Detection Of Peripheral Arterial Disease Research Articles

Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events.

We have previously shown that polygenic risk scores (PRS) can improve risk stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential of PRS in improving the detection of PAD and prediction of major adverse cardiovascular and cerebrovascular events (MACCE) and adverse events (AE) in an institutional patient cohort. We created a cohort of 278 patients (52 cases and 226 controls) and fit a PAD-specific PRS based on the weighted sum of risk alleles. We built traditional clinical risk models and machine learning (ML) models using clinical and genetic variables to detect PAD, MACCE, and AE. The models' performances were measured using the area under the curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), and Brier score. We also evaluated the clinical utility of our PAD model using decision curve analysis (DCA). We found a modest, but not statistically significant improvement in the PAD detection model's performance with the inclusion of PRS from 0.902 (95% CI: 0.846-0.957) (clinical variables only) to 0.909 (95% CI: 0.856-0.961) (clinical variables with PRS). The PRS inclusion significantly improved risk re-classification of PAD with an NRI of 0.07 (95% CI: 0.002-0.137), p = 0.04. For our ML model predicting MACCE, the addition of PRS did not significantly improve the AUC, however, NRI analysis demonstrated significant improvement in risk re-classification (p = 2e-05). Decision curve analysis showed higher net benefit of our combined PRS-clinical model across all thresholds of PAD detection. Including PRS to a clinical PAD-risk model was associated with improvement in risk stratification and clinical utility, although we did not see a significant change in AUC. This result underscores the potential clinical utility of incorporating PRS data into clinical risk models for prevalent PAD and the need for use of evaluation metrics that can discern the clinical impact of using new biomarkers in smaller populations.

Detection of Peripheral Arterial Disease Using Ankle Brachial Index and Its Correlation with Glycated Haemoglobin, Low-Density Lipoprotein, High-Density Lipoprotein and Triglycerides Level in Patients with Type-2 Diabetes

Detection of Peripheral Arterial Disease Using Ankle Brachial Index and Its Correlation with Glycated Haemoglobin, Low-Density Lipoprotein, High-Density Lipoprotein and Triglycerides Level in Patients with Type-2 Diabetes

Peripheral Arterial Disease Detection Efforts with Ankle Brachial Index (ABI) Examination

Peripheral Arterial Disease (PAD) is the third leading cause of atheroskeric vascular morbidity in coronary heart disease. The ankle-brachial index (ABI) is one way to assess peripheral arteries. This activity aims to identify people who are at risk or suffer from PAD. The method used is a medical examination, which includes ABI values, blood pressure, cholesterol levels, blood sugar, and uric acid. This medical examination was conducted on 41 people in RT 02 RW 06 Pasar Ambacang, Padang City. The screening results found that 17.07% of respondents had ABI values higher than 1.3 (severely calcified vessel), and 14.62% had ABI values in the range of 0.71-0.9 (mild obstruction). This activity also found respondents with first-stage hypertension (29.26%), second-stage hypertension (2.44%), high cholesterol levels (56.09%), high blood sugar levels (7.31%), and uric acid levels exceeding normal limits (7.31%). Screening efforts can be a preventive measure.

A powerful Peripheral Arterial Disease detection using machine learning-based severity level classification model and hyper parameter optimization methods

This study addresses the critical need for improved detection and assessment of Peripheral Arterial Disease (PAD) using Artificial Intelligence (AI) algorithms, aiming to reduce global mortality rates associated with this condition. The research focuses on enhancing the efficiency of machine learning (ML) models for PAD detection and severity categorization by employing optimization techniques. Notably, this study introduces the application of Synthetic Minority Oversampling Technique (SMOTE) to mitigate imbalanced data challenges in PAD detection. The primary objective of this research is to develop highly efficient ML models utilizing six distinct AI classifiers and Hyper Parameter Optimization (HPO), specifically targeting PAD identification and severity level categorization. Leveraging the Cleveland and Statlog datasets, the study constructs and evaluates these models using all relevant features. The principal findings indicate remarkable advancements in PAD detection accuracy. The proposed models, optimized using SMOTE and Enhanced Decision Tree (EDT) through HPO, surpass state-of-the-art methodologies, achieving exceptional accuracies of 99.20% and 98.52% on the Cleveland and Statlog datasets, respectively. These models outperform existing approaches, assessed using comprehensive metrics such as F1 score, Accuracy, Precision, Recall, Specificity, and MCC. In conclusion, this study's innovative approach significantly improves the capability to assess PAD and categorize its severity levels accurately. The proposed models hold promise in aiding physicians to evaluate patients' cardiac conditions effectively, enabling early interventions that have the potential to mitigate mortality rates associated with cardiovascular diseases.

Better Detection of Peripheral Arterial Disease with Toe-Brachial Index Compared to Ankle-Brachial Index among Taiwanese Patients with Diabetic Kidney Disease

Background: Ankle-brachial index (ABI) is a simple method for diagnosing peripheral artery disease (PAD) but has limited reliability in patients with diabetes mellitus (DM) because of medial artery calcification. Our study aims to investigate whether the toe brachial index (TBI) or the cardio-ankle vascular index (CAVI) has a better detection over the ABI for diagnosing PAD in diabetic kidney disease (DKD). Materials and Methods: A cohort of 368 patients (mean age 68.59 ± 13.14 years, 190 males and 178 females) with type 2 DM underwent ABI, TBI, and CAVI measurements at our outpatient clinic. Results: Of all enrolled patients, the TBI is significant in evaluating PAD, especially in patients whose chronic kidney disease (CKD) stage 3a with adjusted odds ratio (AOR) = 6.50, 95% confidence interval (CI) 1.63–25.97, p = 0.0080, stage 3b AOR = 7.47, 95% CI 1.52–36.81, p = 0.0135, and stage 4–5 AOR = 20.13, 95% CI 1.34–94.24, p = 0.0116. CAVI is also significant in CKD stage 1 with AOR = 0.16, 95% CI 0.03–0.77, p = 0.0223, stage 2 with AOR = 0.18, 95% CI 0.04–0.74, p = 0.0180, and stage 3a AOR = 0.31, 95% CI 0.10–0.93, p = 0.0375. Conclusion: TBI has a better yield of detection of PAD compared to ABI among Taiwanese patients with DKD. CAVI may play a role in the early stage of DKD.

Assessing Automatic Plethysmographic Ankle-Brachial Index Devices in Peripheral Artery Disease Detection: A Comparative Study with Doppler Ankle-Brachial Index Measurements.

BACKGROUND The ankle-brachial index (ABI) is a critical diagnostic test for peripheral artery disease (PAD), albeit requiring technical expertise and dedicated resources. The advent of automatic ABI devices proposes a more accessible approach, necessitating fewer resources and less expertise. This study was conducted to gather data on PAD prevalence and to evaluate the correlation and efficacy of automatic ABI measurements vs traditional Doppler ABI measurements to understand their potential role in primary care settings. MATERIAL AND METHODS ABI measurements were obtained using both the Doppler method and an automatic plethysmographic device (Dopplex ABility, Huntleigh Healthcare). RESULTS Of the 290 participants (mean age 67.6±7.4 years), Doppler ABI method identified 16.8% with abnormal results (<0.9), while the automatic method identified only 5.9%. The mean Doppler ABI was 1.05±0.15, and the mean automatic ABI was 1.12±0.13. The sensitivity of the automatic ABI measurements was 22.2%, and the specificity was 96.8%, with a positive predictive value of 57.1%, and a negative predictive value of 86.9%. Adjustments in the automatic assessment and inclusion of pulse wave velocity enhanced the diagnostic capabilities of the automatic ABI device. CONCLUSIONS While the automatic plethysmographic ABI device may lack the necessary diagnostic capabilities to replace the traditional Doppler ABI device as a standalone test in PAD diagnosis, it could still offer significant value in primary care settings if integrated with adjusted cut-off points and pulse wave velocity analysis.

True Non-Contrast Phase versus Virtual-Non Contrast: “Lights and Shadows” of Dual Energy CT Angiography in Peripheral Arterial Disease

(1) Background: The value of dual-energy CT angiography (DE-CTA) in the detection of peripheral arterial disease (PAD) has been widely recognized. We aim to evaluate the diagnostic accuracy of virtual non-contrast (VNC) imaging of DE-CTA compared to true non-contrast phase (TNC). (2) Methods: Our Internal Review Board (IRB) approved prospective study enrolled 40 patients (28 men, 12 women; median age 69 y, range 41–93 y) who underwent lower extremity DE-CTA for symptomatic PAD. Mean attenuation values of TNC and VNC were obtained by placing circular regions of interest (ROI) at five levels from the aortic to the popliteal arterial lumen, reported in Hounsfield units (HU), and compared using a two-sample t-test. The subjective quality of VNC images was assessed by two independent radiologists with 10 and 4 years of CTA-imaging experience according to a 4-point scale and verified by the intra-class correlation coefficient (ICC). Dose Length Product (DLP) values of each DE-CTA examination were also considered. (3) Results: Except for the external iliac artery, VNC attenuation values were significantly lower than the corresponding TNC values at all levels, with a mean difference ranging from 14.1 and 8.7 HU. At qualitative analysis, VNC images were considered excellent to diagnose in 40%, good in 50%, and sufficient in 10% of cases. No cases of non-diagnostic VNC imaging were reported. Avoiding the TNC phase, a mean reduction in DLP of 54% for each DE-CTA was estimated. (4) Conclusions: TNC and VNC images showed comparable reliability and diagnostic accuracy in the detection of PAD. VNC may be considered a promising substitute for TNC from the perspectives of dose reduction and workflow optimization.

Performance of systolic pressure index for lower limb peripheral arterial disease in a group of elderly in sub-Saharan Africa

BackgroundThe automatic measurement of the ankle-brachial index (ABI) constitutes a reliable, simple, safe, rapid, and inexpensive alternative diagnostic screening test compared with the Doppler method for peripheral arterial disease (PAD). We aimed to compare the diagnostic performance of automatic ABI measurement tests to Doppler ultrasound for PAD in a group of patients aged 65 years and above, in Sub-Saharan Africa. MethodsThis was an experimental comparative study of the performance of Doppler ultrasound to the automated ABI test in the diagnosis of PAD in patients aged ≥ 65 years followed-up at the Yaoundé Central Hospital, Cameroon between January to June 2018. An ABI threshold < 0.90 is defined as a PAD. We compare the sensitivity, and specificity of the high ankle-brachial index (ABI-HIGH), low ankle-brachial index (ABI-LOW), and the mean ankle-brachial index (ABI-MEAN) for both tests. ResultsWe included 137 subjects with an average age of 71.7 ± 6.8 years. In the ABI-HIGH mode, the automatic device had a sensitivity of 55% and a specificity of 98.35% with a difference between the two techniques of d = 0.024 (p = 0.016). In the ABI-MEAN mode, it had a sensitivity of 40.63% and a specificity of 99.15%; d = 0.071 (p < 0.0001). In the ABI-LOW mode, it had a sensitivity of 30.95% and a specificity of 99.11%; d = 0.119 (p < 0.0001). ConclusionThe Automatic measurement of systolic pressure index has a better diagnostic performance in the detection of Peripheral Arterial Disease compared to the reference method by continuous Doppler in sub-Saharan African subjects aged ≥ 65 years.

Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records

Peripheral artery disease (PAD) is a common cardiovascular disorder that is frequently underdiagnosed, which can lead to poorer outcomes due to lower rates of medical optimization. We aimed to develop an automated tool to identify undiagnosed PAD and evaluate physician acceptance of a dashboard representation of risk assessment. Data were derived from electronic health records (EHR). We developed and compared traditional risk score models to novel machine learning models. For usability testing, primary and specialty care physicians were recruited and interviewed until thematic saturation. Data from 3168 patients with PAD and 16,863 controls were utilized. Results showed a deep learning model that utilized time engineered features outperformed random forest and traditional logistic regression models (average AUCs 0.96, 0.91 and 0.81, respectively), P < 0.0001. Of interviewed physicians, 75% were receptive to an EHR-based automated PAD model. Feedback emphasized workflow optimization, including integrating risk assessments directly into the EHR, using dashboard designs that minimize clicks, and providing risk assessments for clinically complex patients. In conclusion, we demonstrate that EHR-based machine learning models can accurately detect risk of PAD and that physicians are receptive to automated risk detection for PAD. Future research aims to prospectively validate model performance and impact on patient outcomes.

Measurement of Ankle Brachial Index with Oscillometric Method for Early Detection of Peripheral Artery Disease

Peripheral Arterial Disease (PAD) is a blood vessel disease caused by blockage or plaque accumulation around the artery walls. PAD is included in the category of diseases that are often diagnosed too late and affect more severe cases, such as the death of certain tissues or body parts. The Ankle Brachial Index (ABI) is an accurate non-invasive method for diagnosing PAD, in practice, ABI is usually performed in certain hospitals and is still difficult to find due to limited tools. Therefore, a tool is made that can detect the condition of a person's PAD based on the ABI value. The tool is made using two MPX5050GP sensors to detect oscillometric pulses, a DC pump and solenoid valve as an actuator to pump and deflate the cuff, ADS1115 as an external ADC to increase the accuracy of sensor readings, as well as an LCD and buzzer as tool indicators. The output is displayed in the form of a print out from a thermal printer, with an emergency stop that functions as a safety system to power off the supply when a failure occurs in the measurement process. Oscillometric method is used to detect systolic and diastolic pressure. The accuracy of the tool is 95.5%. This accuracy result is obtained by comparing the readings of systolic and diastolic values using a sphygmomanometer which is commonly used.

Color Doppler and Ankle-brachial Index in the Diagnosis of Peripheral Artery Disease in Type II Diabetic Subject

Objective: To determine the association of color doppler and ankle-brachial index (ABI) in the diagnosis of peripheral artery disease in type ii diabetic subject.&#x0D; Methods: The study was carried out at ultrasound department of Advanced Radiology Clinic (ARC), Karachi, from January 2020 to December 2020. Patients of diagnosed diabetes mellitus for at least 4 years of either gender between 30-70 years of age were included. All patients were instructed to avoid heavy exercise, smoking and caffeinated beverages before the examination for at the minimum of two hours. All the cases underwent Standard Doppler examination of lower limb peripheral arteries performed by radiologist having experience of more than 10 years. Spectral waveform analysis was evaluated and presence of Biphasic flow or monophasic flow on color Doppler was labelled as peripheral artery disease. The ABI was computed for each patient by measuring the systolic blood pressure in supine position starting with the right arm then proceeding towards right leg, left leg, and finally for the left arm. ABI is a ratio obtained from dividing the higher of the ankle blood pressures for each leg by the highest of the right and left arm’s brachial systolic blood pressures. It was labelled as peripheral artery disease if the value was &lt;0.9. This information along with the demographics was entered in the Performa by the researcher. Data was analyzed by using SPSS Version 26.&#x0D; Result: Mean age of enrolled participants was 58.21±6.74 year. There were 78.6% were males and 21.4% were females; mean duration of diabetes was 11.24±5.62 years. Out of all 121 cases were diagnosed color doppler and 118 cases found positive on ABI. Although ankle-brachial index shows almost similar diagnosis like color doppler ultrasound in the assessment of peripheral arterial disease (P-0.649).&#x0D; Conclusion: This study concludes that ankle-brachial index is a feasible, non-invasive technique almost similar to color Doppler ultrasound method for the detection of peripheral arterial disease. The ankle-brachial index technique is most important in periphery areas where color Doppler ultrasound is not available.

Development of a polygenic risk score to improve detection of peripheral artery disease.

Peripheral artery disease (PAD) is a major cause of cardiovascular morbidity and mortality, yet timely diagnosis is elusive. Larger genome-wide association studies (GWAS) have now provided the ability to evaluate whether genetic data, in the form of genome-wide polygenic risk scores (PRS), can help improve our ability to identify patients at high risk of having PAD. Using summary statistic data from the largest PAD GWAS from the Million Veteran Program, we developed PRSs with genome data from UK Biobank. We then evaluated the clinical utility of adding the best-performing PRS to a PAD clinical risk score. A total of 487,320 participants (5759 PAD cases) were included in our final genetic analysis. Compared to participants in the lowest 10% of PRS, those in the highest decile had 3.1 higher odds of having PAD (95% CI, 3.06-3.21). Additionally, a PAD PRS was associated with increased risk of having coronary artery disease, congestive heart failure, and cerebrovascular disease. The PRS significantly improved a clinical risk model (Net Reclassification Index = 0.07, p < 0.001), with most of the performance seen in downgrading risk of controls. Combining clinical and genetic data to detect risk of PAD resulted in a model with an area under the curve of 0.76 (95% CI, 0.75-0.77). We demonstrate that a genome-wide PRS can discriminate risk of PAD and other cardiovascular diseases. Adding a PAD PRS to clinical risk models may help improve detection of prevalent, but undiagnosed disease.

Comparison of Color Doppler and computed tomography angiography (CTA) in arterial disease in patients with lower limb ischemia.

Objective: To compare the diagnostic accuracy of color Doppler ultrasound with computed tomography angiography in patients with lower limb ischemia and to assess the severity of stenosis. Study Design: Cross Sectional Analytical study. Setting: Shalamar Hospital, Lahore. Period: May 2020 to October 2020. Material &amp; Methods: Data were collected according to the Age, Height, Weight, BMI, Duration of diabetes, Total Cholesterol, LDL, HDL, Triglyceride, Gender, Socioeconomic status, Diabetes, Hypertension, Stenosis, Collateral, calcification. Sample size of46 patients were included in this research comprising 32 males (69.6%) and 14 females (30.4%).Data entry and analysis will be done by using SPSS version-23. Results: Total numbers of 46 patients were included in this research comprising 32 males (69.6%) and 14 females (30.4%). According to the result analysis 34 patients had shown peripheral arterial disease at color Doppler and 12 patients had not shown peripheral arterial disease at color doppler. 38 patients had shown peripheral arterial disease at CTA and 8 patients had not shown peripheral arterial disease at CTA. Conclusion: This study concludes that computed tomography angiography for detection of peripheral arterial disease as the gold standard, MDCT angiography shows higher sensitivity (82.6%) than color-coded Doppler ultrasonography (73.9%) in the assessment of peripheral arterial disease.

The Role Of Doppler Ultrasound, CT Angiography and Conventional Angiography in Detection of Peripheral Arterial Disease for CME credit September 2021

The Role Of Doppler Ultrasound, CT Angiography and Conventional Angiography in Detection of Peripheral Arterial Disease for CME credit September 2021

The Role of Doppler Ultrasound, CT Angiography, and Conventional Angiography in Detection of Peripheral Arterial Disease

The purpose of this study is to analyze how accurate duplex ultrasonography using color Doppler and computed tomography (CT) angiography are in detection of peripheral arterial disease (PAD) in comparison with the Gold Standard of digital subtraction angiography (DSA). This is a single-center prospective, analytical study done on patients with symptoms of PAD referred to the Department of Radiodiagnosis of Medical Trust Hospital (n = 53). All patients were imaged with color Doppler, CT angiography, and DSA. The peak systolic velocity (PSV) ratio was calculated by Doppler ultrasound, and the percentage stenosis for the same vascular segments was calculated using CT angiography and DSA. To test the statistical significance between the results, chi-square test was used. A P value &lt;.05 indicates statistical significance. The PSV ratio for each grade—normal (&lt;1.5), mild (1.5-2.8), moderate (2.9-4.9), and severe (≥5)—and the percentage of stenosis for each grade observed on CT angiography—normal (&lt;20% stenosis), mild (20%-49% stenosis), moderate (50%-74% stenosis), severe (75%-99% stenosis), and total occlusion (100% stenosis)—were found to be highly sensitive and specific with good positive predictive value, negative predictive value, and accuracy level when compared with DSA with narrow confidence intervals for each range. The P value was &lt;.001 for both color Doppler and CT angiography. Computed tomography angiography can be an effective tool as an alternative to DSA for gradation of stenosis if the artifacts resulting from vascular calcification can be avoided. Duplex ultrasonography can be utilized for gradation of stenosis by using the value of PSV ratio and spectral pattern together. However, it can only act as an adjunct to CT angiography because it is incapable of imaging the full length of the arterial segments in one frame.

Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes.

Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.

Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study

Objective. A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG (‘DLPPG’) classification method to detect peripheral arterial disease (PAD) using toe PPG signals. Approach. PPG spectrogram images derived from our previously published multi-site PPG datasets (214 participants; 31.3% legs with PAD by ankle brachial pressure index (ABPI)) were input into a pretrained 8-layer (five convolutional layers + three fully connected layers) AlexNet as tailored to the 2-class problem with transfer learning to fine tune the convolutional neural network (CNN). k-fold random cross validation (CV) was performed (for k = 5 and k = 10), with each evaluated over k training/validation runs. Overall test sensitivity, specificity, accuracy, and Cohen’s Kappa statistic with 95% confidence interval ranges were calculated and compared, as well as sensitivities in detecting mild-moderate (0.5 ≤ ABPI < 0.9) and major (ABPI < 0.5) levels of PAD. Main results. CV with either k = 5 or 10 folds gave similar diagnostic performances. The overall test sensitivity was 86.6%, specificity 90.2% and accuracy 88.9% (Kappa: 0.76 [0.70–0.82]) (at k = 5). The sensitivity to mild-moderate disease was 83.0% (75.5%–88.9%) and to major disease was 100.0% (90.5%–100.0%). Significance. Substantial agreements have been demonstrated between the DL-based PPG classification technique and the ABPI PAD diagnostic reference. This novel automatic approach, requiring minimal pre-processing of the pulse waveforms before PPG trace classification, could offer significant benefits for the diagnosis of PAD in a variety of clinical settings where low-cost, portable and easy-to-use diagnostics are desirable.

Detection of peripheral artery disease in patients with ischemic heart disease. A quick guide for medical practitioners

In this manual, the authors focused on the principal methods for diagnosis of peripheral artery disease in cardiological patients, from the interview and physical examination to functional tests and vascular visualization. Diagnostic and prognostic value of each method, its potentialities for reducing the risk of cardiovascular events (CVE), including myocardial infarction (MI), ischemic stroke (IS) or extremity amputation in critical ischemia, and overall mortality are discussed. The authors provided current information about a possibility of reducing the risk of CVE by intensifying the antithrombotic therapy according to results of the COMPASS study.

Variation in four limb blood pressure and central aortic pressure: An observational study on North Indian semi-urban males

Background: Measurement of ankle pressure along with measurement of both arm blood pressure (BP) can be helpful to for calculation of ankle-brachial index (ABI) and detection of early peripheral diseases among the patients. Non-invasive measurement of central aortic pressures is possible as considered as better marker for central hemodynamic situation. Studies on normal subjects are scant. Aim and Objectives: The present study was conducted to evaluate the difference of BP in four limbs obtained from simultaneous recording and evaluate the association with central aortic BP components and peripheral BP components in normal Indian healthy male subjects. Materials and Methods: The study was conducted in the Department of Physiology of Mayo Institute of Medical Sciences, Barabanki, with 25 healthy clinically normal subjects. Four limb BP was recorded and ABI and central aortic pressures were estimated using periscope arterial health and cardiovascular analysis system. Results: Ankle systolic BP (SBP) was significantly high for both sides. Inter-ankle mean arterial pressure difference found to be significantly associated with ABI. Central aortic systolic pressure was found to be lower than the peripheral SBP. Conclusion: Ankle BP measurement to be considered for early detection of peripheral arterial disease and should be performed routinely in hypertension clinics. Central aortic pressure gives a better picture for identifying the hypertensive state of the body.

Comparative study of ankle brachial pressure index and blood sugar profile among offspring of diabetic and non-diabetic parents in early detection of peripheral arterial disease

Background: Type 2 diabetes mellitus (T2DM) is a complex disorder which is caused by a composite combination of genetic, epigenetic, and environmental factors. One of the most contributing quantitative risk factors is family history. The prevalence of peripheral arterial disease (PAD) in T2DM ranges from 20% to 30% and the lifetime risk of developing diabetes is up to 40% if either of parents is type 2 diabetic. Aim and Objectives: The aim of the study was to know whether non-diabetic offsprings of diabetic parents having chances of becoming diabetic and to have PAD in future part of life. Material and Methods: Taking all inclusion and exclusion criteria into consideration, 50 offsprings of diagnosed diabetic parents (>5 years) were taken as cases, and 50 healthy age-matched offsprings were taken from non-diabetic parents as controls. A thorough physical and systemic examination were done. After getting informed written consent, anthropometric measurements, FBS, PPBS, HbA1c, were measured. By taking ankle systolic blood pressure (SBP) and brachial SBP, ankle-brachial pressure index (ABPI) was calculated using a hand held vascular Doppler. Statistical analysis was done by Student’s t-test in SPSS software. Results: The mean HbA1c (%) level for controls was 5.68 ± 0.35 and for cases was 5.85 ± 0.44 with a P-value of 0.035 (significant). Similarly mean values for ankle SBP (mm Hg) were 148.4 ± 17.33 in controls and 159.8 ± 23.66 in cases with a P-value 0.0071 (significant). About 32% of controls and 50% of cases showed a non-compressible ABPI (ncABPI) arteries of >1.3, whereas only 2% of cases showed a typical ABPI of PAD, that is,