Mansour, H., Shahen, S., Sultan, H., AboElNeel, H., Shehata, H. (2022). Uric Acid as a Predictor of Peripheral Arterial Disease as Indicated by Ankle Brachial Index. The Egyptian Journal of Hospital Medicine, 88(1), 2781-2787. doi: 10.21608/ejhm.2022.241952
Hazem Mansour; Sameh Shahen; Heba Sultan; Hamdy AbdelAzeem AboElNeel; Hassen Shehata. "Uric Acid as a Predictor of Peripheral Arterial Disease as Indicated by Ankle Brachial Index". The Egyptian Journal of Hospital Medicine, 88, 1, 2022, 2781-2787. doi: 10.21608/ejhm.2022.241952
Mansour, H., Shahen, S., Sultan, H., AboElNeel, H., Shehata, H. (2022). 'Uric Acid as a Predictor of Peripheral Arterial Disease as Indicated by Ankle Brachial Index', The Egyptian Journal of Hospital Medicine, 88(1), pp. 2781-2787. doi: 10.21608/ejhm.2022.241952
Mansour, H., Shahen, S., Sultan, H., AboElNeel, H., Shehata, H. Uric Acid as a Predictor of Peripheral Arterial Disease as Indicated by Ankle Brachial Index. The Egyptian Journal of Hospital Medicine, 2022; 88(1): 2781-2787. doi: 10.21608/ejhm.2022.241952
Uric Acid as a Predictor of Peripheral Arterial Disease as Indicated by Ankle Brachial Index
Departments of 1Cardiology and 2Vascular Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt
Abstract
Background: Peripheral arterial disease (PAD) is a chronic atherosclerotic progressive disorder that affects the arterial tree especially those of the lower limb which can be screened by the ankle-brachial index (ABI). Generally, uric acid (UA) has been accused of the initiation and progression of atherosclerosis in various arterial segments however, it is a less studied risk factor in PAD. Objective: Our study aimed to evaluate the correlation between increased serum uric acid levels and PAD as indicated by ABI and whether there is a cut-off value for UA to predict PAD. Patients and methods: A case-control study compared 100 patients with PAD as indicated by ABI with 100 cross-matched controls as regards serum UA levels and other risk factors. Moreover, the receiver operating characteristic curve (ROC) was plotted to determine the best cut-off point for UA to detect PAD as indicated by ABI. Results: The BMI, DM, and dyslipidemia were highly significant among the patient's group (P- value= 0.001). Moreover, UA was significantly correlated to low ABI (P- value=0.003). Besides, UA cut-off value > 6.5 exhibited a specificity of 90% and a positive predictive value of 80% to diagnose PAD. Conclusion: Low ABI was significantly associated with UA denoting its probable relation with lower limb atherosclerosis, with a good positive predictive value to predict it.