106.21 ± 11.99, p < 0.05; API: 7.06 ± 1.70 vs. 12.34 ± 3.27, p < 0.05). Discriminant analysis using API and BPI could successfully screen all diabetes patients with LEAD with a sensitivity of 100% and specificity of 80.77%. CONCLUSIONS: We established a LEPA model that could successfully assess lower-extremity microvascular perfusion in diabetes patients. This model has important application value for the recognition of early-stage LEAD in patients with diabetes.

The predictive value of vascular calcification on plain radiographs of the ankle to diagnose diabetes mellitus.

INTRODUCTION: There are nearly 500,000 people with undiagnosed diabetes mellitus in the UK. A common complication of diabetes is vascular calcification. The incidental finding of vascular calcification on plain radiographs in patients with undiagnosed diabetes has the potential to alter patient management. We hypothesised that the presence of vascular calcification on plain radiographs of the foot may predict the diagnosis of diabetes and aimed to determine the positive predictive value of vascular calcification to diagnose diabetes. METHODS: A retrospective case control study compared 130 diabetic patients to 130 non-diabetic patients that were matched for age and gender. The presence of vascular calcification in anterior, posterior or plantar vessels was measured on plain radiographs. McNemar's Chi-squared test and positive predictive values were calculated. Conditional logistic regression models estimated the association between calcification and diabetes RESULTS: The overall mean age was 58.0 % and 31.5 % were females. 89.2 % of those with diabetes had calcification present, and 23.1 % in those who did not have diabetes had calcification. McNemar's test for independence gives p < 0.001. Calcification in both anterior and posterior vessels predicts diabetes with a positive predictive value of 91.2 % (95 % CI 76.9-97.0 %). The odds ratio for having diabetes is 78 (95 % CI: 7.8 - 784) times higher in a person who has calcification in the blood vessels of their ankle than in a person without calcification after adjusting for confounders CONCLUSION: This study has demonstrated that vascular calcification in the anterior and posterior blood vessels is over 90 % predictive of a diagnosis of diabetes. This screening test could be used in future clinics when interpreting radiographs, aiding in the diagnosis of diabetes and altering patient management.

Anatomical and Functional Discrepancy in Diabetic Patients With Intermediate Coronary Lesions　- An Intravascular Ultrasound and Quantitative Flow Ratio Study.

BACKGROUND: Data regarding the performance of computational fractional flow reserve in patients with diabetes mellitus (DM) remain scarce. This study sought to explore the impact of DM on quantitative flow ratio (QFR) and its association with intravascular ultrasound (IVUS)-derived anatomical references.Methods and Results: IVUS and QFR were retrospectively analyzed in 237 non-diabetic and 93 diabetic patients with 250 and 102 intermediate lesions, respectively. Diabetics were further categorized based on adequate (HbA1c <7.0%: 47 patients with 53 lesions) or poor (HbA1c ≥7.0%: 46 patients with 49 lesions) glycemic control. Lesions with QFR ≤0.8 or minimum lumen area (MLA) ≤4.0 mm2and plaque burden (PB, %) ≥70 were considered functionally or anatomically significant, respectively. PB increased, and MLA decreased stepwise across non-diabetics, diabetics with adequate glycemic control and those with poor glycemic control. In contrast, QFR was similar among the 3 groups. PB correlated significantly with the QFR for lesions in non-diabetics, but not for lesions in diabetics. DM was independently correlated with the functionally non-significant lesions (QFR >0.8) with high-risk IVUS features (MLA ≤4.0 mm2and PB ≥70; OR 2.053, 95% CI: 1.137-3.707, P=0.017). When considering the effect of glycemic control, HbA1c was an independent predictor of anatomical-functional discordance (OR 1.347, 95% CI: 1.089-1.667, P=0.006). CONCLUSIONS: Anatomical-functional discordance of intermediate coronary lesions assessed by IVUS and QFR is exacerbated in patients with diabetes, especially when glycemia is poorly controlled.