Insulin detection in diabetes mellitus: challenges and new prospects

Abstract

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

Fig. 1. Major challenges facing insulin therapy.

a, Insulin action curves are shown that correspond to natural endogenous insulin secretion in healthy people, with basal (dark grey) and prandial (light grey) insulin secretion depicted. Also shown are predicted variations in exogenous insulin (blue line, basal levels of insulin; blue triangles, basal insulin infusions; red line, bolus levels of insulin; red triangles, bolus insulin infusions) in a patient with diabetes mellitus, as well as a hypothetical example of real insulin variations (yellow dots) in a patient with diabetes mellitus who experiences insulin stacking and is at risk of hypoglycaemia. b, Factors affecting insulin sensitivity, which represent the main contributors to intrapersonal and interpersonal variations in response to insulin therapy. c, Current insulin analysis procedure in clinical practice: sample collection in the clinic by a nurse, transportation of the sample to a centralized analytical laboratory and enzyme-linked immunosorbent assay (ELISA)-based insulin analysis by trained personnel, eventually making the whole insulin testing process complex, costly and time-consuming.

Fig. 2. Current insulin analysis approaches used in clinical practice and clinical research.

a, Conventional analytical methodologies and tools for insulin quantification used in centralized laboratory settings, including high-performance liquid chromatography (HPLC), mass spectrometry, capillary electrophoresis, microfluidics and immunoassays. Equipment for chromatography, electrophoresis and spectrometry is usually bulky and heavy and requires trained professionals for operation, whereas immunoassays and microfluidic-assisted insulin detection rely on compact equipment and facilitate hassle-free insulin monitoring. b, Natural and synthetic insulin receptors available for achieving highly selective insulin measurements, including antibodies, aptamers, molecularly imprinted polymers (MIPs) and insulin receptors. Part b is adapted from RCSB PDB number 2WFU (10.2210/pdb2WFU/pdb), CC0 1.0 (https://creativecommons.org/publicdomain/zero/1.0/).

Fig. 3. Our future vision for decentralized insulin monitoring and diabetes mellitus management.

a, Advances in insulin detection technologies could lead to the development of biosensor approaches for home-based self-testing of insulin using capillary blood and saliva, for example, lateral flow tests and strip-based insulin meters. b, A theoretical needle-type continuous insulin monitoring (CIM) sensor device for continuous real-time insulin measurements in subcutaneous interstitial fluid. c, A theoretical multi-analyte microneedle sensor array for simultaneous monitoring of multiple diabetes mellitus biomarkers in interstitial fluid. d, In the future, we envision the development of personalized closed-loop autonomous artificial pancreas ‘sense–act’ systems that integrate wearable devices for diabetes mellitus care (biosensors and insulin pumps) within an ‘Internet of things’ (interconnection of devices via the Internet for sharing and managing data) platform. The system relies on multimodal sensory chemical and physical inputs and a multitude of personal variables (regarding meals, exercise and other activities), along with data-driven machine-learning decision-making algorithms towards optimal (timely and accurate) personalized insulin dosing and efficient glucose regulation. C, control line; DIA, diastolic; ECG, electrocardiogram; S, sample well; SYS, systolic; T, test line.