Accurate spectroscopic calibration for noninvasive glucose monitoring by modeling the physiological glucose dynamics

Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy

In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.

Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?

Diabetes has no well-established cure; thus, its management is critical for avoiding severe health complications involving multiple organs. This requires frequent glycaemia monitoring, and the gold standards for this are fingerstick tests. During the last decades, several blood-withdrawal-free platforms have been being studied to replace this test and to improve significantly the quality of life of people with diabetes (PWD). Devices estimating glycaemia level targeting blood or biofluids such as tears, saliva, breath and sweat, are gaining attention; however, most are not reliable, user-friendly and/or cheap. Given the complexity of the topic and the rise of diabetes, a careful analysis is essential to track scientific and industrial progresses in developing diabetes management systems. Here, we summarize the emerging blood glucose level (BGL) measurement methods and report some examples of devices which have been under development in the last decades, discussing the reasons for them not reaching the market or not being really non-invasive and continuous. After discussing more in depth the history of Raman spectroscopy-based researches and devices for BGL measurements, we will examine if this technique could have the potential for the development of a user-friendly, miniaturized, non-invasive and continuous blood glucose-monitoring device, which can operate reliably, without inter-patient variability, over sustained periods.

Closed-Loop Diabetes Minipatch Based on a Biosensor and an Electroosmotic Pump on Hollow Biodegradable Microneedles

Developing a miniaturized, low-cost, and smart closed-loop system for diabetes could significantly improve life quality and benefit millions of people. Conventional closed-loop devices are large in size and exorbitant. Here, we unprecedentedly demonstrate an electrically controlled flexible closed-loop patch for continuous diabetes management by integrating hollow biodegradable microneedles with a biosensing device and an electroosmotic pump. The hollow microneedles were fabricated using a combination of soft lithography and micromachining. The outer layer of the microneedles was functionalized to serve as a biosensing device for the in situ sensitive and accurate monitoring of interstitial glucose. The inner layer of the microneedles was integrated with a flexible electroosmotic pump to deliver insulin, and the delivery rate was electrically controlled by the glucose level from the biosensing device. The closed-loop system successfully stabilized the blood glucose levels of diabetic rats in a normal and safe range. The system is painless, miniaturized, cost-effective, and flexible. It is anticipated that it could open up exciting new avenues for fundamental studies of new closed-loop devices as well as practical applications for diabetes management.

Noninvasive Blood and Tissue Analysis: Raman Spectroscopy, One Perspective for Monitoring of Glucose and Beyond

Noninvasive in vivo blood and tissue analysis remains a challenge to medical technology epitomized by the ongoing quest to replace fingerstick self-monitoring of blood glucose. Recent developments warrant comment on near-term prospects for using Raman spectroscopy to meet that challenge. These developments combined with 20 years of experimentation with noninvasive blood and tissue analysis suggest that it may be possible and practical to perform noninvasive in vivo glucose analysis with improvements in (1) the enabling technologies for making Raman measurements and (2) an underlying anatomical-physiological model of how in vivo spectroscopic measurements are made and interpreted. We review the substantial progress made toward meeting the challenge and the personal, public health, and economic implications of these ongoing efforts.

A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels

Raman spectra of human skin obtained by laser excitation have been used to non-invasively detect blood glucose. In previous reports, however, Raman spectra thus obtained were mainly derived from the epidermis and interstitial fluid as a result of the shallow penetration depth of lasers in skin. The physiological process by which glucose in microvessels penetrates into the interstitial fluid introduces a time delay, which inevitably introduces errors in transcutaneous measurements of blood glucose. We focused the laser directly on the microvessels in the superficial layer of the human nailfold, and acquired Raman spectra with multiple characteristic peaks of blood, which indicated that the spectra obtained predominantly originated from blood. Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively. The mean prediction performance of the 12 volunteers was obtained as an RMSEP of 0.45 mmol/L and R² of 0.95. It was no time lag between the predicted blood glucose and the actual blood glucose in the oral glucose tolerance test (OGTT). We also applied the procedure to data from all 12 volunteers regarded as one set, and the total predicted performance was obtained with an RMSEP of 0.27 mmol/L and an R² of 0.98, which is better than that of the individual model for each volunteer. This suggested that anatomical differences between volunteer fingernails do not reduce the prediction accuracy and 100% of the predicted glucose concentrations fall within Region A and B of the Clarke error grid, allowing acceptable predictions in a clinically relevant range. The Raman spectroscopy detection of blood glucose from microvessels is of great significance of non-invasive blood glucose detection of Raman spectroscopy. This innovative method may also facilitate non-invasive detection of other blood components.

Non-invasive monitoring of blood glucose using optical methods for skin spectroscopy-opportunities and recent advances

Diabetes mellitus is a widespread disease with greatly rising patient numbers expected in the future, not only for industrialized countries but also for regions in the developing world. There is a need for efficient therapy, which can be via self-monitoring of blood glucose levels to provide tight glycemic control for reducing the risks of severe health complications. Advancements in diabetes technology can nowadays offer different sensor approaches, even for continuous blood glucose monitoring. Non-invasive blood glucose assays have been promised for many years and various vibrational spectroscopy-based methods of the skin are candidates for achieving this goal. Due to the small spectral signatures of the glucose hidden among a largely variable background, the largest signal-to-noise ratios and multivariate calibration are essential to provide the method applicability for self-monitoring of blood glucose. Besides multiparameter approaches, recently presented devices based on photoplethysmography with wavelengths in the visible and near-infrared range are evaluated for their potential of providing reliable blood glucose concentration predictions. Graphical abstract ᅟ.

Noninvasive Continuous Glucose Monitoring Using a Multisensor-Based Glucometer and Time Series Analysis

Daily continuous glucose monitoring is very helpful in the control of glucose levels for people with diabetes and impaired glucose tolerance. In this study, a multisensor-based, noninvasive continuous glucometer was developed, which can continuously estimate glucose levels via monitoring of physiological parameter changes such as impedance spectroscopy at low and high frequency, optical properties, temperature and humidity. Thirty-three experiments were conducted for six healthy volunteers and three volunteers with diabetes. Results showed that the average correlation coefficient between the estimated glucose profiles and reference glucose profiles reached 0.8314, with a normalized root mean squared error (NRMSE) of 14.6064. The peak time of postprandial glucose was extracted from the glucose profile, and its estimated value had a correlation coefficient of 0.9449 with the reference value, wherein the root mean square error (RMSE) was 6.8958 min. Using Clarke error grid (CEG) analysis, 100% of the estimated glucose values fell in the clinically acceptable zones A and B, and 92.86% fell in zone A. The application of a multisensor-based, noninvasive continuous glucometer and time series analysis can endure the time delay between human physiological parameters and glucose level changes, so as to potentially accomplish noninvasive daily continuous glucose monitoring.

Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy

The successful development of a noninvasive blood glucose sensor that can operate reliably over sustained periods of time has been a much sought after but elusive goal in diabetes management. Since diabetes has no well-established cure, control of elevated glucose levels is critical for avoiding severe secondary health complications in multiple organs including the retina, kidney and vasculature. While fingerstick testing continues to be the mainstay of blood glucose detection, advances in electrochemical sensing-based minimally invasive approaches have opened the door for alternate methods that would considerably improve the quality of life for people with diabetes. In the quest for better sensing approaches, optical technologies have surfaced as attractive candidates as researchers have sought to exploit the endogenous contrast of glucose, notably its absorption, scattering, and polarization properties. Vibrational spectroscopy, especially spontaneous Raman scattering, has exhibited substantial promise due to its exquisite molecular specificity and minimal interference of water in the spectral profiles acquired from the blood-tissue matrix. Yet, it has hitherto been challenging to leverage the Raman scattering signatures of glucose for prediction in all but the most basic studies and under the least demanding conditions. In this Account, we discuss the newly developed array of methodologies that address the key challenges in measuring blood glucose accurately using Raman spectroscopy and unlock new prospects for translation to sustained noninvasive measurements in people with diabetes. Owing to the weak intensity of spontaneous Raman scattering, recent research has focused on enhancement of signals from the blood constituents by designing novel excitation-collection geometries and tissue modulation methods while our attempts have led to the incorporation of nonimaging optical elements. Additionally, invoking mass transfer modeling into chemometric algorithms has not only addressed the physiological lag between the actual blood glucose and the measured interstitial fluid glucose values but also offered a powerful tool for predictive measurements of hypoglycemia. This framework has recently been extended to provide longitudinal tracking of glucose concentration without necessitating extensive a priori concentration information. These findings are advanced by the results of recent glucose tolerance studies in human subjects, which also hint at the need for designing nonlinear calibration models that can account for subject-to-subject variations in skin heterogeneity and hematocrit levels. Together, the emerging evidence underscores the promise of a blood withdrawal-free optical platform-featuring a combination of high-throughput Raman spectroscopic instrumentation and data analysis of subtle variations in spectral expression-for diabetes screening in the clinic and, ultimately, for personalized monitoring.

Modeling and Measurement of Correlation between Blood and Interstitial Glucose Changes

One of the most effective methods for continuous blood glucose monitoring is to continuously measure glucose in the interstitial fluid (ISF). However, multiple physiological factors can modulate glucose concentrations and affect the lag phase between blood and ISF glucose changes. This study aims to develop a compensatory tool for measuring the delay in ISF glucose variations in reference to blood glucose changes. A theoretical model was developed based on biophysics and physiology of glucose transport in the microcirculation system. Blood and interstitial fluid glucose changes were measured in mice and rats by fluorescent and isotope methods, respectively. Computer simulation mimicked curves were fitted with data resulting from fluorescent measurements of mice and isotope measurements of rats, indicating that there were lag times for ISF glucose changes. It also showed that there was a required diffusion distance for glucose to travel from center of capillaries to interstitial space in both mouse and rat models. We conclude that it is feasible with the developed model to continuously monitor dynamic changes of blood glucose concentration through measuring glucose changes in ISF with high accuracy, which requires correct parameters for determining and compensating for the delay time of glucose changes in ISF.

Emerging trends in optical sensing of glycemic markers for diabetes monitoring

In the past decade, considerable attention has been focused on the measurement of glycemic markers, such as glycated hemoglobin and glycated albumin, that provide retrospective indices of average glucose levels in the bloodstream. While these biomarkers have been regularly used to monitor long-term glucose control in established diabetics, they have also gained traction in diabetic screening. Detection of such glycemic markers is challenging, especially in a point-of-care setting, due to the stringent requirements for sensitivity and robustness. A number of non-separation based measurement strategies were recently proposed, including photonic tools that are well suited to reagent-free marker quantitation. Here, we critically review these methods while focusing on vibrational spectroscopic methods, which offer highly specific molecular fingerprinting capability. We examine the underlying principles and the utility of these approaches as reagentless assays capable of multiplexed detection of glycemic markers and also the challenges in their eventual use in the clinic.

Spectroscopic approach for dynamic bioanalyte tracking with minimal concentration information

Vibrational spectroscopy has emerged as a promising tool for non-invasive, multiplexed measurement of blood constituents - an outstanding problem in biophotonics. Here, we propose a novel analytical framework that enables spectroscopy-based longitudinal tracking of chemical concentration without necessitating extensive a priori concentration information. The principal idea is to employ a concentration space transformation acquired from the spectral information, where these estimates are used together with the concentration profiles generated from the system kinetic model. Using blood glucose monitoring by Raman spectroscopy as an illustrative example, we demonstrate the efficacy of the proposed approach as compared to conventional calibration methods. Specifically, our approach exhibits a 35% reduction in error over partial least squares regression when applied to a dataset acquired from human subjects undergoing glucose tolerance tests. This method offers a new route at screening gestational diabetes and opens doors for continuous process monitoring without sample perturbation at intermediate time points.

Raman spectroscopy as a promising tool for noninvasive point-of-care glucose monitoring

Self-monitoring of glucose is important for managing diabetes. Noninvasive glucose monitors are not yet available, but patients would benefit highly from such a device. We present results that may lead to a novel, point-of-care noninvasive system to measure blood glucose based on Raman spectroscopy. A hospitalized cohort of 111 subjects was measured using a custom-made Raman spectrometer system. Blood glucose reference samples were used to correlate Raman data to glucose levels, using advanced preprocessing and analysis algorithms. A correlation coefficient (R (2)) of .83 was found correlating independent Raman-based predictions on reference blood glucose for the full cohort. Stratification of the cohort in gender-specific groups raised correlation levels to .88 (females) and .94 (males). Glucose could be measured noninvasively with average errors as low as 0.9 mM. We conclude that this novel system shows promising results for the advance of noninvasive, point-of-care glucose monitoring.

Applications of ultrasensitive wavelength-modulated differential photothermal radiometry to noninvasive glucose detection in blood serum

Wavelength-Modulated Differential Laser Photothermal Radiometry (WM-DPTR) has been designed for noninvasive glucose measurements in the mid-infrared (MIR) range. Glucose measurements in human blood serum in the physiological range (20-320 mg/dl) with predicted error <10.3 mg/dl demonstrated high sensitivity and accuracy to meet wide clinical detection requirements, ranging from hypoglycemia to hyperglycemia. The glucose sensitivity and specificity of WM-DPTR stem from the subtraction of the simultaneously measured signals from two excitation laser beams at wavelengths near the peak and the baseline of the strongest interference-free glucose absorption band in the MIR range. It was found that the serum glucose sensitivity and measurement precision strongly depend on the tunability and stability of the intensity ratio and the phase shift of the two laser beams. This level of accuracy was favorably compared to other MIR techniques. WM-DPTR has shown excellent potential to be developed into a clinically viable noninvasive glucose biosensor.

A facile and real-time spectroscopic method for biofluid analysis in point-of-care diagnostics

BACKGROUND

Accurate and real-time information is critical for decision making, especially in medical applications, where any delay in diagnosis due to collection, transport and storage of biofluids can have substantial ramifications for disease management.

RESULTS

We present a facile method for point-of-care biofluid diagnostics based on the spectroscopic analysis of cotton-swab contents using a Raman probe. A PCA algorithm was developed in order to understand the clustering behavior of different off-the-shelf pharmaceutical formulations based on the recorded spectral data. Furthermore, we employed the Raman probe to detect antibiotics in a human urine sample. Our observations suggest that it is possible to provide quantitative concentration determination of Raman-active analytes by using cotton swabs as a sampling probe, which offers a wealth of possibility for real-time measurement in clinical situations.

CONCLUSION

We envision that the intrinsic simplicity of the proposed approach in conjunction with its capability for accurate analyte determination in biofluids will lead to its clinical translation and application in point-of-care settings in the near future.

Continuous Glucose Monitoring Systems: A Review

There have been continuous advances in the field of glucose monitoring during the last four decades, which have led to the development of highly evolved blood glucose meters, non-invasive glucose monitoring (NGM) devices and continuous glucose monitoring systems (CGMS). Glucose monitoring is an integral part of diabetes management, and the maintenance of physiological blood glucose concentration is the only way for a diabetic to avoid life-threatening diabetic complications. CGMS have led to tremendous improvements in diabetic management, as shown by the significant lowering of glycated hemoglobin (HbA1c) in adults with type I diabetes. Most of the CGMS have been minimally-invasive, although the more recent ones are based on NGM techniques. This manuscript reviews the advances in CGMS for diabetes management along with the future prospects and the challenges involved.

A method for accurate in vivo micro-Raman spectroscopic measurements under guidance of advanced microscopy imaging

The movement from the subjects during in vivo confocal Raman spectral measurements could change the measurement volume, leading to non-specific signals and inaccurate interpretation of the acquired spectrum. Here we introduce a generally applicable method that includes (1) developing a multimodal system to achieve real-time monitoring of every spectral measurement with reflectance confocal microscopy (RCM) and multiphoton microscopy (MPM) imaging; (2) performing region-of-interest measurement by scanning an area of the tissue during spectral acquisition. The developed method has been validated by measuring different micro-structures of in vivo human skin. Our results demonstrated great consistency between RCM images and confocal Raman spectra. The superior quality of the images and spectra allows us to derive blood flow velocity and blood glucose level. We believe this method is valuable for realizing accurate microscopic spectral measurement and have great potential to be adapted into clinic to achieve non-invasive measurement of important biological parameters.

Noninvasive glucose detection in human skin using wavelength modulated differential laser photothermal radiometry

Noninvasive glucose monitoring will greatly improve diabetes management. We applied Wavelength-Modulated Differential Laser Photothermal Radiometry (WM-DPTR) to noninvasive glucose measurements in human skin in vitro in the mid-infrared range. Glucose measurements in human blood serum diffused into a human skin sample (1 mm thickness from abdomen) in the physiological range (21-400 mg/dl) demonstrated high sensitivity and accuracy to meet wide clinical detection requirements. It was found that the glucose sensitivity could be tuned by adjusting the intensity ratio and phase difference of the two laser beams in the WM-DPTR system. The measurement results demonstrated the feasibility of the development of WM-DPTR into a clinically viable noninvasive glucose biosensor.

In vivo blood glucose quantification using Raman spectroscopy

We here propose a novel Raman spectroscopy method that permits the noninvasive measurement of blood glucose concentration. To reduce the effects of the strong background signals produced by surrounding tissue and to obtain the fingerprint Raman lines formed by blood analytes, a laser was focused on the blood in vessels in the skin. The Raman spectra were collected transcutaneously. Characteristic peaks of glucose (1125 cm^-1) and hemoglobin (1549 cm^-1) were observed. Hemoglobin concentration served as an internal standard, and the ratio of the peaks that appeared at 1125 cm^-1 and 1549 cm^-1 peaks was used to calculate the concentration of blood glucose. We studied three mouse subjects whose blood glucose levels became elevated over a period of 2 hours using a glucose test assay. During the test, 25 Raman spectra were collected transcutaneously and glucose reference values were provided by a blood glucose meter. Results clearly showed the relationship between Raman intensity and concentration. The release curves were approximately linear with a correlation coefficient of 0.91. This noninvasive methodology may be useful for the study of blood glucose in vivo.

Italian contributions to the development of continuous glucose monitoring sensors for diabetes management

Monitoring glucose concentration in the blood is essential in the therapy of diabetes, a pathology which affects about 350 million people around the World (three million in Italy), causes more than four million deaths per year and consumes a significant portion of the budget of national health systems (10% in Italy). In the last 15 years, several sensors with different degree of invasiveness have been proposed to monitor glycemia in a quasi-continuous way (up to 1 sample/min rate) for relatively long intervals (up to 7 consecutive days). These continuous glucose monitoring (CGM) sensors have opened new scenarios to assess, off-line, the effectiveness of individual patient therapeutic plans from the retrospective analysis of glucose time-series, but have also stimulated the development of innovative on-line applications, such as hypo/hyper-glycemia alert systems and artificial pancreas closed-loop control algorithms. In this review, we illustrate some significant Italian contributions, both from industry and academia, to the growth of the CGM sensors research area. In particular, technological, algorithmic and clinical developments performed in Italy will be discussed and put in relation with the advances obtained in the field in the wider international research community.

Investigation of noise-induced instabilities in quantitative biological spectroscopy and its implications for noninvasive glucose monitoring

Over the past decade, optical spectroscopy has been employed in combination with multivariate chemometric models to investigate a wide variety of diseases and pathological conditions, primarily due to its excellent chemical specificity and lack of sample preparation requirements. Despite promising results in several proof-of-concept studies, its translation to the clinical setting has often been hindered by inadequate accuracy of the conventional spectroscopic models. To address this issue and the possibility of curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture spectra (due to fluctuations in sample and environmental conditions), support vector machine-based least-squares nonlinear regression (LS-SVR) has been recently proposed. In this paper, we investigate the robustness of this methodology to noise-induced instabilities and present an analytical formula for estimating modeling precision as a function of measurement noise and model parameters. This formalism can be readily used to evaluate uncertainty in information extracted from spectroscopic measurements, particularly important for rapid-acquisition biomedical applications. Subsequently, using field data (Raman spectra) acquired from a glucose clamping study on an animal model subject, we perform the first systematic investigation of the relative effect of additive interference components (namely, noise in prediction spectra, calibration spectra, and calibration concentrations) on the prediction error of nonlinear spectroscopic models. Our results show that the LS-SVR method gives more accurate results and is substantially more robust to additive noise when compared with conventional regression methods such as partial least-squares regression (PLS), when careful selection of the LS-SVR model parameters are performed. We anticipate that these results will be useful for uncertainty estimation in similar biomedical applications where the precision of measurements and its response to noise in the data set is as important, if not more so, than the generic accuracy level.

Green pharmacy and pharmEcovigilance: prescribing and the planet

Active pharmaceutical ingredients (APIs) are ubiquitous environmental contaminants, resulting primarily from excretion and bathing and from disposal of leftover drugs by consumers and healthcare facilities. Although prudent disposal of leftover drugs has attracted the most attention for reducing API levels in the aquatic environment, a more effective approach would prevent the generation of leftover drugs in the first place. Many aspects of the practice of medicine and pharmacy can be targeted for reducing environmental contamination by APIs. These same modifications--focused on treating humans and the environment as a single, integral patient--could also have collateral outcomes with improved therapeutic outcomes, and with a reduced incidence of unintended poisonings, drug interactions and drug diversion, and lower consumer costs.

Materials for diabetes therapeutics

This review is focused on the materials and methods used to fabricate closed-loop systems for type 1 diabetes therapy. Herein, we give a brief overview of current methods used for patient care and discuss two types of possible treatments and the materials used for these therapies-(i) artificial pancreases, comprised of insulin producing cells embedded in a polymeric biomaterial, and (ii) totally synthetic pancreases formulated by integrating continuous glucose monitors with controlled insulin release through degradable polymers and glucose-responsive polymer systems. Both the artificial and the completely synthetic pancreas have two major design requirements: the device must be both biocompatible and be permeable to small molecules and proteins, such as insulin. Several polymers and fabrication methods of artificial pancreases are discussed: microencapsulation, conformal coatings, and planar sheets. We also review the two components of a completely synthetic pancreas. Several types of glucose sensing systems (including materials used for electrochemical, optical, and chemical sensing platforms) are discussed, in addition to various polymer-based release systems (including ethylene-vinyl acetate, polyanhydrides, and phenylboronic acid containing hydrogels).

Non-invasive glucose monitoring technology in diabetes management: a review

The frequent monitoring of glucose is an essential part of diabetes management. Despite the fact that almost all the commercially successful blood glucose monitoring devices are invasive, there is an immense need to develop non-invasive glucose monitoring (NGM) devices that will alleviate the pain and suffering of diabetics associated with the frequent pricking of skin for taking the blood sample for glucose testing. There have been numerous developments in the field of NGM during the last decade, which stress the need for a critical review. This manuscript aims to review the various NGM techniques and devices. The challenges and future trends in NGM are also discussed.

Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin

In recent years, glycated hemoglobin (HbA1c) has been increasingly accepted as a functional metric of mean blood glucose in the treatment of diabetic patients. Importantly, HbA1c provides an alternate measure of total glycemic exposure due to the representation of blood glucose throughout the day, including post-prandially. In this article, we propose and demonstrate the potential of Raman spectroscopy as a novel analytical method for quantitative detection of HbA1c, without using external dyes or reagents. Using the drop coating deposition Raman (DCDR) technique, we observe that the nonenzymatic glycosylation (glycation) of the hemoglobin molecule results in subtle but discernible and highly reproducible changes in the acquired spectra, which enable the accurate determination of glycated and nonglycated hemoglobin using standard chemometric methods. The acquired Raman spectra display excellent reproducibility of spectral characteristics at different locations in the drop and show a linear dependence of the spectral intensity on the analyte concentration. Furthermore, in hemolysate models, the developed multivariate calibration models for HbA1c show a high degree of prediction accuracy and precision--with a limit of detection that is a factor of ~15 smaller than the lowest physiological concentrations encountered in clinical practice. The excellent accuracy and reproducibility achieved in this proof-of-concept study opens substantive avenues for characterization and quantification of the glycosylation status of (therapeutic) proteins, which are widely used for biopharmaceutical development. We also envision that the proposed approach can provide a powerful tool for high-throughput HbA1c sensing in multicomponent mixtures and potentially in hemolysate and whole blood lysate samples.

Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation

We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.

Analyzing near-infrared scattering from human skin to monitor changes in hematocrit

Probing tissue with near-infrared radiation (NIR) simultaneously produces remitted fluorescence and Raman scattering (IE) plus Rayleigh∕Mie light scattering (EE) that noninvasively give chemical and physical information about the materials and objects within. We model tissue as a three-phase system: plasma and red blood cell (RBC) phases that are mobile and a static tissue phase. In vivo, any volume of tissue naturally experiences spatial and temporal fluctuations of blood plasma and RBC content. Plasma and RBC fractions may be discriminated from each other on the basis of their physical, chemical, and optical properties. Thus, IE and EE from NIR probing yield information about these fractions. Assuming there is no void volume in viable tissue, or that void volume is constant, changes in plasma and RBC volume fractions may be calculated from simultaneous measurements of the two observables, EE and IE. In a previously published analysis we showed the underlying phenomenology but did not provide an algorithm for calculating volume fractions from experimental data. Now, we present a simple analysis that allows monitoring of fluid fraction and hematocrit (Hct) changes by measuring IE and EE, and apply it to some experimental in vivo measurements.

Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy

While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future.

Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements

Although several in vivo blood glucose measurement studies have been performed by different research groups using near-infrared (NIR) absorption and Raman spectroscopic techniques, prospective prediction has proven to be a challenging problem. An important issue in this case is the demonstration of causality of glucose concentration to the spectral information, especially as the intrinsic glucose signal is smaller compared with that of the other analytes in the blood-tissue matrix. Furthermore, time-dependent physiological processes make the relation between glucose concentration and spectral data more complex. In this article, chance correlations in Raman spectroscopy-based calibration model for glucose measurements are investigated for both in vitro (physical tissue models) and in vivo (animal model and human subject) cases. Different spurious glucose concentration profiles are assigned to the Raman spectra acquired from physical tissue models, where the glucose concentration is intentionally held constant. Analogous concentration profiles, in addition to the true concentration profile, are also assigned to the datasets acquired from an animal model during a glucose clamping study as well as a human subject during an oral glucose tolerance test. We demonstrate that the spurious concentration profile-based calibration models are unable to provide prospective predictions, in contrast to those based on actual concentration profiles, especially for the physical tissue models. We also show that chance correlations incorporated by the calibration models are significantly less in Raman as compared to NIR absorption spectroscopy, even for the in vivo studies. Finally, our results suggest that the incorporation of chance correlations for in vivo cases can be largely attributed to the uncontrolled physiological sources of variations. Such uncontrolled physiological variations could either be intrinsic to the subject or stem from changes in the measurement conditions.

Detecting vital signs with wearable wireless sensors

The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented.

Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose

Sample-to-sample variability has proven to be a major challenge in achieving calibration transfer in quantitative biological Raman spectroscopy. Multiple morphological and optical parameters, such as tissue absorption and scattering, physiological glucose dynamics and skin heterogeneity, vary significantly in a human population introducing nonanalyte specific features into the calibration model. In this paper, we show that fluctuations of such parameters in human subjects introduce curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture Raman spectra. To account for these curved effects, we propose the use of support vector machines (SVM) as a nonlinear regression method over conventional linear regression techniques such as partial least-squares (PLS). Using transcutaneous blood glucose detection as an example, we demonstrate that application of SVM enables a significant improvement (at least 30%) in cross-validation accuracy over PLS when measurements from multiple human volunteers are employed in the calibration set. Furthermore, using physical tissue models with randomized analyte concentrations and varying turbidities, we show that the fluctuations in turbidity alone causes curved effects which can only be adequately modeled using nonlinear regression techniques. The enhanced levels of accuracy obtained with the SVM based calibration models opens up avenues for prospective prediction in humans and thus for clinical translation of the technology.