Feasibility of home-based tracking of insulin resistance from vascular stiffness estimated from the photoplethysmographic finger pulse waveform

Koppula, Aditya and Asif, Abdur Rehman and Barra, Ram Reddy and Sridharan, Kousik Sarathy (2022) Feasibility of home-based tracking of insulin resistance from vascular stiffness estimated from the photoplethysmographic finger pulse waveform. Physiological Measurement, 43 (6). ISSN 0967-3334

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Abstract

Objective. In this study we explored the utility of post-prandial vascular stiffness as a surrogate measure for estimating insulin resistance, which is a pre-diabetic condition. Approach. A cohort of 51 healthy young adults with varying body mass index (BMI) values was studied using fasting plasma values of insulin and glucose, fasting and post-meal finger photoplethysmography (PPG) and electrocardiogram (ECG). Insulin resistance was estimated by homeostatic model assessment for insulin resistance 2 (HOMA-IR2) using fasting plasma insulin and glucose. Vascular stiffness was estimated by reciprocal of pulse arrival time (rPAT) from ECG and finger PPG at five time points from fasting to 2 h post-oral glucose ingestion. We examined if insulin resistance correlates with meal-induced vascular stiffness changes, supporting the feasibility of using finger PPG to estimate insulin resistance. Main results. HOMA-IR2 was positively correlated with an early rise (0 to 30 min post-meal) and delayed fall (30 to 120 min post-meal) of rPAT. Correlation persisted even after the effect of BMI has been partialled out in subgroup analysis. We conclude that finger PPG-based pulse waveform and single-lead ECG has the potential to be used as a non-invasive method for the assessment of insulin resistance. Significance. As both signals, namely ECG and PPG, can be easily acquired using wearable and other low-cost sensing systems, the present study can serve as a pointer to develop accessible strategies for monitoring and longitudinal tracking of insulin resistance in health and pathophysiological states. © 2022 Institute of Physics and Engineering in Medicine.

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